notebooks/crrt-notebook.ipynb

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   "source": [
    "# Continuous renal replacement therapy (CRRT)\n",
    "\n",
    "This notebook overviews the process of defining CRRT: a treatment used to dialyse or filter a patient's blood continuously. Key to CRRT is its lower speed compared to conventional dialysis: avoidance of rapid solute/fluid loss is suspected to be the main reason why CRRT tends to be tolerated better than intermittent hemodialysis.\n",
    "\n",
    "The primary aim of this notebook is to define the start and end times of CRRT for patients in the MIMIC-III database v1.4.\n",
    "\n",
    "A secondary aim of this notebook is to provide insight into how to extract clinical concepts from the MIMIC-III database.\n",
    "\n",
    "Many thanks to Sharon O'Donoghue for her invaluable advice in the creation of this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outline\n",
    "\n",
    "The main steps in defining a clinical concept in MIMIC-III are as follows:\n",
    "\n",
    "1. Identification of key terms and phrases which describe the concept\n",
    "2. Search for these terms in D_ITEMS (or D_LABITEMS if searching for a laboratory measurement)\n",
    "3. Extraction of the data from tables specified in the `LINKSTO` column of D_ITEMS\n",
    "4. Definition of the concept using rules applied to the data extracted\n",
    "5. Validation of the concepts by individual inspection and aggregate statistics\n",
    "\n",
    "This process is iterative and not as clear cut as the above - validation may lead you to redefine data extraction, and so on. Furthermore, in the case of MIMIC-III v1.4, this process must be repeated twice: once for Metavision, once for CareVue.\n",
    "\n",
    "## MetaVision vs. CareVue\n",
    "\n",
    "One issue in MIMIC-III is that it is a combination of two ICU database systems. As a result, concepts are split among different `ITEMID` values. For example, a patient's heart rate is a relatively simple concept to extract, however, if we look in the D_ITEMS table for labels matching 'heart rate', we find at least two `ITEMID`:\n",
    "\n",
    "itemid |          label          |  abbreviation   |  dbsource  |   linksto\n",
    "--------|-------------------------|-----------------|------------|-------------\n",
    "211 | Heart Rate              |                 | carevue    | chartevents\n",
    "220045 | Heart Rate              | HR              | metavision | chartevents\n",
    "\n",
    "Both these `ITEMID` values capture heart rate - but one is used for the CareVue database system (`dbsource = 'carevue'`) and one is used for the MetaVision database system (`dbsource = 'metavision'`). The data extraction step must be repeated twice: once for `dbsource = 'carevue'` and once for `dbsource = 'metavision'`. In general, it is recommended to extract data from MetaVision first, as the data is better structured and provides useful information for what data elements to include. For example, `ITEMID` values in MetaVision have abbrevations with each label - these abbreviations can then be used to search for data elements in CareVue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 0: import libraries, connect to the database"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import psycopg2\n",
    "from IPython.display import display, HTML # used to print out pretty pandas dataframes\n",
    "import matplotlib.dates as dates\n",
    "import matplotlib.lines as mlines\n",
    "\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot') \n",
    "\n",
    "# specify user/password/where the database is\n",
    "sqluser = 'postgres'\n",
    "sqlpass = 'postgres'\n",
    "dbname = 'mimic'\n",
    "schema_name = 'mimiciii'\n",
    "host = 'localhost'\n",
    "\n",
    "query_schema = 'SET search_path to ' + schema_name + ';'\n",
    "\n",
    "# connect to the database\n",
    "con = psycopg2.connect(dbname=dbname, user=sqluser, password=sqlpass, host=host)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1: Identification of key terms\n",
    "\n",
    "We are interested in continuous renal replacement therapy (CRRT). First, we look for 'CRRT' in the database, isolating ourselves to metavision data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
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   "outputs": [
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      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>itemid</th>\n",
       "      <th>label</th>\n",
       "      <th>category</th>\n",
       "      <th>linksto</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>227290</td>\n",
       "      <td>CRRT mode</td>\n",
       "      <td>Dialysis</td>\n",
       "      <td>chartevents</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>225436</td>\n",
       "      <td>CRRT Filter Change</td>\n",
       "      <td>Dialysis</td>\n",
       "      <td>procedureevents_mv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>227525</td>\n",
       "      <td>Calcium Gluconate (CRRT)</td>\n",
       "      <td>Medications</td>\n",
       "      <td>inputevents_mv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>225802</td>\n",
       "      <td>Dialysis - CRRT</td>\n",
       "      <td>Dialysis</td>\n",
       "      <td>procedureevents_mv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>227536</td>\n",
       "      <td>KCl (CRRT)</td>\n",
       "      <td>Medications</td>\n",
       "      <td>inputevents_mv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>225956</td>\n",
       "      <td>Reason for CRRT Filter Change</td>\n",
       "      <td>Dialysis</td>\n",
       "      <td>chartevents</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   itemid                          label     category             linksto\n",
       "0  227290                      CRRT mode     Dialysis         chartevents\n",
       "1  225436             CRRT Filter Change     Dialysis  procedureevents_mv\n",
       "2  227525       Calcium Gluconate (CRRT)  Medications      inputevents_mv\n",
       "3  225802                Dialysis - CRRT     Dialysis  procedureevents_mv\n",
       "4  227536                     KCl (CRRT)  Medications      inputevents_mv\n",
       "5  225956  Reason for CRRT Filter Change     Dialysis         chartevents"
      ]
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "query = query_schema + \"\"\"\n",
    "select itemid, label, category, linksto\n",
    "from d_items\n",
    "where dbsource = 'metavision'\n",
    "and lower(label) like '%crrt%'\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(query,con)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above gives us some hints to expand our initial search:\n",
    "\n",
    "* `category = 'Dialysis'`\n",
    "* `lower(label) like '%dialysis%'`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2: Extraction of `ITEMID`s from tables\n",
    "\n",
    "## Get list of `itemid` related to CRRT"
   ]
  },
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      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>itemid</th>\n",
       "      <th>label</th>\n",
       "      <th>category</th>\n",
       "      <th>linksto</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>225740</td>\n",
       "      <td>Dialysis Catheter Discontinued</td>\n",
       "      <td>Access Lines - Invasive</td>\n",
       "      <td>chartevents</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>227357</td>\n",
       "      <td>Dialysis Catheter Dressing Occlusive</td>\n",
       "      <td>Access Lines - Invasive</td>\n",
       "      <td>chartevents</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>225776</td>\n",
       "      <td>Dialysis Catheter Dressing Type</td>\n",
       "      <td>Access Lines - Invasive</td>\n",
       "      <td>chartevents</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>226118</td>\n",
       "      <td>Dialysis Catheter placed in outside facility</td>\n",
       "      <td>Access Lines - Invasive</td>\n",
       "      <td>chartevents</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>227753</td>\n",
       "      <td>Dialysis Catheter Placement Confirmed by X-ray</td>\n",
       "      <td>Access Lines - Invasive</td>\n",
       "      <td>chartevents</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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   "source": [
    "query = query_schema + \"\"\"\n",
    "select itemid, label, category, linksto\n",
    "from d_items di\n",
    "where dbsource = 'metavision'\n",
    "and (lower(label) like '%dialy%'\n",
    "or category = 'Dialysis'\n",
    "or lower(label) like '%crrt%'\n",
    ")\n",
    "order by linksto, category, label\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(query,con)\n",
    "\n",
    "HTML(df.head().to_html().replace('NaN', ''))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manually label above `itemid`\n",
    "\n",
    "The above is a list of all the potential data elements which could be used to define CRRT. The next step is to identify the specific elements which can be used to define start/stop time. This process requires clinical expertise in the area.\n",
    "\n",
    "The following tables are a result of reviewing all `ITEMID` labels and flagging them as \"consider for further review\" or \"not relevant\".\n",
    "\n",
    "\n",
    "### Links to CHARTEVENTS\n",
    "\n",
    "itemid | label | category | linksto | Included/comment\n",
    "--- | --- | --- | --- | ---\n",
    "225740 | Dialysis Catheter Discontinued | Access Lines - Invasive | chartevents | No - access line\n",
    "227357 | Dialysis Catheter Dressing Occlusive | Access Lines - Invasive | chartevents | No - access line\n",
    "225776 | Dialysis Catheter Dressing Type | Access Lines - Invasive | chartevents | No - access line\n",
    "226118 | Dialysis Catheter placed in outside facility | Access Lines - Invasive | chartevents | No - access line\n",
    "227753 | Dialysis Catheter Placement Confirmed by X-ray | Access Lines - Invasive | chartevents | No - access line\n",
    "225323 | Dialysis Catheter Site Appear | Access Lines - Invasive | chartevents | No - access line\n",
    "225725 | Dialysis Catheter Tip Cultured | Access Lines - Invasive | chartevents | No - access line\n",
    "227124 | Dialysis Catheter Type | Access Lines - Invasive | chartevents | No - access line\n",
    "225126 | Dialysis patient | Adm History/FHPA | chartevents | No - admission information\n",
    "224149 | Access Pressure | Dialysis | chartevents | Yes - CRRT setting\n",
    "224404 | ART Lumen Volume | Dialysis | chartevents | Yes - CRRT setting\n",
    "224144 | Blood Flow (ml/min) | Dialysis | chartevents | Yes - CRRT setting\n",
    "228004 | Citrate (ACD-A) | Dialysis | chartevents | Yes - CRRT setting\n",
    "227290 | CRRT mode | Dialysis | chartevents | Yes - CRRT setting\n",
    "225183 | Current Goal | Dialysis | chartevents | Yes - CRRT setting\n",
    "225977 | Dialysate Fluid | Dialysis | chartevents | Yes - CRRT setting\n",
    "224154 | Dialysate Rate | Dialysis | chartevents | Yes - CRRT setting\n",
    "224135 | Dialysis Access Site | Dialysis | chartevents | No - access line\n",
    "225954 | Dialysis Access Type | Dialysis | chartevents | No - access line\n",
    "224139 | Dialysis Site Appearance | Dialysis | chartevents | No - access line\n",
    "225810 | Dwell Time (Peritoneal Dialysis) | Dialysis | chartevents | No - peritoneal dialysis\n",
    "224151 | Effluent Pressure | Dialysis | chartevents | Yes - CRRT setting\n",
    "224150 | Filter Pressure | Dialysis | chartevents | Yes - CRRT setting\n",
    "226499 | Hemodialysis Output | Dialysis | chartevents | No - hemodialysis\n",
    "225958 | Heparin Concentration (units/mL) | Dialysis | chartevents | Yes - CRRT setting\n",
    "224145 | Heparin Dose (per hour) | Dialysis | chartevents | Yes - CRRT setting\n",
    "224191 | Hourly Patient Fluid Removal | Dialysis | chartevents | Yes - CRRT setting\n",
    "225952 | Medication Added #1 (Peritoneal Dialysis) | Dialysis | chartevents | No - peritoneal dialysis\n",
    "227638 | Medication Added #2 (Peritoneal Dialysis) | Dialysis | chartevents | No - peritoneal dialysis\n",
    "225959 | Medication Added Amount #1 (Peritoneal Dialysis) | Dialysis | chartevents | No - peritoneal dialysis\n",
    "227639 | Medication Added Amount #2 (Peritoneal Dialysis) | Dialysis | chartevents | No - peritoneal dialysis\n",
    "225961 | Medication Added Units #1 (Peritoneal Dialysis) | Dialysis | chartevents | No - peritoneal dialysis\n",
    "227640 | Medication Added Units #2 (Peritoneal Dialysis) | Dialysis | chartevents | No - peritoneal dialysis\n",
    "228005 | PBP (Prefilter) Replacement Rate | Dialysis | chartevents | Yes - CRRT setting\n",
    "225965 | Peritoneal Dialysis Catheter Status | Dialysis | chartevents | No - peritoneal dialysis\n",
    "225963 | Peritoneal Dialysis Catheter Type | Dialysis | chartevents | No - peritoneal dialysis\n",
    "225951 | Peritoneal Dialysis Fluid Appearance | Dialysis | chartevents | No - peritoneal dialysis\n",
    "228006 | Post Filter Replacement Rate | Dialysis | chartevents | Yes - CRRT setting\n",
    "225956 | Reason for CRRT Filter Change | Dialysis | chartevents | Yes - CRRT setting\n",
    "225976 | Replacement Fluid | Dialysis | chartevents | Yes - CRRT setting\n",
    "224153 | Replacement Rate | Dialysis | chartevents | Yes - CRRT setting\n",
    "224152 | Return Pressure | Dialysis | chartevents | Yes - CRRT setting\n",
    "225953 | Solution (Peritoneal Dialysis) | Dialysis | chartevents | No - peritoneal dialysis\n",
    "224146 | System Integrity | Dialysis | chartevents | Yes - CRRT setting\n",
    "226457 | Ultrafiltrate Output | Dialysis | chartevents | Yes - CRRT setting\n",
    "224406 | VEN Lumen Volume | Dialysis | chartevents | Yes - CRRT setting\n",
    "225806 | Volume In (PD) | Dialysis | chartevents | No - peritoneal dialysis\n",
    "227438 | Volume not removed | Dialysis | chartevents | No - peritoneal dialysis\n",
    "225807 | Volume Out (PD) | Dialysis | chartevents | No - peritoneal dialysis\n",
    "\n",
    "### Links to DATETIMEEVENTS\n",
    "\n",
    "itemid | label | category | linksto | Included/comment\n",
    "--- | --- | --- | --- | ---\n",
    "225318 | Dialysis Catheter Cap Change | Access Lines - Invasive | datetimeevents | No - access lines\n",
    "225319 | Dialysis Catheter Change over Wire Date | Access Lines - Invasive | datetimeevents | No - access lines\n",
    "225321 | Dialysis Catheter Dressing Change | Access Lines - Invasive | datetimeevents | No - access lines\n",
    "225322 | Dialysis Catheter Insertion Date | Access Lines - Invasive | datetimeevents | No - access lines\n",
    "225324 | Dialysis CatheterTubing Change | Access Lines - Invasive | datetimeevents | No - access lines\n",
    "225128 | Last dialysis | Adm History/FHPA | datetimeevents | No - admission information\n",
    "\n",
    "### Links to INPUTEVENTS_MV\n",
    "\n",
    "itemid | label | category | linksto | Included/comment\n",
    "--- | --- | --- | --- | ---\n",
    "227525 | Calcium Gluconate (CRRT) | Medications | inputevents_mv | Yes - CRRT setting\n",
    "227536 | KCl (CRRT) | Medications | inputevents_mv | Yes - CRRT setting\n",
    "\n",
    "### Links to PROCEDUREEVENTS_MV\n",
    "\n",
    "itemid | label | category | linksto | Included/comment\n",
    "--- | --- | --- | --- | ---\n",
    "225441 | Hemodialysis | 4-Procedures | procedureevents_mv | No - hemodialysis\n",
    "224270 | Dialysis Catheter | Access Lines - Invasive | procedureevents_mv | No - access lines\n",
    "225436 | CRRT Filter Change | Dialysis | procedureevents_mv | Yes - CRRT setting\n",
    "225802 | Dialysis - CRRT | Dialysis | procedureevents_mv | Yes - CRRT setting\n",
    "225803 | Dialysis - CVVHD | Dialysis | procedureevents_mv | Yes - CRRT setting\n",
    "225809 | Dialysis - CVVHDF | Dialysis | procedureevents_mv | Yes - CRRT setting\n",
    "225955 | Dialysis - SCUF | Dialysis | procedureevents_mv | Yes - CRRT setting\n",
    "225805 | Peritoneal Dialysis | Dialysis | procedureevents_mv | No - peritoneal dialysis\n",
    "\n",
    "## Reasons for inclusion/exclusion\n",
    "\n",
    "* CRRT Setting - yes (included) - these settings are only documented when a patient is receiving CRRT.\n",
