Pediatric Nephrology · Clinical Calculator · CKD Progression

Pediatric CKD Progression Time to Kidney Replacement Therapy (CKiD Model)

The Chronic Kidney Disease in Children (CKiD) study built six linked survival models that estimate when children with CKD will start kidney replacement therapy (KRT), dialysis or transplant, from the diagnosis group, eGFR, and proteinuria, sharpened by blood pressure, last year's eGFR change, anemia, and serum chemistry when available. In 2026 the models held up in Korean children, the first validation in an East Asian population.

Published: References: 3 Read time:

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Instructions
  1. Select the diagnosis group and enter the current CKiD U25 eGFR and urine protein-to-creatinine ratio (UPCR): these three are required.
  2. Add any of the optional predictors. The tool automatically uses the best-fitting model (lowest Akaike information criterion, AIC) for the data entered, as the original online tool does.
  3. Read the time by which 10%, 25%, and 50% of children with the same profile are expected to start KRT, and the 2- and 5-year risk.

All computation runs in your browser; no values are stored or transmitted beyond your own device.

When to Use

Use for children and adolescents with stable CKD under pediatric nephrology care, eGFR roughly 15–90 mL/min/1.73 m², to frame prognosis with families and to time dialysis access, transplant evaluation, and preemptive living-donor work-up.

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When NOT to rely on it

Not for acute kidney injury, children already on KRT, or adults. Only two diagnosis groups are modeled; responses to specific treatments (for example, for focal segmental glomerulosclerosis, lupus nephritis, or atypical hemolytic uremic syndrome) are not captured. Estimates are percentiles for a population with the same profile, not a deterministic forecast; times beyond 10 years are not estimated.

Pearls & Pitfalls
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How the diagnosis group is coded

Hemolytic uremic syndrome (HUS) behaves like non-glomerular disease (congenital anomalies of the kidney and urinary tract, cystic and other non-glomerular causes) and is grouped with it; "glomerular" means glomerular disease other than HUS. The diagnosis group changes the slope of eGFR and the shape of the survival curve, so do not read any single coefficient in isolation.

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Use the right eGFR

The models were built on the CKiD under-25 (U25) equations (Pierce et al., 2021), preferably combining creatinine and cystatin C. Adult CKD-EPI or the older bedside Schwartz equation will shift the prediction. Get the input from the CKiD U25 eGFR calculator.

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Pitfalls

(1) Blood pressure must be classified with the 2017 American Academy of Pediatrics (AAP) percentiles; use the pediatric BP calculator. (2) In Korean validation, discrimination was excellent but calibration was imperfect; the enriched model with eGFR slope aligned best with observed risk. (3) The models predict the timing of a clinical decision (starting KRT), which also depends on local practice and access.

CKiD Time to KRT

mg/mmol ÷ 113 = mg/mg.

Optional predictors

Used with blood pressure (needs BP).
Albumin, chloride, and CO₂ are used together.
Next Steps
  • Median under about 2 years or 2-year risk high: start KRT preparation now: family education on dialysis modalities, vascular access or peritoneal dialysis catheter planning, and transplant referral with living-donor evaluation to allow a preemptive transplant.
  • Modifiable predictors: treat hypertension to AAP targets, reduce proteinuria with renin-angiotensin system blockade, correct metabolic acidosis and anemia, and support nutrition and growth; re-run the model after a year to see the effect of a slower eGFR slope.
  • Counseling: share the 25th and 50th percentile times as a range, not a date, and revisit at each annual review.
  • Calculate inputs with the CKiD U25 eGFR and pediatric BP calculators.
Evidence

Model structure (Ng et al., 2023)

Time to KRT follows a generalized gamma distribution: log T = β + σ·W. All predictors modify the location β; diagnosis group also modifies the scale σ and the shape κ. eGFR enters as log(eGFR/45) with a change in slope above 45; UPCR as log(UPCR/0.5) with a change in slope above 0.5; the eGFR slope as the annual change in log eGFR.

ModelPredictors (beyond diagnosis, eGFR, UPCR)AICc-statistic
ElementaryNone1476.50.865
Partially enriched 1BP, eGFR change1470.10.866
Partially enriched 2BP, anemia1457.70.874
Partially enriched 3BP, albumin, chloride, CO₂1456.70.871
Partially enriched 4BP, anemia, albumin, chloride, CO₂1446.20.875
EnrichedAll of the above plus eGFR change1445.10.875

Derivation: 890 children in CKiD (median age 11.4 years, median eGFR 50 mL/min/1.73 m², 29% started KRT over a median of 5.2 years). External validation of the elementary model in the European ESCAPE cohort: c-statistic 0.854, calibration slope 0.996.

Korean validation (Park et al., 2026)

533 children in KNOW-Ped CKD (median age 10.8 years, median eGFR 57.6 mL/min/1.73 m²); 32.1% started KRT over a median of 4.8 years. C-statistics were 0.911–0.972 across models and calibration slopes exceeded 0.9, but the goodness-of-fit test showed miscalibration; models including the eGFR slope matched observed risk most closely.

Implementation check: this page reproduces both worked examples in Ng et al. (2023): median 3.3 years for a non-glomerular profile with eGFR 30 and UPCR 2 (elementary model), and 2.5, 5.1, and 10.1 years for the published enriched-model glomerular profile.

Important: This calculator is an educational aid for licensed clinicians caring for children with CKD. Predictions describe groups of children with the same profile in North American research cohorts and are not individual certainties. Decisions about dialysis and transplant timing rest on the whole clinical picture.
References 3 sources
  1. Ng, D. K., Matheson, M. B., Schwartz, G. J., Wang, F. M., Mendley, S. R., Furth, S. L., Warady, B. A., & CKiD Investigators (2023). Development of an adaptive clinical web-based prediction tool for kidney replacement therapy in children with chronic kidney disease. Kidney International, 104(5), 985–994. https://doi.org/10.1016/j.kint.2023.06.020
  2. Park, P. G., Kim, J., Choi, N., Kim, J. H., Lim, S. H., Lee, J. H., Park, M. J., Baek, H. S., Cho, M. H., Lee, K. H., Shin, J. I., Han, K. H., Kim, J. Y., Song, J. Y., Yang, E. M., Kim, S. H., Ahn, Y. H., Kang, H. G., & Park, E. (2026). Cross-population validation of the pediatric CKD risk-prediction tool. Kidney International Reports, 11, 106373. https://doi.org/10.1016/j.ekir.2026.106373
  3. Pierce, C. B., Muñoz, A., Ng, D. K., Warady, B. A., Furth, S. L., & Schwartz, G. J. (2021). Age- and sex-dependent clinical equations to estimate glomerular filtration rates in children and young adults with chronic kidney disease. Kidney International, 99(4), 948–956. https://doi.org/10.1016/j.kint.2020.10.047
Dr. W Rivero, MD

W Rivero, MD, FPCP, DPSN

Specialist in Internal Medicine, Nephrology, and Clinical Nutrition. Practicing integrative and evidence-based nephrology across Quezon City, Pampanga, and Bulacan.

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