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bp

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An R package for analyzing blood pressure data

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# bp

[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/bp)](https://cran.r-project.org/package=bp)
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## bp: Blood Pressure Analysis for R

Cardiovascular disease (CVD) is the leading cause of death worldwide
with Hypertension, specifically, affecting over 1.1 billion people
annually. The goal of the package is to provide a comprehensive toolbox
for analyzing blood pressure (BP) data using a variety of statistical
metrics and visualizations to bring more clarity to CVD.

### Installation

You can install the released version of bp from
[CRAN](https://CRAN.R-project.org) with:

``` r
install.packages("bp")
```

You can install the development version from
[GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("johnschwenck/bp")
```

For installation with vignettes:

``` r
devtools::install_github("johnschwenck/bp", build_vignettes = TRUE)
```

### Sample Data

The package includes six sample data sets:

- `bp_hypnos`: The data set contains ABPM measurements from n= 5subjects
  with type 2diabetes and obstructive sleep apnea. The recordings are
  taken every hour during the 24-hour period for each of the two visits
  that are 3 months apart. Additional information includes sleep/wake
  indicator for each recording inferred from a wrist-worn actigraphy
  device. The aim of original study (Rooney et al.2021) is to determine
  the effect of positive airway pressure treatment of sleep apnea on
  glycemic control and blood pressure of patients with type 2diabetes.
  The included data is a subset of the full data described in [Rooneyet
  al.(2021)](https://clinicaltrials.gov/ct2/show/NCT02454153)
- `bp_jhs`: The data set consists of HBPM measurements from a [2019
  single-subject pilot
  study](https://dataverse.harvard.edu/dataverse/r4r) during an endured
  aerobic (endurance) exercise: cycling 5,775 miles from New York City,
  New York to Seward, Alaska over the course of 95 days. Data was
  collected using an Omron Evolv wireless blood pressure monitor twice
  per day (in the morning upon waking up and in the evening before bed).
  The aim of original study (Schwenck 2019) is to assess blood pressure
  variability across different environments through time.
- `bp_ghana`: The data set contains HBPM measurements on n= 757subjects
  across 32 community health centers from cluster-randomized trial in
  Ghana: 389 subjects are in the health insurance coverage (HIC) group
  and 368 subjects are in another group consisting of a combination of
  HIC with a nurse-led task-shifting strategy for hypertension control
  (TASSH)(this group is denoted TASSH + HIC). Baseline blood pressure
  measurements were collected, with 85% of subjects having available 12
  month follow-up measurements. The aim of original study (Ogedegbe et
  al.2018) is to assess the comparative effectiveness of HIC alone
  versus the combination of TASSH + HIC on reducing systolic blood
  pressure among patients with uncontrolled hypertension in Ghana.
- `bp_preg`: The data set contains HBPM measurements from n= 209women,
  each of whom were recorded every 30 minutes during the Pregnancy Day
  Assessment Clinic (PDAC) observation window for up to a maximum of 240
  minutes (i.e. a maximum of 8 total readings per subject per
  observation window) in addition to an initial “booking” reading before
  the PDAC assessment. The aim of the original study (McCarthy et
  al.2015) is to investigate the pregnancy-induced hypertension and
  pre-eclampsia prediction to determine whether the blood pressure
  assessment of the first observation window of 1 hour (60 minutes) is
  sufficiently accurate relative to the standard 4 hour (240 minute)
  window.
- `bp_children`: The data set contains HBPM measurements on n= 1,283
  children from Bristol, UK. Three blood pressure readings per visit
  were collected over the course of two observation periods (at ages 9
  and 11). Additionally, information on their physical activity has been
  collected. The aim of original study (Solomon-Moore et al.2020) is to
  examine how sedentary behavior affect children progressing through
  primary school, and to understand the relationship between elevated
  blood pressure in children and its impact on the development of
  cardiovascular disease into adulthood.
- `bp_rats`: The data set contains AP measurements sampled at 100 Hz of
  the SS (n1= 9) and SS.13 (n2= 6) genetic strains of Dahl rats. Each
  mouse was administered either a low sodium or a high sodium diet. The
  aim of original study (Bugenhagen et al.2010) is to investigate the
  connection between the dysfunction of the baroreflex control system in
  Dahl rats and salt-sensitive Hypertension

### Intended Functionality

The `bp` package is designed to allow the user to initialize a processed
dataframe through the `process_data` function by specifying any
combination of the following variables present in the user-supplied data
set (with the minimum requirement that `SBP` and `DBP` are included).
The package will then utilize the processed dataframe to calculate
various metrics from medical and statistical literature and provide
visualizations. Perhaps the most useful user-friendly feature of the
package is the ability to generate a visualization report to discern
relationships and assess blood pressure stage progression among
subjects.

