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Compute lung volume reference values for given demographics

Source: R/lung_volumes.R
pft_volumes.Rd

pft_volumes() computes ATS-compliant upper and lower normal limits for lung volume measures including FRC, TLC, RV, ERV, IC, and VC.

Usage

pft_volumes(data, sex = sex, age = age, height = height)

Arguments

data

A data frame containing columns for sex ("M","F"), age (in years, in the range 5-80 per the GLI 2021 spline tables) and height (in centimeters). If data also contains any of frc_measured, tlc_measured, rv_measured, rv_tlc_measured, erv_measured, ic_measured, vc_measured, the corresponding measured value is used to compute a z-score and percent-predicted (see Value).

sex, age, height

Column references. By default pft_volumes() reads from sex, age, and height. Override via a bare name (sex = Sex), a string (sex = "Sex"), or an rlang injection (sex = !!my_var). The user's original column names are preserved in the output.

Value

The original data frame with extra columns appended for each measure:

  • <measure>_pred: predicted (median) value.

  • <measure>_lln: lower limit of normal (5th percentile).

  • <measure>_uln: upper limit of normal (95th percentile). If a <measure>_measured column was supplied in data, two additional columns are emitted:

  • <measure>_zscore: LMS z-score ((measured/M)^L - 1) / (L*S).

  • <measure>_pctpred: percent predicted (measured / pred) * 100.

References

Hall GL, Filipow N, Ruppel G, et al. Official ERS technical standard: Global Lung Function Initiative reference values for static lung volumes in individuals of European ancestry. Eur Respir J. 2021;57(3):2000289. doi:10.1183/13993003.00289-2020 .

See also

pft_spirometry() and pft_diffusion() for the analogous reference-value functions. pft_classify() uses TLC and its LLN (produced by this function) to identify restrictive impairments. pft_interpret() composes all three reference functions in one call.

Examples

data <- data.frame(sex=c("M","F"),
                   age=c(30,5.1),
                   height=c(178,50))
pft_volumes(data)
#> # A tibble: 2 × 24
#>   sex     age height frc_pred frc_lln frc_uln tlc_pred tlc_lln tlc_uln rv_pred
#>   <chr> <dbl>  <dbl>    <dbl>   <dbl>   <dbl>    <dbl>   <dbl>   <dbl>   <dbl>
#> 1 M      30      178   3.31    2.25     4.65     7.13    5.73    8.56    1.52 
#> 2 F       5.1     50   0.0948  0.0640   0.135    0.247   0.192   0.308   0.182
#> # ℹ 14 more variables: rv_lln <dbl>, rv_uln <dbl>, rv_tlc_pred <dbl>,
#> #   rv_tlc_lln <dbl>, rv_tlc_uln <dbl>, erv_pred <dbl>, erv_lln <dbl>,
#> #   erv_uln <dbl>, ic_pred <dbl>, ic_lln <dbl>, ic_uln <dbl>, vc_pred <dbl>,
#> #   vc_lln <dbl>, vc_uln <dbl>