In dewittpe/pedbp: Pediatric Blood Pressure
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knitr::opts_chunk$set(collapse = TRUE, fig.align = "center")
library(qwraps2)
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:
- Patient sex (male/female) required
- Systolic blood pressure (mmHg) required
- Diastolic blood pressure (mmHg) required
- 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.
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.
args(p_bp)
# Quantile Function
args(q_bp)
Both methods expect an age in months and an indicator for sex.
r qwraps2::backtick(height) %s% ", "
in centimeters, is used preferentially over
r qwraps2::backtick(height_percentile) %s% "."
The
r qwraps2::backtick(default_height_percentile) %s% "."
is set to 50 by default to match the flowchart above, but can be adjusted
here to meet the end users needs.
The reference look up tables for the @nhlbi2011expert and @flynn2017clinical
require height percentiles.
If
r qwraps2::backtick(height)
is entered, then the height percentile is determined via an LMS
method for age and sex using corresponding LMS data from either the Centers
for Disease control (CDC) or the World Health Organization (WHO) based on
age. Under 36 months use the WHO data to estimate the height percentile and
for 36 months and over use the CDC data. The look up table will use the
percentile nearest the calculated value. Look up height percentiles values
are: 5, 10, 25, 50, 75, 90, and 95.
If you want to restrict to CDC or WHO values regardless of age, we recommend
using
r qwraps2::backtick(p_height_for_age)
and
r qwraps2::backtick(p_length_for_age)
to get height (stature) percentiles and pass the result to the
r qwraps2::backtick(height_percentile)
argument.
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)
Those percentiles would be modified if height was 103 cm:
p_bp(q_sbp = 100, q_dbp = 60, age = 44, male = 1, height = 103)
For the age and sex, the height of 103 is approximately the
r frmt(as.integer(100 * p_height_for_age(103, male = 1, age = 44))) %s% "th"
percentile.
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.
bp_cdf(sbp = 90, dbp = 55, age = 44, male = 1, height = 103, source = "nhlbi")
Vectors of blood pressures can be used as well. NA values will return NA.
bps <-
p_bp(
q_sbp = c(100, NA, 90)
, q_dbp = c(60, 82, 48)
, age = 44
, male = 1
, height_percentile = 0.80
)
bps
If you want to know which data source was used in computing each of the
percentile estimates you can look at the
r qwraps2::backtick(bp_params)
attribute:
attr(bps, "bp_params")
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
, height_percentile = 0.80
)
Working With More Than One Patient
The
r qwraps2::backtick(p_bp)
and
r 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.
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$height
)
Select Source Data
The default method for estimating blood pressure percentiles is based on the
method of @martin2022machine and @martin2022development which uses three
different references depending on age and known/unknown stature. If you want
to use a specific reference then you can do so by using the
r qwraps2::backtick(source)
argument.
If you have a project with the want/need to use a specific source and you'd
to use you can set it as an option:
options(pedbp_bp_source = "martin2022") # default
There are four sources:
- @gemelli1990longitudinal for kids under one year of age.
- @lo2013prehypertension for kids over three years of age and when height is unknown.
- @nhlbi2011expert for kids 1 through 18 years of age with known stature.
- @flynn2017clinical for kids 1 through 18 years of age with known stature.
The data from @flynn2017clinical and @nhlbi2011expert are similar but for one
major difference. @flynn2017clinical excluded overweight and obese ( BMI
above the 85th percentile) children.