    "* Access lines- no (excluded) - these ITEMIDs are not included as the presence of an access line does *not* guarantee that CRRT is being delivered. While having an access line is a requirement of performing CRRT, these lines are present even when a patient is not actively being hemodialysed.\n",
    "* Peritoneal dialysis - no (excluded) - Peritoneal dialysis is a different form of dialysis, and is not CRRT\n",
    "* Hemodialysis - no (excluded) - Similar as above, hemodialysis is a different form of dialysis and is not CRRT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define rules based upon ITEMIDs\n",
    "\n",
    "Above, we acquired a list of `itemid` which we determined to be related to administration of CRRT. The next step is to determine *how* these `itemid` relate to CRRT: do they indicate it is started, stopped, continuing, or something else.\n",
    "\n",
    "We will evaluate `itemid` from three tables, in turn: CHARTEVENTS, INPUTEVENTS_MV, and PROCEDUREEVENTS_MV. Note that the \\_MV subscript indicates that the table only has data from MetaVision (half the patients), while \\_CV indicates the table only has data from CareVue (the other half of patients). Note that after we extract data from MetaVision patients, we will repeat this exercise for CareVue patients.\n",
    "\n",
    "### table 1 of 3: `itemid` from CHARTEVENTS\n",
    "\n",
    "These are the included CRRT settings in CHARTEVENTS:\n",
    "\n",
    "itemid |              label               | param_type \n",
    "--------|----------------------------------|------------\n",
    "224144 | Blood Flow (ml/min)              | Numeric\n",
    "224145 | Heparin Dose (per hour)          | Numeric\n",
    "224146 | System Integrity                 | Text\n",
    "224149 | Access Pressure                  | Numeric\n",
    "224150 | Filter Pressure                  | Numeric\n",
    "224151 | Effluent Pressure                | Numeric\n",
    "224152 | Return Pressure                  | Numeric\n",
    "224153 | Replacement Rate                 | Numeric\n",
    "224154 | Dialysate Rate                   | Numeric\n",
    "224191 | Hourly Patient Fluid Removal     | Numeric\n",
    "224404 | ART Lumen Volume                 | Numeric\n",
    "224406 | VEN Lumen Volume                 | Numeric\n",
    "225183 | Current Goal                     | Numeric\n",
    "225956 | Reason for CRRT Filter Change    | Text\n",
    "225958 | Heparin Concentration (units/mL) | Text\n",
    "225976 | Replacement Fluid                | Text\n",
    "225977 | Dialysate Fluid                  | Text\n",
    "226457 | Ultrafiltrate Output             | Numeric\n",
    "227290 | CRRT mode                        | Text\n",
    "228004 | Citrate (ACD-A)                  | Numeric\n",
    "228005 | PBP (Prefilter) Replacement Rate | Numeric\n",
    "228006 | Post Filter Replacement Rate     | Numeric\n",
    "\n",
    "First, we examine the numeric fields. These fields are the core CRRT settings which, according to clinical advice, should be documented hourly for patients actively on CRRT:"
   ]
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      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>icustay_id</th>\n",
       "      <th>label</th>\n",
       "      <th>charttime</th>\n",
       "      <th>value</th>\n",
       "      <th>valueuom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>246866</td>\n",
       "      <td>ART Lumen Volume</td>\n",
       "      <td>2161-12-11 20:00:00</td>\n",
       "      <td>1.3</td>\n",
       "      <td>mL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>246866</td>\n",
       "      <td>VEN Lumen Volume</td>\n",
       "      <td>2161-12-11 20:00:00</td>\n",
       "      <td>1.2</td>\n",
       "      <td>mL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>246866</td>\n",
       "      <td>Access Pressure</td>\n",
       "      <td>2161-12-11 23:43:00</td>\n",
       "      <td>-87</td>\n",
       "      <td>mmHg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>246866</td>\n",
       "      <td>Blood Flow (ml/min)</td>\n",
       "      <td>2161-12-11 23:43:00</td>\n",
       "      <td>200</td>\n",
       "      <td>ml/min</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>246866</td>\n",
       "      <td>Citrate (ACD-A)</td>\n",
       "      <td>2161-12-11 23:43:00</td>\n",
       "      <td>0</td>\n",
       "      <td>ml/hr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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   "source": [
    "query = query_schema + \"\"\"\n",
    "select\n",
    "  ce.icustay_id, di.label, ce.charttime\n",
    "  , ce.value\n",
    "  , ce.valueuom\n",
    "from chartevents ce\n",
    "inner join d_items di\n",
    "on ce.itemid = di.itemid\n",
    "where ce.icustay_id = 246866\n",
    "and ce.itemid in\n",
    "(\n",
    "        224404, -- | ART Lumen Volume\n",
    "        224406, -- | VEN Lumen Volume\n",
    "        228004, -- | Citrate (ACD-A)\n",
    "        224145, -- | Heparin Dose (per hour)\n",
    "        225183, -- | Current Goal\n",
    "        224149, -- | Access Pressure\n",
    "        224144, -- | Blood Flow (ml/min)\n",
    "        224154, -- | Dialysate Rate\n",
    "        224151, -- | Effluent Pressure\n",
    "        224150, -- | Filter Pressure\n",
    "        224191, -- | Hourly Patient Fluid Removal\n",
    "        228005, -- | PBP (Prefilter) Replacement Rate\n",
    "        228006, -- | Post Filter Replacement Rate\n",
    "        224153, -- | Replacement Rate\n",
    "        224152, -- | Return Pressure\n",
    "        226457  -- | Ultrafiltrate Output\n",
    ")\n",
    "order by ce.icustay_id, ce.charttime, di.label;\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(query,con)\n",
    "\n",
    "HTML(df.head().to_html().replace('NaN', ''))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above we can see that `ART Lumen Volume and VEN Lumen Volume` are documented at a drastically different time than the other settings. Upon discussion with a clinical expert, they confirmed that this is expected, as these volumes indicate settings to keep open the line and are not directly relevant to the administration of CRRT - at best they are superfluous and at worst they can mislead the start/stop times. As a result `ART Lumen Volume` and `VEN Lumen Volume` are excluded. This leaves us with the final set of `ITEMID`s:\n",
    "\n",
    "```sql\n",
    "224149, -- Access Pressure\n",
    "224144, -- Blood Flow (ml/min)\n",
    "228004, -- Citrate (ACD-A)\n",
    "225183, -- Current Goal\n",
    "224154, -- Dialysate Rate\n",
    "224151, -- Effluent Pressure\n",
    "224150, -- Filter Pressure\n",
    "224145, -- Heparin Dose (per hour)\n",
    "224191, -- Hourly Patient Fluid Removal\n",
    "228005, -- PBP (Prefilter) Replacement Rate\n",
    "228006, -- Post Filter Replacement Rate\n",
    "224153, -- Replacement Rate\n",
    "224152, -- Return Pressure\n",
    "226457  -- Ultrafiltrate Output\n",
    "```\n",
    "\n",
    "The next step is to examine the remaining text based `ITEMID`:\n",
    "\n",
    "\n",
    "\n",
    "itemid |              label               | param_type \n",
    "--------|----------------------------------|------------\n",
    "224146 | System Integrity                 | Text\n",
    "225956 | Reason for CRRT Filter Change    | Text\n",
    "225958 | Heparin Concentration (units/mL) | Text\n",
    "225976 | Replacement Fluid                | Text\n",
    "225977 | Dialysate Fluid                  | Text\n",
    "227290 | CRRT mode                        | Text\n",
    "\n",
    "We define a helper function which prints out the number of observations for a given `itemid`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_itemid_info(con, itemid):\n",
    "    # get name of itemid\n",
    "    query = query_schema + \"\"\"\n",
    "    select label\n",
    "    from d_items\n",
    "    where itemid = \"\"\" + str(itemid)\n",
    "    \n",
    "    df = pd.read_sql_query(query,con)\n",
    "    print('Values for {} - {}...'.format(itemid, df['label'][0]))\n",
    "    \n",
    "    \n",
    "    query = query_schema + \"\"\"\n",
    "    select value\n",
    "    , count(distinct icustay_id) as number_of_patients\n",
    "    , count(icustay_id) as number_of_observations\n",
    "    from chartevents\n",
    "    where itemid = \"\"\" + str(itemid) + \"\"\"    \n",
    "    group by value\n",
    "    order by value\n",
    "    \"\"\"\n",
    "    df = pd.read_sql_query(query,con)\n",
    "    display(HTML(df.to_html().replace('NaN', '')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 224146 - System Integrity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values for 224146 - System Integrity...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>number_of_patients</th>\n",
       "      <th>number_of_observations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Active</td>\n",
       "      <td>539</td>\n",
       "      <td>48072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Clots Increasing</td>\n",
       "      <td>245</td>\n",
       "      <td>1419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Clots Present</td>\n",
       "      <td>427</td>\n",
       "      <td>16836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Clotted</td>\n",
       "      <td>233</td>\n",
       "      <td>441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Discontinued</td>\n",
       "      <td>339</td>\n",
       "      <td>771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Line pressure inconsistent</td>\n",
       "      <td>127</td>\n",
       "      <td>431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>New Filter</td>\n",
       "      <td>357</td>\n",
       "      <td>1040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>No Clot Present</td>\n",
       "      <td>275</td>\n",
       "      <td>2615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Recirculating</td>\n",
       "      <td>172</td>\n",
       "      <td>466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Reinitiated</td>\n",
       "      <td>336</td>\n",
       "      <td>1207</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_itemid_info(con, 224146)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In discussion with a clinical expert, each of these settings indicate different stages of the CRRT treatment. We can simplify them into three modes: started, stopped, or active. Since active implies that the CRRT is running, the first active event could also be a start time, therefore we call it \"active/started\". Here we list the manually curated mapping:\n",
    "\n",
    "value | count | interpretation\n",
    "--- | --- | ---\n",
    "Active                     |   539 | CRRT active/started\n",
    "Clots Increasing           |   245 | CRRT active/started\n",
    "Clots Present              |   427 | CRRT active/started\n",
    "Clotted                    |   233 | CRRT **stopped**\n",
    "Discontinued               |   339 | CRRT **stopped**\n",
    "Line pressure inconsistent |   127 | CRRT active/started\n",
    "New Filter                 |   357 | CRRT **started**\n",
    "No Clot Present            |   275 | CRRT active/started\n",
    "Recirculating              |   172 | CRRT **stopped**\n",
    "Reinitiated                |   336 | CRRT **started**\n",
    "\n",
    "Later on we will code special rules to incorporate this `itemid`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 225956 - Reason for CRRT Filter Change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values for 225956 - Reason for CRRT Filter Change...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>number_of_patients</th>\n",
       "      <th>number_of_observations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Clotted</td>\n",
       "      <td>50</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Line changed</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Procedure</td>\n",
       "      <td>20</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_itemid_info(con, 225956)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above is a **stop** time as the filter needed to be changed at this time. Any subsequent CRRT would be a restart of CRRT - and not a continuation of an ongoing CRRT session."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 225958 - Heparin Concentration (units/mL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values for 225958 - Heparin Concentration (units/mL)...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>number_of_patients</th>\n",
       "      <th>number_of_observations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>16</td>\n",
       "      <td>995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>41</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Not applicable</td>\n",
       "      <td>120</td>\n",
       "      <td>8796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_itemid_info(con, 225958)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above is a normal setting and can be combined with the numeric fields."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 225976 - Replacement Fluid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values for 225976 - Replacement Fluid...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>number_of_patients</th>\n",
       "      <th>number_of_observations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Normal Saline 0.9%</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prismasate K0</td>\n",
       "      <td>78</td>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prismasate K2</td>\n",
       "      <td>459</td>\n",
       "      <td>27603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prismasate K4</td>\n",
       "      <td>387</td>\n",
       "      <td>30872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sodium Bicarb 150/D5W</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sodium Bicarb 75/0.45NS</td>\n",
       "      <td>6</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_itemid_info(con, 225976)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above is a normal setting and can be combined with the numeric fields."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 225977 - Dialysate Fluid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values for 225977 - Dialysate Fluid...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>number_of_patients</th>\n",
       "      <th>number_of_observations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>97</td>\n",
       "      <td>6025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Normal Saline</td>\n",
       "      <td>32</td>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prismasate K0</td>\n",
       "      <td>89</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prismasate K2</td>\n",
       "      <td>438</td>\n",
       "      <td>24271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prismasate K4</td>\n",
       "      <td>357</td>\n",
       "      <td>27320</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_itemid_info(con, 225977)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above is a normal setting and can be combined with the numeric fields."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 227290 - CRRT mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values for 227290 - CRRT mode...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>number_of_patients</th>\n",
       "      <th>number_of_observations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CVVH</td>\n",
       "      <td>40</td>\n",
       "      <td>1280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CVVHD</td>\n",
       "      <td>24</td>\n",
       "      <td>583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CVVHDF</td>\n",
       "      <td>498</td>\n",
       "      <td>25533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SCUF</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_itemid_info(con, 227290)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While all of this looks good, it's feasible that the documentation of the CRRT mode is not done directly concurrent to the actual administration of CRRT. We thus investigate whether CRRT mode is available for all patients with a CRRT setting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_icustay_id</th>\n",
       "      <th>num_with_mode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>784</td>\n",
       "      <td>533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examining CRRT mode\n",
    "query = query_schema + \"\"\"\n",
    "with t1 as \n",
