Abstract
BackgroundResting 12-lead electrocardiography is widely used for the detection of cardiac diseases. Electrocardiogram readings have been reported to be affected by aging and, therefore, can predict patient mortality.MethodsA total of 12,837 patients without structural heart disease who underwent electrocardiography at baseline were identified in the Shinken Database among those registered between 2010 and 2017 (n = 19,170). Using 438 electrocardiography parameters, predictive models for all-cause death and cardiovascular (CV) death were developed by a support vector machine (SVM) algorithm.ResultsDuring the observation period of 320.4 days, 55 all-cause deaths and 23 CV deaths were observed. In the SVM prediction model, the mean c-statistics of 10 cross-validation models with training and testing datasets were 0.881 ± 0.027 and 0.927 ± 0.101, respectively, for all-cause death and 0.862 ± 0.029 and 0.897 ± 0.069, respectively for CV death. For both all-cause and CV death, high values of permutation importance in the ECG parameters were concentrated in the QRS complex and ST-T segment.ConclusionsParameters acquired from 12-lead resting electrocardiography could be applied to predict the all-cause and CV deaths of patients without structural heart disease. The ECG parameters that greatly contributed to the prediction were concentrated in the QRS complex and ST-T segment.
Highlights
Resting 12-lead electrocardiography is widely used for the detection of cardiac diseases
We developed a predictive model for all-cause and cardiovascular (CV) death using 438 ECG parameters according to the following steps
Predictive models for all‐cause and CV death Step 1: The Wald statistics of 438 ECG parameters in the univariable logistic regression analysis for all-cause death and CV death are shown in the order of ECG time phases (P, QRS, and ST-T) in Fig. 1a, b, respectively
Summary
Resting 12-lead electrocardiography is widely used for the detection of cardiac diseases. A number of predictive models for all-cause death using ECG parameters have been reported [6,7,8,9,10] that are based on the concept that abnormal ECG changes represent serious comorbidities that increase the risk of all-cause death. These models have used one or a few parameters, mostly categorical, and were limited to a specific ECG lead [6,7,8,9,10]. Very recently, reported studies have applied machine learning algorithms to large populations and a large numbers of parameters [11]
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