Abstract
To demonstrate the application of American Association for Thoracic Surgery Quality Gateway (AQG) outcomes models to a Surgeon Case Study of quality assurance in adult cardiac surgery.
The case study includes 6989 cardiac and thoracic aorta operations performed in adults at Cleveland Clinic by a single surgeon between 2001 and 2023. AQG models were used to predict expected probabilities for operative mortality and major morbidity and to compare hospital outcomes, surgery type, risk profile, and individual risk factor levels using virtual (digital) twin causal inference. These models were based on postoperative procedural outcomes after 52,792 cardiac operations performed in 19 hospitals of 3 high-performing hospital systems with overall hospital mortality of 2.0%, analyzed by advanced machine learning for rare events.
For individual surgeons, their patients, hospitals, and hospital systems, the Surgeon Case Study demonstrated that AQG provides expected outcomes across the entire spectrum of cardiac surgery, from single-component primary operations to complex multicomponent reoperations. Actionable opportunities for quality improvement based on virtual twins are illustrated for patients, surgeons, hospitals, risk profile groups, operations, and risk factors vis-à-vis other hospitals.
Using minimal data collection and models developed using advanced machine learning, this case study shows that probabilities can be generated for operative mortality and major morbidity after virtually all adult cardiac operations. It demonstrates the utility of 21st century causal inference (virtual [digital] twin) tools for assessing quality for surgeons asking “how am I doing?,” their patients asking “what are my chances?,” and the profession asking “how can we get better?”