    "(\n",
    "select icustay_id,\n",
    "    max(case when itemid = 227290 then 1 else 0 end) as HasMode\n",
    "from chartevents ce\n",
    "where itemid in\n",
    "(\n",
    "        227290, --  CRRT mode\n",
    "        228004, --  Citrate (ACD-A)\n",
    "        225958, --  Heparin Concentration (units/mL)\n",
    "        224145, --  Heparin Dose (per hour)\n",
    "        225183, --  Current Goal -- always there\n",
    "        224149, --  Access Pressure\n",
    "        224144, --  Blood Flow (ml/min)\n",
    "        225977, --  Dialysate Fluid\n",
    "        224154, --  Dialysate Rate\n",
    "        224151, --  Effluent Pressure\n",
    "        224150, --  Filter Pressure\n",
    "        224191, --  Hourly Patient Fluid Removal\n",
    "        228005, --  PBP (Prefilter) Replacement Rate\n",
    "        228006, --  Post Filter Replacement Rate\n",
    "        225976, --  Replacement Fluid\n",
    "        224153, --  Replacement Rate\n",
    "        224152, --  Return Pressure\n",
    "        226457  --  Ultrafiltrate Output\n",
    ")\n",
    "group by icustay_id\n",
    ")\n",
    "select count(icustay_id) as Num_ICUSTAY_ID\n",
    ", sum(hasmode) as Num_With_Mode\n",
    "from t1\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(query,con)\n",
    "\n",
    "HTML(df.to_html().replace('NaN', ''))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can take this analysis a bit further and ask: is CRRT mode is present when *none* of the other settings are present?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numobs</th>\n",
       "      <th>both</th>\n",
       "      <th>onlycrrtmode</th>\n",
       "      <th>nocrrtmode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81162</td>\n",
       "      <td>27446</td>\n",
       "      <td>1</td>\n",
       "      <td>53778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = query_schema + \"\"\"\n",
    "with t1 as \n",
    "(\n",
    "select icustay_id, charttime\n",
    "    , max(case when itemid = 227290 then 1 else 0 end) as HasCRRTMode\n",
    "    , max(case when itemid != 227290 then 1 else 0 end) as OtherITEMID\n",
    "from chartevents ce\n",
    "where itemid in\n",
    "(\n",
    "        227290, --  CRRT mode\n",
    "        228004, --  Citrate (ACD-A)\n",
    "        225958, --  Heparin Concentration (units/mL)\n",
    "        224145, --  Heparin Dose (per hour)\n",
    "        225183, --  Current Goal -- always there\n",
    "        224149, --  Access Pressure\n",
    "        224144, --  Blood Flow (ml/min)\n",
    "        225977, --  Dialysate Fluid\n",
    "        224154, --  Dialysate Rate\n",
    "        224151, --  Effluent Pressure\n",
    "        224150, --  Filter Pressure\n",
    "        224191, --  Hourly Patient Fluid Removal\n",
    "        228005, --  PBP (Prefilter) Replacement Rate\n",
    "        228006, --  Post Filter Replacement Rate\n",
    "        225976, --  Replacement Fluid\n",
    "        224153, --  Replacement Rate\n",
    "        224152, --  Return Pressure\n",
    "        226457  --  Ultrafiltrate Output\n",
    ")\n",
    "group by icustay_id, charttime\n",
    ")\n",
    "select count(icustay_id) as NumObs\n",
    ", sum(case when HasCRRTMode = 1 and OtherITEMID = 1 then 1 else 0 end) as Both\n",
    ", sum(case when HasCRRTMode = 1 and OtherITEMID = 0 then 1 else 0 end) as OnlyCRRTMode\n",
    ", sum(case when HasCRRTMode = 0 and OtherITEMID = 1 then 1 else 0 end) as NoCRRTMode\n",
    "from t1\n",
    "\"\"\"\n",
    "df = pd.read_sql_query(query,con)\n",
    "\n",
    "HTML(df.to_html().replace('NaN', ''))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As CRRT mode is relatively redundant, doesn't necessarily indicate CRRT is being actively performed, and documentation for it is not 100% compliant, we exclude it from the list of `ITEMID`.\n",
    "\n",
    "### CHARTEVENTS wrap up\n",
    "\n",
    "The following is the final set of `ITEMID` from CHARTEVENTS which indicate CRRT is started/ongoing:\n",
    "\n",
    "```sql\n",
    "224149, -- Access Pressure\n",
    "224144, -- Blood Flow (ml/min)\n",
    "228004, -- Citrate (ACD-A)\n",
    "225183, -- Current Goal\n",
    "225977, -- Dialysate Fluid\n",
    "224154, -- Dialysate Rate\n",
    "224151, -- Effluent Pressure\n",
    "224150, -- Filter Pressure\n",
    "225958, -- Heparin Concentration (units/mL)\n",
    "224145, -- Heparin Dose (per hour)\n",
    "224191, -- Hourly Patient Fluid Removal\n",
    "228005, -- PBP (Prefilter) Replacement Rate\n",
    "228006, -- Post Filter Replacement Rate\n",
    "225976, -- Replacement Fluid\n",
    "224153, -- Replacement Rate\n",
    "224152, -- Return Pressure\n",
    "226457  -- Ultrafiltrate Output\n",
    "```\n",
    "\n",
    "The following `ITEMID` are the final set which indicate CRRT is started/stopped/ongoing (i.e. require special rules):\n",
    "\n",
    "```sql\n",
    "224146, -- System Integrity\n",
    "225956  -- Reason for CRRT Filter Change\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### table 2 of 3: INPUTEVENTS_MV\n",
    "\n",
    "The following is the final set of ITEMID from INPUTEVENTS_MV:\n",
    "\n",
    "```sql\n",
    "227525,-- Calcium Gluconate (CRRT)\n",
    "227536 -- KCl (CRRT)\n",
    "```\n",
    "\n",
    "No special examination is required for these fields - they are guaranteed to be CRRT (as verified by a clinician) - we can use these to indicate that CRRT is active/started."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### table 3 of 3: PROCEDUREEVENTS_MV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following are the set of `itemid` from above related to PROCEDUREEVENTS_MV:\n",
    "\n",
    "itemid | label\n",
    "--- | ---\n",
    "225436 | CRRT Filter Change\n",
    "225802 | Dialysis - CRRT\n",
    "225803 | Dialysis - CVVHD\n",
    "225809 | Dialysis - CVVHDF\n",
    "225955 | Dialysis - SCUF\n",
    "\n",
    "The only contentious `ITEMID` is 225436 (CRRT Filter Change). This `ITEMID` indicates a break from CRRT, and it reinitiates at the end of this change. While in principle this could be used as an end time, documentation on it is not 100%, and as recommended by staff it's easier to ignore this and use the filter change field from CHARTEVENTS to define the end of CRRT events.\n",
    "\n",
    "The final set of `ITEMID` used for CRRT are:\n",
    "\n",
    "```sql\n",
    "225802, -- Dialysis - CRRT\n",
    "225803, -- Dialysis - CVVHD\n",
    "225809, -- Dialysis - CVVHDF\n",
    "225955  -- Dialysis - SCUF\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 4: definition of concept using rules\n",
    "\n",
    "Let's review the goal of this notebook. We would like to define the duration of CRRT for each patient. Concretely, this means we must define, for each `ICUSTAY_ID`:\n",
    "\n",
    "* a `STARTTIME`\n",
    "* an `ENDTIME`\n",
    "\n",
    "As CRRT can be started/stopped throughout a patient's stay, there may be multiple `STARTTIME` and `ENDTIME` for a single `ICUSTAY_ID` - but they should not overlap.\n",
    "\n",
    "Recall that CHARTEVENTS stores data at charted times (`CHARTTIME`), and as a result the settings are stored at a single point in time. For CHARTEVENTS, the main task thus becomes converting a series of `CHARTTIME` into pairs of `STARTTIME` and `ENDTIME`. Intuitively this can be done by looking for consecutive settings each hour, and combining these into a single CRRT event. The first observed `CHARTTIME` becomes the `STARTTIME`, and the last observed `CHARTTIME` becomes the `ENDTIME`. However, CHARTEVENTS is not the only source of data. To improve the accuracy of our calculation, we also include data from INPUTEVENTS_MV and PROCEDUREEVENTS_MV. For INPUTEVENTS_MV, this does not complicate things too much. Each observation in INPUTEVENTS_MV is also stored at a single `CHARTTIME`, and so we simply need to combine this table with CHARTEVENTS before proceeding (likely by using the SQL `UNION` command).\n",
    "\n",
    "PROCEDUREEVENTS_MV is more complicated as it actually stores data with a `STARTTIME` and an `ENDTIME` column already. We need to merge the extracted data from CHARTEVENTS/INPUTEVENTS_MV with this already nicely formatted data from PROCEDUREEVENTS_MV.\n",
    "\n",
    "With the task laid out, let's get started. We will:\n",
    "\n",
    "1. Aggregate INPUTEVENTS_MV into durations\n",
    "2. Convert CHARTEVENTS into durations\n",
    "2. Compare these durations with PROCEDUREVENTS_MV and decide on a rule for merging the two\n",
    "3. Merge PROCEDUREEVENTS_MV with INPUTEVENTS_MV/CHARTEVENTS for a final durations table for Metavision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define the example ICUSTAY_ID for the below code\n",
    "# originally, this was 246866 - if changed, the interpretation provided will no longer make sense\n",
    "query_where_clause = \"and icustay_id = 246866\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make sure we don't display data we don't have to, we define a function which: (i) doesn't display icustay_id, and (ii) simplifies the date by removing the month/year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def display_df(df):\n",
    "    col = [x for x in df.columns if x != 'icustay_id']\n",
    "    df_tmp = df[col].copy()\n",
    "    for c in df_tmp.columns:\n",
    "        if '[ns]' in str(df_tmp[c].dtype):\n",
    "            df_tmp[c] = df_tmp[c].dt.strftime('Day %d, %H:%M')\n",
    "    \n",
    "    display(HTML(df_tmp.to_html().replace('NaN', '')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aggregating INPUTEVENTS_MV\n",
    "\n",
    "First, let's look at INPUTEVENTS_MV. Each entry is stored with a `starttime` and an `endtime`. Note we have to exclude `statusdescription = 'Rewritten'` as these are undelivered medications which have been rewritten (useful for auditing purposes but does not give you information about drugs delivered to the patient)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Durations from INPUTEVENTS for one patient with KCl...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>linkorderid</th>\n",
       "      <th>orderid</th>\n",
       "      <th>label</th>\n",
       "      <th>starttime</th>\n",
       "      <th>endtime</th>\n",
       "      <th>rate</th>\n",
       "      <th>rateuom</th>\n",
       "      <th>statusdescription</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8522257</td>\n",
       "      <td>8522257</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 11, 21:30</td>\n",
       "      <td>Day 12, 02:30</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9370484</td>\n",
       "      <td>9370484</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 11, 23:45</td>\n",
       "      <td>Day 12, 02:41</td>\n",
       "      <td>10.002273</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3507252</td>\n",
       "      <td>3507252</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 02:41</td>\n",
       "      <td>Day 12, 05:36</td>\n",
       "      <td>9.997713</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9525961</td>\n",
       "      <td>9525961</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 05:36</td>\n",
       "      <td>Day 12, 08:31</td>\n",
       "      <td>10.285715</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7118985</td>\n",
       "      <td>7118985</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 08:31</td>\n",
       "      <td>Day 12, 11:29</td>\n",
       "      <td>10.112360</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5395095</td>\n",
       "      <td>5395095</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 11:29</td>\n",
       "      <td>Day 12, 14:28</td>\n",
       "      <td>10.055866</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8065541</td>\n",
       "      <td>8065541</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 14:28</td>\n",
       "      <td>Day 12, 17:25</td>\n",
       "      <td>10.169492</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5758899</td>\n",
       "      <td>5758899</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 17:25</td>\n",
       "      <td>Day 12, 20:24</td>\n",
       "      <td>10.055866</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7157126</td>\n",
       "      <td>7157126</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 20:24</td>\n",
       "      <td>Day 12, 20:30</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Paused</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7157126</td>\n",
       "      <td>3853798</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 21:30</td>\n",
       "      <td>Day 12, 21:35</td>\n",
       "      <td>9.997634</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Changed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7157126</td>\n",
       "      <td>6292957</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 21:35</td>\n",
       "      <td>Day 13, 02:08</td>\n",
       "      <td>6.190549</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>271343</td>\n",
       "      <td>271343</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 02:08</td>\n",
       "      <td>Day 13, 07:06</td>\n",
       "      <td>6.040268</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9407942</td>\n",
       "      <td>9407942</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 07:06</td>\n",
       "      <td>Day 13, 12:03</td>\n",
       "      <td>6.006060</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9182119</td>\n",
       "      <td>9182119</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 12:03</td>\n",
       "      <td>Day 13, 16:29</td>\n",
       "      <td>6.005904</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Stopped</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>9720623</td>\n",
       "      <td>9720623</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 18:15</td>\n",
       "      <td>Day 13, 23:15</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2194578</td>\n",
       "      <td>2194578</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 15:28</td>\n",
       "      <td>Day 14, 18:47</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7567525</td>\n",
       "      <td>7567525</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 18:47</td>\n",
       "      <td>Day 14, 19:01</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Changed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7567525</td>\n",
       "      <td>4605649</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 19:01</td>\n",
       "      <td>Day 14, 23:04</td>\n",
       "      <td>4.007380</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Changed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>7567525</td>\n",
       "      <td>5699592</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 23:04</td>\n",
       "      <td>Day 14, 23:18</td>\n",
       "      <td>5.871428</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8743715</td>\n",
       "      <td>8743715</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 23:18</td>\n",
       "      <td>Day 15, 02:28</td>\n",
       "      <td>5.905264</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4080709</td>\n",
       "      <td>4080709</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 02:28</td>\n",
       "      <td>Day 15, 05:44</td>\n",
       "      <td>5.969388</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4644782</td>\n",
       "      <td>4644782</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 05:44</td>\n",
       "      <td>Day 15, 08:57</td>\n",
       "      <td>5.906736</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>741589</td>\n",
       "      <td>741589</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 08:57</td>\n",
       "      <td>Day 15, 12:08</td>\n",
       "      <td>5.905759</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6190220</td>\n",
       "      <td>6190220</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 12:08</td>\n",
       "      <td>Day 15, 15:17</td>\n",
       "      <td>5.904762</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1921010</td>\n",
       "      <td>1921010</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 15:17</td>\n",
       "      <td>Day 15, 18:34</td>\n",
       "      <td>5.908629</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3011912</td>\n",
       "      <td>3011912</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 18:34</td>\n",
       "      <td>Day 15, 21:46</td>\n",
