Table of Contents

• KFRE Risk Prediction Tools (R)
• Installation
• GitHub (Development)
• Dependencies
• Quick Start
  1. Toy Data
  2. Vectorized Predictions with RiskPredictor
  3. Single-Person Predictions
  4. Add KFRE Risk Columns to a DataFrame
  5. CKD Staging & ESRD Outcome Labels
  6. uPCR → uACR Conversion
  7. Evaluation Metrics (AUC-ROC, AP, Brier…)
  8. Plot ROC / PR Curves
• Running Tests
• API Surface (Exports)
• Notes on Parity with Python
• References
• License

KFRE Risk Prediction Tools (R)

kfre is an R implementation of helpers around the Kidney Failure Risk Equation (KFRE), including:

• Vectorized and per-person KFRE predictions (4-, 6-, and 8-variable models; 2- and 5-year horizons)
• Convenience wrappers to add KFRE risk columns to a data.frame
• uPCR → uACR conversion utility
• CKD stage labeling utilities
• Evaluation helpers (AUC-ROC, Average Precision, Brier, etc.)
• Publication-style ROC and Precision–Recall plots

Installation

r install.packages("kfre")

GitHub (Development)

```r

install.packages("remotes")

remotes::installgithub("lshpaner/kfrer") ```

Dependencies

Core imports: R6, stats, ggplot2, pROC, precrec Suggested for tests/vignettes: testthat (>= 3.0.0), knitr, rmarkdown

Quick Start

1. Toy data

```r toy <- data.frame( age = c(55, 72), sex_txt = c("male", "female"), eGFR = c(45, 28), uACR = c(120, 800), dm = c(1, 0), htn = c(1, 1), albumin = c(4.2, 3.4), phosphorous = c(3.3, 4.6), bicarbonate = c(24, 22), calcium = c(9.1, 9.8), stringsAsFactors = FALSE )

cols <- list( age = "age", sex = "sex_txt", eGFR = "eGFR", uACR = "uACR", dm = "dm", htn = "htn", albumin = "albumin", phosphorous = "phosphorous", bicarbonate = "bicarbonate", calcium = "calcium" ) ```

2. Vectorized predictions with RiskPredictor

```r rp <- RiskPredictor$new(df = toy, columns = cols)

4-variable KFRE (2-year), North America constants

p42y <- rp$predictkfre( years = 2, isnorthamerican = TRUE, useextravars = FALSE, num_vars = 4 )

6-variable KFRE (5-year)

p65y <- rp$predictkfre( years = 5, isnorthamerican = TRUE, useextravars = TRUE, num_vars = 6 )

8-variable KFRE (2-year)

p82y <- rp$predictkfre( years = 2, isnorthamerican = TRUE, useextravars = TRUE, num_vars = 8 )

p42y p65y p8_2y ```

3. Single-person predictions

```r

Male, 55yo, 2-year risk (4-var)

rp$kfreperson( age = 55, ismale = TRUE, eGFR = 45, uACR = 120, isnorthamerican = TRUE, years = 2 )

Female, 72yo, 5-year risk (6-var)

rp$kfreperson( age = 72, ismale = FALSE, eGFR = 28, uACR = 800, isnorthamerican = TRUE, years = 5, dm = 0, htn = 1 )

Female, 72yo, 2-year risk (8-var)

rp$kfreperson( age = 72, ismale = FALSE, eGFR = 28, uACR = 800, isnorthamerican = TRUE, years = 2, albumin = 3.4, phosphorous = 4.6, bicarbonate = 22, calcium = 9.8 ) ```

4. Add KFRE risk columns to a data.frame

```r toykfre <- addkfreriskcol( df = toy, agecol = "age", sexcol = "sextxt", eGFRcol = "eGFR", uACRcol = "uACR", dmcol = "dm", htncol = "htn", albumincol = "albumin", phosphorouscol = "phosphorous", bicarbonatecol = "bicarbonate", calciumcol = "calcium", numvars = c(4, 6, 8), years = c(2, 5), isnorthamerican = TRUE, copy = TRUE )

names(toykfre) head(toykfre)

Adds:

kfre4var2year, kfre4var5year,

kfre6var2year, kfre6var5year,

kfre8var2year, kfre8var5year

