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

Objectives: This 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.

Setting: We retrieved the anonymised electronic health records of a population-based retrospective cohort in Hong Kong.

Participants: A total of 26 197 patients were included in the analysis.

Primary and secondary outcome measures: The 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.

Results: During 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.

Conclusions: This 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.

Keywords: diabetic nephropathy & vascular disease; epidemiology; primary care; statistics & research methods.

Figure 1

Flowchart of data clean and analysis procedure for CKD progression. CKD, chronic kidney disease; DM, diabetes mellitus; eGFR, estimated glomerular filtration rate; HbA1c, haemoglobin A1c; MRAM, Metabolic Risk Assessment Module; RAMP-DM, Risk Assessment and Management Programme for Patients with Diabetes Mellitus; T2DM, type 2 diabetes mellitus; UACR, urine albumin-to-creatinine ratio.

Figure 2

Adjusted incidence rates per 1000 person-years (PY) of CKD new-onset or progression over time since the diagnosis of previous stages in female patients, at the HbA1c levels ranging from 6% to 9%, for new-onset (red solid line), progression from stage 1 to 2 (green solid line) and from stage 2 to 3 (blue solid line). Upper panel (A–D) shows adjusted incidence rates in patients aged below 65 years. Lower panel (E–H) shows adjusted incidence rates in patients aged 65 years or above. The broken line and shadow area indicate the 95% CI. CKD, chronic kidney disease; HbA1c, haemoglobin A1c.

Figure 3

Adjusted incidence rates per 1000 person-years (PY) of CKD new-onset or progression over time since the diagnosis of previous stages in male patients, at the HbA1c levels ranging from 6% to 9%, for new-onset (red solid line), progression from stage 1 to 2 (green solid line) and from stage 2 to 3 (blue solid line). Upper panel (A–D) shows adjusted incidence rates in patients aged below 65 years. Lower panel (E–H) shows adjusted incidence rates in patients aged 65 years or above. The broken line and shadow area indicate the 95% CI. CKD, chronic kidney disease; HbA1c, haemoglobin A1c.