Amiodarone Cohort Analysis III - Exploring the Longitudinal Data.ipynb

{
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   "source": [
    "## An OMOP Cohort: Patients Exposed to Amiodarone Hydrochloride > 30 Days\n",
    "\n",
    "### Part III: Exploring the Longitudinal Data\n",
    "\n",
    "An examination of de-identified longitudinal visit and procedure data (derived to OMOP from Medicare DeSYNPuf data, 2008-2010, by OHDSI) and associated ICD9CM diagnosis codes to analyze the incidence and prevalence of AEs associated with amiodarone toxicity to the lung, thyroid and liver.\n",
    "\n",
    "Kyle P. Rasku MS BSN RN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fa9d242d",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "14dac4f4",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>treatment_start</th>\n",
       "      <th>treatment_period</th>\n",
       "      <th>visit_id</th>\n",
       "      <th>vstart</th>\n",
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       "      <th>diagnosis_1</th>\n",
       "      <th>diagnosis_2</th>\n",
       "      <th>diagnosis_3</th>\n",
       "      <th>...</th>\n",
       "      <th>pdesc_16</th>\n",
       "      <th>sentinel_condition_code</th>\n",
       "      <th>sentinel_condition_name</th>\n",
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       "      <th>sentinel_condition_type</th>\n",
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       "      <th>condition_end_date</th>\n",
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       "      <td>2008-01-28</td>\n",
       "      <td>50058.0</td>\n",
       "      <td>11234.0</td>\n",
       "      <td>Vomiting</td>\n",
       "      <td>Epidemic vertigo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2008-01-15</td>\n",
       "      <td>90</td>\n",
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       "      <td>2008-01-28</td>\n",
       "      <td>315573.0</td>\n",
       "      <td>11283.0</td>\n",
       "      <td>Atrial fibrillation</td>\n",
       "      <td>Paroxysmal supraventricular tachycardia</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>111598</td>\n",
       "      <td>2008-01-15</td>\n",
       "      <td>90</td>\n",
       "      <td>5351386</td>\n",
       "      <td>2008-01-31</td>\n",
       "      <td>218450.0</td>\n",
       "      <td>11234.0</td>\n",
       "      <td>Atrial fibrillation</td>\n",
       "      <td>Premature beats</td>\n",
       "      <td>Type 2 diabetes mellitus</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>111598</td>\n",
       "      <td>2008-01-15</td>\n",
       "      <td>90</td>\n",
       "      <td>5351403</td>\n",
       "      <td>2008-02-05</td>\n",
       "      <td>382137.0</td>\n",
       "      <td>28276.0</td>\n",
       "      <td>Angina decubitus</td>\n",
       "      <td>Mitral and aortic stenosis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>111598</td>\n",
       "      <td>2008-01-15</td>\n",
       "      <td>90</td>\n",
       "      <td>5351324</td>\n",
       "      <td>2008-02-05</td>\n",
       "      <td>447710.0</td>\n",
       "      <td>11222.0</td>\n",
       "      <td>Conduction disorder of the heart</td>\n",
       "      <td>Atrial fibrillation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "<p>5 rows × 65 columns</p>\n",
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      "text/plain": [
       "   patient_id treatment_start  treatment_period  visit_id     vstart  \\\n",
       "0      111598      2008-01-15                90   5351372 2008-01-28   \n",
       "1      111598      2008-01-15                90   5351385 2008-01-28   \n",
       "2      111598      2008-01-15                90   5351386 2008-01-31   \n",
       "3      111598      2008-01-15                90   5351403 2008-02-05   \n",
       "4      111598      2008-01-15                90   5351324 2008-02-05   \n",
       "\n",
       "   vprovider  care_site                       diagnosis_1  \\\n",
       "0    50058.0    11234.0                          Vomiting   \n",
       "1   315573.0    11283.0               Atrial fibrillation   \n",
       "2   218450.0    11234.0               Atrial fibrillation   \n",
       "3   382137.0    28276.0                  Angina decubitus   \n",
       "4   447710.0    11222.0  Conduction disorder of the heart   \n",
       "\n",
       "                               diagnosis_2               diagnosis_3  ...  \\\n",
       "0                         Epidemic vertigo                       NaN  ...   \n",
       "1  Paroxysmal supraventricular tachycardia                       NaN  ...   \n",
       "2                          Premature beats  Type 2 diabetes mellitus  ...   \n",
       "3               Mitral and aortic stenosis                       NaN  ...   \n",
       "4                      Atrial fibrillation                       NaN  ...   \n",
       "\n",
       "  pdesc_16 sentinel_condition_code sentinel_condition_name  \\\n",
       "0      NaN                     NaN                     NaN   \n",
       "1      NaN                     NaN                     NaN   \n",
       "2      NaN                     NaN                     NaN   \n",
       "3      NaN                     NaN                     NaN   \n",
       "4      NaN                     NaN                     NaN   \n",
       "\n",
       "  sentinel_snomed_name sentinel_condition_type sentinel_snomed_code  \\\n",
       "0                  NaN                     NaN                  NaN   \n",
       "1                  NaN                     NaN                  NaN   \n",
       "2                  NaN                     NaN                  NaN   \n",
       "3                  NaN                     NaN                  NaN   \n",
       "4                  NaN                     NaN                  NaN   \n",
       "\n",
       "  condition_start_date condition_end_date condition_interval_days  \\\n",
