Albumin-Corrected Calcium

Calculates the albumin-adjusted (corrected) serum calcium level, accounting for hypoalbuminemia, using the Payne formula.

Usage

corrected_calcium(
  data,
  col_map = list(calcium = "Ca", albumin = "Alb"),
  units = c("auto", "conventional", "si"),
  na_action = c("keep", "omit", "error", "ignore", "warn"),
  check_extreme = FALSE,
  extreme_action = c("warn", "cap", "error", "ignore", "NA"),
  extreme_rules = NULL,
  verbose = FALSE
)

Arguments

data

A data.frame or tibble containing serum calcium and albumin.

col_map

Named list with calcium and albumin indicating column names.

units

One of c("auto","conventional","si"). "auto" attempts unit detection.

na_action

One of c("keep","omit","error","ignore","warn").

check_extreme

Logical; if TRUE, scan inputs for plausible ranges (applied on working units).

extreme_action

One of c("warn","cap","error","ignore","NA").

extreme_rules

Optional overrides for defaults in working units: list(ca_mgdl = c(4, 15), alb_gdl = c(2, 5)).

verbose

Logical; if TRUE, emits progress via hm_inform().

Value

A tibble with one column: corrected_calcium (numeric, in mg/dL for conventional input or mmol/L for SI / auto-SI input).

Details

Payne formula (conventional units): Corrected Ca (mg/dL) = measured Ca (mg/dL) + 0.8 * (4.0 - albumin (g/dL)). If inputs appear to be in SI units (calcium mmol/L, albumin g/L), they are converted to mg/dL and g/dL (using 1 mmol/L ~= 4 mg/dL; 1 g/L = 0.1 g/dL) for the correction and converted back to mmol/L for output.

References

Payne RB, Little AJ, Williams RB, Milner JR (1973). “Interpretation of serum calcium in patients with abnormal serum proteins.” British Medical Journal, 4(5893), 643–646. doi:10.1136/bmj.4.5893.643 .

Examples

df <- data.frame(Ca = c(2.3, 2.5, 2.1), Alb = c(38, 42, 30))
corrected_calcium(df)
#> Warning: corrected_calcium(): auto-detected SI units Ca=mmol/L, Alb=g/L; output standardized to mmol/L.
#> # A tibble: 3 × 1
#>   corrected_calcium
#>               <dbl>
#> 1              2.34
#> 2              2.46
#> 3              2.3