       "      <td>5.906250</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1107318</td>\n",
       "      <td>1107318</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 21:46</td>\n",
       "      <td>Day 16, 01:01</td>\n",
       "      <td>5.907693</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>609665</td>\n",
       "      <td>609665</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 01:01</td>\n",
       "      <td>Day 16, 04:18</td>\n",
       "      <td>5.908629</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4995198</td>\n",
       "      <td>4995198</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 04:18</td>\n",
       "      <td>Day 16, 07:36</td>\n",
       "      <td>5.903030</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4667423</td>\n",
       "      <td>4667423</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 07:36</td>\n",
       "      <td>Day 16, 10:54</td>\n",
       "      <td>5.903030</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4802077</td>\n",
       "      <td>4802077</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 10:54</td>\n",
       "      <td>Day 16, 14:12</td>\n",
       "      <td>5.903030</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>8427007</td>\n",
       "      <td>8427007</td>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 14:12</td>\n",
       "      <td>Day 16, 16:04</td>\n",
       "      <td>5.905613</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Stopped</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Durations from INPUTEVENTS for one patient with KCl...\")\n",
    "query = query_schema + \"\"\"\n",
    "select \n",
    "    linkorderid\n",
    "  , orderid\n",
    "  , case when itemid = 227525 then 'Calcium' else 'KCl' end as label\n",
    "  , starttime, endtime\n",
    "  , rate, rateuom\n",
    "  , statusdescription\n",
    "from inputevents_mv\n",
    "where itemid in\n",
    "(\n",
    "  --227525,-- Calcium Gluconate (CRRT)\n",
    "  227536 -- KCl (CRRT)\n",
    ")\n",
    "and statusdescription != 'Rewritten'\n",
    "\"\"\" + query_where_clause + \"\"\"\n",
    "order by starttime, endtime\n",
    "\"\"\"\n",
    "ie = pd.read_sql_query(query,con)\n",
    "\n",
    "display_df(ie)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normally `linkorderid` links together administrations which are consecutive but may have changes in rate, but from the above we can note that `linkorderid` seems to rarely group entries.  Rows 8-10 and 16-18 are grouped (i.e. they are sequential administrations where the rate may or may not have changed), but many aren't *even though* they occur sequentially. We'd like to merge together sequential events to simplify the durations - and it appears we can greatly simplify this data by merging two rows if `endtime(row-1) == starttime(row)`.\n",
    "\n",
    "We can do this in three steps:\n",
    "\n",
    "1. Create a binary flag that indicates when new \"events\" occur, where an \"event\" is defined as a continuous segment of administration, i.e. the binary flag is 1 if the row does not immediately follow the previous row, and 0 if the row does immediately follow the previous row\n",
    "2. Aggregate this binary flag so each individual event is assigned a unique integer (i.e. create a partition over these events)\n",
    "3. Create an integer to identify the last row in the event (so we can get useful information from this row)\n",
    "4. Group the data based off the partition to result in a single `starttime` and `endtime` for each continguous medication administration\n",
    "\n",
    "Now we'll go through the code for doing this step by step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: create a binary flag for new events\n",
    "\n",
    "```sql\n",
    "with t1 as\n",
    "(\n",
    "select icustay_id\n",
    "  , case when itemid = 227525 then 'Calcium' else 'KCl' end as label\n",
    "  , starttime\n",
    "  , endtime\n",
    "  , case\n",
    "        when lag(endtime) over (partition by icustay_id, itemid order by starttime, endtime) = starttime\n",
    "            then 0\n",
    "        else 1 end\n",
    "    as new_event_flag\n",
    "  , rate, rateuom\n",
    "  , statusdescription\n",
    "from inputevents_mv\n",
    "where itemid in\n",
    "(\n",
    "  --227525,-- Calcium Gluconate (CRRT)\n",
    "  227536 -- KCl (CRRT)\n",
    ")\n",
    "and statusdescription != 'Rewritten'\n",
    "\"\"\" + query_where_clause + \"\"\"\n",
    "```\n",
    "\n",
    "This selects data from INPUTEVENTS_MV for just KCl using a single patient specified by the `query_where_clause` (this is so it can act as an example - you can omit the single patient and it will work on all the data).\n",
    "\n",
    "The key code block is here:\n",
    "\n",
    "```sql\n",
    "  , case\n",
    "        when lag(endtime) over (partition by icustay_id, itemid order by starttime, endtime) = starttime\n",
    "            then 0\n",
    "        else 1 end\n",
    "    as new_event_flag\n",
    "```\n",
    "\n",
    "This creates a boolean flag which is 1 every time the current `starttime` is not equal to the previous `endtime`, i.e. it marks new \"events\". We can see it in action here:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Durations from INPUTEVENTS_MV, new events noted with time_partition...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>starttime</th>\n",
       "      <th>endtime</th>\n",
       "      <th>endtime_lag</th>\n",
       "      <th>new_event_flag</th>\n",
       "      <th>rate</th>\n",
       "      <th>rateuom</th>\n",
       "      <th>statusdescription</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 11, 21:30</td>\n",
       "      <td>Day 12, 02:30</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 11, 23:45</td>\n",
       "      <td>Day 12, 02:41</td>\n",
       "      <td>Day 12, 02:30</td>\n",
       "      <td>1</td>\n",
       "      <td>10.002273</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 02:41</td>\n",
       "      <td>Day 12, 05:36</td>\n",
       "      <td>Day 12, 02:41</td>\n",
       "      <td>0</td>\n",
       "      <td>9.997713</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 05:36</td>\n",
       "      <td>Day 12, 08:31</td>\n",
       "      <td>Day 12, 05:36</td>\n",
       "      <td>0</td>\n",
       "      <td>10.285715</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 08:31</td>\n",
       "      <td>Day 12, 11:29</td>\n",
       "      <td>Day 12, 08:31</td>\n",
       "      <td>0</td>\n",
       "      <td>10.112360</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 11:29</td>\n",
       "      <td>Day 12, 14:28</td>\n",
       "      <td>Day 12, 11:29</td>\n",
       "      <td>0</td>\n",
       "      <td>10.055866</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 14:28</td>\n",
       "      <td>Day 12, 17:25</td>\n",
       "      <td>Day 12, 14:28</td>\n",
       "      <td>0</td>\n",
       "      <td>10.169492</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 17:25</td>\n",
       "      <td>Day 12, 20:24</td>\n",
       "      <td>Day 12, 17:25</td>\n",
       "      <td>0</td>\n",
       "      <td>10.055866</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 20:24</td>\n",
       "      <td>Day 12, 20:30</td>\n",
       "      <td>Day 12, 20:24</td>\n",
       "      <td>0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Paused</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 21:30</td>\n",
       "      <td>Day 12, 21:35</td>\n",
       "      <td>Day 12, 20:30</td>\n",
       "      <td>1</td>\n",
       "      <td>9.997634</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Changed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 21:35</td>\n",
       "      <td>Day 13, 02:08</td>\n",
       "      <td>Day 12, 21:35</td>\n",
       "      <td>0</td>\n",
       "      <td>6.190549</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 02:08</td>\n",
       "      <td>Day 13, 07:06</td>\n",
       "      <td>Day 13, 02:08</td>\n",
       "      <td>0</td>\n",
       "      <td>6.040268</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 07:06</td>\n",
       "      <td>Day 13, 12:03</td>\n",
       "      <td>Day 13, 07:06</td>\n",
       "      <td>0</td>\n",
       "      <td>6.006060</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 12:03</td>\n",
       "      <td>Day 13, 16:29</td>\n",
       "      <td>Day 13, 12:03</td>\n",
       "      <td>0</td>\n",
       "      <td>6.005904</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Stopped</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 18:15</td>\n",
       "      <td>Day 13, 23:15</td>\n",
       "      <td>Day 13, 16:29</td>\n",
       "      <td>1</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 15:28</td>\n",
       "      <td>Day 14, 18:47</td>\n",
       "      <td>Day 13, 23:15</td>\n",
       "      <td>1</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 18:47</td>\n",
       "      <td>Day 14, 19:01</td>\n",
       "      <td>Day 14, 18:47</td>\n",
       "      <td>0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Changed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 19:01</td>\n",
       "      <td>Day 14, 23:04</td>\n",
       "      <td>Day 14, 19:01</td>\n",
       "      <td>0</td>\n",
       "      <td>4.007380</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Changed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 23:04</td>\n",
       "      <td>Day 14, 23:18</td>\n",
       "      <td>Day 14, 23:04</td>\n",
       "      <td>0</td>\n",
       "      <td>5.871428</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 23:18</td>\n",
       "      <td>Day 15, 02:28</td>\n",
       "      <td>Day 14, 23:18</td>\n",
       "      <td>0</td>\n",
       "      <td>5.905264</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 02:28</td>\n",
       "      <td>Day 15, 05:44</td>\n",
       "      <td>Day 15, 02:28</td>\n",
       "      <td>0</td>\n",
       "      <td>5.969388</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 05:44</td>\n",
       "      <td>Day 15, 08:57</td>\n",
       "      <td>Day 15, 05:44</td>\n",
       "      <td>0</td>\n",
       "      <td>5.906736</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 08:57</td>\n",
       "      <td>Day 15, 12:08</td>\n",
       "      <td>Day 15, 08:57</td>\n",
       "      <td>0</td>\n",
       "      <td>5.905759</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 12:08</td>\n",
       "      <td>Day 15, 15:17</td>\n",
       "      <td>Day 15, 12:08</td>\n",
       "      <td>0</td>\n",
       "      <td>5.904762</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 15:17</td>\n",
       "      <td>Day 15, 18:34</td>\n",
       "      <td>Day 15, 15:17</td>\n",
       "      <td>0</td>\n",
       "      <td>5.908629</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 18:34</td>\n",
       "      <td>Day 15, 21:46</td>\n",
       "      <td>Day 15, 18:34</td>\n",
       "      <td>0</td>\n",
       "      <td>5.906250</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 21:46</td>\n",
       "      <td>Day 16, 01:01</td>\n",
       "      <td>Day 15, 21:46</td>\n",
       "      <td>0</td>\n",
       "      <td>5.907693</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 01:01</td>\n",
       "      <td>Day 16, 04:18</td>\n",
       "      <td>Day 16, 01:01</td>\n",
       "      <td>0</td>\n",
       "      <td>5.908629</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 04:18</td>\n",
       "      <td>Day 16, 07:36</td>\n",
       "      <td>Day 16, 04:18</td>\n",
       "      <td>0</td>\n",
       "      <td>5.903030</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 07:36</td>\n",
       "      <td>Day 16, 10:54</td>\n",
       "      <td>Day 16, 07:36</td>\n",
       "      <td>0</td>\n",
       "      <td>5.903030</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 10:54</td>\n",
       "      <td>Day 16, 14:12</td>\n",
       "      <td>Day 16, 10:54</td>\n",
       "      <td>0</td>\n",
       "      <td>5.903030</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 14:12</td>\n",
       "      <td>Day 16, 16:04</td>\n",
       "      <td>Day 16, 14:12</td>\n",
       "      <td>0</td>\n",
       "      <td>5.905613</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Stopped</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Durations from INPUTEVENTS_MV, new events noted with time_partition...\")\n",
    "query = query_schema + \"\"\"\n",
    "with t1 as\n",
    "(\n",
    "select \n",
    "  icustay_id\n",
    "  , case when itemid = 227525 then 'Calcium' else 'KCl' end as label\n",
    "  , starttime, endtime\n",
    "  , lag(endtime) over (partition by icustay_id, itemid order by starttime, endtime) as endtime_lag\n",
    "  , case\n",
    "        when lag(endtime) over (partition by icustay_id, itemid order by starttime, endtime) = starttime\n",
    "            then 0\n",
    "        else 1 end\n",
    "    as new_event_flag\n",
    "  , rate, rateuom\n",
    "  , statusdescription\n",
    "from inputevents_mv\n",
    "where itemid in\n",
    "(\n",
    "  --227525,-- Calcium Gluconate (CRRT)\n",
    "  227536 -- KCl (CRRT)\n",
    ")\n",
    "and statusdescription != 'Rewritten'\n",
    "\"\"\" + query_where_clause + \"\"\"\n",
    ")\n",
    "select \n",
    "  label\n",
    ", starttime\n",
    ", endtime\n",
    ", endtime_lag\n",
    ", new_event_flag\n",
    ", rate, rateuom\n",
    ", statusdescription\n",
    "from t1\n",
    "\"\"\"\n",
    "ie = pd.read_sql_query(query,con)\n",
    "\n",
    "display_df(ie)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note we have added the `endtime_lag` column to give a clearer idea of how the query is working. We can see the first row starts with `new_event_flag = 1` since `endtime_lag` is null. Next, the `endtime_lag != starttime`, so `new_event_flag` is again `= 1`.\n",
    "\n",
    "Finally, for row 2 (marked by **2** on the far left), the `endtime_lag == starttime` - and so `new_event_flag` is 0. This continues all the way until row **9**, where we can again see `endtime_lag != starttime`. Note that the `statusdescription` on row **8** even informs us why: it states that the administration has been \"Paused\". This is why we mentioned earlier that we were interested in the last row from an event.\n",
    "\n",
    "### Step 2: create a binary flag for new events\n",
    "\n",
    "With SQL, in order to aggregate groups of rows, we need a *partition*. That is, we need some key (usually an integer) which is unique for that set of rows. Once we have this unique key, we can do all the standard SQL aggregations like `max()`, `min()`, and so on (note: SQL \"window\" functions operate on the same principle, except you define the partition explicitly from a combination of columns).\n",
    "\n",
    "With this in mind, our next step is to use this flag to create a unique integer for each set of rows we'd like grouped. Since we'd like to group new events together, we can run a cumulative sum along `new_event_flag`: every time a new event occurs, the integer will increase and consequently that event will all have the same unique key. The code to do this is:\n",
    "\n",
    "```sql\n",
    "SUM(new_event_flag) OVER (partition by icustay_id, label order by starttime, endtime) as time_partition \n",
    "```\n",
    "\n",
    "Let's see this in action:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Durations from INPUTEVENTS for one patient with KCl...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>starttime</th>\n",
       "      <th>endtime</th>\n",
       "      <th>new_event_flag</th>\n",
       "      <th>time_partition</th>\n",
       "      <th>rate</th>\n",
       "      <th>rateuom</th>\n",
       "      <th>statusdescription</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 11, 21:30</td>\n",
       "      <td>Day 12, 02:30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 11, 23:45</td>\n",
       "      <td>Day 12, 02:41</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10.002273</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 02:41</td>\n",
       "      <td>Day 12, 05:36</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>9.997713</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 05:36</td>\n",
       "      <td>Day 12, 08:31</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>10.285715</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 08:31</td>\n",