       "0                  NaN                NaN                     NaN   \n",
       "1                  NaN                NaN                     NaN   \n",
       "2                  NaN                NaN                     NaN   \n",
       "3                  NaN                NaN                     NaN   \n",
       "4                  NaN                NaN                     NaN   \n",
       "\n",
       "  treatment_condition_interval_days  \n",
       "0                               NaN  \n",
       "1                               NaN  \n",
       "2                               NaN  \n",
       "3                               NaN  \n",
       "4                               NaN  \n",
       "\n",
       "[5 rows x 65 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "condition_visits_ds = pd.read_csv(\"conditions_visits_procedures.csv\", parse_dates=[\"treatment_start\", \"vstart\"])\n",
    "condition_visits_ds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4ebd4a8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5617 entries, 0 to 5616\n",
      "Data columns (total 65 columns):\n",
      " #   Column                             Non-Null Count  Dtype         \n",
      "---  ------                             --------------  -----         \n",
      " 0   patient_id                         5617 non-null   int64         \n",
      " 1   treatment_start                    5617 non-null   datetime64[ns]\n",
      " 2   treatment_period                   5617 non-null   int64         \n",
      " 3   visit_id                           5617 non-null   int64         \n",
      " 4   vstart                             5617 non-null   datetime64[ns]\n",
      " 5   vprovider                          5608 non-null   float64       \n",
      " 6   care_site                          5612 non-null   float64       \n",
      " 7   diagnosis_1                        5617 non-null   object        \n",
      " 8   diagnosis_2                        4279 non-null   object        \n",
      " 9   diagnosis_3                        2137 non-null   object        \n",
      " 10  diagnosis_4                        1033 non-null   object        \n",
      " 11  diagnosis_5                        443 non-null    object        \n",
      " 12  diagnosis_6                        187 non-null    object        \n",
      " 13  diagnosis_7                        86 non-null     object        \n",
      " 14  diagnosis_8                        35 non-null     object        \n",
      " 15  diagnosis_9                        10 non-null     object        \n",
      " 16  diagnosis_10                       2 non-null      object        \n",
      " 17  diagnosis_11                       1 non-null      object        \n",
      " 18  snomed_1                           5617 non-null   object        \n",
      " 19  snomed_2                           4279 non-null   object        \n",
      " 20  snomed_3                           2137 non-null   object        \n",
      " 21  snomed_4                           1033 non-null   object        \n",
      " 22  snomed_5                           443 non-null    object        \n",
      " 23  snomed_6                           187 non-null    float64       \n",
      " 24  snomed_7                           86 non-null     float64       \n",
      " 25  snomed_8                           35 non-null     float64       \n",
      " 26  snomed_9                           10 non-null     float64       \n",
      " 27  snomed_10                          2 non-null      float64       \n",
      " 28  snomed_11                          1 non-null      float64       \n",
      " 29  icd9_1                             5617 non-null   object        \n",
      " 30  icd9_2                             4306 non-null   object        \n",
      " 31  icd9_3                             2096 non-null   object        \n",
      " 32  icd9_4                             997 non-null    object        \n",
      " 33  icd9_5                             418 non-null    object        \n",
      " 34  icd9_6                             179 non-null    object        \n",
      " 35  icd9_7                             82 non-null     object        \n",
      " 36  icd9_8                             33 non-null     object        \n",
      " 37  icd9_9                             10 non-null     float64       \n",
      " 38  icd9_10                            1 non-null      float64       \n",
      " 39  icd9_11                            1 non-null      float64       \n",
      " 40  pdesc_1                            4346 non-null   object        \n",
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      " 42  pdesc_3                            1415 non-null   object        \n",
      " 43  pdesc_4                            651 non-null    object        \n",
      " 44  pdesc_5                            298 non-null    object        \n",
      " 45  pdesc_6                            127 non-null    object        \n",
      " 46  pdesc_7                            66 non-null     object        \n",
      " 47  pdesc_8                            41 non-null     object        \n",
      " 48  pdesc_9                            22 non-null     object        \n",
      " 49  pdesc_10                           11 non-null     object        \n",
      " 50  pdesc_11                           8 non-null      object        \n",
      " 51  pdesc_12                           5 non-null      object        \n",
      " 52  pdesc_13                           4 non-null      object        \n",
      " 53  pdesc_14                           4 non-null      object        \n",
      " 54  pdesc_15                           2 non-null      object        \n",
      " 55  pdesc_16                           1 non-null      object        \n",
      " 56  sentinel_condition_code            277 non-null    float64       \n",
      " 57  sentinel_condition_name            277 non-null    object        \n",
