{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "13da7a02-2a1b-493d-8c29-2008d1801303",
   "metadata": {},
   "source": [
    "# Analysis of pre-stroke modified Rankin Scale\n",
    "\n",
    "Distributions of mRS (modified Rankin Scale) are shown for:\n",
    "* All patients\n",
    "* Ischaemic and haemorrhagic stroke\n",
    "* Ischaemic patients with NIHSS of 0-10 (surrogate for non large vessel occlusions)\n",
    "* Ischaemic patients with NIHSS of 11+ (surrogate for large vessel occlusions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ee72b51f-5ae0-4960-bba2-c297c55642da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Change default colour scheme:\n",
    "plt.style.use('seaborn-colorblind')\n",
    "\n",
    "# import data\n",
    "data = pd.read_csv(\n",
    "    './../data/2019-11-04-HQIP303-Exeter_MA.csv', low_memory=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b26feeb-c80a-439e-b67d-1c1f44708853",
   "metadata": {},
   "source": [
    "## Get distribution of mRS before stroke for all patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8566a0dd-edda-46e7-a850-5eef1dde898e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>all</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mRS</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.533043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.107781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.121422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.063581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.017870</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          all\n",
       "mRS          \n",
       "0    0.533043\n",
       "1    0.156302\n",
       "2    0.107781\n",
       "3    0.121422\n",
       "4    0.063581\n",
       "5    0.017870"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count, division = np.histogram(data['S2RankinBeforeStroke'], bins=range(0,7))\n",
    "\n",
    "results = pd.DataFrame()\n",
    "results.index.name='mRS'\n",
    "results['all'] = count / count.sum()\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c62a928-0837-4d0d-892b-91cafae3f660",
   "metadata": {},
   "source": [
    "## Get distribution of mRS before stroke by stroke type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4881a9f1-2bec-41bb-b5f1-28cb4cb4770e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>all</th>\n",
       "      <th>infarction</th>\n",
       "      <th>haemorrhage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mRS</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.156302</td>\n",
       "      <td>0.157958</td>\n",
       "      <td>0.144905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.107781</td>\n",
       "      <td>0.108075</td>\n",
       "      <td>0.106007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.121422</td>\n",
       "      <td>0.119199</td>\n",
       "      <td>0.137272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.063581</td>\n",
       "      <td>0.062649</td>\n",
       "      <td>0.068735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.017870</td>\n",
       "      <td>0.017196</td>\n",
       "      <td>0.021706</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "          all  infarction  haemorrhage\n",
       "mRS                                   \n",
       "0    0.533043    0.534923     0.521374\n",
       "1    0.156302    0.157958     0.144905\n",
       "2    0.107781    0.108075     0.106007\n",
       "3    0.121422    0.119199     0.137272\n",
       "4    0.063581    0.062649     0.068735\n",
       "5    0.017870    0.017196     0.021706"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Infarction\n",
    "mask = data['S2StrokeType'] == 'Infarction'\n",
    "count, division = np.histogram(\n",
    "    data[mask]['S2RankinBeforeStroke'], bins=range(0,7))\n",
    "results['infarction'] = count / count.sum()\n",
    "\n",
    "# Haemorrhage\n",
    "mask = data['S2StrokeType'] == 'Primary Intracerebral Haemorrhage'\n",
    "count, division = np.histogram(\n",
    "    data[mask]['S2RankinBeforeStroke'], bins=range(0,7))\n",
    "results['haemorrhage'] = count / count.sum()\n",
    "\n",
    "# Show results\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2d87d84-8c3e-46fa-b85f-b1c1375d2579",
   "metadata": {},
   "source": [
    "## mRS for ischaemic stroke, by stroke severity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d7834fa8-1a0e-4a88-9cf2-8f69dcd4057f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>all</th>\n",
       "      <th>infarction</th>\n",
       "      <th>haemorrhage</th>\n",
       "      <th>nlvo</th>\n",
       "      <th>lvo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mRS</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>0.582771</td>\n",
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       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>0.156302</td>\n",
       "      <td>0.157958</td>\n",
       "      <td>0.144905</td>\n",
       "      <td>0.163513</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.121422</td>\n",
       "      <td>0.119199</td>\n",
       "      <td>0.137272</td>\n",
       "      <td>0.101109</td>\n",
       "      <td>0.166050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.063581</td>\n",
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       "      <td>0.017196</td>\n",
       "      <td>0.021706</td>\n",
       "      <td>0.006822</td>\n",
       "      <td>0.044460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          all  infarction  haemorrhage      nlvo       lvo\n",
       "mRS                                                       \n",
       "0    0.533043    0.534923     0.521374  0.582771  0.407796\n",
       "1    0.156302    0.157958     0.144905  0.163513  0.143538\n",
       "2    0.107781    0.108075     0.106007  0.103988  0.120133\n",
       "3    0.121422    0.119199     0.137272  0.101109  0.166050\n",
       "4    0.063581    0.062649     0.068735  0.041796  0.118023\n",
       "5    0.017870    0.017196     0.021706  0.006822  0.044460"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter data\n",
    "mask = data['S2StrokeType'] == 'Infarction'\n",
    "data = data[mask]\n",
    "\n",
    "# NIHSS 0-10 is a surrogate for non large vessel occlusions\n",
    "mask = data['S2NihssArrival'] < 11 \n",
    "count, division = np.histogram(\n",
    "    data[mask]['S2RankinBeforeStroke'], bins=range(0,7))\n",
    "results['nlvo'] = count / count.sum()\n",
    "\n",
    "# NIHSS 11+ is a surrogate for non large vessel occlusions\n",
    "mask = data['S2NihssArrival'] > 10\n",
    "count, division = np.histogram(\n",
    "    data[mask]['S2RankinBeforeStroke'], bins=range(0,7))\n",
    "results['lvo'] = count / count.sum()\n",
    "\n",
    "# Show results\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "659982c0-ecd9-452e-9a97-89aab32dba50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,5))\n",
    "ax = fig.add_subplot()\n",
    "width = 0.17\n",
    "x = results.index\n",
    "ax.bar(x - width * 2, results['all'], width, label='all')\n",
    "ax.bar(x, results['haemorrhage'], width, label='haemorrhage')\n",
    "ax.bar(x + width, results['nlvo'], width, label='nLVO (NIHSS 0-10')\n",
    "ax.bar(x + width * 2, results['lvo'], width, label='LVO (NIHSS 11+)')\n",
    "ax.set_ylabel('Fraction of patients')\n",
    "ax.set_xlabel('mRS before stroke')\n",
    "ax.legend()\n",
    "plt.savefig('./output/mrs_before_by_stroke_type.jpg', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "17bf0859-00df-445e-96ab-4500e53a2f49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,5))\n",
    "ax = fig.add_subplot()\n",
    "width = 0.17\n",
    "x = results.index\n",
    "#ax.plot(results['all'], label='all')\n",
    "ax.plot(results['haemorrhage'], marker='o', label='haemorrhage')\n",
    "ax.plot(results['nlvo'], marker = 's', label='nLVO (NIHSS 0-10')\n",
    "ax.plot(results['lvo'], marker = '^', label='LVO (NIHSS 11+)')\n",
    "ax.grid()\n",
    "ax.set_ylabel('Fraction of patients')\n",
    "ax.set_xlabel('mRS before stroke')\n",
    "ax.legend()\n",
    "plt.savefig('./output/mrs_before_by_stroke_type.jpg', dpi=300)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}