Validation of a Risk Prediction Equation for Incident Chronic Kidney Disease in a Hypertensive Non-Diabetes Cohort in Singapore Primary Care Patients.

Abstract

Accurate identification of individuals at risk of developing chronic kidney disease (CKD) may improve clinical care. Nelson et al. developed prediction equations to estimate the risk of incident eGFR of less than 60 mL/min/1.73 m2 in diabetic and non-diabetes patients using data from 34 multinational cohorts. We aim to validate the non-diabetes equation in our local multi-ethnic cohort and develop further prediction models. Demographics, clinical and laboratory data of hypertensive non-diabetes patients with baseline eGFR ≥60 mL/min/1.73 m2 on follow-up with primary care clinics between 2010 and 2015 were collected. Follow-up was 5 years from entry to study. We validated Nelson's equation and developed our own model which we subsequently validated. The developmental cohort included patients between 2010 and 2014 while the validation cohort included patients in 2015. Variables included age, sex, eGFR, history of cardiovascular disease, ever smoker, body mass index, albuminuria, cholesterol, and treatment. Primary outcome was incident eGFR <60/min/1.73 m2 within 5 years. Model performance was evaluated by C-statistics and calibration was assessed. In the developmental cohort of 27,800 patients, 2823 (10.2%) developed the outcome during a mean follow-up of 4.4 years while 638 (12.8%) patients developed the outcome in the validation cohort of 4,994 patients. Applicability of Nelson's equation was limited by missing albuminuria, absence of black race, and exclusion of non-hypertensive patients in our cohort. Nonetheless, the modified Nelson's model demonstrated C-statistic of 0.85 (95% CI: 0.84-0.86). The C-statistic of our bespoke model was 0.85 (0.85-0.86) and 0.87 (0.85-0.88) for the developmental cohort and validation cohort, respectively. Calibration was suboptimal as the predicted risk exceeded the observed risk. The modified Nelson's equation and our locally derived novel model demonstrated high discrimination. Both models may potentially be used in predicting risk of CKD in hypertensive patients who are managed in primary care, allowing for early interventions in high-risk population.