clinical_application.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2: Clinical Application\n",
    "\n",
    "Using the CAST dataset, investigate the trend that average resting ehart rate increase up until middle age then decrease into old age.\n",
    "\n",
    "### Dataset (CAST)\n",
    "\n",
    "The data from this project comes from the [Cardiac Arrythmia Suppression Trial (CAST)](https://physionet.org/content/crisdb/1.0.0/), which was sponsored by the National Heart, Lung, and Blood Institute (NHLBI). CAST collected 24 hours of heart rate data from ECGs from people who have had a myocardial infarction (MI) within the past two years.[1] This data has been smoothed and resampled to more closely resemble PPG-derived pulse rate data from a wrist wearable.[2]\n",
    "\n",
    "1. **CAST RR Interval Sub-Study Database Citation** - Stein PK, Domitrovich PP, Kleiger RE, Schechtman KB, Rottman JN. Clinical and demographic determinants of heart rate variability in patients post myocardial infarction: insights from the Cardiac Arrhythmia Suppression Trial (CAST). Clin Cardiol 23(3):187-94; 2000 (Mar)\n",
    "2. **Physionet Citation** - Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals (2003). Circulation. 101(23):e215-e220.\n",
    "\n",
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code\n",
    "#### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load the dataset\n",
    "\n",
    "The dataset is stored as [.npz](https://docs.scipy.org/doc/numpy/reference/generated/numpy.savez.html) files. Each file contains roughly 24 hours of heart rate data in the 'hr' array sampled at 1Hz. The subject ID is the name of the file. You will use these files to compute resting heart rate.\n",
    "\n",
    "Demographics metadata is stored in a file called 'metadata.csv'. This CSV has three columns, one for subject ID, age group, and sex. You will use this file to make the association between resting heart rate and age group for each gender.\n",
    "\n",
    "Find the dataset in `../datasets/crisdb/`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "hr_filenames = glob.glob('/data/crisdb/*.npz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load Metadata\n",
    "Load the metadata file into a datastructure that allows for easy lookups from subject ID to age group and sex."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_filename = '/data/crisdb/metadata.csv'\n",
    "\n",
    "with open(metadata_filename, 'r') as f:\n",
    "    metadata = pd.read_csv(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>e198a</td>\n",
       "      <td>20-24</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>e198b</td>\n",
       "      <td>20-24</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>e028b</td>\n",
       "      <td>30-34</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>e028a</td>\n",
       "      <td>30-34</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>e061b</td>\n",
       "      <td>30-34</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject    age   sex\n",
       "0   e198a  20-24  Male\n",
       "1   e198b  20-24  Male\n",
       "2   e028b  30-34  Male\n",
       "3   e028a  30-34  Male\n",
       "4   e061b  30-34  Male"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute Resting Heart Rate\n",
    "For each subject we want to compute the resting heart rate while keeping track of which age group this subject belongs to. An easy, robust way to compute the resting heart rate is to use the lowest 5th percentile value in the heart rate timeseries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def AgeAndRHR(metadata, filename):\n",
    "\n",
    "    # Load the heart rate timeseries\n",
    "    hr_data = np.load(filename)['hr']\n",
    "    \n",
    "    # Compute the resting heart rate from the timeseries by finding the lowest 5th percentile value in hr_data\n",
    "    rhr = np.percentile(hr_data, 5)\n",
    "\n",
    "    # Find the subject ID from the filename.\n",
    "    subject = filename.rsplit('/')[-1].rstrip('.npz')\n",
    "\n",
    "    # Find the age group for this subject in metadata.\n",
    "    age_group = metadata.loc[metadata['subject'] == subject]['age'].values[0]\n",
    "    \n",
    "    # Find the sex for this subject in metadata.\n",
    "    sex = metadata.loc[metadata['subject'] == subject]['sex'].values[0]\n",
    "\n",
    "    return age_group, sex, rhr\n",
    "\n",
    "df = pd.DataFrame(data=[AgeAndRHR(metadata, filename) for filename in hr_filenames],\n",
    "                  columns=['age_group', 'sex', 'rhr'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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>age_group</th>\n",
       "      <th>sex</th>\n",
       "      <th>rhr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60-64</td>\n",
       "      <td>Female</td>\n",
       "      <td>89.302326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65-69</td>\n",
       "      <td>Male</td>\n",
       "      <td>65.641026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45-49</td>\n",
       "      <td>Male</td>\n",
