Artificial intelligence-enhanced ECG score for perioperative risk assessment in non-cardiac surgery

Abstract Aims The role of electrocardiography (ECG) has been limited in the preoperative risk evaluation in noncardiac surgery due to its low prognostic value. We aimed to evaluate the utility of an AI-enabled ECG (QCG-Critical score) in predicting 30-day postoperative mortality in non-cardiac surgery and compare its performance with traditional perioperative risk-assessment tools. Methods and results A retrospective cohort of 46 135 adults who underwent non-cardiac surgery at a tertiary centre between 2020 and 2021 was analysed. Preoperative ECG images acquired within 30 days before surgery were used as input to previously developed CNN-based deep-learning algorithm to generate QCG-Critical score that reflects the risk for critical illness. The primary outcome was 30-day mortality, which occurred in 0.34% of patients. Individuals with QCG-Critical scores 40 had a markedly higher mortality rate of 11.7%. The QCG-Critical score demonstrated strong predictive performance for 30-day mortality (AUROC: 0.909), outperforming the ESC surgical category (0.728) and RCRI (0.725), and was comparable to the ASA classification (0.886). The performance of QCG-Critical score remained consistent across subgroups stratified by age, sex, emergency operation, anaesthesia type, and conventional risk groups. The QCG-Critical score also demonstrated good performance for predicting 7-day mortality (AUROC: 0.933), unplanned PCI (0.857), prolonged mechanical ventilation (0.829), and presumed heart failure (0.774). Conclusion The preoperative QCG-Critical score accurately predicted postoperative mortality and other adverse outcomes, outperforming conventional risk-stratification tools. The QCG-Critical score may serve as a fast, accessible, and integrable tool for perioperative risk assessments in routine surgical care.