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<p>We evaluated the performance of 12-channel ECG in predicting sudden cardiac death across different time intervals using a retrospective data set of 17,625 high-risk cardiac patients who underwent coronary angiography (2007–2018) with follow-up data until 2022. Extreme gradient boosting using 12SL Marquette software-derived parameters from digital ECG recording was used to train and validate models using a random 80/20 split. Model performance was evaluated in both unbalanced and risk-factor-balanced case-control sets. Using single ECG, both long-term (from baseline ECG) and short-term predictions (from the last recorded ECG) achieved a modest area under the curve (AUC) of 0.68 in the unbalanced validation and 0.59/0.63 in the balanced validation (long-/short-term). Adding clinical risk factor data resulted in AUC 0.70/0.71 (unbalanced) and 0.64/0.62 (balanced) for long- and short-term prediction. Adding data of observed ECG changes during follow-up for short-term prediction resulted in the best model performance (0.72/0.66; unbalanced/balanced).</p>

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Performance of the 12-lead ECG in predicting short- and long-term risk of sudden cardiac death

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月5日

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  • Jussi A. Hernesniemi,
  • Teemu Pukkila,
  • Jani Rankinen,
  • Antti Kallonen,
  • Mikko Uimonen,
  • Leo-Pekka Lyytikäinen,
  • Kjell Nikus,
  • Esa Räsänen,
  • Juho Tynkkynen

摘要

We evaluated the performance of 12-channel ECG in predicting sudden cardiac death across different time intervals using a retrospective data set of 17,625 high-risk cardiac patients who underwent coronary angiography (2007–2018) with follow-up data until 2022. Extreme gradient boosting using 12SL Marquette software-derived parameters from digital ECG recording was used to train and validate models using a random 80/20 split. Model performance was evaluated in both unbalanced and risk-factor-balanced case-control sets. Using single ECG, both long-term (from baseline ECG) and short-term predictions (from the last recorded ECG) achieved a modest area under the curve (AUC) of 0.68 in the unbalanced validation and 0.59/0.63 in the balanced validation (long-/short-term). Adding clinical risk factor data resulted in AUC 0.70/0.71 (unbalanced) and 0.64/0.62 (balanced) for long- and short-term prediction. Adding data of observed ECG changes during follow-up for short-term prediction resulted in the best model performance (0.72/0.66; unbalanced/balanced).