Development and Validation of a Prediction Model for Kidney Failure in Long-Term Survivors of Childhood Cancer

PURPOSE Kidney failure is a rare but serious late effect following treatment for childhood cancer. We developed a model using demographic and treatment characteristics to predict individual risk of kidney failure among 5-year survivors of childhood cancer. METHODS Five-year survivors from the Childhood Cancer Survivor Study (CCSS) without history of kidney failure (n = 25,483) were assessed for subsequent kidney failure (ie, dialysis, kidney transplantation, or kidney-related death) by age 40 years. Outcomes were identified by self-report and linkage with the Organ Procurement and Transplantation Network and the National Death Index. A sibling cohort (n = 5,045) served as a comparator. Piecewise exponential models accounting for race/ethnicity, age at diagnosis, nephrectomy, chemotherapy, radiotherapy, congenital genitourinary anomalies, and early-onset hypertension estimated the relationships between potential predictors and kidney failure, using area under the curve (AUC) and concordance (C) statistic to evaluate predictive power. Regression coefficient estimates were converted to integer risk scores. The St Jude Lifetime Cohort Study and the National Wilms Tumor Study served as validation cohorts. RESULTS Among CCSS survivors, 204 developed late kidney failure. Prediction models achieved an AUC of 0.65-0.67 and a C-statistic of 0.68-0.69 for kidney failure by age 40 years. Validation cohort AUC and C-statistics were 0.88/0.88 for the St Jude Lifetime Cohort Study (n = 8) and 0.67/0.64 for the National Wilms Tumor Study (n = 91). Risk scores were collapsed to form statistically distinct low- (n = 17,762), moderate- (n = 3,784), and high-risk (n = 716) groups, corresponding to cumulative incidences in CCSS of kidney failure by age 40 years of 0.6% (95% CI, 0.4 to 0.7), 2.1% (95% CI, 1.5 to 2.9), and 7.5% (95% CI, 4.3 to 11.6), respectively, compared with 0.2% (95% CI, 0.1 to 0.5) among siblings. CONCLUSION Prediction models accurately identify childhood cancer survivors at low, moderate, and high risk for late kidney failure and may inform screening and interventional strategies.