The package has the ability to make use of the following physiological
variables (expressed as integers):

- Systolic Blood Pressure (`SBP`) measured in mmHg
- Diastolic Blood Pressure (`DBP`) measured in mmHg
- Arterial Pressure (`AP`) (if applicable)
- Heart Rate (`HR`) measured in bpm
- Pulse Pressure (`PP`) measured in mmHg which is calculated as SBP -
  DBP
- Mean Arterial Pressure (`MAP`) measured in mmHg
- Rate Pressure Product (`RPP`) which is calculated as SBP multiplied by
  resting HR

There are also processing functionality for arterial pressure (AP) data
which include the following inputs: \* A column corresponding to the AP
data (`AP`) \* Time elapsed (`time_elap`)

Furthermore, there are a suite of processing capabilities within the
`process_data` function such as:

- `DATE_TIME`: A column coreresponding to a date/time object (typically
  `as.POSIXct` format) such as `12/1/2020 13:42:07`
- `ID`: Identification of individuals
- `VISIT`: The visit of each individual, if more than one (integer)
- `WAKE`: A binary indicator where 1 denotes awake and 0 denotes asleep
  (binary 1 or 0)
- `GROUP`: An arbitrary column that can be used to group data according
  to this category (such as gender or age)
- `EOD`: Denotes the end-of-day which can be adjusted for situations
  where individuals may take readings after midnight
- `DATA_SCREEN`: A screening proceedure to remove outliers based on
  upper and lower limits of BP readings
- `AGG`: An aggregation option to average consecutive readings taken
  within quick succession
- `CHRON_ORDER`: Adjust the chronology of the readings (chronological vs
  reverse-chronological)
- `TZ`: Time zone adjustment

After all available variables are identified and processed, the
resulting processed dataframe is used for all other functions.

Unique to the `bp` package is the ability to create additional columns
that might not originally be present in the supplied data set. At
current, the following additional columns will be created:

- `TIME_OF_DAY` - Corresponds to the Time of Day (Morning, Afternoon,
  Evening, or Night) based on `DATE_TIME` column
- `DAY_OF_WEEK` - Corresponds to the Day of the week: a useful column
  for table visuals. Based on `DATE_TIME` column
- `SBP_CATEGORY` - Systolic Blood Pressure Stages (Low, Normal,
  Elevated, Stage 1, Stage 2, Crisis) as defined by the American Heart
  Association (AHA)
- `DBP_CATEGORY` - Diastolic Blood Pressure Stages (Low, Normal,
  Elevated, Stage 1, Stage 2, Crisis) as defined by the American Heart
  Association (AHA)
- `BP_CLASS` - A two-to-one mapping of SBP and DBP readings that expands
  on the original AHA categories using a more recent adaptation by (Lee
  et al 2018)

See examples below for further details.

### Available Metrics

The package will then utilize the above variables to calculate various
metrics from medical and statistical literature in order to quantify and
classify the variability of the readings into their respective
categories of hypertension (normal, elevated, or hypertensive).

The following metrics are currently offered through the `bp` package:

| Function           | Metric Name                                | Source                                                                        |
|--------------------|--------------------------------------------|-------------------------------------------------------------------------------|
| `bp_arv`           | Average Real Variability                   | [Mena et al (2005)](https://doi.org/10.1097/01.hjh.0000160205.81652.5a)       |
| `bp_center`        | Mean and Median                            | [Amaro Lijarcio et al (2006)](https://doi.org/10.1016/j.ejim.2006.07.023)     |
| `bp_cv`            | Coefficient of Variation                   | [Munter et al (2011)](https://doi.org/10.1097/HJH.0b013e32834cf213)           |
| `bp_mag`           | Blood Pressure Magnitude (peak and trough) | [Munter et al (2011)](https://doi.org/10.1097/HJH.0b013e32834cf213)           |
| `bp_range`         | Blood Pressure Range                       | [Levitan et al (2013)](https://doi.org/10.1038/jhh.2013.19)                   |
| `bp_sv`            | Successive Variation                       | [Munter et al (2011)](https://doi.org/10.1097/HJH.0b013e32834cf213)           |
| `bp_sleep_metrics` | Blood Pressure Sleep Metrics               | (Multiple - see documentation)                                                |
| `bp_stages`        | Blood Pressure Stages Classification       | American Heart Association                                                    |
| `bp_stats`         | Aggregation of statistical summaries       | N/A                                                                           |
| `dip_calc`         | Nocturnal Dipping % and Classification     | [Okhubo et al (1997)](https://academic.oup.com/ajh/article/10/11/1201/148906) |

The following visualization functions are currently offered through the
`bp` package:

| Function         | Visualization Type              |
|------------------|---------------------------------|
| `bp_scatter`     | Scatter plot of BP stages       |
| `bp_ts_plots`    | Time series plots               |
| `bp_hist`        | Histograms of BP stages         |
| `dip_class_plot` | Dipping % category plot         |
| `bp_report`      | Exportable report of BP summary |

## Example - HYPNOS data

There are two main steps involved with the `bp` package: The data
processing step and the functionality / analysis step.

1.  Load and process data into a new usable dataframe for all further
    analysis using the `process_data` function

