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

Background: 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.

Development and deployment of a nationwide predictive model for chronic kidney disease progression in diabetic patients.

Aim: Chronic kidney disease (CKD) is a major complication of diabetes and a significant disease burden on the healthcare system. The aim of this work was to apply a predictive model to identify high-risk patients in the early stages of CKD as a means to provide early intervention to avert or delay kidney function deterioration.

Materials And Methods: Using the data from the National Diabetes Database in Singapore, we applied a machine-learning algorithm to develop a predictive model for CKD progression in diabetic patients and to deploy the model nationwide.

Cost-effectiveness of screening for chronic kidney disease in the general adult population: a systematic review.

Introduction: Chronic kidney disease (CKD) is a significant public health problem, with rising incidence and prevalence worldwide, and is associated with increased morbidity and mortality. Early identification and treatment of CKD can slow its progression and prevent complications, but it is not clear whether CKD screening is cost-effective. The aim of this study is to conduct a systematic review of the cost-effectiveness of CKD screening strategies in general adult populations worldwide, and to identify factors, settings and drivers of cost-effectiveness in CKD screening.