OBJECTIVEDiabetes has become the leading cause of end-stage renal disease (ESRD). Renal risk stratification could assist in earlier identification and targeted prevention. This study aimed to derive risk models to predict ESRD events in type 2 diabetes in primary care.RESEARCH DESIGN AND METHODSThe nationwide derivation cohort included adults with type 2 diabetes from the New Zealand Diabetes Cohort Study initially assessed during 2000–2006 and followed until December 2010, excluding those with pre-existing ESRD. The outcome was fatal or nonfatal ESRD event (peritoneal dialysis or hemodialysis for ESRD, renal transplantation, or death from ESRD). Risk models were developed using Cox proportional hazards models, and their performance was assessed in a separate validation cohort.RESULTSThe derivation cohort included 25,736 individuals followed for up to 11 years (180,497 person-years; 86% followed for ≥5 years). At baseline, mean age was 62 years, median diabetes duration 5 years, and median HbA1c 7.2% (55 mmol/mol); 37% had albuminuria; and median estimated glomerular filtration rate (eGFR) was 77 mL/min/1.73 m2. There were 637 ESRD events (2.5%) during follow-up. Models that included sex, ethnicity, age, diabetes duration, albuminuria, serum creatinine, systolic blood pressure, HbA1c, smoking status, and previous cardiovascular disease status performed well with good discrimination and calibration in the derivation cohort and the validation cohort (n = 5,877) (C-statistics 0.89–0.92), improving predictive performance compared with previous models.CONCLUSIONSThese 5-year renal risk models performed very well in two large primary care populations with type 2 diabetes. More accurate risk stratification could facilitate earlier intervention than using eGFR and/or albuminuria alone.

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INTRODUCTION: New Zealand (NZ) guidelines recommend treating people for cardiovascular disease (CVD) risk on the basis of five-year absolute risk using a NZ adaptation of the Framingham risk equation. A diabetes-specific Diabetes Cohort Study (DCS) CVD predictive risk model has been developed and validated using NZ Get Checked data. AIM: To revalidate the DCS model with an independent cohort of people routinely assessed using PREDICT, a web-based CVD risk assessment and management programme. METHODS: People with Type 2 diabetes without pre-existing CVD were identified amongst people who had a PREDICT risk assessment between 2002 and 2005. From this group we identified those with sufficient data to allow estimation of CVD risk with the DCS models. We compared the DCS models with the NZ Framingham risk equation in terms of discrimination, calibration, and reclassification implications. RESULTS: Of 3044 people in our study cohort, 1829 people had complete data and therefore had CVD risks calculated. Of this group, 12.8% (235) had a cardiovascular event during the five-year follow-up. The DCS models had better discrimination than the currently used equation, with C-statistics being 0.68 for the two DCS models and 0.65 for the NZ Framingham model. DISCUSSION: The DCS models were superior to the NZ Framingham equation at discriminating people with diabetes who will have a cardiovascular event. The adoption of a DCS model would lead to a small increase in the number of people with diabetes who are treated with medication, but potentially more CVD events would be avoided. KEYWORDS: Cardiovascular disease; diabetes; prevention; risk assessment; reliability and validity

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