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OBJECTIVE: To develop comprehensive quality assurance models for procedural outcomes after adult cardiac surgery. METHODS: Based on 52,792 cardiac operations in adults performed in 19 hospitals of 3 high-performing hospital systems, models were developed for operative mortality (n=1,271), stroke (n=895), deep sternal wound infection (n=122), prolonged intubation (6,182), renal failure (1,265), prolonged postoperative stay (n=5,418), and reoperations (n=1,693). Random forest quantile classification, a method tailored for challenges of rare events, and model-free variable priority screening were used to identify predictors of events. RESULTS: A small set of preoperative variables was sufficient to model procedural outcomes for virtually all cardiac operations, including older age; advanced symptoms; left ventricular, pulmonary, renal, and hepatic dysfunction; lower albumin; higher acuity; and greater complexity of the planned operation. Geometric mean performance ranged from .63 to .76. Calibration covered large areas of probability. Continuous risk factors provided high information content, and their association with outcomes was visualized with partial plots. These risk factors differed in strength and configuration among hospitals, as did their risk-adjusted outcomes according to patient risk as determined by counterfactual causal inference within a framework of virtual (digital) twins. CONCLUSIONS: Using a small set of variables and contemporary machine-learning methods, comprehensive models for procedural operative mortality and major morbidity after adult cardiac surgery were developed based on data from 3 exemplary hospital systems. They provide surgeons, their patients, and hospital and hospital systems with 21st century tools for assessing their risks compared to these advanced hospital systems and improving cardiac surgery quality.
RESUMO
OBJECTIVE: To demonstrate applying American Association for Thoracic Surgery Quality Gateway (AQG) outcomes models to a Surgeon Case Study of quality assurance in adult cardiac surgery. METHODS: The case study includes 6,989 cardiac and thoracic aorta operations performed in adults at Cleveland Clinic by one surgeon from 2001 to 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. RESULTS: 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 multi-component reoperations. Actionable opportunities for quality improvement based on virtual twins is illustrated for patients, surgeons, hospitals, risk profile groups, operations, and risk factors vis-à-vis other hospitals. CONCLUSIONS: 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?"