Individualised risk prediction model for new-onset, progression and regression of chronic kidney disease in a retrospective cohort of patients with type 2 diabetes under primary care in Hong Kong

Abstract

ObjectivesThis study is aimed to develop and validate a prediction model for multistate transitions across different stages of chronic kidney disease (CKD) in patients with type 2 diabetes mellitus under primary care.SettingWe retrieved the anonymised electronic health records of a population-based retrospective cohort in Hong Kong.ParticipantsA total of 26 197 patients were included in the analysis.Primary and secondary outcome measuresThe new-onset, progression and regression of CKD were defined by the transitions of four stages that were classified by combining glomerular filtration rate and urine albumin-to-creatinine ratio. We applied a multiscale multistate Poisson regression model to estimate the rates of the stage transitions by integrating the baseline demographic characteristics, routine laboratory test results and clinical data from electronic health records.ResultsDuring the mean follow-up time of 1.8 years, there were 2632 patients newly diagnosed with CKD, 1746 progressed to the next stage and 1971 regressed into an earlier stage. The models achieved the best performance in predicting the new-onset and progression with the predictors of sex, age, body mass index, systolic blood pressure, diastolic blood pressure, serum creatinine, haemoglobin A1c, total cholesterol, low-density lipoprotein, high-density lipoprotein, triglycerides and drug prescriptions.ConclusionsThis study demonstrated that individual risks of new-onset and progression of CKD can be predicted from the routine physical and laboratory test results. The individualised prediction curves developed from this study could potentially be applied to routine clinical practices, to facilitate clinical decision making, risk communications with patients and early interventions.