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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...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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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. |
format | Online Article Text |
id | pubmed-10062335 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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