``` r
#devtools::install_github("johnschwenck/bp")
library(bp)

## Load bp_hypnos
data(bp_hypnos)

## Process bp_hypnos
hypnos_proc <- process_data(bp_hypnos, 
                     sbp = 'syst', 
                     dbp = 'diast', 
                     date_time = 'date.time', 
                     hr = 'hr', 
                     pp = 'PP', 
                     map = 'MaP', 
                     rpp = 'Rpp', 
                     id = 'id', 
                     visit = 'Visit', 
                     wake = 'wake')
#> 2 values that exceeded the DUL or DLL thresholds were coerced to NA.
```

**NOTE:** the `process_data` function is insensitive to capitalization
of the supplied data column names. For this example, even though the
original column name “SYST” exists in the `bp_hypnos`, “syst” is still
an acceptable name to be given to the function as shown. For emphasis,
all of the above column names were intentionally entered using the wrong
capitalization.

`SBP` and `DBP` must be specified for any other functions to work
properly.

2.  Using the newly processed `hypnos_proc`, we can now calculate
    various metrics. Now that the included raw `bp_hypnos` dataset has
    been processed into `hypnos_proc`, we can now instead rely on this
    new dataframe to calculate various metrics and visualizations. The
    calculation of the nocturnal dipping classification is shown below,
    using a subset of only two of the subjects for comparison (subjects
    70417 and 70435):

``` r
dip_calc(hypnos_proc, subj = c(70417, 70435))
#> [[1]]
#> # A tibble: 8 × 6
#> # Groups:   ID, VISIT [4]
#>   ID    VISIT WAKE  avg_SBP avg_DBP     N
#>   <fct> <fct> <fct>   <dbl>   <dbl> <int>
#> 1 70417 1     0        123.    60.5    10
#> 2 70417 1     1        128     66.6    20
#> 3 70417 2     0        136.    60.5     8
#> 4 70417 2     1        136.    65.6    17
#> 5 70435 1     0        106.    63       6
#> 6 70435 1     1        129.    82.1    23
#> 7 70435 2     0        136.    79.2     9
#> 8 70435 2     1        123.    72.5    20
#> 
#> [[2]]
#> # A tibble: 4 × 6
#> # Groups:   ID [2]
#>   ID    VISIT  dip_sys class_sys  dip_dias class_dias
#>   <fct> <fct>    <dbl> <chr>         <dbl> <chr>     
#> 1 70417 1      0.0359  non-dipper   0.0916 non-dipper
#> 2 70417 2     -0.00450 reverse      0.0784 non-dipper
#> 3 70435 1      0.179   dipper       0.233  extreme   
#> 4 70435 2     -0.104   reverse     -0.0927 reverse
```

In terms of statistical metrics, the `bp_stats` function aggregates many
of the variability and center metrics into one table which makes
comparing the different measures to one another very convenient. Let’s
suppose for this example that we wanted to further analyze these two
subjects by their `BP_CLASS`: we would include `add_groups = "BP_CLASS"`
as an additional argument (note that capitalization does not matter).

