Prediction of First Cardiovascular Disease Event in Type 1 Diabetes Mellitus

Abstract

Background— Patients with type 1 diabetes mellitus are at increased risk of developing cardiovascular disease (CVD), but they are currently undertreated. There are no risk scores used on a regular basis in clinical practice for assessing the risk of CVD in type 1 diabetes mellitus. Methods and Results— From 4306 clinically diagnosed adult patients with type 1 diabetes mellitus, we developed a prediction model for estimating the risk of first fatal or nonfatal 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 by using a 2-stage approach. First, a nonparametric, data-driven approach was used to identify potentially informative risk factors and interactions (random forest and survival tree analysis). Second, 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 2119 patients with type 1 diabetes mellitus. During a median follow-up of 6.8 years (interquartile range, 2.9–10.9) a total of 793 (18.4%) patients developed CVD. The final prediction model included age, sex, diabetes duration, systolic blood pressure, low-density lipoprotein cholesterol, hemoglobin A 1c , albuminuria, glomerular filtration rate, smoking, and exercise. Discrimination was excellent for a 5-year CVD event with a C-statistic of 0.826 (95% confidence interval, 0.807–0.845) in the derivation data and a C-statistic of 0.803 (95% confidence interval, 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 for the implementation of decision rules in a clinical setting.

Type 1 diabetes: Developing the first risk-estimation model for predicting silent myocardial ischemia. The potential role of insulin resistance

“…In T1DM, smoking has been associated with several cardiovascular risk factors (such as physical inactivity, worse glycaemic control or more atherogenic lipid profile and endothelial dysfunction)[39,40]. The role of smoking as cardiovascular risk factor in T1DM is strongly supported by a recent risk estimation model for predicting clinical cardiovascular events (a composite of fatal and non-fatal events of CAD, ischemic stroke, heart failure and peripheral artery disease) developed in a large cohort of T1DM subjects, in which smoking was identified as one of its main predictor factors [41]. However, there are no previous studies evaluating the effects of smoking on SMI in T1DM.…”

Electrocardiographic Abnormalities and Cardiovascular Disease Risk in Type 1 Diabetes: The Epidemiology of Diabetes Interventions and Complications (EDIC) Study

“…Nevertheless, patients with type 1 diabetes vary in their individual risk profile and susceptibility to adverse outcomes (1,5). Hence, patients with type 1 diabetes must be stratified according to their clinical characteristics and risk factor profile for effective patient management.…”

“…Currently there are no widely used CVD risk prediction algorithms specific to patients with type 1 diabetes, despite some efforts to develop such algorithms (5,19,20). In the absence of data to the contrary, the current common approach to identifying CVD risk in patients with type 1 diabetes has been to apply the same risk assessment and diagnostic strategies used in the general population (1).…”

“…Increased CVD risk is not, however, uniform in all patients with type 1 diabetes; it varies according to individual characteristics and risk profile (1,5). Identifying these predictive characteristics and risk markers is necessary to improve our ability to identify patients with type 1 diabetes who are at a higher risk.…”

The Prediction of Atherosclerotic Cardiovascular Disease in Type 1 Diabetes Mellitus

“…Thus, any model that is developed must be based on observational data only. The Steno Risk Engine, as described in this issue of Circulation, 2 is the second attempt at estimating ASCVD risk in T1D patients and differs somewhat in design and application from that previously created from the Swedish National Diabetes Register (NDR). 3 …”