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<p>PreOpNet is a novel deep-learning algorithm using 12-lead digital electrocardiogram (ECG) for preoperative risk assessment of all-cause death and major adverse cardiac events (MACE) within 30 days. Its performance in European high-risk patients undergoing major non-cardiac surgery—the target population for guideline-recommended risk assessment—and comparison to high-sensitivity cardiac troponin T (hs-cTnT), is unknown. In a prospective European study (2014–2019), 6098 high-risk patients with available ECGs were enrolled. PreOpNet showed moderate discrimination for death (AUC 0.707) and MACE (0.675), but overestimated risk. It outperformed the revised cardiac risk index (RCRI) for death (AUC 0.644), but not for MACE (0.662). Hs-cTnT remained superior for both outcomes (AUC 0.762 and 0.743). Importantly, PreOpNet provided incremental prognostic value when combined with RCRI and/or hs-cTnT. PreOpNet has limited benefit for preoperative risk stratification in high-risk surgical patients as a stand-alone test. However, it holds promise when used in conjunction with RCRI and hs-cTnT. <b>Clinical Trial Registration</b>: ClinicalTrials.gov number: NCT02573532; <a href="https://www.clinicaltrials.gov/study/NCT02573532">https://www.clinicaltrials.gov/study/NCT02573532</a>.</p>

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External validation of PreOpNet to predict 30-day mortality after major non-cardiac surgery using digital electrocardiogram

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月16日

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  • Arnaud Champetier,
  • Pedro Lopez-Ayala,
  • Christian Puelacher,
  • Koray Durak,
  • Emel Kaplan,
  • Danielle Gualandro,
  • Noemi Glarner,
  • Mirjam Pargger,
  • Gabrielle Huré,
  • Katrin Burri-Winkler,
  • Daniel Bolliger,
  • Luzius A. Steiner,
  • Stefan Schären,
  • Edin Mujagic,
  • Martin Clauss,
  • Andreas M. Mueller,
  • Didier Lardinois,
  • Jasper Boeddinghaus,
  • Felix Mahfoud,
  • Ivo Strebel,
  • Christian Mueller

摘要

PreOpNet is a novel deep-learning algorithm using 12-lead digital electrocardiogram (ECG) for preoperative risk assessment of all-cause death and major adverse cardiac events (MACE) within 30 days. Its performance in European high-risk patients undergoing major non-cardiac surgery—the target population for guideline-recommended risk assessment—and comparison to high-sensitivity cardiac troponin T (hs-cTnT), is unknown. In a prospective European study (2014–2019), 6098 high-risk patients with available ECGs were enrolled. PreOpNet showed moderate discrimination for death (AUC 0.707) and MACE (0.675), but overestimated risk. It outperformed the revised cardiac risk index (RCRI) for death (AUC 0.644), but not for MACE (0.662). Hs-cTnT remained superior for both outcomes (AUC 0.762 and 0.743). Importantly, PreOpNet provided incremental prognostic value when combined with RCRI and/or hs-cTnT. PreOpNet has limited benefit for preoperative risk stratification in high-risk surgical patients as a stand-alone test. However, it holds promise when used in conjunction with RCRI and hs-cTnT. Clinical Trial Registration: ClinicalTrials.gov number: NCT02573532; https://www.clinicaltrials.gov/study/NCT02573532.