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<p>In-hospital cardiac arrest (IHCA) is often unexpected, and existing early warning scores based mainly on vital signs have limited predictive performance. We developed and internally evaluated clinical, deep-learning electrocardiographic, and hybrid models for IHCA prediction in a retrospective, single-center case–control study at National Taiwan University Hospital. The cohort included 835 adult patients with IHCA and 3,935 randomly selected non-IHCA controls from 2011 to 2018. The clinical model used demographic characteristics, comorbidities, vital signs, and conventional ECG measurements. The ECG model was a convolutional neural network incorporating residual and squeeze-and-excitation architectures trained on raw 12-lead ECG waveforms. The hybrid model entered the CNN-derived probability as a continuous predictor in the multivariable logistic regression model. The clinical model achieved an AUC of 0.872, with high specificity but lower sensitivity. The CNN achieved an AUC of 0.811 and higher sensitivity. The hybrid model had the highest observed performance (AUC 0.892; F1 score 0.602), although its improvement over the clinical model was not statistically significant. CNN-derived ECG information may modestly complement clinical predictors for IHCA risk stratification, but external validation and prospective evaluation are required before clinical implementation.</p>

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Development and evaluation of a hybrid clinical and deep-learning ECG model for predicting in-hospital cardiac arrest

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月5日

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  • Chung-Yu Lin,
  • Chih-Yang Li,
  • Kai-Yuan Hsiao,
  • Yu-Tin Yang,
  • Ting-An Lin,
  • Wei-Shan Chang,
  • Lian-Yu Lin,
  • Ruey-Feng Chang,
  • MingChih Chen,
  • Ben-Chang Shia

摘要

In-hospital cardiac arrest (IHCA) is often unexpected, and existing early warning scores based mainly on vital signs have limited predictive performance. We developed and internally evaluated clinical, deep-learning electrocardiographic, and hybrid models for IHCA prediction in a retrospective, single-center case–control study at National Taiwan University Hospital. The cohort included 835 adult patients with IHCA and 3,935 randomly selected non-IHCA controls from 2011 to 2018. The clinical model used demographic characteristics, comorbidities, vital signs, and conventional ECG measurements. The ECG model was a convolutional neural network incorporating residual and squeeze-and-excitation architectures trained on raw 12-lead ECG waveforms. The hybrid model entered the CNN-derived probability as a continuous predictor in the multivariable logistic regression model. The clinical model achieved an AUC of 0.872, with high specificity but lower sensitivity. The CNN achieved an AUC of 0.811 and higher sensitivity. The hybrid model had the highest observed performance (AUC 0.892; F1 score 0.602), although its improvement over the clinical model was not statistically significant. CNN-derived ECG information may modestly complement clinical predictors for IHCA risk stratification, but external validation and prospective evaluation are required before clinical implementation.