Multimodal deep learning to predict postoperative major adverse cardiac and cerebrovascular events after noncardiac...

Major adverse cardiovascular and cerebrovascular events (MACCEs) after noncardiac surgery can lead to substantial morbidity, mortality, and health care costs. Therefore, accurate and rapid risk prediction is crucial for targeted perioperative management. This study aimed to develop and validate a minimally burdensome multimodal deep learning model integrating demographic data, the International Classification of Diseases (ICD)-10 procedure codes, and raw preoperative 12-lead electrocardiogram (ECG) waveforms to predict 30-day MACCEs and to compare its performance with the established risk indices.

This retrospective cohort study at a single tertiary academic center included adult patients who underwent noncardiac surgery under regional or general anesthesia from 2006 to 2020. Preoperative 12-lead ECGs were acquired within 3 months before surgery. A transformer-based deep neural network processed raw ECG signals, while a gradient boosting machine (GBM) combined ECG-derived latent features with basic demographic variables (age and sex) and simplified ICD-10 procedure codes. The primary outcome was 30-day MACCEs (cardiac arrest, acute myocardial infarction, congestive heart failure, new arrhythmia, angina, stroke, or cardiovascular/cerebrovascular death). Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), precision-recall curves, sensitivity, specificity, F1 scores, and calibration metrics.

Among the 165 577 cases, 54.5% were female, the median age was 56 years, and 0.6% developed 30-day MACCEs. The multimodal GBM model demonstrated a significantly higher AUROC of 0.902 [95% confidence interval (CI), 0.898-0.906] than the baseline GBM [0.842 (0.838-0.847)]. It also outperformed the Revised Cardiac Risk Index [0.812 (0.807-0.818)] and the American Society of Anesthesiologists class [0.759 (0.753-0.765)].

A multimodal deep learning model combining raw ECG waveforms with minimal clinical data yielded superior 30-day MACCE risk prediction compared to that of the conventional indices. This approach could facilitate broad clinical adoption by minimizing data collection requirements while enhancing perioperative risk stratification.