``` r
bp_stats(hypnos_proc, subj = c(70417, 70435), add_groups = "bp_class", bp_type = "both")
#> # A tibble: 35 × 27
#> # Groups:   ID, VISIT, WAKE [8]
#>    ID        N VISIT WAKE  BP_CLASS SBP_mean DBP_mean SBP_med DBP_med SD_SBP
#>    <fct> <int> <fct> <fct> <ord>       <dbl>    <dbl>   <dbl>   <dbl>  <dbl>
#>  1 70417     5 1     0     Normal       114.     54.6     114    56     3.51
#>  2 70417     3 1     0     Elevated     125.     60.3     125    64     1.53
#>  3 70417     2 1     0     ISH - S2     143      75.5     143    75.5   2.83
#>  4 70417     3 1     1     Normal       116.     67       118    68     4.04
#>  5 70417     9 1     1     Elevated     125.     65.2     124    66     2.54
#>  6 70417     6 1     1     ISH - S1     134.     67.8     133    67.5   2.42
#>  7 70417     2 1     1     ISH - S2     144      68.5     144    68.5   1.41
#>  8 70417     3 2     0     Elevated     124      57.7     122    57     4.36
#>  9 70417     2 2     0     ISH - S1     133      56.5     133    56.5   1.41
#> 10 70417     3 2     0     ISH - S2     151      66       151    65     0   
#> # ℹ 25 more rows
#> # ℹ 17 more variables: SD_DBP <dbl>, ARV_SBP <dbl>, ARV_DBP <dbl>,
#> #   SV_SBP <dbl>, SV_DBP <dbl>, CV_SBP <dbl>, CV_DBP <dbl>, SBP_max <dbl>,
#> #   SBP_min <dbl>, DBP_max <dbl>, DBP_min <dbl>, SBP_range <dbl>,
#> #   DBP_range <dbl>, Peak_SBP <dbl>, Peak_DBP <dbl>, Trough_SBP <dbl>,
#> #   Trough_DBP <dbl>
```

Here is an example of the `bp_scatter` function for subject 70417:

``` r
bp_scatter(hypnos_proc, subj = 70417)
#> Warning in geom_text(aes(x = min(xlim_breaks) + 5, y = low_y_lim[2], label = "Low"), : All aesthetics have length 1, but the data has 55 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.
#> Warning in geom_text(aes(x = min(xlim_breaks) + 5, y = norm_y_lim[2], label = "Normal"), : All aesthetics have length 1, but the data has 55 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.
#> Warning in geom_text(aes(x = min(xlim_breaks) + 5, y = 130, label = "Elevated"), : All aesthetics have length 1, but the data has 55 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.
#> Warning in geom_text(aes(x = 90, y = 140, label = "S1"), color = "black", : All aesthetics have length 1, but the data has 55 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.
#> Warning in geom_text(aes(x = s2_x_lim[2] - 5, y = s2_y_lim[2] - 5, label = "S2"), : All aesthetics have length 1, but the data has 55 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.
#> Warning in geom_text(aes(x = min(xlim_breaks) + 5, y = 140, label = "ISH - S1"), : All aesthetics have length 1, but the data has 55 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.
#> Warning in geom_text(aes(x = min(xlim_breaks) + 5, y = s2_y_lim[2] - 5, : All aesthetics have length 1, but the data has 55 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.
#> Warning in geom_text(aes(x = 90, y = 90, label = "IDH\n S1"), color = "black", : All aesthetics have length 1, but the data has 55 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.
#> Warning in geom_text(aes(x = s2_x_lim[2] - 5, y = 90, label = "IDH\n S2"), : All aesthetics have length 1, but the data has 55 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.
#> Warning in geom_text(aes(x = xlim_breaks[xlim_breaks_length], y = ylim_breaks[ylim_breaks_length], : All aesthetics have length 1, but the data has 55 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.
```

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