For example, the estimated percentile for a blood pressure of 92/60 in a 39.2
month old female in the 95th height percentile are:
d <- data.frame(source = c("martin2022", "gemelli1990", "lo2013", "nhlbi", "flynn2017"),
p_sbp = NA_real_,
p_dbp = NA_real_)
for(i in 1:nrow(d)) {
bp <- p_bp(q_sbp = 92, q_dbp = 60, age = 39.2, male = 0, height_percentile = 95, source = d$source[i])
d[i, "p_sbp"] <- bp$sbp
d[i, "p_dbp"] <- bp$dbp
}
d
The estimated 85th quantile SBP/DBP for a 39.2 month old female, who is in
the 95th height percentile are:
d <- data.frame(source = c("martin2022", "gemelli1990", "lo2013", "nhlbi", "flynn2017"),
q_sbp = NA_real_,
q_dbp = NA_real_)
for(i in 1:nrow(d)) {
bp <- q_bp(p_sbp = 0.85, p_dbp = 0.85, age = 39.2, male = 0, height_percentile = 95, source = d$source[i])
d[i, "q_sbp"] <- bp$sbp
d[i, "q_dbp"] <- bp$dbp
}
d
Comparing to Published Percentiles
The percentiles published in @nhlbi2011expert and @flynn2017clinical where
used to estimate a Gaussian mean and standard deviation. This was in part to
be consistent with the values from @gemelli1990longitudinal and
@lo2013prehypertension. As a result, the calculated percentiles and
quantiles from the pedbp package for @nhlbi2011expert and @flynn2017clinical
will be slightly different from the
published values.
Flynn et al.
fq <-
q_bp(
p_sbp = flynn2017$bp_percentile/100,
p_dbp = flynn2017$bp_percentile/100,
male = flynn2017$male,
age = flynn2017$age,
height_percentile = flynn2017$height_percentile,
source = "flynn2017")
fp <-
p_bp(
q_sbp = flynn2017$sbp,
q_dbp = flynn2017$dbp,
male = flynn2017$male,
age = flynn2017$age,
height_percentile = flynn2017$height_percentile,
source = "flynn2017")
f_bp <-
cbind(flynn2017,
pedbp_sbp = fq$sbp,
pedbp_dbp = fq$dbp,
pedbp_sbp_p = fp$sbp_p * 100,
pedbp_dbp_p = fp$dbp_p * 100
)
All the quantile estimates are within 2 mmHg:
summary(f_bp$pedbp_sbp - f_bp$sbp)
summary(f_bp$pedbp_dbp - f_bp$dbp)
stopifnot(max(abs(f_bp$pedbp_sbp - f_bp$sbp)) < 2)
stopifnot(max(abs(f_bp$pedbp_dbp - f_bp$dbp)) < 2)
All the percentiles estimates are within are within 2 percentile points:
summary(f_bp$pedbp_sbp_p - f_bp$bp_percentile)
summary(f_bp$pedbp_dbp_p - f_bp$bp_percentile)
stopifnot(max(abs(f_bp$pedbp_sbp_p - f_bp$bp_percentile)) < 2)
stopifnot(max(abs(f_bp$pedbp_dbp_p - f_bp$bp_percentile)) < 2)
A helpful set of graphics are shown below. Panels A and C show the estimated
blood pressure quantiles provide by the
r qwraps2::Rpkg(pedbp)
package (y-axis) against the published quantiles from @flynn2017clinical for
systolic and diastolic blood pressures respectively.
Panels B and D are Bland-Altman plots showing the difference vs average
between the two estimates.
fsbp <- ggplot2::ggplot(f_bp) +
ggplot2::theme_bw() +
ggplot2::aes(x = sbp, y = pedbp_sbp) +
ggplot2::geom_point() +
ggplot2::geom_abline(intercept = 0, slope = 1) +
ggplot2::ylab("pedbp Package\nSystolic Blood Pressure (mmHg)") +
ggplot2::xlab("Published Flynn (2017)\nSystolic Blood Pressure (mmHg)")