```

5. CKD staging & ESRD outcome labels

```r

ESRD outcome within 2 years (duration is in days → converted to years)

out <- data.frame( eGFR = c(95, 25), ESRDflag = c(1, 1), followupdays = c(200, 1000) )

out <- classesrdoutcome( df = out, col = "ESRDflag", years = 2, durationcol = "followupdays", prefix = "esrd", createyears_col = TRUE )

Adds: ESRDdurationyears and esrd2year_outcome

CKD stage labels

out <- classckdstages( df = out, egfrcol = "eGFR", stagecol = "stage", combinedstagecol = "stage_combined" )

table(out$stage) table(out$stage_combined) ```

6. uPCR → uACR conversion

```r df_pcr <- data.frame( sex = c("female","male","female"), dm = c(1,0,1), htn = c(1,1,0), pcr = c(150, 600, 50) )

acr <- upcruacr( dfpcr, sexcol = "sex", diabetescol = "dm", hypertensioncol = "htn", upcrcol = "pcr", female_str = "female" )

acr ```

7. Evaluation metrics (AUC-ROC, AP, Brier…)

Your data.frame must include:

• Outcome columns named like *_2_year_outcome / *_5_year_outcome
• Prediction columns named kfre_{n}var_{year}year, e.g. kfre_4var_2year

```r met <- evalkfremetrics( df = toykfre, # must contain truth + prediction columns nvarlist = c(4, 6, 8), outcomeyears = c(2, 5), decimal_places = 4 )

met

Rows: Metrics; Cols: "{2year|5year}{4|6|8}var_kfre"

```

8. Plot ROC / PR curves

```r

Basic: compute & plot both ROC and PR (no files written)

plotkfremetrics( df = toykfre, numvars = c(4, 6, 8), plottype = "allplots", mode = "both", # compute + plot show_years = c(2, 5) )

Save to disk (PNG/SVG)

plotkfremetrics( df = toykfre, numvars = c(4, 6), plottype = "aucroc", mode = "both", showyears = c(2, 5), saveplots = TRUE, imagepathpng = "plots", image_prefix = "kfre" ) ```

Running Tests

If you’ve cloned the repo:

r library(devtools) devtools::load_all(".") devtools::test()

You should see unit tests for both the end-to-end flow and the evaluation utilities.

API surface (exports)

• RiskPredictor (R6)
  • $predict_kfre(years, is_north_american, use_extra_vars, num_vars)
  • $kfre_person(...)
• Wrappers:
  • predict_kfre(df, columns, years, is_north_american, use_extra_vars, num_vars)
  • add_kfre_risk_col(...)
• Utilities:
• upcr_uacr(...)
• perform_conversions(...)
• class_esrd_outcome(...)
• class_ckd_stages(...)
• eval_kfre_metrics(...)
• plot_kfre_metrics(...)

Notes on parity with Python

The R implementations are designed to mirror the Python versions (naming, shapes, and expected columns). Where packages differ (e.g., ROC/PR computation), we use pROC and precrec to maintain metric parity.

References

• Tangri N, Grams ME, Levey AS, et al. (2016). Multinational assessment of accuracy of equations for predicting risk of kidney failure: A meta-analysis. JAMA, 315(2), 164–174. doi:10.1001/jama.2015.18202

• Tangri N, Stevens LA, Griffith J, et al. (2011). A predictive model for progression of chronic kidney disease to kidney failure. JAMA, 305(15), 1553–1559. doi:10.1001/jama.2011.451

• Sumida K, Nadkarni GN, Grams ME, et al. (2020). Conversion of urine protein-creatinine ratio or urine dipstick protein to urine albumin-creatinine ratio for use in CKD screening and prognosis. Ann Intern Med, 173(6), 426–435. doi:10.7326/M20-0529

License

kfre is distributed under the MIT License. See LICENSE for more information.