The Journal of thoracic and cardiovascular surgery
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J. Thorac. Cardiovasc. Surg. · Sep 2024
The Landscape of Congenital Heart Disease Treated with the Ross Procedure.
The Ross procedure has excellent outcomes in the pediatric population. Some series have reported age- and anatomy-dependent outcomes, but a comprehensive analysis stratified by these variables has not been reported to date. We sought to describe the landscape of congenital heart disease (CHD) treated with the Ross procedure and identify the patients best served by this operation. ⋯ Among pediatric patients, those with isolated AV disease are best served with the Ross procedure, regardless of age. Complex CHD is associated with lower survival and increased risk of LVOT reintervention.
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J. Thorac. Cardiovasc. Surg. · Sep 2024
Hospital and Surgeon Surgical Valvar Volume and Survival after Multi-Valve Cardiac Surgery in Medicare Beneficiaries.
Long-term outcomes after multivalve cardiac surgery remain underevaluated. ⋯ Survival varied significantly by type of multivalve surgery, worsened with addition of concomitant interventions and improved substantially with increasing annual hospital and surgeon volume. Hospital volume was associated with an improved early hazard for death that abated beyond 3 months post surgery, while surgeon volume was associated with an improved hazard for death that persisted even beyond the first postoperative year. Consideration should be given to referring multivalve cases to high-volume hospitals and surgeons.
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J. Thorac. Cardiovasc. Surg. · Sep 2024
Five Steps in Performing Machine Learning for Binary Outcomes.
The use of machine learning (ML) in cardiovascular and thoracic surgery is evolving rapidly. Maximizing the capabilities of ML can help improve patient risk stratification and clinical decision making, improve accuracy of predictions, and improve resource utilization in cardiac surgery. The many nuances and intricacies of ML modeling need to be understood to appropriately implement these technologies in the clinical research setting. This primer provides an educational framework of ML for generating predicted probabilities in clinical research and illustrates it with a real-world clinical example. ⋯ Collaboration among surgeons, care providers, statisticians, data scientists, and information technology professionals can help to maximize the impact of ML as a powerful tool in cardiac surgery.