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, performed well internally and externally, further external validation of enriched models is needed.

Automated summary

Clinicians need an improved prediction model for estimating time to kidney replacement therapy for children with chronic kidney disease. This study developed and validated a prediction tool based on common clinical variables using statistical learning methods, and also designed an online calculator for clinical use. The models performed well in both internal and external validation.