A new deep learning model predicts postoperative cardiac events after noncardiac surgery with high accuracy, improving patient safety.

• The model showed an AUROC of 0.902 for 30-day major adverse cardiac and cerebrovascular events (MACCE), outperforming traditional methods.
• Only 0.6% of 165,577 patients experienced MACCE, indicating the model’s focus on high-risk patients may enhance targeting for preoperative interventions.

This tool could streamline preoperative assessments and reduce complications by accurately identifying at-risk patients with minimal data collection.

• It combines ECG waveforms with basic demographic data, making it easy to implement in routine practice.