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

Abstract

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.

Keywords: kidney failure; kidney replacement therapy; pediatric chronic kidney disease; pediatric nephrology; prediction; risk stratification.

Figure 1.

Results from random survival forest plotting metrics of predictive value (minimum depth of maximal subtree versus variable importance) to identify candidate predictors for parametric survival models. Minimum depth of maximal subtree indicates how early in the branching process a predictor is selected, on average; smaller values correspond to earlier selection and thus greater predictive value (y-axis is descending). Importance measures how much prediction error is introduced by randomly permuting the values of a predictor within the dataset; larger values indicate more such error, attributing greater importance to the correct ordering of the values. Groups of variables are denoted and variables listed were included in best subset selection methods of parametric survival models.

Figure 2.

Decision tree describing adaptive approach for model based on availability of data for use in the online calculator. Akaike’s Information Criterion (AIC) statistics for each model are presented and correspond to improved penalized model fit (i.e., lower corresponds to lower error).

Figure 3.

Model validation assessment based on 10-fold cross-validation of model building process for the Enriched Model including calibration plot depicting observed risk on predicted risk from at 5-year risk of kidney replacement therapy (3a) and survival function of standardized residual times for participants (3b). The calibration plot demonstrates close correspondence between observed and predicted 5-year risk and the survival function aligns closely with the expected standard exponential for strong model fit.

Figure 4.

Comparison of Elementary Model with Partially Enriched Model 1 including blood pressure and longitudinal GFR. The example profile is a child with non-glomerular or HUS diagnosis, a current GFR of 30 ml/min|1.73m² and a urine protein:creatinine ratio (UPCR) of 2 mg/mgCr. The y-axis represents the median predicted time to KRT and the x-axis represents GFR from one year ago with the dashed line indicating no change. The three lines represent different models: the elementary model (light grey) is constant across previous GFR, and the partially enriched model 1 (dark grey) is depicted by blood pressure status (normal or elevated).