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Compute carbon monoxide diffusion capacity or transfer factor reference values for given demographics

Source: R/diffusion_capacity.R
pft_diffusion.Rd

pft_diffusion() computes ATS-compliant upper and lower normal limits for carbon monoxide measured diffusion capacity and European equivalents including DLCO (or TLCO), KCO, and VA.

Usage

pft_diffusion(data, SI.units = FALSE, sex = sex, age = age, height = height)

Arguments

data

A data frame containing columns for sex ("M","F"), age (in years, in the range 5-90 per the GLI 2017 spline tables) and height (in centimeters). If data also contains a <measure>_measured column for any of the active measures (tlco, kco_si, va under SI units; dlco, kco_tr, va under traditional), the measured value is used to compute z-score and percent-predicted (see Value).

SI.units

A boolean. Returns the reference values in SI units if TRUE . and Traditional units if FALSE.

sex, age, height

Column references. By default pft_diffusion() 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

Stanojevic S, Graham BL, Cooper BG, et al. Official ERS technical standards: Global Lung Function Initiative reference values for the carbon monoxide transfer factor for Caucasians. Eur Respir J. 2017;50(3):1700010. doi:10.1183/13993003.00010-2017 . (Author correction: doi:10.1183/13993003.50010-2017 , applied here.)

See also

pft_spirometry() and pft_volumes() for the analogous reference-value functions. pft_severity() grades DLCO impairment severity from the z-score column produced here. 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_diffusion(data)
#> # A tibble: 2 × 12
#>   sex     age height dlco_pred dlco_lln dlco_uln kco_tr_pred kco_tr_lln
#>   <chr> <dbl>  <dbl>     <dbl>    <dbl>    <dbl>       <dbl>      <dbl>
#> 1 M      30      178     33.0     26.1     41.0         4.99       3.99
#> 2 F       5.1     50      2.64     1.83     3.69       11.0        7.45
#> # ℹ 4 more variables: kco_tr_uln <dbl>, va_pred <dbl>, va_lln <dbl>,
#> #   va_uln <dbl>