fdbp <- ggplot2::ggplot(f_bp) +
ggplot2::theme_bw() +
ggplot2::aes(x = dbp, y = pedbp_dbp) +
ggplot2::geom_point() +
ggplot2::geom_abline(intercept = 0, slope = 1) +
ggplot2::ylab("pedbp Package\nDiastolic Blood Pressure (mmHg)") +
ggplot2::xlab("Published Flynn (2017)\nDiastolic Blood Pressure (mmHg)")
ggpubr::ggarrange(
fsbp, qwraps2::qblandaltman(f_bp[, c("sbp", "pedbp_sbp")]) + ggplot2::theme_bw(),
fdbp, qwraps2::qblandaltman(f_bp[, c("dbp", "pedbp_dbp")]) + ggplot2::theme_bw(),
labels = LETTERS
)
NHLBI/CDC
nq <-
q_bp(
p_sbp = nhlbi_bp_norms$bp_percentile/100,
p_dbp = nhlbi_bp_norms$bp_percentile/100,
male = nhlbi_bp_norms$male,
age = nhlbi_bp_norms$age,
height_percentile = nhlbi_bp_norms$height_percentile,
source = "nhlbi")
np <-
p_bp(
q_sbp = nhlbi_bp_norms$sbp,
q_dbp = nhlbi_bp_norms$dbp,
male = nhlbi_bp_norms$male,
age = nhlbi_bp_norms$age,
height_percentile = nhlbi_bp_norms$height_percentile,
source = "nhlbi")
nhlbi_bp <-
cbind(nhlbi_bp_norms,
pedbp_sbp = nq$sbp,
pedbp_dbp = nq$dbp,
pedbp_sbp_p = np$sbp_p * 100,
pedbp_dbp_p = np$dbp_p * 100
)
All the quantile estimates are within 2 mmHg:
summary(nhlbi_bp$pedbp_sbp - nhlbi_bp$sbp)
summary(nhlbi_bp$pedbp_dbp - nhlbi_bp$dbp)
stopifnot(max(abs(nhlbi_bp$pedbp_sbp - nhlbi_bp$sbp)) < 2)
stopifnot(max(abs(nhlbi_bp$pedbp_dbp - nhlbi_bp$dbp)) < 2)
All the percentiles are within 2 percentile points:
summary(nhlbi_bp$pedbp_sbp_p - nhlbi_bp$bp_percentile)
summary(nhlbi_bp$pedbp_dbp_p - nhlbi_bp$bp_percentile)
stopifnot(max(abs(nhlbi_bp$pedbp_sbp_p - nhlbi_bp$bp_percentile)) < 2)
stopifnot(max(abs(nhlbi_bp$pedbp_dbp_p - nhlbi_bp$bp_percentile)) < 2)
A helpful set of graphics are shown below. Panels A and C show the estimated
blood pressure quantiles provide by the
r qwraps2::Rpkg(pedbp)
package (y-axis) against the published quantiles from @nhlbi2011expert for
systolic and diastolic blood pressures respectively.
Panels B and D are Bland-Altman plots showing the difference vs average
between the two estimates.
nsbp <- ggplot2::ggplot(nhlbi_bp) +
ggplot2::theme_bw() +
ggplot2::aes(x = sbp, y = pedbp_sbp) +
ggplot2::geom_point() +
ggplot2::geom_abline(intercept = 0, slope = 1) +
ggplot2::ylab("pedbp Package\nSystolic Blood Pressure (mmHg)") +
ggplot2::xlab("Published NHLBI\nSystolic Blood Pressure (mmHg)")
ndbp <- ggplot2::ggplot(nhlbi_bp) +
ggplot2::theme_bw() +
ggplot2::aes(x = dbp, y = pedbp_dbp) +
ggplot2::geom_point() +
ggplot2::geom_abline(intercept = 0, slope = 1) +
ggplot2::ylab("pedbp Package\nDiastolic Blood Pressure (mmHg)") +
ggplot2::xlab("Published NHLBI (2017)\nDiastolic Blood Pressure (mmHg)")
ggpubr::ggarrange(
nsbp, qwraps2::qblandaltman(nhlbi_bp[, c("sbp", "pedbp_sbp")]) + ggplot2::theme_bw(),
ndbp, qwraps2::qblandaltman(nhlbi_bp[, c("dbp", "pedbp_dbp")]) + ggplot2::theme_bw(),
labels = LETTERS
)
NHLBI vs Flynn 2017
The NHLBI data included overweight and obese children whereas Flynn excluded
them. As a result, the estimates for blood pressures can differ
significantly between the two sources.
