Spirometry markers: FEV1/FVC, LLN-based obstruction, GOLD grade, bronchodilator response

Usage

spirometry_markers(
  data,
  col_map = NULL,
  na_action = c("keep", "omit", "error", "ignore", "warn"),
  verbose = TRUE
)

Arguments

data: Data frame with spirometry inputs.
col_map: Named list: fev1, fvc, fev1_post, fvc_post, age, height, sex, ethnicity
na_action: One of c("keep","omit","error","ignore","warn").
verbose: Logical; if TRUE (default), emits progress via rlang::inform.

Value

Tibble with ratio_pre, ratio_post, copd_flag_fixed, obstruction_lln, fev1_pp, fvc_pp, fev1_z, fvc_z, ratio_z, gold_grade, bdr_fev1, bdr_fvc.

References

Miller MR, Hankinson J, Brusasco V, et al. (2005). “Standardisation of spirometry.” European Respiratory Journal, 26(2), 319–338. doi:10.1183/09031936.05.00034805 . (spirometry standardisation methodology; background) Quanjer PH, Stanojevic S, Cole TJ, Baur X, Hall GL, Culver BH, et al. (2012). “Multi-ethnic reference values for spirometry for the 3–95-yr age range: the global lung function 2012 equations.” European Respiratory Journal, 40, 1324–1343. doi:10.1183/09031936.00080312 . for Chronic Obstructive Lung Disease (GOLD) GI (2025). “Global strategy for the diagnosis, management, and prevention of COPD.” Online report; no DOI assigned, https://goldcopd.org/2025-gold-report/.

Examples

df <- data.frame(FEV1 = c(3.2, 2.1, 1.5), FVC = c(4.0, 3.0, 2.5))
spirometry_markers(df)
#> spirometry_markers(): reading input 'df' — 3 rows × 2 variables
#> spirometry_markers(): col_map (2 columns — 2 inferred from data)
#>   fev1              ->  'FEV1'    (inferred)
#>   fvc               ->  'FVC'    (inferred)
#> spirometry_markers(): computing markers:
#>   ratio_pre/post  [FEV1/FVC]
#>   copd_flag_fixed [ratio < 0.70]
#>   obstruction_lln [LLN-based]
#>   fev1_pp/fvc_pp  [% predicted]
#>   gold_grade      [GOLD severity]
#> spirometry_markers(): results: ratio_pre 3/3, ratio_post 0/3, copd_flag_fixed 3/3, obstruction_lln 0/3, fev1_pp 0/3, fvc_pp 0/3, fev1_z 0/3, fvc_z 0/3, ratio_z 0/3, gold_grade 0/3, bdr_fev1 0/3, bdr_fvc 0/3
#> # A tibble: 3 × 12
#>   ratio_pre ratio_post copd_flag_fixed obstruction_lln fev1_pp fvc_pp fev1_z
#>       <dbl>      <dbl> <lgl>           <lgl>             <dbl>  <dbl>  <dbl>
#> 1       0.8         NA FALSE           NA                   NA     NA     NA
#> 2       0.7         NA FALSE           NA                   NA     NA     NA
#> 3       0.6         NA TRUE            NA                   NA     NA     NA
#> # ℹ 5 more variables: fvc_z <dbl>, ratio_z <dbl>, gold_grade <chr>,
#> #   bdr_fev1 <dbl>, bdr_fvc <dbl>