Challenges in predictive modelling of chronic kidney disease: A narrative review

Abstract

The exponential rise in the burden of chronic kidney disease (CKD) worldwide has put enormous pressure on the economy. Predictive modeling of CKD can ease this burden by predicting the future disease occurrence ahead of its onset. There are various regression methods for predictive modeling based on the distribution of the outcome variable. However, the accuracy of the predictive model depends on how well the model is developed by taking into account the goodness of fit, choice of covariates, handling of covariates measured on a continuous scale, handling of categorical covariates, and number of outcome events per predictor parameter or sample size. Optimal performance of a predictive model on an independent cohort is desired. However, there are several challenges in the predictive modeling of CKD. Disease-specific methodological challenges hinder the development of a predictive model that is cost-effective and universally applicable to predict CKD onset. In this review, we discuss the advantages and challenges of various regression models available for predictive modeling and highlight those best for future CKD prediction.

Keywords: Chronic kidney disease; Predictive modelling; Regression; Statistical modelling; Methodology

Core Tip: The burden of chronic kidney disease (CKD) is growing rapidly and there is an urgent need to prevent the growth of the disease burden by identifying the individuals at high risk for the development of CKD. A broad spectrum of statistical models exist that can predict the future onset of the disease. This narrative review discusses the practical applicability of various statistical models for CKD prediction.