Development and Validation of Machine Learning Models for Adverse Events after Cardiac Surgery.

Importance: Early recognition of adverse events after cardiac surgery is vital for treatment. However, the widely used Society of Thoracic Surgery (STS) risk model has modest performance in predicting adverse events and only applies <80% of cardiac surgeries.

Objective: To develop and validate machine learning (ML) models for predicting outcomes after cardiac surgery.

Predictive Analytics in Cardiothoracic Care: Enhancing Outcomes with the Healthcare Enabled by Artificial Intelligence in Real Time (HEART) Project.

Problem: Postoperative complications after cardiac surgery significantly impact both the short-term and long-term survival of patients. Cardiovascular diseases are a major health concern, accounting for 12% of health expenditures in the United States. A substantial number of patients with cardiovascular disease undergo invasive procedures, including cardiac surgery, and the incidence of postoperative complications is notable.