Derivation and Validation of a Prediction Model of End-Stage Renal Disease in Patients With Type 2 Diabetes Based on a Systematic Review and Meta-analysis

Abstract

Objectives: To develop and validate a model for predicting the risk of end-stage renal disease (ESRD) in patients with type 2 diabetes. Methods: The derivation cohort was from a meta-analysis. Statistically significant risk factors were extracted and combined to the corresponding risk ratio (RR) to establish a risk assessment model for ESRD in type 2 diabetes. All risk factors were scored according to their weightings to establish the prediction model. Model performance is evaluated using external validation cohorts. The outcome was the occurrence of ESRD defined as eGFR<15 ml min-1 1.73 m-2 or received kidney replacement therapy (dialysis or transplantation). Results: A total of 1,167,317 patients with type 2 diabetes were included in our meta-analysis, with a cumulative incidence of approximately 1.1%. The final risk factors of the prediction model included age, sex, smoking, diabetes mellitus (DM) duration, systolic blood pressure (SBP), hemoglobin A1c (HbA1c), estimated glomerular filtration rate (eGFR), and triglyceride (TG). All risk factors were scored according to their weightings, with the highest score being 36.5. External verification showed that the model has good discrimination, AUC=0.807(95%CI 0.753–0.861). The best cutoff value is 16 points, with the sensitivity and specificity given by 85.33% and 60.45%, respectively. Conclusion: The study established a simple risk assessment model including 8 routinely available clinical parameters for predicting the risk of ESRD in type 2 diabetes.