The graphic below shows the estimated systolic and diastolic blood pressures
provided by the
r qwraps2::Rpkg(pedbp)
package. As expected, the values estimated based on Flynn are lower, on
average, than those estimated by data from the NHLBI.
nhlbi_vs_flynn <- f_bp
names(nhlbi_vs_flynn)[names(nhlbi_vs_flynn) == "bp_percentile"] <- "flynn_bp_percentile"
names(nhlbi_vs_flynn)[names(nhlbi_vs_flynn) == "sbp"] <- "flynn_sbp"
names(nhlbi_vs_flynn)[names(nhlbi_vs_flynn) == "dbp"] <- "flynn_dbp"
names(nhlbi_vs_flynn)[names(nhlbi_vs_flynn) == "pedbp_sbp"] <- "pedbp_flynn_sbp"
names(nhlbi_vs_flynn)[names(nhlbi_vs_flynn) == "pedbp_dbp"] <- "pedbp_flynn_dbp"
names(nhlbi_vs_flynn)[names(nhlbi_vs_flynn) == "pedbp_sbp_p"] <- "pedbp_flynn_sbp_p"
names(nhlbi_vs_flynn)[names(nhlbi_vs_flynn) == "pedbp_dbp_p"] <- "pedbp_flynn_dbp_p"
nhlbi_vs_flynn <-
merge(nhlbi_vs_flynn, nhlbi_bp, all = TRUE, by = c("male", "age", "height_percentile"))
names(nhlbi_vs_flynn)[names(nhlbi_vs_flynn) == "bp_percentile"] <- "nhlbi_bp_percentile"
names(nhlbi_vs_flynn)[names(nhlbi_vs_flynn) == "sbp"] <- "nhlbi_sbp"
names(nhlbi_vs_flynn)[names(nhlbi_vs_flynn) == "dbp"] <- "nhlbi_dbp"
names(nhlbi_vs_flynn)[names(nhlbi_vs_flynn) == "pedbp_sbp"] <- "pedbp_nhlbi_sbp"
names(nhlbi_vs_flynn)[names(nhlbi_vs_flynn) == "pedbp_dbp"] <- "pedbp_nhlbi_dbp"
names(nhlbi_vs_flynn)[names(nhlbi_vs_flynn) == "pedbp_sbp_p"] <- "pedbp_nhlbi_sbp_p"
names(nhlbi_vs_flynn)[names(nhlbi_vs_flynn) == "pedbp_dbp_p"] <- "pedbp_nhlbi_dbp_p"
pA <- ggplot2::ggplot(nhlbi_vs_flynn) +
ggplot2::theme_bw() +
ggplot2::aes(x = pedbp_nhlbi_sbp, y = pedbp_flynn_sbp) + #, color = factor(height_percentile), shape = factor(male, 0:1, c("Female", "Male"))) +
ggplot2::geom_point() +
ggplot2::geom_abline(intercept = 0, slope = 1) +
ggplot2::ylab("pedbp NHLBI Systolic BP") +
ggplot2::xlab("pedbp Flynn (2017) Systolic BP")
pC <- ggplot2::ggplot(nhlbi_vs_flynn) +
ggplot2::theme_bw() +
ggplot2::aes(x = pedbp_nhlbi_dbp, y = pedbp_flynn_dbp) + #, color = factor(height_percentile), shape = factor(male, 0:1, c("Female", "Male"))) +
ggplot2::geom_point() +
ggplot2::geom_abline(intercept = 0, slope = 1) +
ggplot2::ylab("pedbp NHLBI Diastolic BP") +
ggplot2::xlab("pedbp Flynn (2017) Diastolic BP")
ggpubr::ggarrange(
pA, qwraps2::qblandaltman(nhlbi_vs_flynn[, c("pedbp_nhlbi_sbp", "pedbp_flynn_sbp")]) + ggplot2::theme_bw(),
pC, qwraps2::qblandaltman(nhlbi_vs_flynn[, c("pedbp_nhlbi_dbp", "pedbp_flynn_dbp")]) + ggplot2::theme_bw(),
labels = LETTERS
)
Blood Pressure Charts
To you can get blood pressure charts for any combination of inputs using
r qwraps2::backtick(bp_chart) %s% "."
For example, the blood pressure percentiles when using
r qwraps2::backtick("source = 'martin2022'", dequote = TRUE)
and height is unknown are:
bp_chart(p = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.90, 0.95), source = "martin2022") # default
And if height is known (say it is the 25th percentile)
bp_chart(p = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.90, 0.95),
height_percentile = 25,
source = "martin2022")
Additionally, charts for each of the specific data sources can be generated
bp_chart(source = "gemelli1990")
bp_chart(source = "lo2013")
bp_chart(source = "nhlbi")
bp_chart(source = "flynn2017")
Shiny Application
An interactive Shiny application is also
available. After installing the pedbp package and the suggested packages, you
can run the app locally via
shiny::runApp(system.file("shinyapps", "pedbp", package = "pedbp"))
The shiny application allows for interactive exploration of blood pressure
percentiles for an individual patient and allows for batch processing a set
of patients as well.
