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- Title
Relationship between resting 12-lead electrocardiogram and all-cause death in patients without structural heart disease: Shinken Database analysis.
- Authors
Hirota, Naomi; Suzuki, Shinya; Arita, Takuto; Yagi, Naoharu; Otsuka, Takayuki; Kishi, Mikio; Semba, Hiroaki; Kano, Hiroto; Matsuno, Shunsuke; Kato, Yuko; Uejima, Tokuhisa; Oikawa, Yuji; Matsuhama, Minoru; Iida, Mitsuru; Inoue, Tatsuya; Yajima, Junji; Yamashita, Takeshi
- Abstract
<bold>Background: </bold>Resting 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.<bold>Methods: </bold>A 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.<bold>Results: </bold>During 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.<bold>Conclusions: </bold>Parameters 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.
- Subjects
HEART diseases; SUPPORT vector machines; ELECTROCARDIOGRAPHY; CARDIOVASCULAR disease related mortality; PREDICTION models
- Publication
BMC Cardiovascular Disorders, 2021, Vol 21, Issue 1, p1
- ISSN
1471-2261
- Publication type
journal article
- DOI
10.1186/s12872-021-01864-3