       "      <td>Day 12, 11:29</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>10.112360</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 11:29</td>\n",
       "      <td>Day 12, 14:28</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>10.055866</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 14:28</td>\n",
       "      <td>Day 12, 17:25</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>10.169492</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 17:25</td>\n",
       "      <td>Day 12, 20:24</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>10.055866</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 20:24</td>\n",
       "      <td>Day 12, 20:30</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Paused</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 21:30</td>\n",
       "      <td>Day 12, 21:35</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>9.997634</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Changed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 21:35</td>\n",
       "      <td>Day 13, 02:08</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6.190549</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 02:08</td>\n",
       "      <td>Day 13, 07:06</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6.040268</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 07:06</td>\n",
       "      <td>Day 13, 12:03</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6.006060</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 12:03</td>\n",
       "      <td>Day 13, 16:29</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6.005904</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Stopped</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 18:15</td>\n",
       "      <td>Day 13, 23:15</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 15:28</td>\n",
       "      <td>Day 14, 18:47</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 18:47</td>\n",
       "      <td>Day 14, 19:01</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Changed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 19:01</td>\n",
       "      <td>Day 14, 23:04</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4.007380</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Changed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 23:04</td>\n",
       "      <td>Day 14, 23:18</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.871428</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 23:18</td>\n",
       "      <td>Day 15, 02:28</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.905264</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 02:28</td>\n",
       "      <td>Day 15, 05:44</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.969388</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 05:44</td>\n",
       "      <td>Day 15, 08:57</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.906736</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 08:57</td>\n",
       "      <td>Day 15, 12:08</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.905759</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 12:08</td>\n",
       "      <td>Day 15, 15:17</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.904762</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 15:17</td>\n",
       "      <td>Day 15, 18:34</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.908629</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 18:34</td>\n",
       "      <td>Day 15, 21:46</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.906250</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 21:46</td>\n",
       "      <td>Day 16, 01:01</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.907693</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 01:01</td>\n",
       "      <td>Day 16, 04:18</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.908629</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 04:18</td>\n",
       "      <td>Day 16, 07:36</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.903030</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 07:36</td>\n",
       "      <td>Day 16, 10:54</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.903030</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 10:54</td>\n",
       "      <td>Day 16, 14:12</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.903030</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 14:12</td>\n",
       "      <td>Day 16, 16:04</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.905613</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Stopped</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Durations from INPUTEVENTS for one patient with KCl...\")\n",
    "query = query_schema + \"\"\"\n",
    "with t1 as\n",
    "(\n",
    "select icustay_id\n",
    "  , case when itemid = 227525 then 'Calcium' else 'KCl' end as label\n",
    "  , starttime\n",
    "  , endtime\n",
    "  , case\n",
    "        when lag(endtime) over (partition by icustay_id, itemid order by starttime, endtime) = starttime\n",
    "            then 0\n",
    "        else 1 end\n",
    "    as new_event_flag\n",
    "  , rate, rateuom\n",
    "  , statusdescription\n",
    "from inputevents_mv\n",
    "where itemid in\n",
    "(\n",
    "  --227525,-- Calcium Gluconate (CRRT)\n",
    "  227536 -- KCl (CRRT)\n",
    ")\n",
    "and statusdescription != 'Rewritten'\n",
    "\"\"\" + query_where_clause + \"\"\"\n",
    ")\n",
    ", t2 as\n",
    "(\n",
    "    select \n",
    "    icustay_id\n",
    "    , label\n",
    "    , starttime, endtime\n",
    "    , new_event_flag\n",
    "    , SUM(new_event_flag) OVER (partition by icustay_id, label order by starttime, endtime) as time_partition \n",
    "    , rate, rateuom, statusdescription\n",
    "    from t1\n",
    ")\n",
    "select \n",
    "      label\n",
    "    , starttime\n",
    "    , endtime\n",
    "    , new_event_flag\n",
    "    , time_partition \n",
    "    , rate, rateuom, statusdescription\n",
    "\n",
    "from t2\n",
    "order by starttime, endtime\n",
    "\"\"\"\n",
    "ie = pd.read_sql_query(query,con)\n",
    "\n",
    "display_df(ie)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above (hopefully) makes it clear how a unique partition for each continuous segment of KCl administration can be delineated by cumulatively summing `new_event_flag` to create `time_partition`.\n",
    "\n",
    "### Step 3: create an integer to mark the last row of an event\n",
    "\n",
    "From above, it appears as though the *last* `statusdescription` would provide us useful debugging information as to why the administration event stopped - so we have another inline view where we create an integer which is 1 for the last `statusdescription`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Durations from INPUTEVENTS for one patient with KCl...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>starttime</th>\n",
       "      <th>endtime</th>\n",
       "      <th>time_partition</th>\n",
       "      <th>rate</th>\n",
       "      <th>rateuom</th>\n",
       "      <th>statusdescription</th>\n",
       "      <th>lastrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 11, 21:30</td>\n",
       "      <td>Day 12, 02:30</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 11, 23:45</td>\n",
       "      <td>Day 12, 02:41</td>\n",
       "      <td>2</td>\n",
       "      <td>10.002273</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 02:41</td>\n",
       "      <td>Day 12, 05:36</td>\n",
       "      <td>2</td>\n",
       "      <td>9.997713</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 05:36</td>\n",
       "      <td>Day 12, 08:31</td>\n",
       "      <td>2</td>\n",
       "      <td>10.285715</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 08:31</td>\n",
       "      <td>Day 12, 11:29</td>\n",
       "      <td>2</td>\n",
       "      <td>10.112360</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 11:29</td>\n",
       "      <td>Day 12, 14:28</td>\n",
       "      <td>2</td>\n",
       "      <td>10.055866</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 14:28</td>\n",
       "      <td>Day 12, 17:25</td>\n",
       "      <td>2</td>\n",
       "      <td>10.169492</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 17:25</td>\n",
       "      <td>Day 12, 20:24</td>\n",
       "      <td>2</td>\n",
       "      <td>10.055866</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 20:24</td>\n",
       "      <td>Day 12, 20:30</td>\n",
       "      <td>2</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Paused</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 21:30</td>\n",
       "      <td>Day 12, 21:35</td>\n",
       "      <td>3</td>\n",
       "      <td>9.997634</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Changed</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 21:35</td>\n",
       "      <td>Day 13, 02:08</td>\n",
       "      <td>3</td>\n",
       "      <td>6.190549</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 02:08</td>\n",
       "      <td>Day 13, 07:06</td>\n",
       "      <td>3</td>\n",
       "      <td>6.040268</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 07:06</td>\n",
       "      <td>Day 13, 12:03</td>\n",
       "      <td>3</td>\n",
       "      <td>6.006060</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 12:03</td>\n",
       "      <td>Day 13, 16:29</td>\n",
       "      <td>3</td>\n",
       "      <td>6.005904</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Stopped</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 18:15</td>\n",
       "      <td>Day 13, 23:15</td>\n",
       "      <td>4</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 15:28</td>\n",
       "      <td>Day 14, 18:47</td>\n",
       "      <td>5</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 18:47</td>\n",
       "      <td>Day 14, 19:01</td>\n",
       "      <td>5</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Changed</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 19:01</td>\n",
       "      <td>Day 14, 23:04</td>\n",
       "      <td>5</td>\n",
       "      <td>4.007380</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Changed</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 23:04</td>\n",
       "      <td>Day 14, 23:18</td>\n",
       "      <td>5</td>\n",
       "      <td>5.871428</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 23:18</td>\n",
       "      <td>Day 15, 02:28</td>\n",
       "      <td>5</td>\n",
       "      <td>5.905264</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 02:28</td>\n",
       "      <td>Day 15, 05:44</td>\n",
       "      <td>5</td>\n",
       "      <td>5.969388</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 05:44</td>\n",
       "      <td>Day 15, 08:57</td>\n",
       "      <td>5</td>\n",
       "      <td>5.906736</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 08:57</td>\n",
       "      <td>Day 15, 12:08</td>\n",
       "      <td>5</td>\n",
       "      <td>5.905759</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 12:08</td>\n",
       "      <td>Day 15, 15:17</td>\n",
       "      <td>5</td>\n",
       "      <td>5.904762</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 15:17</td>\n",
       "      <td>Day 15, 18:34</td>\n",
       "      <td>5</td>\n",
       "      <td>5.908629</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 18:34</td>\n",
       "      <td>Day 15, 21:46</td>\n",
       "      <td>5</td>\n",
       "      <td>5.906250</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 15, 21:46</td>\n",
       "      <td>Day 16, 01:01</td>\n",
       "      <td>5</td>\n",
       "      <td>5.907693</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 01:01</td>\n",
       "      <td>Day 16, 04:18</td>\n",
       "      <td>5</td>\n",
       "      <td>5.908629</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 04:18</td>\n",
       "      <td>Day 16, 07:36</td>\n",
       "      <td>5</td>\n",
       "      <td>5.903030</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 07:36</td>\n",
       "      <td>Day 16, 10:54</td>\n",
       "      <td>5</td>\n",
       "      <td>5.903030</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 10:54</td>\n",
       "      <td>Day 16, 14:12</td>\n",
       "      <td>5</td>\n",
       "      <td>5.903030</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 16, 14:12</td>\n",
       "      <td>Day 16, 16:04</td>\n",
       "      <td>5</td>\n",
       "      <td>5.905613</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Stopped</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Durations from INPUTEVENTS for one patient with KCl...\")\n",
    "query = query_schema + \"\"\"\n",
    "with t1 as\n",
    "(\n",
    "select icustay_id\n",
    "  , case when itemid = 227525 then 'Calcium' else 'KCl' end as label\n",
    "  , starttime\n",
    "  , endtime\n",
    "  , case\n",
    "        when lag(endtime) over (partition by icustay_id, itemid order by starttime, endtime) = starttime\n",
    "            then 0\n",
    "        else 1 end\n",
    "    as new_event_flag\n",
    "  , rate, rateuom\n",
    "  , statusdescription\n",
    "from inputevents_mv\n",
    "where itemid in\n",
    "(\n",
    "  --227525,-- Calcium Gluconate (CRRT)\n",
    "  227536 -- KCl (CRRT)\n",
    ")\n",
    "and statusdescription != 'Rewritten'\n",
    "\"\"\" + query_where_clause + \"\"\"\n",
    ")\n",
    ", t2 as\n",
    "(\n",
    "    select \n",
    "    icustay_id\n",
    "    , label\n",
    "    , starttime, endtime\n",
    "    , SUM(new_event_flag) OVER (partition by icustay_id, label order by starttime, endtime) as time_partition \n",
    "    , rate, rateuom, statusdescription\n",
    "    from t1\n",
    ")\n",
    ", t3 as\n",
    "(\n",
    "select\n",
    "    icustay_id\n",
    "    , label\n",
    "    , starttime, endtime\n",
    "    , time_partition \n",
    "    , rate, rateuom, statusdescription\n",
    "    , ROW_NUMBER() over (PARTITION BY icustay_id, label, time_partition order by starttime desc, endtime desc) as lastrow\n",
    "from t2\n",
    ")\n",
    "select \n",
    "label\n",
    ", starttime\n",
    ", endtime\n",
    ", time_partition\n",
    ", rate, rateuom\n",
    ", statusdescription\n",
    ", lastrow\n",
    " from t3\n",
    "order by starttime, endtime\n",
    "\"\"\"\n",
    "ie = pd.read_sql_query(query,con)\n",
    "\n",
    "display_df(ie)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: aggregate to merge together contiguous start/end times\n",
    "\n",
    "Now we aggregate the `starttime` and `endtime` together by grouping by `time_partition`, as follows:\n",
    "\n",
    "* we want the first `starttime`, so we use `min(starttime)`\n",
    "* we want the last `endtime`, so we use `max(endtime)`\n",
    "* we want the `statusdescription` at the last row, so we aggregate a column where all rows except the last are null\n",
    "\n",
    "To give more detail on the last step, let's look at the SQL code:\n",
    "\n",
    "```sql\n",
    ", min(case when lastrow = 1 then statusdescription else null end) as statusdescription\n",
    "```\n",
    "\n",
    "Aggregate functions ignore null values, so if we set the column to null for all but `lastrow = 1`, then the aggregate function is guaranteed to only return the value at `lastrow = 1`. The use of aggregate function could be either `min()` or `max()` - since it only effectively operates on a single value.\n",
    "\n",
    "Tying it all together, we have the final query:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Durations from INPUTEVENTS for one patient with KCl...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>starttime</th>\n",
       "      <th>endtime</th>\n",
       "      <th>rate_min</th>\n",
       "      <th>rate_max</th>\n",
       "      <th>rateuom</th>\n",
       "      <th>statusdescription</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 11, 21:30</td>\n",
       "      <td>Day 12, 02:30</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 11, 23:45</td>\n",
       "      <td>Day 12, 20:30</td>\n",
       "      <td>9.997713</td>\n",
       "      <td>10.285715</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Paused</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Calcium</td>\n",
       "      <td>Day 11, 23:45</td>\n",
       "      <td>Day 12, 20:30</td>\n",