      " 58  sentinel_snomed_name               277 non-null    object        \n",
      " 59  sentinel_condition_type            277 non-null    object        \n",
      " 60  sentinel_snomed_code               277 non-null    float64       \n",
      " 61  condition_start_date               277 non-null    object        \n",
      " 62  condition_end_date                 277 non-null    object        \n",
      " 63  condition_interval_days            277 non-null    float64       \n",
      " 64  treatment_condition_interval_days  277 non-null    float64       \n",
      "dtypes: datetime64[ns](2), float64(15), int64(3), object(45)\n",
      "memory usage: 2.8+ MB\n"
     ]
    }
   ],
   "source": [
    "condition_visits_ds.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "293006be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(277, 13)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "sentinel_visits_ds = condition_visits_ds[[\"patient_id\", \"treatment_start\", \"treatment_period\", \"visit_id\", \"vstart\", \n",
    "                                          \"sentinel_condition_code\", \"sentinel_condition_name\", \"sentinel_snomed_name\", \n",
    "                                          \"sentinel_condition_type\", \"condition_start_date\", \"condition_end_date\", \n",
    "                                          \"condition_interval_days\", \"treatment_condition_interval_days\"]].copy()\n",
    "\n",
    "sentinel_visits_ds = sentinel_visits_ds[sentinel_visits_ds[\"condition_start_date\"].notnull()]\n",
    "sentinel_visits_ds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "49b3f7f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 277 entries, 34 to 5613\n",
      "Data columns (total 13 columns):\n",
      " #   Column                             Non-Null Count  Dtype         \n",
      "---  ------                             --------------  -----         \n",
      " 0   patient_id                         277 non-null    int64         \n",
      " 1   treatment_start                    277 non-null    datetime64[ns]\n",
      " 2   treatment_period                   277 non-null    int64         \n",
      " 3   visit_id                           277 non-null    int64         \n",
      " 4   vstart                             277 non-null    datetime64[ns]\n",
      " 5   sentinel_condition_code            277 non-null    float64       \n",
      " 6   sentinel_condition_name            277 non-null    object        \n",
      " 7   sentinel_snomed_name               277 non-null    object        \n",
      " 8   sentinel_condition_type            277 non-null    object        \n",
      " 9   condition_start_date               277 non-null    object        \n",
      " 10  condition_end_date                 277 non-null    object        \n",
      " 11  condition_interval_days            277 non-null    float64       \n",
      " 12  treatment_condition_interval_days  277 non-null    float64       \n",
      "dtypes: datetime64[ns](2), float64(3), int64(3), object(5)\n",
      "memory usage: 30.3+ KB\n"
     ]
    }
   ],
   "source": [
    "sentinel_visits_ds.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43bc8629",
   "metadata": {},
   "source": [
    "## The Impacted Patients\n",
    "\n",
    "The cohort had 97 individuals (48%) who developed a lung, liver or thyroid-related illness following amiodarone exposure.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8f49abd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(277, 19)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohort_ds = pd.read_csv(\"amiodarone_exposed_patients.csv\", parse_dates=[\"patient_dob\"])\n",
    "sentinel_cohort_visits_ds = pd.merge(sentinel_visits_ds, cohort_ds, on=\"patient_id\", how=\"left\")\n",
    "sentinel_cohort_visits_ds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2cbd49e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 277 entries, 0 to 276\n",
      "Data columns (total 19 columns):\n",
      " #   Column                             Non-Null Count  Dtype         \n",
      "---  ------                             --------------  -----         \n",
      " 0   patient_id                         277 non-null    int64         \n",
      " 1   treatment_start                    277 non-null    datetime64[ns]\n",
      " 2   treatment_period                   277 non-null    int64         \n",
      " 3   visit_id                           277 non-null    int64         \n",
      " 4   vstart                             277 non-null    datetime64[ns]\n",
      " 5   sentinel_condition_code            277 non-null    float64       \n",
      " 6   sentinel_condition_name            277 non-null    object        \n",
      " 7   sentinel_snomed_name               277 non-null    object        \n",
      " 8   sentinel_condition_type            277 non-null    object        \n",
      " 9   condition_start_date               277 non-null    object        \n",
      " 10  condition_end_date                 277 non-null    object        \n",
      " 11  condition_interval_days            277 non-null    float64       \n",
      " 12  treatment_condition_interval_days  277 non-null    float64       \n",
      " 13  gender                             277 non-null    object        \n",
      " 14  patient_dob                        277 non-null    datetime64[ns]\n",
      " 15  race                               277 non-null    object        \n",
      " 16  ethnicity                          277 non-null    object        \n",
      " 17  county                             277 non-null    int64         \n",
      " 18  state                              277 non-null    object        \n",
      "dtypes: datetime64[ns](3), float64(3), int64(4), object(9)\n",
      "memory usage: 43.3+ KB\n"
     ]
    }
   ],
   "source": [
    "sentinel_cohort_visits_ds.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b203066a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(97, 2)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patient_visits_ds = sentinel_visits_ds.groupby(\"patient_id\").count().reset_index()\n",
