Distribution and quantile functions for pediatric blood pressure.
Usage
p_bp(
q_sbp,
q_dbp,
age,
male,
height = NA,
height_percentile = NA,
default_height_percentile = 50,
source = getOption("pedbp_bp_source", "martin2022"),
...
)
q_bp(
p_sbp,
p_dbp,
age,
male,
height = NA,
height_percentile = NA,
default_height_percentile = 50,
source = getOption("pedbp_bp_source", "martin2022"),
...
)
z_bp(
q_sbp,
q_dbp,
age,
male,
height = NA,
height_percentile = NA,
default_height_percentile = 50,
source = getOption("pedbp_bp_source", "martin2022"),
...
)Arguments
- q_sbp
a vector of systolic blood pressures
- q_dbp
a vector of diastolic blood pressures
- age
numeric age, in months
- male
integer value, 1 = male, 0 = female
- height
numeric, in centimeters, can be missing. See Details.
- height_percentile
height percentile to use; range from [0, 100]. See Details.
- default_height_percentile
default height percentile to use if
heightis missing; range (0, 100).- source
the method, or data set, to use as the reference. See Details.
- ...
not currently used
- p_sbp
a vector of systolic blood probabilities; range from [0, 1].
- p_dbp
a vector of diastolic blood probabilities; range from [0, 1].
Value
A pedbp_bp object. This is a list of two numeric vectors for
the systolic and diastolic pressure respectively. The names for the vectors
depends on the call. p_bp returns a list of vectors with the names:
sbp_p and dbp_p. q_bp returns a list of vectors with
names: sbp and dbp. z_bp returns a list of vectors with
names: sbp_z and dbp_z.
Additionally, a pedbp_bp object has a bp_params attribute which
provides details on the data source and parameters used in the estimates.
Details
source is used to specify the method or source data sets by which the
distributions are estimated. This can be controlled by the option
pedbp_bp_source.
End users are encouraged to set the option if not using the default so all
calls to these functions will use the same source.
Options:
martin2022(default) uses a combination of references to generate distribution values for ages 1 months through 18 years, without or without known stature. This was the only method implemented in version 1 of the pedbp package.gemelli1990uses only the reference values from Gemelli et al. (1990). These values are applicable to patients from 1 month to 12 months of age. Stature is not used in the look up for the parameters.lo2013uses only the reference values from Lo et al. (2013). This is applicable to patients of at least three years of age. Height is not considered when looking up the parameters.nhlbiuses only reference values from the National Heart, Lung, and Blood Institute [NHLBI] and the Centers for Disease Control and Prevention [CDC] published in 2011. These are for patients of at least one year of age and with a known stature. These values were publishflynn2017uses only reference values from Flynn et al. (2017). These values are similar to the nhlbi values _but_ "do not include children and adolescents with overweight and obesity (ie, those with a BMI >= 85th percentile).
There is a hierarchy for the use of the height,
height_percentile, and default_height_percentile. If
height is provided, it takes precedence over the other two arguments.
height_percentile is used if height is missing and takes
precedence over default_height_percentile. The height is only needed
if using the nhlbi or flynn2017 data sources (including as part
of the martin2022 workflow).
References
Gemelli, Marina, Rosa Manganaro, Carmelo Mamì, and F. De Luca. "Longitudinal study of blood pressure during the 1st year of life." European journal of pediatrics 149 (1990): 318-320.
Lo, Joan C., Alan Sinaiko, Malini Chandra, Matthew F. Daley, Louise C. Greenspan, Emily D. Parker, Elyse O. Kharbanda et al. "Prehypertension and hypertension in community-based pediatric practice." Pediatrics 131, no. 2 (2013): e415-e424.
"Expert panel on integrated guidelines for cardiovascular health and risk reduction in children and adolescents: summary report." Pediatrics 128, no. Suppl 5 (2011): S213. <doi:10.1542/peds.2009-2107C>
The Fourth Report on the Diagnosis, Evaluation, and Treatment of High Blood Pressure in Children and Adolescents National High Blood Pressure Education Program Working Group on High Blood Pressure in Children and Adolescents Pediatrics 2004;114;555-576 <doi:10.1542/peds.114.2.S2.555>
Flynn, Joseph T., David C. Kaelber, Carissa M. Baker-Smith, Douglas Blowey, Aaron E. Carroll, Stephen R. Daniels, Sarah D. De Ferranti et al. "Clinical practice guideline for screening and management of high blood pressure in children and adolescents." Pediatrics 140, no. 3 (2017).