       "      <td>1.201625</td>\n",
       "      <td>2.002708</td>\n",
       "      <td>grams/hour</td>\n",
       "      <td>Paused</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Calcium</td>\n",
       "      <td>Day 12, 21:30</td>\n",
       "      <td>Day 13, 15:54</td>\n",
       "      <td>1.206690</td>\n",
       "      <td>1.805171</td>\n",
       "      <td>grams/hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 12, 21:30</td>\n",
       "      <td>Day 13, 16:29</td>\n",
       "      <td>6.005904</td>\n",
       "      <td>9.997634</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Stopped</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 13, 18:15</td>\n",
       "      <td>Day 13, 23:15</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>FinishedRunning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Calcium</td>\n",
       "      <td>Day 13, 18:15</td>\n",
       "      <td>Day 13, 23:15</td>\n",
       "      <td>1.602136</td>\n",
       "      <td>1.602136</td>\n",
       "      <td>grams/hour</td>\n",
       "      <td>Paused</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>KCl</td>\n",
       "      <td>Day 14, 15:28</td>\n",
       "      <td>Day 16, 16:04</td>\n",
       "      <td>4.007380</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>mEq./hour</td>\n",
       "      <td>Stopped</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Calcium</td>\n",
       "      <td>Day 14, 15:28</td>\n",
       "      <td>Day 16, 16:05</td>\n",
       "      <td>1.196013</td>\n",
       "      <td>1.990426</td>\n",
       "      <td>grams/hour</td>\n",
       "      <td>Stopped</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Durations from INPUTEVENTS for one patient with KCl...\")\n",
    "query = query_schema + \"\"\"\n",
    "with t1 as\n",
    "(\n",
    "select icustay_id\n",
    "  , case when itemid = 227525 then 'Calcium' else 'KCl' end as label\n",
    "  , starttime\n",
    "  , endtime\n",
    "  , case\n",
    "        when lag(endtime) over (partition by icustay_id, itemid order by starttime, endtime) = starttime\n",
    "            then 0\n",
    "        else 1 end\n",
    "    as new_event_flag\n",
    "  , rate, rateuom\n",
    "  , statusdescription\n",
    "from inputevents_mv\n",
    "where itemid in\n",
    "(\n",
    "  227525,-- Calcium Gluconate (CRRT)\n",
    "  227536 -- KCl (CRRT)\n",
    ")\n",
    "and statusdescription != 'Rewritten'\n",
    "\"\"\" + query_where_clause + \"\"\"\n",
    ")\n",
    ", t2 as\n",
    "(\n",
    "    select \n",
    "    icustay_id\n",
    "    , label\n",
    "    , starttime, endtime\n",
    "    , SUM(new_event_flag) OVER (partition by icustay_id, label order by starttime, endtime) as time_partition \n",
    "    , rate, rateuom, statusdescription\n",
    "    from t1\n",
    ")\n",
    ", t3 as\n",
    "(\n",
    "select\n",
    "    icustay_id\n",
    "    , label\n",
    "    , starttime, endtime\n",
    "    , time_partition \n",
    "    , rate, rateuom, statusdescription\n",
    "    , ROW_NUMBER() over (PARTITION BY icustay_id, label, time_partition order by starttime desc, endtime desc) as lastrow\n",
    "from t2\n",
    ")\n",
    "select\n",
    "  label\n",
    "  --, time_partition\n",
    "  , min(starttime) AS starttime\n",
    "  , max(endtime) AS endtime\n",
    "  , min(rate) AS rate_min\n",
    "  , max(rate) AS rate_max\n",
    "  , min(rateuom) AS rateuom\n",
    "  , min(case when lastrow = 1 then statusdescription else null end) as statusdescription\n",
    "from t3\n",
    "group by icustay_id, label, time_partition\n",
    "order by starttime, endtime\n",
    "\"\"\"\n",
    "ie = pd.read_sql_query(query,con)\n",
    "\n",
    "display_df(ie)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above looks good - so we save the query to `query_inputevents` without the clause that isolates the data to one patient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "query_inputevents = query_schema + \"\"\"\n",
    "with t1 as\n",
    "(\n",
    "select icustay_id\n",
    "  , case when itemid = 227525 then 'Calcium' else 'KCl' end as label\n",
    "  , starttime\n",
    "  , endtime\n",
    "  , case\n",
    "        when lag(endtime) over (partition by icustay_id, itemid order by starttime, endtime) = starttime\n",
    "            then 0\n",
    "        else 1 end\n",
    "    as new_event_flag\n",
    "  , rate, rateuom\n",
    "  , statusdescription\n",
    "from inputevents_mv\n",
    "where itemid in\n",
    "(\n",
    "  227525,-- Calcium Gluconate (CRRT)\n",
    "  227536 -- KCl (CRRT)\n",
    ")\n",
    "and statusdescription != 'Rewritten'\n",
    ")\n",
    ", t2 as\n",
    "(\n",
    "    select \n",
    "    icustay_id\n",
    "    , label\n",
    "    , starttime, endtime\n",
    "    , SUM(new_event_flag) OVER (partition by icustay_id, label order by starttime, endtime) as time_partition \n",
    "    , rate, rateuom, statusdescription\n",
    "    from t1\n",
    ")\n",
    ", t3 as\n",
    "(\n",
    "select\n",
    "    icustay_id\n",
    "    , label\n",
    "    , starttime, endtime\n",
    "    , time_partition \n",
    "    , rate, rateuom, statusdescription\n",
    "    , ROW_NUMBER() over (PARTITION BY icustay_id, label, time_partition order by starttime desc, endtime desc) as lastrow\n",
    "from t2\n",
    ")\n",
    "select\n",
    "    icustay_id\n",
    "    , time_partition as num\n",
    "    , min(starttime) AS starttime\n",
    "    , max(endtime) AS endtime\n",
    "    , label\n",
    "    --, min(rate) AS rate_min\n",
    "    --, max(rate) AS rate_max\n",
    "    --, min(rateuom) AS rateuom\n",
    "    --, min(case when lastrow = 1 then statusdescription else null end) as statusdescription\n",
    "from t3\n",
    "group by icustay_id, label, time_partition\n",
    "order by starttime, endtime\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "We now have a good method of combining contiguous events from `INPUTEVENTS_MV`. Note that this is *usually* not required, as the `linkorderid` is meant to partition these events for us. For example, lets look at a very common sedative agent used in the ICU, propofol:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Durations from INPUTEVENTS for one patient given propofol...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>linkorderid</th>\n",
       "      <th>orderid</th>\n",
       "      <th>starttime</th>\n",
       "      <th>endtime</th>\n",
       "      <th>rate</th>\n",
       "      <th>rateuom</th>\n",
       "      <th>amount</th>\n",
       "      <th>amountuom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>1405816</td>\n",
       "      <td>1405816</td>\n",
       "      <td>Day 09, 18:29</td>\n",
       "      <td>Day 10, 00:14</td>\n",
       "      <td>50.002502</td>\n",
       "      <td>mcg/kg/min</td>\n",
       "      <td>17.250863</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>1405816</td>\n",
       "      <td>2101314</td>\n",
       "      <td>Day 10, 01:01</td>\n",
       "      <td>Day 10, 01:05</td>\n",
       "      <td>50.002502</td>\n",
       "      <td>mcg/kg/min</td>\n",
       "      <td>0.200010</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>1405816</td>\n",
       "      <td>7312240</td>\n",
       "      <td>Day 10, 01:05</td>\n",
       "      <td>Day 10, 08:05</td>\n",
       "      <td>40.001221</td>\n",
       "      <td>mcg/kg/min</td>\n",
       "      <td>16.800513</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>1405816</td>\n",
       "      <td>7169415</td>\n",
       "      <td>Day 10, 08:15</td>\n",
       "      <td>Day 10, 12:00</td>\n",
       "      <td>40.001221</td>\n",
       "      <td>mcg/kg/min</td>\n",
       "      <td>9.000275</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>1405816</td>\n",
       "      <td>5852722</td>\n",
       "      <td>Day 10, 12:05</td>\n",
       "      <td>Day 10, 12:40</td>\n",
       "      <td>40.001221</td>\n",
       "      <td>mcg/kg/min</td>\n",
       "      <td>1.400043</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>1405816</td>\n",
       "      <td>3365285</td>\n",
       "      <td>Day 10, 12:40</td>\n",
       "      <td>Day 10, 14:00</td>\n",
       "      <td>20.000627</td>\n",
       "      <td>mcg/kg/min</td>\n",
       "      <td>1.600050</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>522225</td>\n",
       "      <td>522225</td>\n",
       "      <td>Day 10, 14:00</td>\n",
       "      <td>Day 10, 14:01</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>1405816</td>\n",
       "      <td>5245063</td>\n",
       "      <td>Day 10, 14:00</td>\n",
       "      <td>Day 10, 14:07</td>\n",
       "      <td>40.001254</td>\n",
       "      <td>mcg/kg/min</td>\n",
       "      <td>0.280009</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>2703553</td>\n",
       "      <td>2703553</td>\n",
       "      <td>Day 10, 14:05</td>\n",
       "      <td>Day 10, 14:06</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>1405816</td>\n",
       "      <td>6687581</td>\n",
       "      <td>Day 10, 14:07</td>\n",
       "      <td>Day 11, 08:45</td>\n",
       "      <td>30.001253</td>\n",
       "      <td>mcg/kg/min</td>\n",
       "      <td>33.541401</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>4912696</td>\n",
       "      <td>4912696</td>\n",
       "      <td>Day 10, 16:10</td>\n",
       "      <td>Day 10, 16:11</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>3838086</td>\n",
       "      <td>3838086</td>\n",
       "      <td>Day 10, 16:55</td>\n",
       "      <td>Day 10, 16:56</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>5665808</td>\n",
       "      <td>5665808</td>\n",
       "      <td>Day 11, 01:51</td>\n",
       "      <td>Day 11, 01:52</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>1405816</td>\n",
       "      <td>3755617</td>\n",
       "      <td>Day 11, 09:10</td>\n",
       "      <td>Day 11, 13:36</td>\n",
       "      <td>30.001253</td>\n",
       "      <td>mcg/kg/min</td>\n",
       "      <td>7.980333</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Durations from INPUTEVENTS for one patient given propofol...\")\n",
    "query = query_schema + \"\"\"\n",
    "with t1 as\n",
    "(\n",
    "select icustay_id\n",
    "  , di.label\n",
    "  , mv.linkorderid, mv.orderid\n",
    "  , starttime\n",
    "  , endtime\n",
    "  , rate, rateuom\n",
    "  , amount, amountuom\n",
    "from inputevents_mv mv\n",
    "inner join d_items di\n",
    "on mv.itemid = di.itemid\n",
    "and statusdescription != 'Rewritten'\n",
    "\"\"\" + query_where_clause + \"\"\"\n",
    "and mv.itemid = 222168\n",
    ")\n",
    "select \n",
    "    label\n",
    "  , linkorderid, orderid\n",
    "  , starttime\n",
    "  , endtime\n",
    "  , rate, rateuom\n",
    "  , amount, amountuom\n",
    "from t1\n",
    "order by starttime, endtime\n",
    "\"\"\"\n",
    "ie = pd.read_sql_query(query,con)\n",
    "\n",
    "display_df(ie)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see that `linkorderid` nicely delineates contiguous events without us having to put in the effort of above. It also separates *distinct* administrations. Above, at row **6**, we can see a \"1 minute\" delivery of propofol. This is how MetaVision tables (those which end in `_mv`) mark \"instant\" events - in the case of drug delivery, these are boluses of drugs administered to the patient. \n",
    "\n",
    "When using this data, we can group like events on a partition (as we did above), but we don't have to create the partition: it already exists with `linkorderid`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grouped durations from INPUTEVENTS for one patient given propofol...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>linkorderid</th>\n",
       "      <th>starttime</th>\n",
       "      <th>endtime</th>\n",
       "      <th>rate_min</th>\n",
       "      <th>rate_max</th>\n",
       "      <th>rateuom</th>\n",
       "      <th>amount_min</th>\n",
       "      <th>amount_max</th>\n",
       "      <th>amountuom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>1405816</td>\n",
       "      <td>Day 09, 18:29</td>\n",
       "      <td>Day 11, 13:36</td>\n",
       "      <td>20.000627</td>\n",
       "      <td>50.002502</td>\n",
       "      <td>mcg/kg/min</td>\n",
       "      <td>0.200010</td>\n",
       "      <td>33.541401</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>522225</td>\n",
       "      <td>Day 10, 14:00</td>\n",
       "      <td>Day 10, 14:01</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>2703553</td>\n",
       "      <td>Day 10, 14:05</td>\n",
       "      <td>Day 10, 14:06</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>4912696</td>\n",
       "      <td>Day 10, 16:10</td>\n",
       "      <td>Day 10, 16:11</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>3838086</td>\n",
       "      <td>Day 10, 16:55</td>\n",
       "      <td>Day 10, 16:56</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Propofol</td>\n",
       "      <td>5665808</td>\n",
       "      <td>Day 11, 01:51</td>\n",
       "      <td>Day 11, 01:52</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>10.000001</td>\n",
       "      <td>mg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Grouped durations from INPUTEVENTS for one patient given propofol...\")\n",
    "query = query_schema + \"\"\"\n",
    "with t1 as\n",
    "(\n",
    "select icustay_id\n",
    "  , di.itemid, di.label\n",
    "  , mv.linkorderid, mv.orderid\n",
    "  , starttime\n",
    "  , endtime\n",
    "  , amount, amountuom\n",
    "  , rate, rateuom\n",
    "from inputevents_mv mv\n",
    "inner join d_items di\n",
    "on mv.itemid = di.itemid\n",
    "and statusdescription != 'Rewritten'\n",
    "\"\"\" + query_where_clause + \"\"\"\n",
    "and mv.itemid = 222168\n",
    ")\n",
    "select icustay_id\n",
    " , label\n",
    " , linkorderid\n",
    " , min(starttime) as starttime\n",
    " , max(endtime) as endtime\n",
    " , min(rate) as rate_min\n",
    " , max(rate) as rate_max\n",
    " , max(rateuom) as rateuom\n",
    " , min(amount) as amount_min\n",
    " , max(amount) as amount_max\n",
    " , max(amountuom) as amountuom\n",
    "from t1\n",
    "group by icustay_id, itemid, label, linkorderid\n",
    "order by starttime, endtime\n",
    "\"\"\"\n",
    "ie = pd.read_sql_query(query,con)\n",
    "\n",
    "display_df(ie)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's also worth noting that bolus administrations *do not have a `rate`*. They only have an `amount`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert CHARTEVENTS into durations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Durations from CHARTEVENTS...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num</th>\n",
       "      <th>starttime</th>\n",
       "      <th>endtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Day 11, 23:43</td>\n",
       "      <td>Day 12, 20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Day 12, 22:00</td>\n",
       "      <td>Day 13, 16:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Day 13, 18:15</td>\n",
       "      <td>Day 13, 23:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Day 14, 15:27</td>\n",
       "      <td>Day 16, 16:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# convert CHARTEVENTS into durations\n",
    "# NOTE: we only look at a single patient as an exemplar\n",
    "print(\"Durations from CHARTEVENTS...\")\n",
    "query = query_schema + \"\"\"\n",
    "with crrt_settings as\n",
    "(\n",
    "select ce.icustay_id, ce.charttime\n",
    ", max(\n",
    "    case\n",
    "      when ce.itemid in\n",
    "      (\n",
    "        224149, -- Access Pressure\n",
    "        224144, -- Blood Flow (ml/min)\n",
    "        228004, -- Citrate (ACD-A)\n",
    "        225183, -- Current Goal\n",
    "        225977, -- Dialysate Fluid\n",
    "        224154, -- Dialysate Rate\n",
    "        224151, -- Effluent Pressure\n",
    "        224150, -- Filter Pressure\n",
    "        225958, -- Heparin Concentration (units/mL)\n",
    "        224145, -- Heparin Dose (per hour)\n",
    "        224191, -- Hourly Patient Fluid Removal\n",
    "        228005, -- PBP (Prefilter) Replacement Rate\n",
    "        228006, -- Post Filter Replacement Rate\n",
    "        225976, -- Replacement Fluid\n",
    "        224153, -- Replacement Rate\n",
    "        224152, -- Return Pressure\n",
