Compute Blood Pressure Z-score and Percentile

Computes z-scores and percentiles for observed BP values against expected values (\(\mu, \sigma\)) from a specified reference (currently only NHBPEP Fourth Report is available). For ref = "NICE/BHF", z-scores are not defined and NA is returned with a message.

Usage

bp_relative(bp_value, ..., .quiet = FALSE)

Arguments

bp_value

Numeric vector of observed BP values (mmHg).

...

Arguments passed on to .bp_validate_demo_input

bp_type

Character; "systolic" or "diastolic".

sex

Vector of sex codes in your data. Will be mapped to "male" or "female" using male_code and female_code. Values not matching these codes become NA.

male_code,female_code

Scalar values that identify the male and female codes in sex (e.g., 1 and 2, or "M" and "F").

height_z

Numeric vector of height z-scores. Must already be z-transformed (e.g., US CDC, UK-WHO).

height_limit

Positive numeric; z-scores with |z| > height_limit are treated as invalid and set to NA. Default 5.

age_years

Numeric vector of ages in years.

ref

Reference: "Fourth Report" (children/adolescents, 0-17) or "NICE/BHF" (adults, >17).

.quiet

Logical; suppress validation messages (default FALSE).

Value

A tibble with two columns: zscore and percentile (0-100). NA where expected BP could not be computed (e.g., invalid inputs or adult reference).

References

• NHBPEP Fourth Report: Paediatric BP categories

• NICE Guidelines: Adult BP categories

• British Heart Foundation: Adult BP categories

• NDA Methodology: Adult BP Range

• NPDA Methodology: Paediatric BP Range

• RCPCH Growth API: Python library for Paediatric SDS calculation

• childsds package: R package for Paediatric SDS calculation

See also

Other BP functions: .bp_message(), .bp_resolve_limit(), .bp_validate_demo_input(), bp_assess(), bp_category(), bp_expected(), bp_family

Examples

# Z-score and percentile for systolic BP in children using Fourth Report:
bp_relative(
  bp_value = c(95, 110, 130),
  bp_type = "systolic",
  sex = c("M","F","M"),
  male_code = "M", female_code = "F",
  height_z = c(0, 0.5, -1),
  age_years = c(8, 12, 16),
  ref = "Fourth Report"
)
#> # A tibble: 3 × 2
#>   zscore percentile
#>    <dbl>      <dbl>
#> 1 -0.375       35.4
#> 2  0.298       61.7
#> 3  1.53        93.6