    "patient_visits_ds = patient_visits_ds[[\"patient_id\", \"visit_id\"]].copy()\n",
    "patient_visits_ds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "85018376",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
       "      <td>4549</td>\n",
       "      <td>Female</td>\n",
       "      <td>1933-11-01</td>\n",
       "      <td>White</td>\n",
       "      <td>Not Hispanic</td>\n",
       "      <td>15430</td>\n",
       "      <td>IN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5680</td>\n",
       "      <td>Female</td>\n",
       "      <td>1929-04-01</td>\n",
       "      <td>White</td>\n",
       "      <td>Not Hispanic</td>\n",
       "      <td>31150</td>\n",
       "      <td>NJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5894</td>\n",
       "      <td>Male</td>\n",
       "      <td>1948-05-01</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>Not Hispanic</td>\n",
       "      <td>19250</td>\n",
       "      <td>LA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7134</td>\n",
       "      <td>Female</td>\n",
       "      <td>1964-12-01</td>\n",
       "      <td>White</td>\n",
       "      <td>Not Hispanic</td>\n",
       "      <td>10050</td>\n",
       "      <td>FL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7630</td>\n",
       "      <td>Female</td>\n",
       "      <td>1919-08-01</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>Not Hispanic</td>\n",
       "      <td>18550</td>\n",
       "      <td>KY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    patient_id  gender patient_dob                       race     ethnicity  \\\n",
       "4         4549  Female  1933-11-01                      White  Not Hispanic   \n",
       "7         5680  Female  1929-04-01                      White  Not Hispanic   \n",
       "8         5894    Male  1948-05-01  Black or African American  Not Hispanic   \n",
       "10        7134  Female  1964-12-01                      White  Not Hispanic   \n",
       "11        7630  Female  1919-08-01  Black or African American  Not Hispanic   \n",
       "\n",
       "    county state  \n",
       "4    15430    IN  \n",
       "7    31150    NJ  \n",
       "8    19250    LA  \n",
       "10   10050    FL  \n",
       "11   18550    KY  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentinel_patients = list(sentinel_visits_ds[\"patient_id\"].unique())\n",
    "sentinel_cohort_ds = cohort_ds[cohort_ds[\"patient_id\"].isin(sentinel_patients)]\n",
    "sentinel_cohort_ds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e5f31a28",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 3000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.set_theme(style=\"whitegrid\", palette=\"mako\")\n",
    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(30, 10))\n",
    "sb.countplot(data=sentinel_cohort_ds, x=\"gender\", ax=axes[0])\n",
    "sb.countplot(data=sentinel_cohort_ds, x=\"race\", ax=axes[1])\n",
    "sb.countplot(data=sentinel_cohort_ds, x=\"ethnicity\", ax=axes[2])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4b0a7eec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>69</td>\n",
       "      <td>0.71134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>28</td>\n",
       "      <td>0.28866</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count  percentage\n",
       "Female     69     0.71134\n",
       "Male       28     0.28866"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = sentinel_cohort_ds[\"gender\"].value_counts()\n",
    "percs = sentinel_cohort_ds[\"gender\"].value_counts(normalize=True)\n",
    "pd.concat([counts,percs], axis=1, keys=['count', 'percentage'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c76199d5",
   "metadata": {},
   "source": [
    "While there were 63.5% females and 36.5% males in the exposure cohort, there are 71% females and 29% males in the impacted cohort."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "16577113",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>75</td>\n",
       "      <td>0.773196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black or African American</th>\n",
       "      <td>17</td>\n",
       "      <td>0.175258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unspecified</th>\n",
       "      <td>5</td>\n",
       "      <td>0.051546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           count  percentage\n",
       "White                         75    0.773196\n",
       "Black or African American     17    0.175258\n",
       "Unspecified                    5    0.051546"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = sentinel_cohort_ds[\"race\"].value_counts()\n",
    "percs = sentinel_cohort_ds[\"race\"].value_counts(normalize=True)\n",
    "pd.concat([counts,percs], axis=1, keys=['count', 'percentage'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "156a38f0",
   "metadata": {},
   "source": [
    "The exposure cohort was 80% white, much whiter than the total # of Americans on Medicare in 2010 (59% white).  The impacted cohort is 77% white, and 17.5% Black, indicating that proportionally more Black patients were impacted.  \n",
    "\n",
    "100% of the impacted cohort were non-Hispanic, however the exposure cohort only had 3 Hispanic patients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9c7bfb25",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import date\n",
    "\n",
    "def calculate_age(args):\n",
    "    born, visit_date = args\n",
    "    return visit_date.year - born.year - ((visit_date.month, visit_date.day) < (born.month, born.day))\n",
    "\n",
    "# Example: df[['B', 'C']].apply(do_function, axis=1)\n",
    "sentinel_cohort_visits_ds[\"age_at_visit\"] = sentinel_cohort_visits_ds[[\"patient_dob\", \"vstart\"]].apply(calculate_age, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e0f330b",
   "metadata": {},
   "source": [
