Prediction of incident chronic kidney disease in community-based electronic health records: a systematic review and meta-analysis

Abstract Background Chronic Kidney Disease (CKD) is a major global health problem and its early identification would allow timely intervention to reduce complications. We performed a systematic review and meta-analysis of multivariable prediction models derived and/or validated in community-based electronic health records (EHRs) for the prediction of incident CKD in the community. Methods Ovid Medline and Ovid Embase were searched for records from 1947 to 31 January 2024. Measures of discrimination were extracted and pooled by Bayesian meta-analysis, with heterogeneity assessed through a 95% prediction interval (PI). Risk of bias was assessed using Prediction model Risk Of Bias Assessment Tool and certainty in effect estimates by Grading of Recommendations, Assessment, Development and Evaluation. Results Seven studies met inclusion criteria, describing twelve prediction models, with two eligible for meta-analysis including 2 173 202 patients. The CKD-PC (summary c-statistic 0.847; 95% CI 0.827 to 0.867; 95% PI 0.780 to 0.905) and SCORED (summary c-statistic 0.811; 95% CI 0.691 to 0.926; 95% PI 0.514 to 0.992) models had good model discrimination performance. Risk of bias was high in 64% of models and driven by the analysis domain. No model met eligibility for meta-analysis if studies at high risk of bias were excluded, and certainty of effect estimates was ‘low’. No clinical utility analyses or clinical impact studies were found for any of the models. Conclusions Models derived and/or externally validated for prediction of incident CKD in community-based EHRs demonstrate good prediction performance, but assessment of clinical usefulness is limited by high risk of bias, low certainty of evidence and a lack of impact studies.