See also
vignette("bp-distributions", package = "pedbp"),
bp_cdf for plotting cumulative distribution functions for the
blood pressures.
Examples
x <- p_bp(q_sbp = 100, q_dbp = 60, age = 8, male = 0)
x
#> $sbp_p
#> [1] 0.7881446
#>
#> $dbp_p
#> [1] 0.6554217
#>
str(x)
#> List of 2
#> $ sbp_p: num 0.788
#> $ dbp_p: num 0.655
#> - attr(*, "bp_params")='data.frame': 1 obs. of 8 variables:
#> ..$ source : chr "gemelli1990"
#> ..$ male : int 0
#> ..$ age : num 6
#> ..$ sbp_mean : num 92
#> ..$ sbp_sd : num 10
#> ..$ dbp_mean : num 56
#> ..$ dbp_sd : num 10
#> ..$ height_percentile: num NA
#> - attr(*, "class")= chr [1:2] "pedbp_bp" "pedbp_p_bp"
x <- p_bp(q_sbp = c(NA, 82), q_dbp = c(60, 72), age = 9.2, male = 0)
x
#> $sbp_p
#> [1] NA 0.1150697
#>
#> $dbp_p
#> [1] 0.6461698 0.9696036
#>
str(x)
#> List of 2
#> $ sbp_p: num [1:2] NA 0.115
#> $ dbp_p: num [1:2] 0.646 0.97
#> - attr(*, "bp_params")='data.frame': 2 obs. of 8 variables:
#> ..$ source : chr [1:2] "gemelli1990" "gemelli1990"
#> ..$ male : int [1:2] 0 0
#> ..$ age : num [1:2] 9 9
#> ..$ sbp_mean : num [1:2] 94 94
#> ..$ sbp_sd : num [1:2] 10 10
#> ..$ dbp_mean : num [1:2] 57 57
#> ..$ dbp_sd : num [1:2] 8 8
#> ..$ height_percentile: num [1:2] NA NA
#> - attr(*, "class")= chr [1:2] "pedbp_bp" "pedbp_p_bp"
x <- p_bp(q_sbp = c(NA, 82), q_dbp = c(60, 72), age = 29.2, male = 0, height = 82.8)
x
#> $sbp_p
#> [1] NA 0.3847185
#>
#> $dbp_p
#> [1] 0.9398825 0.9959809
#>
str(x)
#> List of 2
#> $ sbp_p: num [1:2] NA 0.385
#> $ dbp_p: num [1:2] 0.94 0.996
#> - attr(*, "bp_params")='data.frame': 2 obs. of 8 variables:
#> ..$ source : chr [1:2] "nhlbi" "nhlbi"
#> ..$ male : int [1:2] 0 0
#> ..$ age : num [1:2] 24 24
#> ..$ sbp_mean : num [1:2] 85 85
#> ..$ sbp_sd : num [1:2] 10.2 10.2
#> ..$ dbp_mean : num [1:2] 43 43
#> ..$ dbp_sd : num [1:2] 10.9 10.9
#> ..$ height_percentile: num [1:2] 5 5
#> - attr(*, "class")= chr [1:2] "pedbp_bp" "pedbp_p_bp"
x <- q_bp(p_sbp = 0.78, p_dbp = 0.65, age = 8, male = 0)
x
#> $sbp
#> [1] 99.72193
#>
#> $dbp
#> [1] 59.8532
#>
str(x)
#> List of 2
#> $ sbp: num 99.7
#> $ dbp: num 59.9
#> - attr(*, "bp_params")='data.frame': 1 obs. of 8 variables:
#> ..$ source : chr "gemelli1990"
#> ..$ male : int 0
#> ..$ age : num 6
#> ..$ sbp_mean : num 92
#> ..$ sbp_sd : num 10
#> ..$ dbp_mean : num 56
#> ..$ dbp_sd : num 10
#> ..$ height_percentile: num NA
#> - attr(*, "class")= chr [1:2] "pedbp_bp" "pedbp_q_bp"
#############################################################################
# compare results when height is known or unknown
p_bp(q_sbp = rep(100, 2),
q_dbp = rep( 60, 2),
age = rep(35.75, 2),
male = c(0, 0),
height = c(NA, 100))
#> $sbp_p
#> [1] 0.8799462 0.9289864
#>
#> $dbp_p
#> [1] 0.9151897 0.9398825
#>
#############################################################################
# Working with multiple patients records
d <- read.csv(system.file("example_data", "for_batch.csv", package = "pedbp"))
d
#> pid age_months male height..cm. sbp..mmHg. dbp..mmHg.