    "### Impacted Patient Ages at Sentinel Visits\n",
    "\n",
    "Mean age at diagnosis, 77 years.\n",
    "\n",
    "Median age at diagnosis, 80 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1d574ec9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.set(rc={\"figure.figsize\":(25, 5)})\n",
    "sb.set_theme(style=\"whitegrid\")\n",
    "sb.countplot(data=sentinel_cohort_visits_ds, x=\"age_at_visit\", palette=\"mako\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1afa870f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>statistic</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>count</td>\n",
       "      <td>277.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mean</td>\n",
       "      <td>77.122744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>std</td>\n",
       "      <td>14.905721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>min</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25%</td>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50%</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>75%</td>\n",
       "      <td>87.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>max</td>\n",
       "      <td>101.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  statistic       value\n",
       "0     count  277.000000\n",
       "1      mean   77.122744\n",
       "2       std   14.905721\n",
       "3       min   29.000000\n",
       "4       25%   72.000000\n",
       "5       50%   80.000000\n",
       "6       75%   87.000000\n",
       "7       max  101.000000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desc = sentinel_cohort_visits_ds[\"age_at_visit\"].describe()\n",
    "desc_df = desc.to_frame().reset_index()\n",
    "desc_df.columns=[\"statistic\", \"value\"]\n",
    "desc_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e6da5a34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>num_patients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AR</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AZ</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CA</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DC</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state  num_patients\n",
       "0    AL             3\n",
       "1    AR             2\n",
       "2    AZ             3\n",
       "3    CA            11\n",
       "4    DC             1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "patients_by_state = sentinel_cohort_ds.groupby([\"state\"])[\"patient_id\"].count()\n",
    "patients_by_state = patients_by_state.reset_index()\n",
    "patients_by_state.columns = [\"state\", \"num_patients\"]\n",
    "patients_by_state.head()"
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  },
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.set_theme(style=\"whitegrid\", palette=\"mako\")\n",
    "import plotly.express as px\n",
    "from textwrap import wrap\n",
    "\n",
    "named_colorscales = px.colors.named_colorscales()\n",
    "#print(\"\\n\".join(wrap(\"\".join('{:<12}'.format(c) for c in named_colorscales), 96)))\n",
    "\n",
    "fig = px.choropleth(patients_by_state,  # Input Pandas DataFrame\n",
    "                    locations = \"state\",  # DataFrame column with locations\n",
    "                    color = \"num_patients\",  # DataFrame column with color values\n",
    "                    color_continuous_scale = 'viridis',\n",
    "                    hover_name = \"state\", # DataFrame column hover info\n",
    "                    locationmode = 'USA-states') # Set to plot as US States\n",
    "fig.update_layout(\n",
    "    title_text = 'Impacted Patients by State', # Create a Title\n",
    "    geo_scope ='usa',  # Plot only the USA instead of globe\n",
    ")\n",
    "fig.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61e9fe19",
   "metadata": {},
   "source": [
    "Impacted patient state proportions similar to the original exposure cohort."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99e6abfc",
   "metadata": {},
   "source": [
    "## The Sentinel Condition Visits\n",
    "\n",
    "277 visits document these diagnoses & conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3bd0e1c6",
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 277 entries, 34 to 5613\n",
      "Data columns (total 13 columns):\n",
      " #   Column                             Non-Null Count  Dtype         \n",
      "---  ------                             --------------  -----         \n",
      " 0   patient_id                         277 non-null    int64         \n",
      " 1   treatment_start                    277 non-null    datetime64[ns]\n",
      " 2   treatment_period                   277 non-null    int64         \n",
      " 3   visit_id                           277 non-null    int64         \n",
      " 4   vstart                             277 non-null    datetime64[ns]\n",
      " 5   sentinel_condition_code            277 non-null    float64       \n",
      " 6   sentinel_condition_name            277 non-null    object        \n",
      " 7   sentinel_snomed_name               277 non-null    object        \n",
      " 8   sentinel_condition_type            277 non-null    object        \n",
      " 9   condition_start_date               277 non-null    object        \n",
      " 10  condition_end_date                 277 non-null    object        \n",
      " 11  condition_interval_days            277 non-null    float64       \n",
      " 12  treatment_condition_interval_days  277 non-null    float64       \n",
      "dtypes: datetime64[ns](2), float64(3), int64(3), object(5)\n",
      "memory usage: 30.3+ KB\n"
     ]
    }
   ],
   "source": [
    "sentinel_visits_ds.info()"
   ]
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   "cell_type": "markdown",
   "id": "ca8af90f",
   "metadata": {},
   "source": [
    "### Treatment Condition Interval Months\n",
    "\n",