References
dewittpe/pedbp documentation built on Jan. 26, 2025, 8:02 p.m.
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################################################################################ # !!! 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)
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:
- Patient sex (male/female) required
- Systolic blood pressure (mmHg) required
- Diastolic blood pressure (mmHg) required
- 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.
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.
args(p_bp) # Quantile Function args(q_bp)
Both methods expect an age in months and an indicator for sex.
r qwraps2::backtick(height) %s% ", "
in centimeters, is used preferentially over
r qwraps2::backtick(height_percentile) %s% "."
The
r qwraps2::backtick(default_height_percentile) %s% "."
is set to 50 by default to match the flowchart above, but can be adjusted
here to meet the end users needs.
The reference look up tables for the @nhlbi2011expert and @flynn2017clinical
require height percentiles.
If
r qwraps2::backtick(height)
is entered, then the height percentile is determined via an LMS
method for age and sex using corresponding LMS data from either the Centers
for Disease control (CDC) or the World Health Organization (WHO) based on
age. Under 36 months use the WHO data to estimate the height percentile and
for 36 months and over use the CDC data. The look up table will use the
percentile nearest the calculated value. Look up height percentiles values
are: 5, 10, 25, 50, 75, 90, and 95.
If you want to restrict to CDC or WHO values regardless of age, we recommend
using
r qwraps2::backtick(p_height_for_age)
and
r qwraps2::backtick(p_length_for_age)
to get height (stature) percentiles and pass the result to the
r qwraps2::backtick(height_percentile)
argument.
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)
Those percentiles would be modified if height was 103 cm:
p_bp(q_sbp = 100, q_dbp = 60, age = 44, male = 1, height = 103)
For the age and sex, the height of 103 is approximately the
r frmt(as.integer(100 * p_height_for_age(103, male = 1, age = 44))) %s% "th"
percentile.
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.
bp_cdf(sbp = 90, dbp = 55, age = 44, male = 1, height = 103, source = "nhlbi")
Vectors of blood pressures can be used as well. NA values will return NA.
bps <- p_bp( q_sbp = c(100, NA, 90) , q_dbp = c(60, 82, 48) , age = 44 , male = 1 , height_percentile = 0.80 ) bps
If you want to know which data source was used in computing each of the
percentile estimates you can look at the
r qwraps2::backtick(bp_params)
attribute:
attr(bps, "bp_params")
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 , height_percentile = 0.80 )
Working With More Than One Patient
The
r qwraps2::backtick(p_bp)
and
r 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.
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 )
Select Source Data
The default method for estimating blood pressure percentiles is based on the
method of @martin2022machine and @martin2022development which uses three
different references depending on age and known/unknown stature. If you want
to use a specific reference then you can do so by using the
r qwraps2::backtick(source)
argument.
If you have a project with the want/need to use a specific source and you'd to use you can set it as an option:
options(pedbp_bp_source = "martin2022") # default
There are four sources:
- @gemelli1990longitudinal for kids under one year of age.
- @lo2013prehypertension for kids over three years of age and when height is unknown.
- @nhlbi2011expert for kids 1 through 18 years of age with known stature.
- @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.
