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CKD staging (GFR and albuminuria) and KDIGO risk

Source: R/ckd_stage.R
ckd_stage.Rd

Categorizes eGFR into G1-G5, albuminuria into A1-A3 (by UACR mg/g), and maps KDIGO risk.

Usage

ckd_stage(
  data,
  col_map = NULL,
  na_action = c("keep", "omit", "error"),
  verbose = TRUE
)

Arguments

data

Data frame with renal measures.

col_map

Named list with required key: eGFR; optional key: UACR.

na_action

One of:

  • "keep" (retain rows; stages become NA where inputs missing)

  • "omit" (drop rows with any missing eGFR/UACR that are mapped)

  • "error" (abort if any mapped input missing)

verbose

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

Value

Tibble with CKD_stage, Albuminuria_stage, KDIGO_risk.

References

Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group (2013). “KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease.” Kidney International Supplements, 3(1), 1–150. doi:10.1038/kisup.2012.73 . Related synopsis: Stevens and Levin (2013), Ann Intern Med, doi:10.7326/0003-4819-158-11-201306040-00007, https://kdigo.org/guidelines/ckd-evaluation-and-management/.

Examples

df <- data.frame(eGFR = c(95, 50), UACR = c(10, 200))
ckd_stage(df, list(eGFR = "eGFR", UACR = "UACR"))
#> ckd_stage(): reading input 'df' — 2 rows × 2 variables
#> ckd_stage(): col_map (2 columns — 2 specified)
#>   eGFR              ->  'eGFR'
#>   UACR              ->  'UACR'
#> ckd_stage(): computing markers:
#>   CKD_stage          [eGFR G-stage]
#>   Albuminuria_stage  [UACR A-stage]
#>   KDIGO_risk         [combined KDIGO risk category]
#> ckd_stage(): results: CKD_stage 2/2, Albuminuria_stage 2/2, KDIGO_risk 2/2
#> # A tibble: 2 × 3
#>   CKD_stage Albuminuria_stage KDIGO_risk
#>   <fct>     <fct>             <fct>     
#> 1 G1        A1                Low       
#> 2 G3a       A2                High