The Journal of thoracic and cardiovascular surgery
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J. Thorac. Cardiovasc. Surg. · Sep 2024
Early Outcomes in Heart Transplantation using Donation after Circulatory Death Donors in Patients Bridged with Durable Left Ventricular Assist Devices.
Donation after circulatory death heart transplantation potentially increases donor allografts, especially for patients with lower listing status. We assessed the outcomes of donation after circulatory death heart transplantation in patients bridged with durable left ventricular assist devices. ⋯ Durable left ventricular assist devices may be associated with a higher risk of developing an early inflammatory response in donation after circulatory death heart transplantation; however, 1-year survival was similar between groups.
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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.