       "      <td>56.928458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60-64</td>\n",
       "      <td>Male</td>\n",
       "      <td>68.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50-54</td>\n",
       "      <td>Male</td>\n",
       "      <td>84.395604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  age_group     sex        rhr\n",
       "0     60-64  Female  89.302326\n",
       "1     65-69    Male  65.641026\n",
       "2     45-49    Male  56.928458\n",
       "3     60-64    Male  68.571429\n",
       "4     50-54    Male  84.395604"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot Resting Heart Rate vs. Age Group\n",
    "We'll use [seaborn](https://seaborn.pydata.org/) to plot the relationship. Seaborn is a thin wrapper around matplotlib that enables higher-level statistical plots.\n",
    "\n",
    "We will use [lineplot](https://seaborn.pydata.org/generated/seaborn.lineplot.html#seaborn.lineplot) to plot the mean of the resting heart rates for each age group along with the 95% confidence interval around the mean. More about making plots that show uncertainty [here](https://seaborn.pydata.org/tutorial/relational.html#aggregation-and-representing-uncertainty)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install seaborn==0.9.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb81c334438>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "labels = sorted(np.unique(df.age_group))\n",
    "df['xaxis'] = df.age_group.map(lambda x: labels.index(x)).astype('float')\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.lineplot(x='xaxis', y='rhr', hue='sex', data=df)\n",
    "_ = plt.xticks(np.arange(len(labels)), labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clinical Conclusion\n",
    "\n",
    "For females, we see a notable increase in resting heart rate from ages 35-39 to age 40-44, then a slight decline from 40-44 to 55-59 before a steeper decline from 55-59 to ages higher than 70. We also see a larger confidence interval from ages 40-44 to 55-59, indicating that female resting heart ranges could easily be around 10 RHR higher or lower than average.\n",
    "\n",
    "For males, we see a minor increase in heart rate around age 45-49, and then slight decline into old age.\n",
    "\n",
    "Compared to males, females have a higher average resting heart rate from ages 40-44 to 65-69. Additionally, females have a much wider confidence interval around their average heart rate values compared to men. The trend of increasing heart rate into middle age before decreasing heart rate into old age is much more visible in women.\n",
    "\n",
    "There may be several possible reasons these results. First, women are generally expected to have a higher heart rate than men because females hearts are typically smaller than male hearts and must beat faster to provide similar output of blood per beat. Women also have a slightly faster physiological heart rhythm, causing higher heart rates ([journal source](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4190707/)). Menopausal women may also experience changes in heart rate. \n",
    "\n",
    "It is important to verify that the dataset is representative of the broader population for both men and women. As shown in the bar charts below, females are clearly underrepresented compared to males in the CAST dataset. This could likely be the reason why the confidence interval for women is much wider then it is for men. Additionally, the age distributions for both men and women are both left skewed with most samples taken around middle age (50s to 60s), while younger age groups are underrepresented. This could be expected because the CAST dataset is collected from people who have had a myocardial infarction (MI) within the past two years, which are more common in middle and older aged people. We might improve our results if we chose a larger dataset that is more representative of the broader population age and sex demographics, and compared equal counts of males and females."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb819b25828>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb819bd37f0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, axes = plt.subplots(1, 2, figsize=(15,4))\n",
    "\n",
    "df.groupby('sex').size().plot(kind='bar', ax=axes[0])\n",
    "df.groupby(['age_group', 'sex']).size().unstack(level=1).plot(kind='bar', ax=axes[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We were unable to fully validate for both sexes the trend that average resting heart rate increases up until middle age and then decreases into old age. Our dataset did not contain adequate samples for younger ages, so we did not see an increase in heart rate for men although we did see an increase for women between age groups 35-39 and 40-44. For both sexes, we were able to see in the plots that average resting heart rate decreases from middle age into old age. However, the age and sex demographics of the CAST dataset are not sufficient to fully validate the trend."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}