vignette-spinners/bp-distributions.R

In dewittpe/pedbp: Pediatric Blood Pressure

#'---
#'title: "Overview of Pediatric Blood Pressure Distributions"
#'output:
#'  rmarkdown::html_vignette:
#'    toc: true
#'    number_sections: true
#'bibliography: references.bib
#'vignette: >
#'  %\VignetteIndexEntry{Overview of Pediatric Blood Pressure Distributions}
#'  %\VignetteEngine{knitr::rmarkdown}
#'  %\VignetteEncoding{UTF-8}
#'---
#'
#+ label = "setup", include = FALSE
#/*
devtools::load_all() # load the dev version while editing
#*/
################################################################################
#                        !!! DO NOT EDIT .Rmd files !!!                        #
#                                                                              #
# .Rmd files are generated by their corresponding .R files found in the        #
# vignette-spinners/ directory.  Any changes needed to the .Rmd file need to   #
# be made in the .R file                                                       #
################################################################################
knitr::opts_chunk$set(collapse = TRUE, fig.align = "center")
library(qwraps2)

#'
#'
#+ label = "helper functions", include = FALSE
percentile_factor <- function(p) {
  factor(p, levels = sort(unique(p)), labels = paste0(sort(unique(p)) * 100, "th"))
}

#'
#'
library(pedbp)

#'
#' # Introduction
#'
#' Part of the work of @martin2022machine required transforming blood
#' pressure measurement into percentiles based on published norms.  This
#' work was complicated by the fact that data for pediatric blood pressure
#' percentiles is sparse and generally only applicable to children at least one
#' year of age and requires height, a commonly unavailable data point in
#' electronic health records for a variety of reasons.
#'
#' A solution to building pediatric blood pressure percentiles was developed and
#' is presented here for others to use.  Inputs for the developed method are:
#'
#' 1. Patient sex (male/female) _required_
#' 2. Systolic blood pressure (mmHg) _required_
#' 3. Diastolic blood pressure (mmHg) _required_
#' 4. Patient height (cm) _if known_.
#'
#' Given the inputs, the following logic is used to determine which data sets
#' will be used to inform the blood pressure percentiles.  Under one year of
#' age, the data from @gemelli1990longitudinal will be used; a height input is
#' not required for this patient subset. For those at least one year of age with
#' a known height, data from @nhlbi2011expert (hereafter referred to as
#' 'NHLBI/CDC' as the report incorporates recommendations and inputs from the
#' National Heart, Lung, and Blood Institute [NHLBI] and the Centers for Disease
#' Control and Prevention [CDC]). If height is unknown and age is at least three
#' years, then data from @lo2013prehypertension is used.  Lastly, for children
#' between one and three years of age with unknown height, blood pressure
#' percentiles are estimated by the NHLBI/CDC data using as a default the median
#' height for each patient's sex and age.
#'
#+ echo = FALSE, results = "asis"
#/*
while(FALSE) {
#*/
knitr::include_graphics(system.file("images", "flowchart.png", package = "pedbp"))
#/*
}
#*/

#'
#' With version 2.0.0 and later, the option to select the specific reference
#' source data was introduced along with the additional @flynn2017clinical
#' reference.
#'
#' The sources are:
#'
#' 1. @gemelli1990longitudinal for kids under one year of age.
#' 2. @lo2013prehypertension for kids over three years of age and when height is unknown.
#' 3. @nhlbi2011expert for kids 1 through 18 years of age with known stature.
#' 4. @flynn2017clinical for kids 1 through 18 years of age with known stature.
#'
#' The data from @flynn2017clinical and @nhlbi2011expert are similar but for one
#' major difference.  @flynn2017clinical excluded overweight and obese ( BMI
#' above the 85th percentile) children and @nhlbi2011expert included overweight
#' and obese children.
#'
#' # Estimating Pediatric Blood Pressure Distributions
#'
#' There are two functions provided for working with blood pressure
#' distributions.  These methods use Gaussian distributions for both systolic
#' and diastolic blood pressures with means and standard deviations either
#' explicitly provided in an aforementioned source or derived by optimizing the parameters
#' such that the sum of squared errors between the provided quantiles from an
#' aforementioned source and the distribution quantiles is minimized.  The
#' provided functions, a distribution function and a quantile function, follow a
#' similar naming convention to the distribution functions found in the stats
#' library in R.
#'
#' ## Percentiles
#'
#' What percentile for systolic and diastolic blood pressure is 100/60 for a 44
#' month old male with unknown height?
#'
p_bp(q_sbp = 100, q_dbp = 60, age = 44, male = 1)

