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Improved prediction of sudden cardiac death in patients with heart failure through digital processing of electrocardiography

AIMS: Available predictive models for sudden cardiac death (SCD) in heart failure (HF) patients remain suboptimal. We assessed whether the electrocardiography (ECG)-based artificial intelligence (AI) could better predict SCD, and also whether the combination of the ECG-AI index and conventional pred...

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Autores principales: Shiraishi, Yasuyuki, Goto, Shinichi, Niimi, Nozomi, Katsumata, Yoshinori, Goda, Ayumi, Takei, Makoto, Saji, Mike, Sano, Motoaki, Fukuda, Keiichi, Kohno, Takashi, Yoshikawa, Tsutomu, Kohsaka, Shun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10062335/
https://www.ncbi.nlm.nih.gov/pubmed/36610062
http://dx.doi.org/10.1093/europace/euac261
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author Shiraishi, Yasuyuki
Goto, Shinichi
Niimi, Nozomi
Katsumata, Yoshinori
Goda, Ayumi
Takei, Makoto
Saji, Mike
Sano, Motoaki
Fukuda, Keiichi
Kohno, Takashi
Yoshikawa, Tsutomu
Kohsaka, Shun
author_facet Shiraishi, Yasuyuki
Goto, Shinichi
Niimi, Nozomi
Katsumata, Yoshinori
Goda, Ayumi
Takei, Makoto
Saji, Mike
Sano, Motoaki
Fukuda, Keiichi
Kohno, Takashi
Yoshikawa, Tsutomu
Kohsaka, Shun
author_sort Shiraishi, Yasuyuki
collection PubMed
description AIMS: Available predictive models for sudden cardiac death (SCD) in heart failure (HF) patients remain suboptimal. We assessed whether the electrocardiography (ECG)-based artificial intelligence (AI) could better predict SCD, and also whether the combination of the ECG-AI index and conventional predictors of SCD would improve the SCD stratification among HF patients. METHODS AND RESULTS: In a prospective observational study, 4 tertiary care hospitals in Tokyo enrolled 2559 patients hospitalized for HF who were successfully discharged after acute decompensation. The ECG data during the index hospitalization were extracted from the hospitals’ electronic medical record systems. The association of the ECG-AI index and SCD was evaluated with adjustment for left ventricular ejection fraction (LVEF), New York Heart Association (NYHA) class, and competing risk of non-SCD. The ECG-AI index plus classical predictive guidelines (i.e. LVEF ≤35%, NYHA Class II and III) significantly improved the discriminative value of SCD [receiver operating characteristic area under the curve (ROC-AUC), 0.66 vs. 0.59; P = 0.017; Delong’s test] with good calibration (P = 0.11; Hosmer–Lemeshow test) and improved net reclassification [36%; 95% confidence interval (CI), 9–64%; P = 0.009]. The Fine–Gray model considering the competing risk of non-SCD demonstrated that the ECG-AI index was independently associated with SCD (adjusted sub-distributional hazard ratio, 1.25; 95% CI, 1.04–1.49; P = 0.015). An increased proportional risk of SCD vs. non-SCD with an increasing ECG-AI index was also observed (low, 16.7%; intermediate, 18.5%; high, 28.7%; P for trend = 0.023). Similar findings were observed in patients aged ≤75 years with a non-ischaemic aetiology and an LVEF of >35%. CONCLUSION: To improve risk stratification of SCD, ECG-based AI may provide additional values in the management of patients with HF.
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spelling pubmed-100623352023-03-31 Improved prediction of sudden cardiac death in patients with heart failure through digital processing of electrocardiography Shiraishi, Yasuyuki Goto, Shinichi Niimi, Nozomi Katsumata, Yoshinori Goda, Ayumi Takei, Makoto Saji, Mike Sano, Motoaki Fukuda, Keiichi Kohno, Takashi Yoshikawa, Tsutomu Kohsaka, Shun Europace Clinical Research AIMS: Available predictive models for sudden cardiac death (SCD) in heart failure (HF) patients remain suboptimal. We assessed whether the electrocardiography (ECG)-based artificial intelligence (AI) could better predict SCD, and also whether the combination of the ECG-AI index and conventional predictors of SCD would improve the SCD stratification among HF patients. METHODS AND RESULTS: In a prospective observational study, 4 tertiary care hospitals in Tokyo enrolled 2559 patients hospitalized for HF who were successfully discharged after acute decompensation. The ECG data during the index hospitalization were extracted from the hospitals’ electronic medical record systems. The association of the ECG-AI index and SCD was evaluated with adjustment for left ventricular ejection fraction (LVEF), New York Heart Association (NYHA) class, and competing risk of non-SCD. The ECG-AI index plus classical predictive guidelines (i.e. LVEF ≤35%, NYHA Class II and III) significantly improved the discriminative value of SCD [receiver operating characteristic area under the curve (ROC-AUC), 0.66 vs. 0.59; P = 0.017; Delong’s test] with good calibration (P = 0.11; Hosmer–Lemeshow test) and improved net reclassification [36%; 95% confidence interval (CI), 9–64%; P = 0.009]. The Fine–Gray model considering the competing risk of non-SCD demonstrated that the ECG-AI index was independently associated with SCD (adjusted sub-distributional hazard ratio, 1.25; 95% CI, 1.04–1.49; P = 0.015). An increased proportional risk of SCD vs. non-SCD with an increasing ECG-AI index was also observed (low, 16.7%; intermediate, 18.5%; high, 28.7%; P for trend = 0.023). Similar findings were observed in patients aged ≤75 years with a non-ischaemic aetiology and an LVEF of >35%. CONCLUSION: To improve risk stratification of SCD, ECG-based AI may provide additional values in the management of patients with HF. Oxford University Press 2023-01-04 /pmc/articles/PMC10062335/ /pubmed/36610062 http://dx.doi.org/10.1093/europace/euac261 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Clinical Research
Shiraishi, Yasuyuki
Goto, Shinichi
Niimi, Nozomi
Katsumata, Yoshinori
Goda, Ayumi
Takei, Makoto
Saji, Mike
Sano, Motoaki
Fukuda, Keiichi
Kohno, Takashi
Yoshikawa, Tsutomu
Kohsaka, Shun
Improved prediction of sudden cardiac death in patients with heart failure through digital processing of electrocardiography
title Improved prediction of sudden cardiac death in patients with heart failure through digital processing of electrocardiography
title_full Improved prediction of sudden cardiac death in patients with heart failure through digital processing of electrocardiography
title_fullStr Improved prediction of sudden cardiac death in patients with heart failure through digital processing of electrocardiography
title_full_unstemmed Improved prediction of sudden cardiac death in patients with heart failure through digital processing of electrocardiography
title_short Improved prediction of sudden cardiac death in patients with heart failure through digital processing of electrocardiography
title_sort improved prediction of sudden cardiac death in patients with heart failure through digital processing of electrocardiography
topic Clinical Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10062335/
https://www.ncbi.nlm.nih.gov/pubmed/36610062
http://dx.doi.org/10.1093/europace/euac261
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