背景:静息心电图(ECG)是临床医学中用于评估患者静息时心脏电活动的有价值的非侵入性诊断工具。心电图异常可能与临床生物标志物有关,并可以预测疾病的早期阶段。在这项研究中,我们评估了心电图特征之间的关联,临床生物标志物,和疾病,并制定了风险评分,以预测卡塔尔生物银行发生冠状动脉疾病(CAD)的风险。
方法:本研究使用了13,827名参与者的12导联心电图数据。用于关联分析的心电图性状为RR,PR,QRS,QTc,PW,还有JT.使用回归模型进行心电图变量与血清电解质之间的关联分析,糖,糖脂质,血压(BP),血液和炎症生物标志物,和疾病(例如,2型糖尿病,CAD,和中风)。制定了基于ECG和临床风险评分,并对其性能进行评估以预测CAD。经典回归和机器学习模型用于风险评分的开发。
结果:观察到与ECG特征显著相关。RR显示关联数量最多:例如,与碳酸氢盐呈正相关,氯化物,HDL-C,和单核细胞,与葡萄糖呈负相关,胰岛素,中性粒细胞,钙,和T2D的风险。QRS与磷呈正相关,碳酸氢盐,和CAD的风险。在CAD患者中观察到QTc升高,而QTc的降低与钙和钾水平的降低有关。使用回归模型开发的风险评分优于机器学习模型。使用包含ECG特征的机器学习模型,接收器工作曲线下的面积达到0.84,糖,糖脂质,血清电解质,和心血管疾病的危险因素。CAD风险评分的前十分位数与其余十分位数的比值比为13.99。
结论:心电图异常与血清电解质有关,糖,糖脂质,血液和炎症生物标志物。在T2D和CAD患者中也观察到这些异常。风险评分在预测CAD方面显示出良好的预测性能。
BACKGROUND: Resting electrocardiogram (ECG) is a valuable non-invasive diagnostic tool used in clinical medicine to assess the electrical activity of the heart while the patient is resting. Abnormalities in ECG may be associated with clinical biomarkers and can predict early stages of diseases. In this study, we evaluated the association between ECG traits, clinical biomarkers, and diseases and developed risk scores to predict the risk of developing coronary artery disease (CAD) in the Qatar Biobank.
METHODS: This study used 12-lead ECG data from 13,827 participants. The ECG traits used for association analysis were RR, PR, QRS, QTc, PW, and JT. Association analysis using regression models was conducted between ECG variables and serum electrolytes, sugars, lipids, blood pressure (BP), blood and inflammatory biomarkers, and diseases (e.g., type 2 diabetes, CAD, and stroke). ECG-based and clinical risk scores were developed, and their performance was assessed to predict CAD. Classical regression and machine-learning models were used for risk score development.
RESULTS: Significant associations were observed with ECG traits. RR showed the largest number of associations: e.g., positive associations with bicarbonate, chloride, HDL-C, and monocytes, and negative associations with glucose, insulin, neutrophil, calcium, and risk of T2D. QRS was positively associated with phosphorus, bicarbonate, and risk of CAD. Elevated QTc was observed in CAD patients, whereas decreased QTc was correlated with decreased levels of calcium and potassium. Risk scores developed using regression models were outperformed by machine-learning models. The area under the receiver operating curve reached 0.84 using a machine-learning model that contains ECG traits, sugars, lipids, serum electrolytes, and cardiovascular disease risk factors. The odds ratio for the top decile of CAD risk score compared to the remaining deciles was 13.99.
CONCLUSIONS: ECG abnormalities were associated with serum electrolytes, sugars, lipids, and blood and inflammatory biomarkers. These abnormalities were also observed in T2D and CAD patients. Risk scores showed great predictive performance in predicting CAD.