#'
#' Using the default source of
{{ qwraps2::backtick(martin2022) }}
#' the data source for the above is @lo2013prehypertension since height was not
#' specified.  The same result could be found by explicitly using the
{{ qwraps2::backtick(lo2013) }}
#' source.
p_bp(q_sbp = 100, q_dbp = 60, age = 44, male = 1, source = "lo2013")

#'
#' Those percentiles would be modified if height was 103 cm:
p_bp(q_sbp = 100, q_dbp = 60, age = 44, male = 1, height = 103)
p_bp(q_sbp = 100, q_dbp = 60, age = 44, male = 1, height = 103, source = "nhlbi")

#'
#' If you don't have the height, but you do have the height percentiles you can
#' use that instead:
p_height_for_age(103, male = 1, age = 44)
x <- p_bp(q_sbp = 100, q_dbp = 60, age = 44, male = 1, height_percentile = 0.80, source = "nhlbi")
x

#'
#' A plotting method to show where the observed blood pressures are on the
#' distribution function is also provided.
#+ fig.width = 5, fig.height = 5
bp_cdf(sbp = 100, dbp = 60, age = 44, male = 1, height_percentile = 0.80, source = "nhlbi")

#'
#' Vectors of blood pressures can be used as well.
{{qwraps2::backtick(NA)}}
#' values will return
{{qwraps2::backtick(NA) %s% "."}}
#'
bps <-
  p_bp(
         q_sbp  = c(100, NA, 90)
       , q_dbp  = c(60, 82, 48)
       , age    = 44
       , male   = 1
      )
bps


#'
#' If you want to know which data source was used in computing each of the
#' percentile estimates you can look at the
{{ qwraps2::backtick(bp_params) }}
#' attribute:
attr(bps, "bp_params")
str(bps)

#'
#' ## Quantiles
#'
#' If you have a percentile value and want to know the associated
#' systolic and diastolic blood pressures:
#'
q_bp(
       p_sbp = c(0.701, NA, 0.36)
     , p_dbp = c(0.85, 0.99, 0.50)
     , age = 44
     , male = 1
    )
#'
#'
#' ## Working With More Than One Patient
#'
#' The
{{ qwraps2::backtick(p_bp) }}
#' and
{{ qwraps2::backtick(q_bp) }}
#' methods are designed accept vectors for each of the arguments.
#' These methods expected each argument to be length 1 or all the same length.
#'
#+ label = "bp_batch_example"
eg_data <- read.csv(system.file("example_data", "for_batch.csv", package = "pedbp"))
eg_data

bp_percentiles <-
  p_bp(
         q_sbp  = eg_data$sbp..mmHg.
       , q_dbp  = eg_data$dbp..mmHg.
       , age    = eg_data$age
       , male   = eg_data$male
       , height = eg_data$height
       )
bp_percentiles

str(bp_percentiles)

#'
#' Going from percentiles back to quantiles:
q_bp(
       p_sbp  = bp_percentiles$sbp_p
     , p_dbp  = bp_percentiles$dbp_p
     , age    = eg_data$age
     , male   = eg_data$male
     , height = eg_data$height
     )

#'
#' # Blood Pressure Charts
#'
#' Percentiles over age:
bp_chart()

#'
#' The percentiles curves for a males in the 75th height percentile based on the
#' @flynn2017clinical data:
bp_chart(male = 1, height_percentile = 75, source = "flynn2017")

#'
#' # References
#'<div id="refs"></div>
#'
# /*
#' # Session Info
#+ label = "sessioninfo"
sessionInfo()
# */