The current and future role of artificial intelligence in optimizing donor organ utilization and recipient outcomes in heart transplantation

Abstract

Heart failure (HF) is a leading cause of morbidity and mortality in the United States. While medical management and mechanical circulatory support have undergone significant advancement in recent years, orthotopic heart transplantation (OHT) remains the most definitive therapy for refractory HF. OHT has seen steady improvement in patient survival and quality of life (QoL) since its inception, with one-year mortality now under 8%. However, a significant number of HF patients are unable to receive OHT due to scarcity of donor hearts. The United Network for Organ Sharing has recently revised its organ allocation criteria in an effort to provide more equitable access to OHT. Despite these changes, there are many potential donor hearts that are inevitably rejected. Arbitrary regulations from the centers for Medicare and Medicaid services and fear of repercussions if one-year mortality falls below established values has led to a current state of excessive risk aversion for which organs are accepted for OHT. Furthermore, non-standardized utilization of extended criteria donors and donation after circulatory death, exacerbate the organ shortage. Data-driven systems can improve donor-recipient matching, better predict patient QoL post-OHT, and decrease needless organ waste through more uniform application of acceptance criteria. Thus, we propose a data-driven future for OHT and a move to patient-centric and holistic transplantation care processes.

Revolutionizing Heart Transplantation: A Multidisciplinary Approach to Xenotransplantation, Immunosuppression, Regenerative Medicine, Artificial Intelligence, and Economic Sustainability

“…Unlike traditional AI systems, which are programmed to perform specific tasks, ML systems are designed to learn from data and improve over time. They analyze extensive datasets to identify patterns, make predictions, and optimize task efficiency, thereby becoming more accurate and effective as they are exposed to more data [ 27 ].…”

The Impact of Artificial Intelligence on Optimizing Diagnosis and Treatment Plans for Rare Genetic Disorders

“…AI and ML have emerged as transformative forces within the healthcare domain, extending well beyond conventional diagnostic and therapeutic methodologies. These technologies, although often used interchangeably, possess distinct attributes and are collectively reshaping medical decision-making through their capacity to harness extensive datasets and computational capabilities [23].…”

“…Furthermore, as implantable and wearable medical technology continues to advance, ML can facilitate realtime patient monitoring, potentially reducing hospitalizations and enhancing post-operative care. This convergence of technology and healthcare holds the promise of not only improving patient outcomes but also alleviating the burden on healthcare systems by streamlining processes and enhancing overall efficiency [23].…”