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Calculate urine-only renal and tubular markers (research-ready)

Source: R/urine_markers.R
urine_markers.Rd

Computes (urine-only):

  • UACR (Albumin-to-Creatinine Ratio, mg/g)

  • albuminuria_stage (KDIGO A1/A2/A3 by UACR)

  • microalbuminuria flag ("normal" vs "micro")

  • UPCR (Urine Protein-to-Creatinine Ratio, mg/g; if urine_protein available)

  • U_Na_K_ratio (urine Na+/K+; if urine_Na and urine_K available)

  • Creatinine-normalized tubular markers (if present, per g creatinine): NGAL_per_gCr, KIM1_per_gCr, NAG_per_gCr, Beta2Micro_per_gCr, A1Micro_per_gCr, IL18_per_gCr, L_FABP_per_gCr

Usage

urine_markers(
  data,
  verbose = FALSE,
  na_action = c("keep", "omit", "error"),
  na_warn_prop = 0.2,
  check_extreme = FALSE,
  extreme_action = c("warn", "cap", "error", "ignore"),
  extreme_rules = NULL
)

Arguments

data

A data.frame or tibble with at least urine_albumin and urine_creatinine.

verbose

Logical; if TRUE, prints progress messages and a completion summary. Default FALSE.

na_action

One of c("keep","omit","error") for handling missing values in required inputs. Default "keep".

na_warn_prop

Proportion \([0,1]\) to trigger high-missingness warnings for required inputs. Default 0.2.

check_extreme

Logical; if TRUE, scan inputs for extreme values. Default FALSE.

extreme_action

One of c("warn","cap","error","ignore") when extremes detected. Default "warn".

extreme_rules

Optional named list of c(min,max) bounds for inputs. If NULL, broad defaults are used.

Value

A tibble with columns: UACR, albuminuria_stage, microalbuminuria, UPCR, U_Na_K_ratio, NGAL_per_gCr, KIM1_per_gCr, NAG_per_gCr, Beta2Micro_per_gCr, A1Micro_per_gCr, IL18_per_gCr, L_FABP_per_gCr

Details

Inputs are validated, missingness handled via na_action, divisions are safeguarded (Inf/NaN -> NA) with a consolidated zero-denominator warning, and an optional extremes scan/cap is available.

Expected units:

  • urine_albumin: mg/L

  • urine_protein: mg/L (optional)

  • urine_creatinine: mg/dL

  • urine_Na, urine_K: mmol/L (optional)

  • Optional tubular markers above assumed mg/L when normalized per g creatinine

References

Original derivations

  • Mogensen CE. Microalbuminuria predicts clinical proteinuria and early mortality in maturity-onset diabetes. N Engl J Med. 1984;310(6):356-360. doi:10.1056/NEJM198402093100602 (UACR and microalbuminuria concept)

  • Ginsberg JM, Chang BS, Matarese RA, Garella S. Use of single voided urine samples to estimate quantitative proteinuria. N Engl J Med. 1983;309(25):1543-1546. doi:10.1056/NEJM198312223092503 (UPCR derivation and validation)

  • Bokenkamp A, Domanetzki M, Zinck R, Schumann G, Byrd D, Brodehl J. Reference values for urinary albumin excretion in healthy children. Pediatr Nephrol. 1998;12(6):478-483. doi:10.1007/s004670050480 (Albumin excretion normative values)

Validation and consensus

  • 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. Guideline without reliable DOI metadata in Crossref; related synopsis PMID: 23732715, https://kdigo.org/guidelines/ckd-evaluation-and-management/. (Albuminuria stages A1-A3; UACR cutoffs)

  • de Zeeuw D, Parving HH, Henning RH. Microalbuminuria as an early marker for cardiovascular disease. J Am Soc Nephrol. 2006;17(8):2100-2105. doi:10.1681/ASN.2006040388 (Prognostic validation of UACR)

  • Ichimura T, Hung CC, Yang SA, Stevens JL, Bonventre JV. Kidney injury molecule-1: a tissue and urinary biomarker for nephrotoxicant-induced renal injury. Am J Physiol Renal Physiol. 2004;286(3):F552-F563. doi:10.1152/ajprenal.00285.2002 (KIM-1 as tubular marker)

  • Portilla D, Dent C, Sugaya T, et al. Liver fatty acid-binding protein as a biomarker of acute kidney injury after cardiac surgery. Kidney Int. 2008;73(4):465-472. doi:10.1038/sj.ki.5002688 (L-FABP biomarker validation)

Examples

df <- tibble::tibble(
  urine_albumin    = 30,
  urine_creatinine = 1.2,
  serum_creatinine = 0.9,
  plasma_Na        = 140,
  urine_Na         = 100,
  age              = 55,
  sex              = 2,
  urine_protein    = 150
)
urine_markers(df)
#> # A tibble: 1 × 12
#>    UACR albuminuria_stage microalbuminuria  UPCR U_Na_K_ratio NGAL_per_gCr
#>   <dbl> <fct>             <fct>            <dbl>        <dbl>        <dbl>
#> 1 25000 A3                normal           12500           NA           NA
#> # ℹ 6 more variables: KIM1_per_gCr <dbl>, NAG_per_gCr <dbl>,
#> #   Beta2Micro_per_gCr <dbl>, A1Micro_per_gCr <dbl>, IL18_per_gCr <dbl>,
#> #   L_FABP_per_gCr <dbl>