For example, the estimated percentile for a blood pressure of 92/60 in a 39.2 month old female in the 95th height percentile are:
d <- data.frame(source = c("martin2022", "gemelli1990", "lo2013", "nhlbi", "flynn2017"), p_sbp = NA_real_, p_dbp = NA_real_) for(i in 1:nrow(d)) { bp <- p_bp(q_sbp = 92, q_dbp = 60, age = 39.2, male = 0, height_percentile = 95, source = d$source[i]) d[i, "p_sbp"] <- bp$sbp d[i, "p_dbp"] <- bp$dbp } d
The estimated 85th quantile SBP/DBP for a 39.2 month old female, who is in the 95th height percentile are:
d <- data.frame(source = c("martin2022", "gemelli1990", "lo2013", "nhlbi", "flynn2017"), q_sbp = NA_real_, q_dbp = NA_real_) for(i in 1:nrow(d)) { bp <- q_bp(p_sbp = 0.85, p_dbp = 0.85, age = 39.2, male = 0, height_percentile = 95, source = d$source[i]) d[i, "q_sbp"] <- bp$sbp d[i, "q_dbp"] <- bp$dbp } d
Comparing to Published Percentiles
The percentiles published in @nhlbi2011expert and @flynn2017clinical where used to estimate a Gaussian mean and standard deviation. This was in part to be consistent with the values from @gemelli1990longitudinal and @lo2013prehypertension. As a result, the calculated percentiles and quantiles from the pedbp package for @nhlbi2011expert and @flynn2017clinical will be slightly different from the published values.
Flynn et al.
fq <- q_bp( p_sbp = flynn2017$bp_percentile/100, p_dbp = flynn2017$bp_percentile/100, male = flynn2017$male, age = flynn2017$age, height_percentile = flynn2017$height_percentile, source = "flynn2017") fp <- p_bp( q_sbp = flynn2017$sbp, q_dbp = flynn2017$dbp, male = flynn2017$male, age = flynn2017$age, height_percentile = flynn2017$height_percentile, source = "flynn2017") f_bp <- cbind(flynn2017, pedbp_sbp = fq$sbp, pedbp_dbp = fq$dbp, pedbp_sbp_p = fp$sbp_p * 100, pedbp_dbp_p = fp$dbp_p * 100 )
All the quantile estimates are within 2 mmHg:
summary(f_bp$pedbp_sbp - f_bp$sbp) summary(f_bp$pedbp_dbp - f_bp$dbp)
stopifnot(max(abs(f_bp$pedbp_sbp - f_bp$sbp)) < 2) stopifnot(max(abs(f_bp$pedbp_dbp - f_bp$dbp)) < 2)
All the percentiles estimates are within are within 2 percentile points:
summary(f_bp$pedbp_sbp_p - f_bp$bp_percentile) summary(f_bp$pedbp_dbp_p - f_bp$bp_percentile)
stopifnot(max(abs(f_bp$pedbp_sbp_p - f_bp$bp_percentile)) < 2) stopifnot(max(abs(f_bp$pedbp_dbp_p - f_bp$bp_percentile)) < 2)
A helpful set of graphics are shown below. Panels A and C show the estimated
blood pressure quantiles provide by the
r qwraps2::Rpkg(pedbp)
package (y-axis) against the published quantiles from @flynn2017clinical for
systolic and diastolic blood pressures respectively.
Panels B and D are Bland-Altman plots showing the difference vs average
between the two estimates.
fsbp <- ggplot2::ggplot(f_bp) + ggplot2::theme_bw() + ggplot2::aes(x = sbp, y = pedbp_sbp) + ggplot2::geom_point() + ggplot2::geom_abline(intercept = 0, slope = 1) + ggplot2::ylab("pedbp Package\nSystolic Blood Pressure (mmHg)") + ggplot2::xlab("Published Flynn (2017)\nSystolic Blood Pressure (mmHg)") fdbp <- ggplot2::ggplot(f_bp) + ggplot2::theme_bw() + ggplot2::aes(x = dbp, y = pedbp_dbp) + ggplot2::geom_point() + ggplot2::geom_abline(intercept = 0, slope = 1) + ggplot2::ylab("pedbp Package\nDiastolic Blood Pressure (mmHg)") + ggplot2::xlab("Published Flynn (2017)\nDiastolic Blood Pressure (mmHg)") ggpubr::ggarrange( fsbp, qwraps2::qblandaltman(f_bp[, c("sbp", "pedbp_sbp")]) + ggplot2::theme_bw(), fdbp, qwraps2::qblandaltman(f_bp[, c("dbp", "pedbp_dbp")]) + ggplot2::theme_bw(), labels = LETTERS )
