Development of an adaptive clinical web-based prediction tool for kidney replacement therapy in children with chronic kidney disease

Clinicians need improved prediction models to estimate time to kidney replacement therapy (KRT) for children with chronic kidney disease (CKD). Here, we aimed to develop and validate a prediction tool based on common clinical variables for time to KRT in children using statistical learning methods and design a corresponding online calculator for clinical use. Among 890 children with CKD in the Chronic Kidney Disease in Children (CKiD) study, 172 variables related to sociodemographics, kidney/cardiovascular health, and therapy use, including longitudinal changes over one year were evaluated as candidate predictors in a random survival forest for time to KRT. An elementary model was specified with diagnosis, estimated glomerular filtration rate and proteinuria as predictors and then random survival forest identified nine additional candidate predictors for further evaluation. Best subset selection using these nine additional candidate predictors yielded an enriched model additionally based on blood pressure, change in estimated glomerular filtration rate over one year, anemia, albumin, chloride and bicarbonate. Four additional partially enriched models were constructed for clinical situations with incomplete data. Models performed well in cross-validation, and the elementary model was then externally validated using data from a European pediatric CKD cohort. A corresponding user-friendly online tool was developed for clinicians. Thus, our clinical prediction tool for time to KRT in children was developed in a large, representative pediatric CKD cohort with an exhaustive evaluation of potential predictors and supervised statistical learning methods. While our models performed well internally and externally, further external validation of enriched models is needed.