    "The # of days between amiodarone treatment start and the onset of the sentinel condition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f768ed57",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['duration'] = (df['end'] - df['start'])/np.timedelta64(1, 'M')\n",
    "sentinel_visits_ds[\"treatment_condition_interval_months\"] = (sentinel_visits_ds['vstart'] - sentinel_visits_ds['treatment_start'])/np.timedelta64(1, 'M')"
   ]
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   "execution_count": 38,
   "id": "dd734338",
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       "      <th>2</th>\n",
       "      <td>std</td>\n",
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       "      <th>3</th>\n",
       "      <td>min</td>\n",
       "      <td>0.032855</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25%</td>\n",
       "      <td>4.139715</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50%</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>75%</td>\n",
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       "      <th>7</th>\n",
       "      <td>max</td>\n",
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       "  statistic       value\n",
       "0     count  277.000000\n",
       "1      mean   10.045885\n",
       "2       std    7.212709\n",
       "3       min    0.032855\n",
       "4       25%    4.139715\n",
       "5       50%    8.377995\n",
       "6       75%   14.784698\n",
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   "source": [
    "tcid_desc = sentinel_visits_ds[\"treatment_condition_interval_months\"].describe()\n",
    "tcid_desc_df = tcid_desc.to_frame().reset_index()\n",
    "tcid_desc_df.columns=[\"statistic\", \"value\"]\n",
    "tcid_desc_df"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "fig = px.histogram(sentinel_visits_ds, x=\"treatment_condition_interval_months\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7842ffb4",
   "metadata": {},
   "source": [
    "The majority of patients who suffered a suspected lung, liver or thyroid complication did so within the first year of amiodarone treatment. A large # of these AEs occurred within the first 6 months."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7baa3e3c",
   "metadata": {},
   "source": [
    "### Sentinel Condition Diagnosis Type \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a3831600",
   "metadata": {},
   "outputs": [
    {
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       "data": [
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Type\"},\"categoryorder\":\"array\",\"categoryarray\":[\"LUNG\",\"LIVER\",\"THYROID\"]},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Diagnosis Count\"}},\"legend\":{\"title\":{\"text\":\"Condition Type\"},\"tracegroupgap\":0},\"margin\":{\"t\":60},\"barmode\":\"relative\"},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts = sentinel_visits_ds[\"sentinel_condition_type\"].value_counts()\n",
    "percs = sentinel_visits_ds[\"sentinel_condition_type\"].value_counts(normalize=True)\n",
    "condition_counts = pd.concat([counts,percs], axis=1, keys=['count', 'percentage']).reset_index()\n",
    "condition_counts.columns = [\"Condition Type\", \"Diagnosis Count\", \"Diagnosis Percentage\"]\n",
    "fig = px.bar(condition_counts, x='Condition Type', y='Diagnosis Count', color='Condition Type')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "67509c75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Condition Type</th>\n",
       "      <th>Diagnosis Count</th>\n",
       "      <th>Diagnosis Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LUNG</td>\n",
       "      <td>251</td>\n",
       "      <td>0.906137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LIVER</td>\n",
       "      <td>16</td>\n",
       "      <td>0.057762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>THYROID</td>\n",
       "      <td>10</td>\n",
       "      <td>0.036101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Condition Type  Diagnosis Count  Diagnosis Percentage\n",
       "0           LUNG              251              0.906137\n",
       "1          LIVER               16              0.057762\n",
       "2        THYROID               10              0.036101"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "condition_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f018155",
   "metadata": {},
   "source": [
    "90.6% of all diagnoses were lung / breathing related diagnoses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "16cbed64",
   "metadata": {},
   "outputs": [],
   "source": [
    "lung_visits = sentinel_visits_ds[sentinel_visits_ds[\"sentinel_condition_type\"]==\"LUNG\"].copy()\n",
    "lung_visits[\"sentinel_condition_code_str\"] = lung_visits[\"sentinel_condition_code\"].astype(\"str\")\n",
    "lung_visits[\"vstart_str\"] = lung_visits[\"vstart\"].dt.strftime(date_format='%m/%d/%Y').astype(\"str\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "56cf50f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "pt_lung_visits = lung_visits.groupby(['patient_id'], as_index = False).agg({'sentinel_condition_code_str': lambda x: ';'.join(set(x)), \n",
    "                                                                            'sentinel_condition_name': lambda x: ';'.join(set(x)),\n",
    "                                                                            'visit_id': 'count',\n",
    "                                                                            'vstart_str' : lambda x: ', '.join(set(x))\n",
    "                                                                           })\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "67885001",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(93, 10)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long = {}\n",
    "long[0] = pt_lung_visits['sentinel_condition_code_str'].str.split(pat=\";\", expand=True)\n",
    "long[1] = pt_lung_visits['sentinel_condition_name'].str.split(pat=\";\",expand=True)\n",
    "long[2] = pt_lung_visits['vstart_str']\n",
    "\n",
    "\n",