NHLBI/CDC
nq <- q_bp( p_sbp = nhlbi_bp_norms$bp_percentile/100, p_dbp = nhlbi_bp_norms$bp_percentile/100, male = nhlbi_bp_norms$male, age = nhlbi_bp_norms$age, height_percentile = nhlbi_bp_norms$height_percentile, source = "nhlbi") np <- p_bp( q_sbp = nhlbi_bp_norms$sbp, q_dbp = nhlbi_bp_norms$dbp, male = nhlbi_bp_norms$male, age = nhlbi_bp_norms$age, height_percentile = nhlbi_bp_norms$height_percentile, source = "nhlbi") nhlbi_bp <- cbind(nhlbi_bp_norms, pedbp_sbp = nq$sbp, pedbp_dbp = nq$dbp, pedbp_sbp_p = np$sbp_p * 100, pedbp_dbp_p = np$dbp_p * 100 )
All the quantile estimates are within 2 mmHg:
summary(nhlbi_bp$pedbp_sbp - nhlbi_bp$sbp) summary(nhlbi_bp$pedbp_dbp - nhlbi_bp$dbp)
stopifnot(max(abs(nhlbi_bp$pedbp_sbp - nhlbi_bp$sbp)) < 2) stopifnot(max(abs(nhlbi_bp$pedbp_dbp - nhlbi_bp$dbp)) < 2)
All the percentiles are within 2 percentile points:
summary(nhlbi_bp$pedbp_sbp_p - nhlbi_bp$bp_percentile) summary(nhlbi_bp$pedbp_dbp_p - nhlbi_bp$bp_percentile)
stopifnot(max(abs(nhlbi_bp$pedbp_sbp_p - nhlbi_bp$bp_percentile)) < 2) stopifnot(max(abs(nhlbi_bp$pedbp_dbp_p - nhlbi_bp$bp_percentile)) < 2)
A helpful set of graphics are shown below. Panels A and C show the estimated
blood pressure quantiles provide by the
r qwraps2::Rpkg(pedbp)
package (y-axis) against the published quantiles from @nhlbi2011expert for
systolic and diastolic blood pressures respectively.
Panels B and D are Bland-Altman plots showing the difference vs average
between the two estimates.
nsbp <- ggplot2::ggplot(nhlbi_bp) + ggplot2::theme_bw() + ggplot2::aes(x = sbp, y = pedbp_sbp) + ggplot2::geom_point() + ggplot2::geom_abline(intercept = 0, slope = 1) + ggplot2::ylab("pedbp Package\nSystolic Blood Pressure (mmHg)") + ggplot2::xlab("Published NHLBI\nSystolic Blood Pressure (mmHg)") ndbp <- ggplot2::ggplot(nhlbi_bp) + ggplot2::theme_bw() + ggplot2::aes(x = dbp, y = pedbp_dbp) + ggplot2::geom_point() + ggplot2::geom_abline(intercept = 0, slope = 1) + ggplot2::ylab("pedbp Package\nDiastolic Blood Pressure (mmHg)") + ggplot2::xlab("Published NHLBI (2017)\nDiastolic Blood Pressure (mmHg)") ggpubr::ggarrange( nsbp, qwraps2::qblandaltman(nhlbi_bp[, c("sbp", "pedbp_sbp")]) + ggplot2::theme_bw(), ndbp, qwraps2::qblandaltman(nhlbi_bp[, c("dbp", "pedbp_dbp")]) + ggplot2::theme_bw(), labels = LETTERS )
NHLBI vs Flynn 2017
The NHLBI data included overweight and obese children whereas Flynn excluded them. As a result, the estimates for blood pressures can differ significantly between the two sources.
The graphic below shows the estimated systolic and diastolic blood pressures
provided by the
r qwraps2::Rpkg(pedbp)
package. As expected, the values estimated based on Flynn are lower, on
average, than those estimated by data from the NHLBI.