    "        226457  -- Ultrafiltrate Output\n",
    "      ) then 1\n",
    "    else 0 end)\n",
    "    as RRT\n",
    "-- Below indicates that a new instance of CRRT has started\n",
    ", max(\n",
    "  case\n",
    "    -- System Integrity\n",
    "    when ce.itemid = 224146 and value in ('New Filter','Reinitiated')\n",
    "      then 1\n",
    "  else 0\n",
    " end ) as RRT_start\n",
    "-- Below indicates that the current instance of CRRT has ended\n",
    ", max(\n",
    "  case\n",
    "    -- System Integrity\n",
    "    when ce.itemid = 224146 and value in ('Discontinued','Recirculating')\n",
    "      then 1\n",
    "    when ce.itemid = 225956\n",
    "      then 1\n",
    "  else 0\n",
    " end ) as RRT_end\n",
    "from chartevents ce\n",
    "where ce.itemid in\n",
    "(\n",
    "  -- MetaVision ITEMIDs\n",
    "  -- Below require special handling\n",
    "  224146, -- System Integrity\n",
    "  225956,  -- Reason for CRRT Filter Change\n",
    "\n",
    "  -- Below are settings which indicate CRRT is started/continuing\n",
    "  224149, -- Access Pressure\n",
    "  224144, -- Blood Flow (ml/min)\n",
    "  228004, -- Citrate (ACD-A)\n",
    "  225183, -- Current Goal\n",
    "  225977, -- Dialysate Fluid\n",
    "  224154, -- Dialysate Rate\n",
    "  224151, -- Effluent Pressure\n",
    "  224150, -- Filter Pressure\n",
    "  225958, -- Heparin Concentration (units/mL)\n",
    "  224145, -- Heparin Dose (per hour)\n",
    "  224191, -- Hourly Patient Fluid Removal\n",
    "  228005, -- PBP (Prefilter) Replacement Rate\n",
    "  228006, -- Post Filter Replacement Rate\n",
    "  225976, -- Replacement Fluid\n",
    "  224153, -- Replacement Rate\n",
    "  224152, -- Return Pressure\n",
    "  226457  -- Ultrafiltrate Output\n",
    ")\n",
    "and ce.value is not null\n",
    "\"\"\" + query_where_clause + \"\"\"\n",
    "group by icustay_id, charttime\n",
    ")\n",
    "\n",
    "-- create the durations for each CRRT instance\n",
    "select icustay_id\n",
    "  , ROW_NUMBER() over (partition by icustay_id order by num) as num\n",
    "  , min(charttime) as starttime\n",
    "  , max(charttime) as endtime\n",
    "from\n",
    "(\n",
    "  select vd1.*\n",
    "  -- create a cumulative sum of the instances of new CRRT\n",
    "  -- this results in a monotonically increasing integer assigned to each CRRT\n",
    "  , case when RRT_start = 1 or RRT=1 or RRT_end = 1 then\n",
    "      SUM( NewCRRT )\n",
    "      OVER ( partition by icustay_id order by charttime )\n",
    "    else null end\n",
    "    as num\n",
    "  --- now we convert CHARTTIME of CRRT settings into durations\n",
    "  from ( -- vd1\n",
    "      select\n",
    "          icustay_id\n",
    "          -- this carries over the previous charttime\n",
    "          , case\n",
    "              when RRT=1 then\n",
    "                LAG(CHARTTIME, 1) OVER (partition by icustay_id, RRT order by charttime)\n",
    "              else null\n",
    "            end as charttime_lag\n",
    "          , charttime\n",
    "          , RRT\n",
    "          , RRT_start\n",
    "          , RRT_end\n",
    "          -- calculate the time since the last event\n",
    "          , case\n",
    "              -- non-null iff the current observation indicates settings are present\n",
    "              when RRT=1 then\n",
    "                CHARTTIME -\n",
    "                (\n",
    "                  LAG(CHARTTIME, 1) OVER\n",
    "                  (\n",
    "                    partition by icustay_id, RRT\n",
    "                    order by charttime\n",
    "                  )\n",
    "                )\n",
    "              else null\n",
    "            end as CRRT_duration\n",
    "\n",
    "          -- now we determine if the current event is a new instantiation\n",
    "          , case\n",
    "              when RRT_start = 1\n",
    "                then 1\n",
    "            -- if there is an end flag, we mark any subsequent event as new\n",
    "              when RRT_end = 1\n",
    "                -- note the end is *not* a new event, the *subsequent* row is\n",
    "                -- so here we output 0\n",
    "                then 0\n",
    "              when\n",
    "                LAG(RRT_end,1)\n",
    "                OVER\n",
    "                (\n",
    "                partition by icustay_id, case when RRT=1 or RRT_end=1 then 1 else 0 end\n",
    "                order by charttime\n",
    "                ) = 1\n",
    "                  then 1\n",
    "                -- if there is less than 2 hours between CRRT settings, we do not treat this as a new CRRT event\n",
    "              when (CHARTTIME - (LAG(CHARTTIME, 1)\n",
    "              OVER\n",
    "              (\n",
    "                partition by icustay_id, case when RRT=1 or RRT_end=1 then 1 else 0 end\n",
    "                order by charttime\n",
    "              ))) <= interval '2' hour\n",
    "                then 0\n",
    "            else 1\n",
    "          end as NewCRRT\n",
    "      -- use the temp table with only settings from chartevents\n",
    "      FROM crrt_settings\n",
    "  ) AS vd1\n",
    "  -- now we can isolate to just rows with settings\n",
    "  -- (before we had rows with start/end flags)\n",
    "  -- this removes any null values for NewCRRT\n",
    "  where\n",
    "    RRT_start = 1 or RRT = 1 or RRT_end = 1\n",
    ") AS vd2\n",
    "group by icustay_id, num\n",
    "having min(charttime) != max(charttime)\n",
    "order by icustay_id, num\n",
    "\"\"\"\n",
    "ce = pd.read_sql_query(query,con)\n",
    "\n",
    "display_df(ce)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# happy with the above query - repeat it without the isolation to a single ICUSTAY_ID\n",
    "query_chartevents = query_schema + \"\"\"\n",
    "with crrt_settings as\n",
    "(\n",
    "select ce.icustay_id, ce.charttime\n",
    ", max(\n",
    "    case\n",
    "      when ce.itemid in\n",
    "      (\n",
    "        224149, -- Access Pressure\n",
    "        224144, -- Blood Flow (ml/min)\n",
    "        228004, -- Citrate (ACD-A)\n",
    "        225183, -- Current Goal\n",
    "        225977, -- Dialysate Fluid\n",
    "        224154, -- Dialysate Rate\n",
    "        224151, -- Effluent Pressure\n",
    "        224150, -- Filter Pressure\n",
    "        225958, -- Heparin Concentration (units/mL)\n",
    "        224145, -- Heparin Dose (per hour)\n",
    "        224191, -- Hourly Patient Fluid Removal\n",
    "        228005, -- PBP (Prefilter) Replacement Rate\n",
    "        228006, -- Post Filter Replacement Rate\n",
    "        225976, -- Replacement Fluid\n",
    "        224153, -- Replacement Rate\n",
    "        224152, -- Return Pressure\n",
    "        226457  -- Ultrafiltrate Output\n",
    "      ) then 1\n",
    "    else 0 end)\n",
    "    as RRT\n",
    "-- Below indicates that a new instance of CRRT has started\n",
    ", max(\n",
    "  case\n",
    "    -- System Integrity\n",
    "    when ce.itemid = 224146 and value in ('New Filter','Reinitiated')\n",
    "      then 1\n",
    "  else 0\n",
    " end ) as RRT_start\n",
    "-- Below indicates that the current instance of CRRT has ended\n",
    ", max(\n",
    "  case\n",
    "    -- System Integrity\n",
    "    when ce.itemid = 224146 and value in ('Discontinued','Recirculating')\n",
    "      then 1\n",
    "    when ce.itemid = 225956\n",
    "      then 1\n",
    "  else 0\n",
    " end ) as RRT_end\n",
    "from chartevents ce\n",
    "where ce.itemid in\n",
    "(\n",
    "  -- MetaVision ITEMIDs\n",
    "  -- Below require special handling\n",
    "  224146, -- System Integrity\n",
    "  225956,  -- Reason for CRRT Filter Change\n",
    "\n",
    "  -- Below are settings which indicate CRRT is started/continuing\n",
    "  224149, -- Access Pressure\n",
    "  224144, -- Blood Flow (ml/min)\n",
    "  228004, -- Citrate (ACD-A)\n",
    "  225183, -- Current Goal\n",
    "  225977, -- Dialysate Fluid\n",
    "  224154, -- Dialysate Rate\n",
    "  224151, -- Effluent Pressure\n",
    "  224150, -- Filter Pressure\n",
    "  225958, -- Heparin Concentration (units/mL)\n",
    "  224145, -- Heparin Dose (per hour)\n",
    "  224191, -- Hourly Patient Fluid Removal\n",
    "  228005, -- PBP (Prefilter) Replacement Rate\n",
    "  228006, -- Post Filter Replacement Rate\n",
    "  225976, -- Replacement Fluid\n",
    "  224153, -- Replacement Rate\n",
    "  224152, -- Return Pressure\n",
    "  226457  -- Ultrafiltrate Output\n",
    ")\n",
    "and ce.value is not null\n",
    "group by icustay_id, charttime\n",
    ")\n",
    "\n",
    "-- create the durations for each CRRT instance\n",
    "select icustay_id\n",
    "  , ROW_NUMBER() over (partition by icustay_id order by num) as num\n",
    "  , min(charttime) as starttime\n",
    "  , max(charttime) as endtime\n",
    "from\n",
    "(\n",
    "  select vd1.*\n",
    "  -- create a cumulative sum of the instances of new CRRT\n",
    "  -- this results in a monotonically increasing integer assigned to each CRRT\n",
    "  , case when RRT_start = 1 or RRT=1 or RRT_end = 1 then\n",
    "      SUM( NewCRRT )\n",
    "      OVER ( partition by icustay_id order by charttime )\n",
    "    else null end\n",
    "    as num\n",
    "  --- now we convert CHARTTIME of CRRT settings into durations\n",
    "  from ( -- vd1\n",
    "      select\n",
    "          icustay_id\n",
    "          -- this carries over the previous charttime\n",
    "          , case\n",
    "              when RRT=1 then\n",
    "                LAG(CHARTTIME, 1) OVER (partition by icustay_id, RRT order by charttime)\n",
    "              else null\n",
    "            end as charttime_lag\n",
    "          , charttime\n",
    "          , RRT\n",
    "          , RRT_start\n",
    "          , RRT_end\n",
    "          -- calculate the time since the last event\n",
    "          , case\n",
    "              -- non-null iff the current observation indicates settings are present\n",
    "              when RRT=1 then\n",
    "                CHARTTIME -\n",
    "                (\n",
    "                  LAG(CHARTTIME, 1) OVER\n",
    "                  (\n",
    "                    partition by icustay_id, RRT\n",
    "                    order by charttime\n",
    "                  )\n",
    "                )\n",
    "              else null\n",
    "            end as CRRT_duration\n",
    "\n",
    "          -- now we determine if the current event is a new instantiation\n",
    "          , case\n",
    "              when RRT_start = 1\n",
    "                then 1\n",
    "            -- if there is an end flag, we mark any subsequent event as new\n",
    "              when RRT_end = 1\n",
    "                -- note the end is *not* a new event, the *subsequent* row is\n",
    "                -- so here we output 0\n",
    "                then 0\n",
    "              when\n",
    "                LAG(RRT_end,1)\n",
    "                OVER\n",
    "                (\n",
    "                partition by icustay_id, case when RRT=1 or RRT_end=1 then 1 else 0 end\n",
    "                order by charttime\n",
    "                ) = 1\n",
    "                  then 1\n",
    "                -- if there is less than 2 hours between CRRT settings, we do not treat this as a new CRRT event\n",
    "              when (CHARTTIME - (LAG(CHARTTIME, 1)\n",
    "              OVER\n",
    "              (\n",
    "                partition by icustay_id, case when RRT=1 or RRT_end=1 then 1 else 0 end\n",
    "                order by charttime\n",
    "              ))) <= interval '2' hour\n",
    "                then 0\n",
    "            else 1\n",
    "          end as NewCRRT\n",
    "      -- use the temp table with only settings from chartevents\n",
    "      FROM crrt_settings\n",
    "  ) AS vd1\n",
    "  -- now we can isolate to just rows with settings\n",
    "  -- (before we had rows with start/end flags)\n",
    "  -- this removes any null values for NewCRRT\n",
    "  where\n",
    "    RRT_start = 1 or RRT = 1 or RRT_end = 1\n",
    ") AS vd2\n",
    "group by icustay_id, num\n",
    "having min(charttime) != max(charttime)\n",
    "order by icustay_id, num\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract durations from PROCEDUREEVENTS_MV\n",
    "\n",
    "PROCEDUREEVENTS_MV contains entries for dialysis. As a reminder from the above, we picked the following `itemid`:\n",
    "\n",
    "* 225802 -- Dialysis - CRRT\n",
    "* 225803 -- Dialysis - CVVHD\n",
    "* 225809 -- Dialysis - CVVHDF\n",
    "* 225955 -- Dialysis - SCUF\n",
    "\n",
    "Extracting data for these entries is straightforward. Each instance of CRRT is documented with a single `starttime` and a single `stoptime`, with no need to merge together different rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Durations from PROCEDUREEVENTS_MV...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num</th>\n",
       "      <th>starttime</th>\n",
       "      <th>endtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Day 11, 23:45</td>\n",
       "      <td>Day 12, 20:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Day 12, 21:30</td>\n",
       "      <td>Day 13, 23:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Day 14, 15:27</td>\n",
       "      <td>Day 16, 16:02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extract the durations from PROCEDUREEVENTS_MV\n",
    "# NOTE: we only look at a single patient as an exemplar\n",
    "print(\"Durations from PROCEDUREEVENTS_MV...\")\n",
    "query = query_schema + \"\"\"\n",
    "select icustay_id\n",
    "  , ROW_NUMBER() over (partition by icustay_id order by starttime, endtime) as num\n",
    "  , starttime, endtime\n",
    "from procedureevents_mv\n",
    "where itemid in\n",
    "(\n",
    "    225802 -- Dialysis - CRRT\n",
    "  , 225803 -- Dialysis - CVVHD\n",
    "  , 225809 -- Dialysis - CVVHDF\n",
    "  , 225955 -- Dialysis - SCUF\n",
    ")\n",
    "\"\"\" + query_where_clause + \"\"\"\n",
    "order by icustay_id, num\n",
    "\"\"\"\n",
    "pe = pd.read_sql_query(query,con)\n",
    "\n",
    "display_df(pe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the above documentation is quite dilligent: the entry pauses between the first and second row for 1 hour representing an actual pause in the administration of CRRT."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# happy with above query\n",
    "query_procedureevents = query_schema + \"\"\"\n",
    "select icustay_id\n",
    "  , ROW_NUMBER() over (partition by icustay_id order by starttime, endtime) as num\n",
    "  , starttime, endtime\n",
    "from procedureevents_mv\n",
    "where itemid in\n",
    "(\n",
    "    225802 -- Dialysis - CRRT\n",
    "  , 225803 -- Dialysis - CVVHD\n",
    "  , 225809 -- Dialysis - CVVHDF\n",
    "  , 225955 -- Dialysis - SCUF\n",
    ")\n",
    "order by icustay_id, num\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Roundup: data from INPUTEVENTS_MV, CHARTEVENTS, and PROCEDUREEVENTS_MV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print(\"Durations from INPUTEVENTS...\")\n",
    "ie = pd.read_sql_query(query_inputevents,con)\n",
    "\n",
    "print(\"Durations from CHARTEVENTS...\")\n",
    "ce = pd.read_sql_query(query_chartevents,con)\n",
    "\n",
    "print(\"Durations from PROCEDUREEVENTS...\")\n",
    "pe = pd.read_sql_query(query_procedureevents,con)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare durations\n",
    "\n",
    "We now need to merge together the above durations into a single, master set of CRRT administrations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num</th>\n",
       "      <th>starttime</th>\n",
       "      <th>endtime</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Day 09, 18:10</td>\n",
       "      <td>Day 13, 15:44</td>\n",
       "      <td>inputevents_ca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>1</td>\n",
       "      <td>Day 09, 18:00</td>\n",
       "      <td>Day 12, 15:15</td>\n",
       "      <td>chartevents</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>2</td>\n",
       "      <td>Day 12, 16:02</td>\n",
       "      <td>Day 12, 19:01</td>\n",
       "      <td>chartevents</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>3</td>\n",
       "      <td>Day 12, 21:00</td>\n",
       "      <td>Day 13, 14:03</td>\n",
