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

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 .

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Publication Details

Journal
npj Digital Medicine
Published
2025-10-16
DOI
https://doi.org/10.1038/s41746-025-01983-7
Primary Topic
Cardiac, Anesthesia and Surgical Outcomes
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Luzius A. Steiner, Martin Clauss, Christian Puelacher, Ivo Strebel et al.
npj Digital Medicine
Cardiac, Anesthesia and Surgical Outcomes
article

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

Luzius A. Steiner, Martin Clauss, Christian Puelacher, Ivo Strebel, Koray Durak, Pedro López‐Ayala, Felix Mahfoud, Noemi Glarner, Stefan Schären, Katrin Burri‐Winkler, Mirjam Pargger, Andreas Mueller, Edin Mujagić, Didier Lardinois, Jasper Boeddinghaus, Daniel Bolliger, Danielle Menosi Gualandro, Christian Mueller, Eliza Kaplan, Arnaud Champetier, Gabrielle Huré
article en

Abstract

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 .

npj Digital MedicineVol. 8(1)
Innsbruck Medical University (AT), University of Basel (CH), Universität Innsbruck (AT), Education and Research Network (IN), University Hospital of Basel (CH), Hospital Base (CL)
AstraZeneca, Fundação de Amparo à Pesquisa do Estado de São Paulo, Schweizerische Herzstiftung
Openalex Percentile: Top 56%
Cardiac, Anesthesia and Surgical Outcomes
DOIOpen Access PDFOpenAlex
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