Why the study?
It was unknown whether adding ECG abnormalities improves the performance of guideline-recommended incident CVD-risk prediction models in people with type 2 diabetes.
Does adding ECG abnormalities improve the performance of incident CVD-risk prediction models in people with type 2 diabetes without prior cardiovascular disease?
Does adding ECG abnormalities improve the performance of incident CVD-risk prediction models in people with type 2 diabetes without prior cardiovascular disease?
Adding ECG abnormalities to established CVD-risk prediction models moderately but consistently improves risk classification for people with type 2 diabetes without prior cardiovascular disease.
AIMS: To investigate if adding ECG abnormalities as a predictor improves the performance of incident CVD-risk prediction models for people with type 2 diabetes (T2D). METHODS: We evaluated the four major prediction models that are recommended by the guidelines of the American College of Cardiology/American Heart Association and European Society of Cardiology, in 11,224 people with T2D without CVD (coronary heart disease, heart failure, stroke, thrombosis) from the Hoorn Diabetes Care System cohort (1998-2018). Baseline measurements included CVD-risk factors and ECG recordings coded according to the Minnesota Classification as no, minor or major abnormalities. After confirming good reference model fit, model performance was assessed before and after addition of ECG abnormalities and compared using c-statistics, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). RESULTS: C-statistics (95%CI) of reference models (ASCVD, AD-ON, ADVANCE and SCORE2-Diabetes) were 0.67 (0.65-0.70), 0.73 (0.71-0.76), 0.71 (0.68-0.74) and 0.67 (0.65-0.69), respectively. Adding ECG abnormalities as predictor improved c-statistics with +0.02 (0.01-0.03), +0.01 (0.00-0.01), +0.02 (0.01-0.03), and +0.02 (0.01-0.02), respectively. Reclassification indicators also showed improvement: categorical NRI (+6%, +3%, +8%, and +5%), continuous NRI (95%CI) 0.25 (0.08-0.37), 0.32 (0.23-0.42), 0.54 (0.34-0.69) and 0.28 (0.09-0.33)), and IDI (95%CI) 0.005 (0.001-0.010), 0.002 (-0.001-0.007), 0.006 (0.001-0.007) and 0.004 (0.000-0.006)). Sensitivity analyses yielded similar results. CONCLUSION: The addition of ECG abnormalities to incident CVD-risk prediction models moderately but consistently improves the ability of models to correctly classify people with T2D in the appropriate CVD-risk category with up to 8%, which is approximately equivalent to many established predictors and (bio)markers.
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Harms et al. (2025) studied this question.
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