#> 1 patient_A 96 1 NA 102 58
#> 2 patient_B 144 0 153 113 NA
#> 3 patient_C 4 0 62 82 43
#> 4 patient_D_1 41 1 NA 96 62
#> 5 patient_D_2 41 1 101 96 62
bp_percentiles <-
p_bp(
q_sbp = d$sbp..mmHg.
, q_dbp = d$dbp..mmHg.
, age = d$age_months
, male = d$male
)
bp_percentiles
#> $sbp_p
#> [1] 0.5533069 0.7320575 0.2622697 0.6195685 0.6195685
#>
#> $dbp_p
#> [1] 0.4120704 NA 0.1356661 0.8028518 0.8028518
#>
# Standard (z) scores:
z_bp(
q_sbp = d$sbp..mmHg.
, q_dbp = d$dbp..mmHg.
, age = d$age_months
, male = d$male
)
#> $sbp_z
#> [1] 0.1340206 0.6190476 -0.6363636 0.3043478 0.3043478
#>
#> $dbp_z
#> [1] -0.2222222 NA -1.1000000 0.8518519 0.8518519
#>
q_bp(
p_sbp = bp_percentiles$sbp_p
, p_dbp = bp_percentiles$dbp_p
, age = d$age_months
, male = d$male
)
#> $sbp
#> [1] 102 113 82 96 96
#>
#> $dbp
#> [1] 58 NA 43 62 62
#>
#############################################################################
# Selecting different source values
# default
p_bp(q_sbp = 92, q_dbp = 60, age = 29.2, male = 0, default_height_percentile = 0.95,
source = "martin2022")
#> $sbp_p
#> [1] 0.7534673
#>
#> $dbp_p
#> [1] 0.9398825
#>
p_bp(q_sbp = 92, q_dbp = 60, age = 29.2, male = 0, default_height_percentile = 0.95,
source = "gemelli1990")
#> $sbp_p
#> [1] NA
#>
#> $dbp_p
#> [1] NA
#>
p_bp(q_sbp = 92, q_dbp = 60, age = 29.2, male = 0, default_height_percentile = 0.95,
source = "lo2013")
#> $sbp_p
#> [1] NA
#>
#> $dbp_p
#> [1] NA
#>
p_bp(q_sbp = 92, q_dbp = 60, age = 29.2, male = 0, default_height_percentile = 0.95,
source = "nhlbi")
#> $sbp_p
#> [1] 0.7534673
#>
#> $dbp_p
#> [1] 0.9398825
#>
p_bp(q_sbp = 92, q_dbp = 60, age = 29.2, male = 0, default_height_percentile = 0.95,
source = "flynn2017")
#> $sbp_p
#> [1] 0.6797595
#>
#> $dbp_p
#> [1] 0.9290757
#>
q_bp(p_sbp = 0.85, p_dbp = 0.85, age = 29.2, male = 0, default_height_percentile = 0.95,
source = "martin2022") # default
#> $sbp
#> [1] 95.58685
#>
#> $dbp
#> [1] 54.33901
#>
q_bp(p_sbp = 0.85, p_dbp = 0.85, age = 29.2, male = 0, default_height_percentile = 0.95,
source = "gemelli1990")
#> $sbp
#> [1] NA
#>
#> $dbp
#> [1] NA
#>
q_bp(p_sbp = 0.85, p_dbp = 0.85, age = 29.2, male = 0, default_height_percentile = 0.95,
source = "lo2013")
#> $sbp
#> [1] NA
#>
#> $dbp
#> [1] NA
#>
q_bp(p_sbp = 0.85, p_dbp = 0.85, age = 29.2, male = 0, default_height_percentile = 0.95,
source = "nhlbi")
#> $sbp
#> [1] 95.58685
#>
#> $dbp
#> [1] 54.33901
#>
q_bp(p_sbp = 0.85, p_dbp = 0.85, age = 29.2, male = 0, default_height_percentile = 0.95,
source = "flynn2017")
#> $sbp
#> [1] 98.08443
#>
#> $dbp
#> [1] 55.5823
#>