    "pt_lung_visit_summary = pd.concat(long, axis=1)\n",
    "pt_lung_visit_summary[\"visit_count\"] = pt_lung_visits[\"visit_id\"].copy()\n",
    "pt_lung_visit_summary[\"patient_id\"] = pt_lung_visits[\"patient_id\"].copy()\n",
    "\n",
    "pt_lung_visit_summary.columns = [\"code_1\", \"code_2\", \"code_3\", \"code_4\", \n",
    "                                 \"diagnosis_1\", \"diagnosis_2\", \"diagnosis_3\", \"diagnosis_4\", \n",
    "                                 \"visit_dates\", \"visit_count\", \"patient_id\"]\n",
    "\n",
    "pt_lung_visit_summary = pt_lung_visit_summary.set_index(\"patient_id\")\n",
    "\n",
    "pt_lung_visit_summary.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "dae6796a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code_1</th>\n",
       "      <th>code_2</th>\n",
       "      <th>code_3</th>\n",
       "      <th>code_4</th>\n",
       "      <th>diagnosis_1</th>\n",
       "      <th>diagnosis_2</th>\n",
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       "      <th>visit_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4549</th>\n",
       "      <td>518.89</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Other diseases of lung, not elsewhere classified</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>12/28/2008</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5680</th>\n",
       "      <td>518.89</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Other diseases of lung, not elsewhere classified</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>09/21/2010</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5894</th>\n",
       "      <td>786.09</td>\n",
       "      <td>786.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Other respiratory abnormalities</td>\n",
       "      <td>Shortness of breath</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>03/23/2009, 06/24/2010</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7134</th>\n",
       "      <td>786.09</td>\n",
       "      <td>786.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Other respiratory abnormalities</td>\n",
       "      <td>Shortness of breath</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>06/02/2009, 09/14/2009, 09/06/2009, 05/08/2009...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7630</th>\n",
       "      <td>516.8</td>\n",
       "      <td>786.09</td>\n",
       "      <td>786.05</td>\n",
       "      <td>None</td>\n",
       "      <td>Other respiratory abnormalities</td>\n",
       "      <td>Shortness of breath</td>\n",
       "      <td>Other specified alveolar and parietoalveolar p...</td>\n",
       "      <td>None</td>\n",
       "      <td>12/22/2009, 05/19/2008, 06/14/2010, 08/22/2008...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111598</th>\n",
       "      <td>518.89</td>\n",
       "      <td>786.09</td>\n",
       "      <td>786.05</td>\n",
       "      <td>None</td>\n",
       "      <td>Other respiratory abnormalities</td>\n",
       "      <td>Other diseases of lung, not elsewhere classified</td>\n",
       "      <td>Shortness of breath</td>\n",
       "      <td>None</td>\n",
       "      <td>01/22/2010, 05/15/2009, 04/14/2009, 04/11/2008...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113140</th>\n",
       "      <td>786.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Shortness of breath</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>01/27/2010</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114821</th>\n",
       "      <td>518.89</td>\n",
       "      <td>786.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Other diseases of lung, not elsewhere classified</td>\n",
       "      <td>Shortness of breath</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>02/11/2010, 05/15/2010</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115881</th>\n",
       "      <td>786.09</td>\n",
       "      <td>786.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Other respiratory abnormalities</td>\n",
       "      <td>Shortness of breath</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>02/16/2009, 04/20/2010, 09/01/2009, 04/26/2009</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116232</th>\n",
       "      <td>786.09</td>\n",
       "      <td>786.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Other respiratory abnormalities</td>\n",
       "      <td>Shortness of breath</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>12/23/2008, 02/13/2009, 01/06/2009</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            code_1  code_2  code_3 code_4  \\\n",
       "patient_id                                  \n",
       "4549        518.89    None    None   None   \n",
       "5680        518.89    None    None   None   \n",
       "5894        786.09  786.05    None   None   \n",
       "7134        786.09  786.05    None   None   \n",
       "7630         516.8  786.09  786.05   None   \n",
       "...            ...     ...     ...    ...   \n",
       "111598      518.89  786.09  786.05   None   \n",
       "113140      786.05    None    None   None   \n",
       "114821      518.89  786.05    None   None   \n",
       "115881      786.09  786.05    None   None   \n",
       "116232      786.09  786.05    None   None   \n",
       "\n",
       "                                                 diagnosis_1  \\\n",
       "patient_id                                                     \n",
       "4549        Other diseases of lung, not elsewhere classified   \n",
       "5680        Other diseases of lung, not elsewhere classified   \n",
       "5894                         Other respiratory abnormalities   \n",
       "7134                         Other respiratory abnormalities   \n",
       "7630                         Other respiratory abnormalities   \n",
       "...                                                      ...   \n",
       "111598                       Other respiratory abnormalities   \n",
