BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
BACKGROUND: 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 patientmortality. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Authors: Ismo Anttila; Kjell Nikus; Tuomo Nieminen; Antti Jula; Veikko Salomaa; Antti Reunanen; Markku Sakari Nieminen; Terho Lehtimäki; Vesa Virtanen; Mika Kähönen Journal: Am J Cardiol Date: 2011-09-08 Impact factor: 2.778
Authors: Antonius M W van Stipdonk; Iris Ter Horst; Marielle Kloosterman; Elien B Engels; Michiel Rienstra; Harry J G M Crijns; Marc A Vos; Isabelle C van Gelder; Frits W Prinzen; Mathias Meine; Alexander H Maass; Kevin Vernooy Journal: Circ Arrhythm Electrophysiol Date: 2018-12
Authors: Peter R Rijnbeek; Gerard van Herpen; Michiel L Bots; Sumche Man; Niek Verweij; Albert Hofman; Hans Hillege; Matthijs E Numans; Cees A Swenne; Jacqueline C M Witteman; Jan A Kors Journal: J Electrocardiol Date: 2014-08-02 Impact factor: 1.438
Authors: Jan Szewieczek; Zbigniew Gąsior; Jan Duława; Tomasz Francuz; Katarzyna Legierska; Agnieszka Batko-Szwaczka; Beata Hornik; Magdalena Janusz-Jenczeń; Iwona Włodarczyk; Krzysztof Wilczyński Journal: Age (Dordr) Date: 2016-04-02