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.

Keywords

• machine learning
• artificial intelligence
• cardiac transplantation
• organ allocation