       "      <td>chartevents</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>1</td>\n",
       "      <td>Day 09, 18:00</td>\n",
       "      <td>Day 13, 15:04</td>\n",
       "      <td>procedureevents</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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AAIAxlFIAAAAAgDGUUgAAAACAMZRSAAAAAIAxlFIAAAAA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\nBQAAAAAYQykFAAAAABjzP2CcIsuK88HEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe95c27de50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# how many PROCEDUREEVENTS_MV dialysis events encapsulate CHARTEVENTS/INPUTEVENTS_MV?\n",
    "# vice-versa?\n",
    "iid = 205508\n",
    "\n",
    "# compare the above durations\n",
    "ce['source'] = 'chartevents'\n",
    "ie['source'] = 'inputevents_kcl'\n",
    "ie.loc[ie['label']=='Calcium','source'] = 'inputevents_ca' \n",
    "pe['source'] = 'procedureevents'\n",
    "df = pd.concat([ie[['icustay_id','num','starttime','endtime','source']], ce, pe])\n",
    "idxDisplay = df['icustay_id'] == iid\n",
    "display_df(df.loc[idxDisplay, :])\n",
    "\n",
    "# 2) how many have no overlap whatsoever?\n",
    "\n",
    "\n",
    "col_dict = {'chartevents': [247,129,191],\n",
    "           'inputevents_kcl': [255,127,0],\n",
    "           'inputevents_ca': [228,26,28],\n",
    "           'procedureevents': [55,126,184]}\n",
    "\n",
    "for c in col_dict:\n",
    "    col_dict[c] = [x/256.0 for x in col_dict[c]]\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=[16,10])\n",
    "m = 0.\n",
    "M = np.sum(idxDisplay)\n",
    "\n",
    "# dummy plots for legend\n",
    "legend_handle = list()\n",
    "for c in col_dict:\n",
    "    legend_handle.append(mlines.Line2D([], [], color=col_dict[c], marker='o',\n",
    "                              markersize=15, label=c))\n",
    "\n",
    "for row in df.loc[idxDisplay,:].iterrows():\n",
    "    # row is a tuple: [index, actual_data], so we use row[1]\n",
    "    plt.plot([row[1]['starttime'].to_pydatetime(), row[1]['endtime'].to_pydatetime()], [0+m/M,0+m/M],\n",
    "            'o-',color=col_dict[row[1]['source']],\n",
    "            markersize=15, linewidth=2)\n",
    "    m=m+1\n",
    "    \n",
    "ax.xaxis.set_minor_locator(dates.HourLocator(byhour=[0,12],interval=1))\n",
    "ax.xaxis.set_minor_formatter(dates.DateFormatter('%H:%M'))\n",
    "ax.xaxis.grid(True, which=\"minor\")\n",
    "ax.xaxis.set_major_locator(dates.DayLocator(interval=1))\n",
    "ax.xaxis.set_major_formatter(dates.DateFormatter('\\n%d\\n%a'))\n",
    "\n",
    "ax.set_ylim([-0.1,1.0])\n",
    "\n",
    "plt.legend(handles=legend_handle,loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ajVh6Jc8VsIZhMGNMAYs37knofv4sy2DGmMKE7+P3+Xw89thjbb63adOmNl+f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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9547b4290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PfzzVajVve9vb8tBDD+W3v/1tkjh8FwAAGGJKpaw75Lzs+tBVW/W1MLVKS9Ye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\nAAAAOIZQCgAAAABwDKEUAAAAAOAYt9M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6oL4AwG6RauexY8fME088Ybq6uowxxpw5cyZi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\nAAA4hlAKAAAAAHAMoRQAAAAA4Jj/BVwJczs0EfLsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe95c03b4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mhHzOoRJs3efPn9cf/vAHnTlzRqNGjdJDDz2k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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe95c25d290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HH3xQP/jBD/STn/xEjuPo0Ucf1Ve/+lVdfPHFmjhxokaOHKnBgwenYxMCAAAAQLdZXk9u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\nsB7NKwAAAADAejSvAAAAAADr0bwCAAAAAKxH8wo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tlZSUaNeuXXK73Ro2bJjmz5+vAQMG\ndBpP7SEekepv/fr12rlzpxzHUXZ2tubPnx/2nmo1NTUqLi5WQ0ODRowYoYULF8rtduv111/X1q1b\nlZGRocGDB2vevHkaMmRIp/FdbbdkyRJ9+OGHuvDCC3X//fcn7DFhj0j194XXXntNJSUl+t3vfqfB\ngwd3Gk/9IVaRau/ll1/W1q1b2+rt+uuv14QJEzqNZ+1FPLqa+9544w1t3rxZLpdLEyZM0I033thp\nPPUXBdPH+Xw+c/DgQWOMMU1NTebOO+80hw8fNocPHzZHjhwxDz/8sDlw4EDE8SUlJaa0tNQYY0xp\naakpKSkxxhhTXV1tGhoajDHG7N692yxevDjs+K62q6ysNAcPHjT33HNPQh8TdohUexUVFSYQCBhj\nQr/rL36/HVF7iEek+jt16lTbNn/605/M6tWrw45fvny5eeedd4wxxqxevdps3rzZGGPMvn37TEtL\nizHGmM2bN5snn3wy7Piuttu7d68pLy83S5cuTehjwh6R6s8YY06cOGF++ctfmnnz5pnPP/887Hjq\nD7GKVHsbNmwwGzdu7HY8ay/iEan+9u3bZx599FHT2tpqjDHm5MmTYcdTf93r828bzsnJ0YgRIyRJ\nmZmZ+upXvyqfz6dzzjkn7E10OyovL9fll18uSbr88stVXl4uSfr617/e7p5VdXV1Ycd3td3o0aPb\nvpfIx4QdItXe+PHjlZGRIUm64IIL5PP5wo6n9hCPSPWXlZXVts2///3vsPdSM8aosrJSkyZNkiRN\nnTq1rRbGjh3bdqP5888/P2L9drXduHHjlJmZmfDHhD0i1Z8kvfjii7rhhhsi3seP+kM8uqq9aLD2\nIh6R6u/NN9/U1Vdf3XaP2uzs7LDjqb/u9fm3DX9ZTU2NPvroI40aNSrqMZ9//rlycnIkSWeddZY+\n//zzTtuUlZW13Qi4K9Ful8jHhB0i1V5ZWZkmT54cdgy1h0TpWH8vvfSStm/frqysLD388MOdtm9o\naFBWVlbbkyxerzfsH39lZWUqLCzs9vGj2S7Rjwl7fLn+ysvL5fV6u3zrGfWHRPly7VVXV2vz5s3a\nvn27RowYoTlz5oT9Q561F4ny5forKSlRdXW11q9fL4/Ho9mzZ4ftR6i/7vX5V16/0NLSouXLl+vm\nm29u98pDTziO0+mZ4v379+utt97SDTfc0OXYaLdL5GPCDpFq75VXXlFGRoamTJnS7T6oPcQqXP1d\nf/31evbZZ/XNb35TmzZtimm/27dv16FDhzRjxoyEbJfIx4Q9vlx/GRkZKi0t1axZs+LeL/WH7nSc\n+77zne9o5cqV+vWvf62cnBytXbu2232w9iJWHesvGAyqsbFRS5Ys0ezZs7VixQoZY7rcB/UXXlq8\n8hoIBLR8+XJNmTJFl156aZfbrlq1Sh999JG8Xq8WL16s7Oxs1dfXKycnR/X19e0uLPHJJ59o9erV\nWrx4sQYNGiRJ2rRpk7Zu3SpJWrx4sbxeb9jtutLTx4S9ItXetm3btGvXLj300ENtkwS1h0Trbu6b\nMmWKli5dquuuu05LlizRyZMnNXLkSP30pz9VU1OTTp8+rYyMDPl8vnYXddq7d69KS0v1yCOPtL0F\n6qWXXtLu3bslSb/5zW8ibhfJoEGDevyYsFvH+vv0009VU1Ojn//855Kkuro6LVq0SEuXLm27wAn1\nh0QIN/edddZZbd+fNm2afvWrX0li7UXihas/r9erSy65RI7jaNSoUXK5XGpoaNC6deuov55K6Rm3\nvSAYDJqVK1eaNWvWhP1+dxdsWrt2bdiTmE+cOGEWLFhgqquru3z87rY7fvx4pxOn431M2CFS7f39\n7383d911V8QLlXyB2kM8ItXfP//5z7Z///nPfzZPPPFE2PEdL16zadMmY4wxhw4dMgsWLGi3n3C6\n227//v3dXjCnp48Je3S39hpjzPz586O+YBP1h2hFqj2fz9f279dee82sWLEi7HjWXsQjUv1t3rzZ\nrF+/3hhjzJEjR8ztt99ugsFgp/HUX/ccY7p5zfoMV11drYceekhf+9rX2l7huv766xUIBPT73/9e\n//rXvzRgwAANHz5cv/jFLzqNb2ho0IoVK1RbW9vu8tHPPfec3n//feXl5UmSMjIytGzZsk7ju9qu\nuLhYVVVVamhoUHZ2tq677jpdeeWVcT8m7BCp9tasWaNAINDuJPi5c+d2Gk/tIR6R6q+srExHjx6V\n4zjKy8vT3Llzw94q5/jx4youLlZjY6POO+88LVy4UB6PR4899pg+/fTTtlcx8vLytGjRok7ju9ru\noYce0pEjR9TS0qJBgwbp9ttvV2FhYdyPCXtEqr8v35rkjjvu0NKlS8PeKof6Q6wi1d67776rjz/+\nWI7jaMiQIZo7d27beX5fxtqLeESqv4KCAq1atUqffPKJ3G63Zs+erbFjx3YaT/11r883rwAAAACA\nM1/aXLAJAAAAAHDmonkFAAAAAFiP5hUAAAAAYD2aVwAAAACA9WheAQAAAADWo3kFAAAAAFiP5hUA\nAAAAYD2aVwAAAACA9f4fzwRX6t0qT4sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe94c05bb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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j8uXLdfjwYS1YsEDz589XPB7X4sWLdemll6q0tFSPPfZYp8fBcRwtXrxY//7v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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe94c05bb90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WvE4AAIB5mjGcttvt3HDDDXnXu96VD3/4w/n617+ehx566EnbjY+P57bbbsvT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\n4erqyuTJk7nnnnsAWL16NUePHsXT05Ply5dXWvP8+fOsXr2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9546cb6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Lf+fk5Mi27ZbHhmEc9d+WZclxHEnxy1+j0ba3o1mwYIFGjx591J+PGjVKb731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\nFYb8+C4AAAAAAMlCKAUAAAAABIZQCgAAAAAIDKE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92Rhjli5dal5//XXjuq5Zvnx5z77S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\nAAAAAIDAEEoBAAAAAIH5/5BNxovkh7vnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe95401f890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nDZEMw+3qEtcZ+s3oNVzq0JN+A4B2gGAKAEBbsMPK2P6y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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe94c14edd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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eTifHGY8fzsh/P5iil27ZSKp15j2AhXPGACBJ0hkfSRo93pikUaSzaXhpAwG1cT4AlpNS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\nXiODUiGEEEIIIYQQXvP/AdYMFmS+u8i2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9542e9110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Ky8tl27Y2bdqkkSNHntKmtLRUL7zwghYsWKCePXt2WLEA\nAAAAgNTR7CnBPp9Pt956q5YtWybHcTRu3Dj17dtXK1euVP/+/TVy5Ei98sorikQievLJJyU1DgXf\nd999HV48AAAAACB5tega1hEjRmjEiBGn/G727Nmxnx988MH2rQoAAPz/7d19jF1lvS/w757OYDsU\nmk5LO9YYlRYUeoUWikCjhB4wET1yKiHAJUAtVuStSAhExCC5GpQEkMpLoQEkOHgvXBOInOhBxPKi\nVEhLM5XSi9hWvKB9sZ0SW2ZwZrrX/WOujaUdWtp5WbPn80km6bTr2f3NeubZv/Vde+21AWDY2+Ml\nwQAAADAYBFYAAABKSWAFAACglARWAAAASklgBQAAoJQEVgAAAEpJYAUAAKCUBFYAAABKSWAFAACg\nlARWAAAASklgBQAAoJQEVgAAAEpJYAUAAKCUBFYAAABKqX6wCwCGuaJIw4Zlafhba+q6tqXaMDpd\nh0xL18QZSaUy2NUBAJTHMDxuEliBwVHtSuOrD+fAFQtT17EplWp3Uu1K6hpS1NWnOmp83j760rR/\n4pykrmGwqwUAGDzD+LhJYAUGXKXr7Yz9r/PSsOnl1HV37PyP1c5Uqp2p2/p/c9AL/yMjVz+aLac9\nlKLhwMEpFgBgEA334ybvYQUGVrWr50n3b627Pum+S113Rxo2tmbsf53XcxYRAGA4cdwksAIDq/HV\nh3vOEG7v3Kvt66qdadj0cka9+nA/VwYAUC6OmwRWYCAVRc97L/ZwhvDd6ro7MnrFwqQo+qkwAICS\ncdyURGAFBlDDhmWp69i0T2PrOjalYcOyPq4IAKCcHDf1cNMlYMA0/K215652+6Cuuz3jH5/dxxUN\nng8OdgH0CfM49JnD2mAea4N57DuVanca/rYiXc3HDXYp+80rrMCAqevaVlM3AQAAKKVqV89xVw3w\nCiswYKoNo3s+G6y6dzcO+FdF3QH5+/HfSvsn5/VDZQNr/Pjx2bRp3y7xoTzM49DX3Nyc9evXD3YZ\n7CdrsTaYx101vnxvDn7xe6nsw3FT6hp6jrtqgFdYgQHTdci0FHX7dp6sqKtP1yFH93FFAADl5Lip\nh8AKDJiuiTNSHTV+n8ZWRx2Srokz+rgiAIByctzUQ2AFBk6lkrePvjTV+lHva1i1flS2HX1JUqn0\nU2EAACXjuCmJwAoMsPZPnJOu8Z9Mte6Avdq+WveBdI0/Kh2fOKefKwMAKBfHTQIrMNDqGrLltIfS\nNWHaHs8YVutHpWvCtGw5raXnZk0AAMOJ4yZ3CQYGXtFwYNr+/X9n1KsPZ/SKhanr2NTz+azVrqSu\nIUVdfaqjDsm2oy/pOUNYQ0+6AADvx3A/bhJYgcFR15COI89PxxHnpWHDsjT8bUXqural2jA6XROm\npWvCsTXz3gsAgP0yjI+bBFZgcFUq6Wo+Ll3Nxw12JQAA5TYMj5u8hxUAAIBSElgBAAAoJYEVAACA\nUhJYAQAAKCWBFQAAgFISWAEAACglgRUAAIBSElgBAAAoJYEVAACAUhJYAQAAKCWBFQAAgFISWAEA\nACglgRUAAIBSElgBAAAoJYEVAACAUhJYAQAAKCWBFQAAgFISWAEAACglgRUAAIBSElgBAAAoJYEV\nAACAUhJYAQAAKCWBFQAAgFISWAEAACglgRUAAIBSElgBAAAoJYEVAACAUhJYAQAAKCWBFQAAgFIS\nWAEAACglgRUAAIBSElgBAAAoJYEVAACAUhJYAQAAKCWBFQAAgFISWAEAACglgRUAAIBSElgBAAAo\nJYEVAACAUhJYAQAAKCWBFQAAgFISWAEAACglgRUAAIBSElgBAAAoJYEVAACAUhJYAQAAKCWBFQAA\ngFISWAEAACglgRUAAIBSElgBAAAoJYEVAACAUhJYAQAAKCWBFQAAgFKqH+wCyqgoiuT3LyevvJK0\ntyeNjcnUqclRn0ylUhns8gAAgGFoOOYUgfVfFN3dKX72ePLjlmTLlqS7u+ervr7na+zYFBecn8p/\nnJ5KvV0HAAD0v+GcU2rrp9kPRXt7iq9fmbz6h+Sdd3b+x66unq+OjmTBD1M88UTywwWpNDYOTrEA\nAMCwMNxzivew5v+fsfj6lcmq/7PrL8G7vfNO8sqqFF+/MkV398AUCAAADDtyyl6+wtra2poHHngg\n1Wo1p5xySmbPnr3Tv3d1deXOO+/M2rVrc9BBB+XKK6/MhAkT+qXg/lD87PGeMxadnXs3oKsrefUP\nKR7/z1TO+FL/FgcAAAxLcspevMJarVZz//3357rrrsttt92W559/Pm+++eZO2yxevDgHHnhg7rjj\njnzhC1/IT37yk34ruK8VRdFzLfiezli82zvvJD/+cc94AACAPiSn9NhjYF29enWam5szceLE1NfX\nZ+bMmVm6dOlO2yxbtiwnn3xykuSEE07IypUrh84O+v3LPW9c3hdtW3rGAwAA9CU5JcleXBLc1taW\ncS3Ah9AAAA8jSURBVOPG7fh+3Lhx+eMf/9jrNiNGjEhjY2O2bt2agw8+uI/L7QevvNJzh6190dGR\nYt5XMxSi+V8HuwD6xMbBLoA+YR5rg3kc+vTG2mAt1gbz2Me6u5NVq5KjjxrsSvbbgN4l+KmnnspT\nTz2VJLnpppsyfvz4gfzvd+vtJG/X0JuSAQCAYa67O41JDhzgvFVfX9/nGW+PgbWpqSmbN2/e8f3m\nzZvT1NS0223GjRuX7du3p729PQcddNAuj3Xqqafm1FNP3fH9pk2b9qf2PlEkPZ9d1NX1/gc3NKQy\n//JU/vs5fV1Wn2tubs769esHuwz20/jx40uxbtg/5rE2mMehT2+sDdZibTCPuyr+5/9Kcedd+5ZT\n6uvTnqRjgPfpu+dx0qRJ+/2Ye3wP6+TJk7Nu3bps3Lgx3d3dWbJkSWbMmLHTNscee2yeeeaZJMkL\nL7yQqVOnplKp7HdxA2Lq1J7Aui/q65Mjj+zbegAAAOSUJHvxCuuIESNy4YUX5sYbb0y1Ws2sWbPy\n4Q9/OI888kgmT56cGTNm5N/+7d9y5513Zv78+Rk9enSuvPLKgai9bxz1yWTs2J4P232/mpp6xgMA\nAPQlOSXJXr6H9Zhjjskxxxyz09+dffbZO/58wAEH5KqrrurbygZIpVJJccH5yYIfvr9bRo8cmVxw\n/tB5JRkAABgy5JQee7wkeDio/MfpySc+njQ07N2AAw5IjvhEKqd/sX8LAwAAhi05RWBNklTq61P5\n4YJk6pE9ZyTey8iRydQjU1lwWyr7ek05AADAHsgpA/yxNmVWaWxM7l6Y4vH/TH78454P2+3u7vmq\nr+/5amrqeXn99C/W1C8BAABQTsM9p9TWT7OfKvX1qZzxpRRfmp38/uWeD9ttb08aG3vu0vXJ/1Yz\n14IDAABDw3DOKQLrblQqleToo3q+AAAASmA45hTvYQUAAKCUBFYAAABKSWAFAACglARWAAAASklg\nBQAAoJQEVgAAAEpJYAUAAKCUBFYAAABKSWAFAACglARWAAAASklgBQAAoJQEVgAAAEpJYAUAAKCU\nBFYAAABKSWAFAACglARWAAAASklgBQAAoJQEVgAAAEpJYAUAAKCUBFYAAABKSWAFAACglARWAAAA\nSklgBQAAoJQEVgAAAEpJYAUAAKCUBFYAAABKSWAFAACglARWAAAASklgBQAAoJQEVgAAAEpJYAUA\nAKCUBFYAAABKSWAFAACglARWAAAASklgBQAAoJQEVgAAAEpJYAUAAKCUBFYAAABKSWAFAACglCpF\nURSDXQQAAAC8m1dYh4mvfOUrg10CfWDRokWDXQJ9wDzWBvM49OmNtcFarA3msTb0xzwKrMNEY2Pj\nYJdAHzj22GMHuwT6gHmsDeZx6NMba4O1WBvMY23oj3kUWIeJAw88cLBLoA/MmDFjsEugD5jH2mAe\nhz69sTZYi7XBPNaG/phHgXWYOPXUUwe7BAAoFb0RoPzcdAkAAIBSqh/sAtg3ra2teeCBB1KtVnPK\nKadk9uzZ2bhxYxYsWJCtW7fm0EMPzfz581Nfv+sUP/bYY1m8eHHq6uoyd+7cTJs2rdfHpP/sbn8X\nRZGHH344L7zwQurq6vLZz342n//853cZ+8wzz+TRRx9Nkpxxxhk5+eSTkyRr167NXXfdlc7Ozkyf\nPj1z585NpVIZyB9r2Fm4cGGWL1+eMWPG5NZbb02StLS05KWXXkp9fX0mTpyYSy+9dLeXHva25vZ2\nLdM3djeHr7/+eu699950dnZmxIgRmTdvXqZMmbLLWGuxXPTG2qA/Dn16Y20oTX8sGHK2b99eXH75\n5cX69euLrq6u4uqrry7eeOON4tZbby1++9vfFkVRFIsWLSp++ctf7jL2jTfeKK6++uqis7Oz2LBh\nQ3H55ZcX27dv7/Ux6R+97e/FixcXd9xxR7F9+/aiKIrirbfe2mXs1q1bi8suu6zYunXrTn8uiqK4\n9tpriz/84Q9FtVotbrzxxmL58uUD+nMNR6+88kqxZs2a4qqrrtrxd62trUV3d3dRFEXR0tJStLS0\n7DLuvdbc3qxl+s7u5vC73/3ujvXz0ksvFTfccMMu46zFctEba4P+WBv0xtpQlv7oPaxD0OrVq9Pc\n3JyJEyemvr4+M2fOzNKlS/PKK6/khBNOSJKcfPLJWbp06S5jly5dmpkzZ6ahoSETJkxIc3NzVq9e\n3etj0j96299PPvlkzjzzzNTV9SzNMWPG7DK2tbU1Rx11VEaPHp3Ro0fnqKOOSmtra7Zs2ZKOjo4c\nfvjhqVQqOemkk8zhADjyyCMzevTonf7u6KOPzogRI5Ikhx9+eNra2nYZ19vvQFEUe7WW6Tu7m8NK\npZKOjo4kSXt7e8aOHbvLOGuxXPTG2qA/1ga9sTa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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe957f35ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print out the above for 10 examples\n",
    "\n",
    "# compare the above durations\n",
    "ce['source'] = 'chartevents'\n",
    "ie['source'] = 'inputevents_kcl'\n",
    "ie.loc[ie['label']=='Calcium','source'] = 'inputevents_ca' \n",
    "pe['source'] = 'procedureevents'\n",
    "df = pd.concat([ie[['icustay_id','num','starttime','endtime','source']], ce, pe])\n",
    "\n",
    "for iid in np.sort(df.icustay_id.unique()[0:10]):\n",
    "    iid = int(iid)\n",
    "    # how many PROCEDUREEVENTS_MV dialysis events encapsulate CHARTEVENTS/INPUTEVENTS_MV?\n",
    "    # vice-versa?\n",
    "    idxDisplay = df['icustay_id'] == iid\n",
    "    \n",
    "    # no need to display here\n",
    "    #display_df(df.loc[idxDisplay, :])\n",
    "    \n",
    "    # 2) how many have no overlap whatsoever?\n",
    "    col_dict = {'chartevents': [247,129,191],\n",
    "               'inputevents_kcl': [255,127,0],\n",
    "               'inputevents_ca': [228,26,28],\n",
    "               'procedureevents': [55,126,184]}\n",
    "\n",
    "    for c in col_dict:\n",
    "        col_dict[c] = [x/256.0 for x in col_dict[c]]\n",
    "\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=[16,10])\n",
    "    m = 0.\n",
    "    M = np.sum(idxDisplay)\n",
    "\n",
    "    # dummy plots for legend\n",
    "    legend_handle = list()\n",
    "    for c in col_dict:\n",
    "        legend_handle.append(mlines.Line2D([], [], color=col_dict[c], marker='o',\n",
    "                                  markersize=15, label=c))\n",
    "\n",
    "    for row in df.loc[idxDisplay,:].iterrows():\n",
    "        # row is a tuple: [index, actual_data], so we use row[1]\n",
    "        plt.plot([row[1]['starttime'].to_pydatetime(), row[1]['endtime'].to_pydatetime()], [0+m/M,0+m/M],\n",
    "                'o-',color=col_dict[row[1]['source']],\n",
    "                markersize=15, linewidth=2)\n",
    "        m=m+1\n",
    "\n",
    "    ax.xaxis.set_minor_locator(dates.HourLocator(byhour=[0,6,12,18],interval=1))\n",
    "    ax.xaxis.set_minor_formatter(dates.DateFormatter('%H:%M'))\n",
    "    ax.xaxis.grid(True, which=\"minor\")\n",
    "    ax.xaxis.set_major_locator(dates.DayLocator(interval=1))\n",
    "    ax.xaxis.set_major_formatter(dates.DateFormatter('\\n%d-%m-%Y'))\n",
    "\n",
    "    ax.set_ylim([-0.1,1.0])\n",
    "\n",
    "    plt.legend(handles=legend_handle,loc='best')\n",
    "    \n",
    "    # if you want to save the figures, uncomment the line below\n",
    "    #plt.savefig('crrt_' + str(iid) + '.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examining the plots, it appears that durations from INPUTEVENTS and PROCEDUREEVENTS_MV are redundant to that in CHARTEVENTS. Furthermore, the durations from CHARTEVENTS appear to better reflect small interruptions due to clotted lines and paused treatment. As a result, we only use the durations from the CHARTEVENTS. The `concepts/durations/crrt-durations.sql` query contains the final duration query which additionally has `itemid` from CareVue. These `itemid` were determined in a similar manner to the above."
   ]
  }
 ],
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   "language": "python",
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    "name": "ipython",
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