## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment  = "#>",
  fig.width  = 6,
  fig.height = 4
)

## -----------------------------------------------------------------------------
library(pft)

## -----------------------------------------------------------------------------
patient <- data.frame(
  sex = "M", age = 45, height = 178
)

ref <- pft_spirometry(patient)
ref[, c("fev1_pred_2022", "fev1_lln_2022", "fev1_uln_2022",
        "fvc_pred_2022",  "fvc_lln_2022",  "fvc_uln_2022")]

## -----------------------------------------------------------------------------
pft_volumes(patient)[, c("frc_pred", "tlc_pred", "rv_pred", "vc_pred")]
pft_diffusion(patient)[, c("dlco_pred", "kco_tr_pred", "va_pred")]

## -----------------------------------------------------------------------------
patient_with_measurements <- data.frame(
  sex = "M", age = 45, height = 178, race = "Caucasian",
  fev1_measured = 2.5,
  fvc_measured  = 3.8
)

out <- pft_spirometry(patient_with_measurements)
out[, c("fev1_pred_2022", "fev1_zscore_2022", "fev1_pctpred_2022",
        "fvc_pred_2022",  "fvc_zscore_2022",  "fvc_pctpred_2022")]

## -----------------------------------------------------------------------------
pft_severity(c(0, -1.7, -3, -5))

## -----------------------------------------------------------------------------
out$fev1_severity_2022 <- pft_severity(out$fev1_zscore_2022)
out$fvc_severity_2022  <- pft_severity(out$fvc_zscore_2022)
out[, c("fev1_zscore_2022", "fev1_severity_2022", "fvc_zscore_2022", "fvc_severity_2022")]

## -----------------------------------------------------------------------------
classification_input <- data.frame(
  fev1 = 2.5,  fev1_lln_2022 = 3.0,
  fvc  = 3.8,  fvc_lln_2022  = 3.5,
  fev1fvc = 0.66, fev1fvc_lln_2022 = 0.70,
  tlc  = 6.0,  tlc_lln = 5.0
)
pft_classify(classification_input)[
  , c("ats_classification", "ats_pattern_combination")
]

## -----------------------------------------------------------------------------
pft_bdr(pre = 2.5, post = 3.0, predicted = 4.0)

## -----------------------------------------------------------------------------
pft_prism(data.frame(
  fev1    = 2.0,  fev1_lln_2022    = 2.5,
  fvc     = 2.6,  fvc_lln_2022     = 3.0,
  fev1fvc = 0.80, fev1fvc_lln_2022 = 0.70
))

## -----------------------------------------------------------------------------
# z dropped from -0.5 to -2.5 over 1 year; r ≈ 0.7 for adult FEV1
pft_change(z1 = -0.5, z2 = -2.5, r = 0.7)

## -----------------------------------------------------------------------------
patient <- data.frame(
  sex = "M", age = 45, height = 178, race = "Caucasian",
  fev1_measured     = 2.5,
  fvc_measured      = 3.8,
  fev1fvc_measured  = 2.5 / 3.8,
  tlc_measured      = 6.0,
  fev1_pre          = 2.5,
  fev1_post         = 2.9
)
result <- pft_interpret(patient)

# A high-level subset of the ~60 columns generated:
result[, c("fev1_pred_2022", "fev1_zscore_2022", "fev1_severity_2022",
           "fvc_zscore_2022", "fvc_severity_2022",
           "ats_classification", "prism",
           "fev1_bdr_pct", "fev1_bdr_significant")]

## ----eval = requireNamespace("ggplot2", quietly = TRUE)-----------------------
pft_plot(result)

## -----------------------------------------------------------------------------
cohort <- data.frame(
  sex    = c("M", "F", "M"),
  age    = c(45, 60, 30),
  height = c(178, 165, 175),
  race   = c("Caucasian", "AfrAm", "Caucasian"),
  fev1_measured = c(2.5, 1.8, 4.0),
  fvc_measured  = c(3.8, 2.4, 5.2),
  fev1fvc_measured = c(2.5/3.8, 1.8/2.4, 4.0/5.2),
  tlc_measured  = c(6.0, 4.5, 6.8)
)

interpreted <- pft_interpret(cohort)
interpreted[, c("sex", "age",
                "fev1_zscore_2022", "fev1_severity_2022",
                "ats_classification", "prism")]

## -----------------------------------------------------------------------------
pft_long(interpreted)[1:6, ]

## -----------------------------------------------------------------------------
patient_dlco <- data.frame(
  sex = "M", age = 50, height = 178, race = "Caucasian",
  dlco_measured   = 6,      # low
  va_measured     = 6,
  kco_tr_measured = 1.0     # also low -> Parenchymal pattern
)
pft_interpret(patient_dlco)$diffusion_category

## -----------------------------------------------------------------------------
citation("pft")