nhlbi_vs_flynn <- f_bp names(nhlbi_vs_flynn)[names(nhlbi_vs_flynn) == "bp_percentile"] <- "flynn_bp_percentile" names(nhlbi_vs_flynn)[names(nhlbi_vs_flynn) == "sbp"] <- "flynn_sbp" names(nhlbi_vs_flynn)[names(nhlbi_vs_flynn) == "dbp"] <- "flynn_dbp" names(nhlbi_vs_flynn)[names(nhlbi_vs_flynn) == "pedbp_sbp"] <- "pedbp_flynn_sbp" names(nhlbi_vs_flynn)[names(nhlbi_vs_flynn) == "pedbp_dbp"] <- "pedbp_flynn_dbp" names(nhlbi_vs_flynn)[names(nhlbi_vs_flynn) == "pedbp_sbp_p"] <- "pedbp_flynn_sbp_p" names(nhlbi_vs_flynn)[names(nhlbi_vs_flynn) == "pedbp_dbp_p"] <- "pedbp_flynn_dbp_p" nhlbi_vs_flynn <- merge(nhlbi_vs_flynn, nhlbi_bp, all = TRUE, by = c("male", "age", "height_percentile")) names(nhlbi_vs_flynn)[names(nhlbi_vs_flynn) == "bp_percentile"] <- "nhlbi_bp_percentile" names(nhlbi_vs_flynn)[names(nhlbi_vs_flynn) == "sbp"] <- "nhlbi_sbp" names(nhlbi_vs_flynn)[names(nhlbi_vs_flynn) == "dbp"] <- "nhlbi_dbp" names(nhlbi_vs_flynn)[names(nhlbi_vs_flynn) == "pedbp_sbp"] <- "pedbp_nhlbi_sbp" names(nhlbi_vs_flynn)[names(nhlbi_vs_flynn) == "pedbp_dbp"] <- "pedbp_nhlbi_dbp" names(nhlbi_vs_flynn)[names(nhlbi_vs_flynn) == "pedbp_sbp_p"] <- "pedbp_nhlbi_sbp_p" names(nhlbi_vs_flynn)[names(nhlbi_vs_flynn) == "pedbp_dbp_p"] <- "pedbp_nhlbi_dbp_p" pA <- ggplot2::ggplot(nhlbi_vs_flynn) + ggplot2::theme_bw() + ggplot2::aes(x = pedbp_nhlbi_sbp, y = pedbp_flynn_sbp) + #, color = factor(height_percentile), shape = factor(male, 0:1, c("Female", "Male"))) + ggplot2::geom_point() + ggplot2::geom_abline(intercept = 0, slope = 1) + ggplot2::ylab("pedbp NHLBI Systolic BP") + ggplot2::xlab("pedbp Flynn (2017) Systolic BP") pC <- ggplot2::ggplot(nhlbi_vs_flynn) + ggplot2::theme_bw() + ggplot2::aes(x = pedbp_nhlbi_dbp, y = pedbp_flynn_dbp) + #, color = factor(height_percentile), shape = factor(male, 0:1, c("Female", "Male"))) + ggplot2::geom_point() + ggplot2::geom_abline(intercept = 0, slope = 1) + ggplot2::ylab("pedbp NHLBI Diastolic BP") + ggplot2::xlab("pedbp Flynn (2017) Diastolic BP") ggpubr::ggarrange( pA, qwraps2::qblandaltman(nhlbi_vs_flynn[, c("pedbp_nhlbi_sbp", "pedbp_flynn_sbp")]) + ggplot2::theme_bw(), pC, qwraps2::qblandaltman(nhlbi_vs_flynn[, c("pedbp_nhlbi_dbp", "pedbp_flynn_dbp")]) + ggplot2::theme_bw(), labels = LETTERS )
Blood Pressure Charts
To you can get blood pressure charts for any combination of inputs using
r qwraps2::backtick(bp_chart) %s% "."
For example, the blood pressure percentiles when using
r qwraps2::backtick("source = 'martin2022'", dequote = TRUE)
and height is unknown are:
bp_chart(p = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.90, 0.95), source = "martin2022") # default
And if height is known (say it is the 25th percentile)
bp_chart(p = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.90, 0.95), height_percentile = 25, source = "martin2022")
Additionally, charts for each of the specific data sources can be generated
bp_chart(source = "gemelli1990") bp_chart(source = "lo2013") bp_chart(source = "nhlbi") bp_chart(source = "flynn2017")
Shiny Application
An interactive Shiny application is also available. After installing the pedbp package and the suggested packages, you can run the app locally via
shiny::runApp(system.file("shinyapps", "pedbp", package = "pedbp"))
The shiny application allows for interactive exploration of blood pressure percentiles for an individual patient and allows for batch processing a set of patients as well.
References
R Package Documentation
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