Prediction of First Cardiovascular Disease Event in Type 1 Diabetes: The Steno T1 Risk Engine
Background—Patients with type 1 diabetes are at increased risk of developing cardiovascular disease (CVD), but are currently undertreated. There are no risk scores used on a regular basis in clinical practice for assessing risk of CVD in type 1 diabetes.
Methods and Results—From 4,306 clinically diagnosed adult type 1 diabetes patients, we developed a prediction model for estimating risk of first fatal or non-fatal CVD event (ischemic heart disease, ischemic stroke, heart failure and peripheral artery disease). Detailed clinical data including lifestyle factors were linked to event data from validated national registers. The risk prediction model was developed using a two-stage approach. First, a non-parametric, data-driven approach was used to identify potentially informative risk factors and interactions (random forest and survival tree analysis). Secondly, based on results from the first step, Poisson regression analysis was used to derive the final model. The final CVD prediction model was externally validated in a different population of 2,119 type 1 diabetes patients. During a median follow-up was 6.8 years (IQR: 2.9-10.9) a total of 793 (18.4%) developed CVD. The final prediction model included age, sex, diabetes duration, systolic blood pressure, LDL cholesterol, HbA1c, albuminuria, glomerular filtration rate, smoking and exercise. Discrimination was excellent for a 5-year CVD event with a C-statistic of 0.826 (95%-CI: 0.807-0.845) in the derivation data and a C-statistic of 0.803 (0.767-0.839) in the validation data. The Hosmer-Lemeshow test showed good calibration (p>0.05) in both cohorts.
Conclusions—This high performing CVD risk model allows the implementation of decision rules in a clinical setting.
- Received August 4, 2015.
- Revision received January 18, 2016.
- Accepted January 21, 2016.