心电图异常对2型糖尿病患者心血管疾病发病风险的预测附加值：霍伦糖尿病护理系统队列研究

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

作者信息

Harms Peter P, Herings Reinier A R, Remmelzwaal Sharon, Rutters Femke, Beulens Joline W J, Nijpels Giel, Elders Petra P J M, Blom Marieke T

机构信息

Amsterdam UMC location Vrije Universiteit Amsterdam, Department of General Practice Medicine, De Boelelaan 1117, Amsterdam, The Netherlands.

Amsterdam Public Health research institute, Health behaviors & chronic diseases and Personalized medicine, Amsterdam, The Netherlands.

出版信息

Eur J Prev Cardiol. 2025 Jan 27. doi: 10.1093/eurjpc/zwaf033.

DOI:10.1093/eurjpc/zwaf033

PMID:39865584

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.

摘要

目的

研究添加心电图异常作为预测指标是否能改善2型糖尿病（T2D）患者心血管疾病（CVD）发病风险预测模型的性能。

方法

我们对美国心脏病学会/美国心脏协会及欧洲心脏病学会指南推荐的四个主要预测模型进行了评估，研究对象为来自霍恩糖尿病护理系统队列（1998 - 2018年）的11224例无CVD（冠心病、心力衰竭、中风、血栓形成）的T2D患者。基线测量包括CVD风险因素以及根据明尼苏达分类法编码为无、轻度或重度异常的心电图记录。在确认参考模型拟合良好后，评估添加心电图异常前后的模型性能，并使用c统计量、净重新分类改善（NRI）和综合判别改善（IDI）进行比较。

结果

参考模型（ASCVD、AD-ON、ADVANCE和SCORE2-Diabetes）的c统计量（95%CI）分别为0.67（0.65 - 0.70）、0.73（0.71 - 0.76）、0.71（0.68 - 0.74）和0.67（0.65 - 0.69）。添加心电图异常作为预测指标后，c统计量分别提高了+0.02（0.01 - 0.03）、+0.01（0.00 - 0.01）、+0.02（0.01 - 0.03）和+0.02（0.01 - 0.02）。重新分类指标也显示出改善：分类NRI（+6%、+3%、+8%和+5%），连续NRI（95%CI）分别为0.25（0.08 - 0.37）、0.32（0.23 - 0.42）、0.54（0.34 - 0.69）和0.28（0.09 - 0.33），IDI（95%CI）分别为0.005（0.001 - 0.010）、0.002（-0.001 - 0.007）、0.006（0.001 - 0.007）和0.004（0.000 - 0.006）。敏感性分析得出了相似的结果。