The added value of ECG abnormalities in predicting incident cardiovascular disease risk for people with type 2 diabetes: The Hoorn Diabetes Care System cohort.

IF 8.4 2区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
Peter P Harms, Reinier A R Herings, Sharon Remmelzwaal, Femke Rutters, Joline W J Beulens, Giel Nijpels, Petra P J M Elders, Marieke T Blom
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引用次数: 0

Abstract

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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来源期刊
European journal of preventive cardiology
European journal of preventive cardiology CARDIAC & CARDIOVASCULAR SYSTEMS-
CiteScore
12.50
自引率
12.00%
发文量
601
审稿时长
3-8 weeks
期刊介绍: European Journal of Preventive Cardiology (EJPC) is an official journal of the European Society of Cardiology (ESC) and the European Association of Preventive Cardiology (EAPC). The journal covers a wide range of scientific, clinical, and public health disciplines related to cardiovascular disease prevention, risk factor management, cardiovascular rehabilitation, population science and public health, and exercise physiology. The categories covered by the journal include classical risk factors and treatment, lifestyle risk factors, non-modifiable cardiovascular risk factors, cardiovascular conditions, concomitant pathological conditions, sport cardiology, diagnostic tests, care settings, epidemiology, pharmacology and pharmacotherapy, machine learning, and artificial intelligence.
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