Clinical Scorecard: Enhancing Prediction and Management of Immune Rejection in Heart Transplantation Through Artificial Intelligence: A Comprehensive Narrative Review
At a Glance
Category
Detail
Condition
Heart Transplantation and Immune Rejection
Key Mechanisms
Utilization of AI for donor-recipient matching, non-invasive rejection monitoring, and improved pathological diagnosis.
Target Population
Patients undergoing heart transplantation.
Care Setting
Transplant centers and clinical research environments.
Key Highlights
AI improves preoperative donor-recipient matching through accurate total cardiac volume measurement.
Non-invasive biomarkers integrated with AI models show high diagnostic accuracy for rejection.
AI enhances sensitivity of high-grade acute cellular rejection detection compared to manual assessment.
Generative adversarial networks address sample scarcity in rejection diagnosis.
Current evidence is limited by methodological weaknesses, including small sample sizes and lack of external validation.
Guideline-Based Recommendations
Diagnosis
Utilize AI models for non-invasive rejection surveillance using biomarkers.
Management
Implement AI-driven tools for optimizing donor-recipient matching.
Monitoring & Follow-up
Adopt AI-enhanced pathological diagnosis to reduce reliance on invasive EMB.
Risks
Acknowledge procedural risks and diagnostic inconsistencies associated with traditional EMB.
Patient & Prescribing Data
Patients with end-stage heart failure requiring transplantation.
AI can reduce unnecessary invasive procedures and improve patient outcomes through better matching and monitoring.
Clinical Best Practices
Prioritize prospective, multi-center studies for validating AI applications in clinical settings.
Standardize biomarker and model reporting for consistency in research.
Despite major advances in guideline-directed medical therapy (GDMT), worsening heart failure continues to drive significant morbidity, repeat hospitalizations and healthcare utilization worldwide.