       "113140                                   Shortness of breath   \n",
       "114821      Other diseases of lung, not elsewhere classified   \n",
       "115881                       Other respiratory abnormalities   \n",
       "116232                       Other respiratory abnormalities   \n",
       "\n",
       "                                                 diagnosis_2  \\\n",
       "patient_id                                                     \n",
       "4549                                                    None   \n",
       "5680                                                    None   \n",
       "5894                                     Shortness of breath   \n",
       "7134                                     Shortness of breath   \n",
       "7630                                     Shortness of breath   \n",
       "...                                                      ...   \n",
       "111598      Other diseases of lung, not elsewhere classified   \n",
       "113140                                                  None   \n",
       "114821                                   Shortness of breath   \n",
       "115881                                   Shortness of breath   \n",
       "116232                                   Shortness of breath   \n",
       "\n",
       "                                                  diagnosis_3 diagnosis_4  \\\n",
       "patient_id                                                                  \n",
       "4549                                                     None        None   \n",
       "5680                                                     None        None   \n",
       "5894                                                     None        None   \n",
       "7134                                                     None        None   \n",
       "7630        Other specified alveolar and parietoalveolar p...        None   \n",
       "...                                                       ...         ...   \n",
       "111598                                    Shortness of breath        None   \n",
       "113140                                                   None        None   \n",
       "114821                                                   None        None   \n",
       "115881                                                   None        None   \n",
       "116232                                                   None        None   \n",
       "\n",
       "                                                  visit_dates  visit_count  \n",
       "patient_id                                                                  \n",
       "4549                                               12/28/2008            1  \n",
       "5680                                               09/21/2010            1  \n",
       "5894                                   03/23/2009, 06/24/2010            2  \n",
       "7134        06/02/2009, 09/14/2009, 09/06/2009, 05/08/2009...            7  \n",
       "7630        12/22/2009, 05/19/2008, 06/14/2010, 08/22/2008...            7  \n",
       "...                                                       ...          ...  \n",
       "111598      01/22/2010, 05/15/2009, 04/14/2009, 04/11/2008...            6  \n",
       "113140                                             01/27/2010            1  \n",
       "114821                                 02/11/2010, 05/15/2010            2  \n",
       "115881         02/16/2009, 04/20/2010, 09/01/2009, 04/26/2009            4  \n",
       "116232                     12/23/2008, 02/13/2009, 01/06/2009            3  \n",
       "\n",
       "[93 rows x 10 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt_lung_visit_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "a3357962",
   "metadata": {},
   "outputs": [],
   "source": [
    "lung_visits2 = pt_lung_visit_summary[[\"diagnosis_1\", \"visit_count\"]].copy()\n",
    "lung_visits2.columns = [\"Primary Diagnosis\", \"Total Visits\"]\n",
    "lung_visits2.sort_values(by=\"Total Visits\", ascending=False, inplace=True)"
   ]
  },
  {
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   "execution_count": 101,
   "id": "8108b8f9",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.bar(lung_visits2, x='Primary Diagnosis', y='Total Visits', color='Primary Diagnosis')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "f277ef81",
   "metadata": {},
   "outputs": [],
   "source": [
    "lung_visits = sentinel_visits_ds[sentinel_visits_ds[\"sentinel_condition_type\"]==\"LUNG\"]\n",
    "\n",
    "code_counts = lung_visits[['sentinel_condition_code', 'visit_id']].groupby(['sentinel_condition_code'], as_index = False).count()\n",
    "code_counts.columns = [\"Condition Code\", \"Visits\"]\n",
    "\n",
    "dx_counts = lung_visits[['sentinel_condition_name', 'visit_id']].groupby(['sentinel_condition_name'], as_index = False).count()\n",
    "dx_counts.columns = [\"Condition Name\", \"Visits\"]\n",
    "\n",
    "patient_dxs = lung_visits[[\"sentinel_condition_name\", \"patient_id\"]].groupby(['sentinel_condition_name']).count()\n",
    "patient_dxs.columns = [\"Patients\"]\n",
    "\n",
    "code_counts[\"Condition Name\"] = dx_counts[\"Condition Name\"].copy()\n",
    "code_counts[\"Patients\"] = patient_dxs[\"Patients\"].copy()\n",
    "\n",
    "code_counts = code_counts[[\"Condition Code\", \"Condition Name\", \"Visits\", \"Patients\"]]\n",
    "\n",
    "code_counts.sort_values(by=\"Visits\", ascending=False, inplace=True)\n",
    "                                         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "84431bbb",
   "metadata": {},
   "outputs": [
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "fig = px.bar(code_counts, x='Condition Name', y='Visits', color='Condition Name')\n",
    "fig.update_traces(showlegend=False)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d094a25",
   "metadata": {},
   "outputs": [],
   "source": []
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