Risk Prediction of Cardiovascular Disease in Type 2 Diabetes

Risk Prediction of Cardiovascular Disease in Type 2 Diabetes A risk equation from the Swedish National Diabetes Register Jan Cederholm , MD, PHD 1 , Katarina Eeg-Olofsson , MD 2 , Björn Eliasson , MD, PHD 2 , Björn Zethelius , MD, PHD 3 , Peter M. Nilsson , MD, PHD 4 , Soffia Gudbjörnsdottir , MD, PHD 2 and on behalf of the Swedish National Diabetes Register 1 Department of Public Health and Caring Sciences, Family Medicine and Clinical Epidemiology, Uppsala University, Uppsala, Sweden 2 Department of Medicine, Sahlgrenska University Hospital, Gothenburg University, Gothenburg, Sweden 3 Department of Public Health and Caring Sciences, Geriatrics, Uppsala University, Uppsala, Sweden 4 Department of Clinical Sciences, University Hospital, Lund University, Malmö, Sweden Corresponding author: Jan Cederholm, jan.cederholm{at}pubcare.uu.se Abstract OBJECTIVE —Risk prediction models obtained in samples from the general population do not perform well in type 2 diabetic patients. Recently, 5-year risk estimates were proposed as being more accurate than 10-year risk estimates. This study presents a diabetes-specific equation for estimation of the absolute 5-year risk of first incident fatal/nonfatal cardiovascular disease (CVD) in type 2 diabetic patients with use of A1C and clinical characteristics. RESEARCH DESIGN AND METHODS —The study was based on 11,646 female and male patients, aged 18–70 years, from the Swedish National Diabetes Register with 1,482 first incident CVD events based on 58,342 person-years with mean follow-up of 5.64 years. RESULTS —This risk equation incorporates A1C, as in the UK Prospective Diabetes Study risk engine, and several clinical characteristics: onset age of diabetes, diabetes duration, sex, BMI, smoking, systolic blood pressure, and antihypertensive and lipid-reducing drugs. All predictors included were associated with the outcome ( P < 0.0001, except for BMI P = 0.0016) with Cox regression analysis. Calibration was excellent when assessed by comparing observed and predicted risk. Discrimination was sufficient, with a receiver operator curve statistic of 0.70. Mean 5-year risk of CVD in all patients was 12.0 ± 7.5%, whereas 54% of the patients had a 5-year risk ≥10%. CONCLUSIONS —This more simplified risk equation enables 5-year risk prediction of CVD based on easily available nonlaboratory predictors in clinical practice and A1C and was elaborated in a large observational study obtained from the normal patient population aged up to