論文

国際誌
2023年1月4日

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

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
  • Yasuyuki Shiraishi
  • Shinichi Goto
  • Nozomi Niimi
  • Yoshinori Katsumata
  • Ayumi Goda
  • Makoto Takei
  • Mike Saji
  • Motoaki Sano
  • Keiichi Fukuda
  • Takashi Kohno
  • Tsutomu Yoshikawa
  • Shun Kohsaka
  • 全て表示

25
3
開始ページ
922
終了ページ
930
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1093/europace/euac261

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.

リンク情報
DOI
https://doi.org/10.1093/europace/euac261
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/36610062
PubMed Central
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10062335
ID情報
  • DOI : 10.1093/europace/euac261
  • PubMed ID : 36610062
  • PubMed Central 記事ID : PMC10062335

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