Deadline for manuscript submissions: 31 July 2026.
Congestive heart failure (CHF) encompasses both reduced and preserved ejection fraction phenotypes. Modern management increasingly demands actionable insights into cardiac function beyond standard vitals. Cardiac time intervals (CTIs), including mitral valve closure (MVC), aortic valve opening (AVO), aortic valve closure (AVC), and mitral valve opening (MVO), as well as isovolumetric contraction time (IVCT) and isovolumetric relaxation time (IVRT), offer a window into the electromechanical timing of systole and diastole. These intervals provide clinically relevant markers of systolic function, diastolic filling dynamics, and chamber compliance. In HFrEF (reduced ejection fraction), CTI monitoring captures deterioration in contractile efficiency; in HFpEF (preserved ejection fraction), diastolic stiffness and shortened filling times can be tracked. Remote CTI monitoring facilitates timely therapy adjustments, prevents hospitalizations, empowers patients in their disease management, and provides clinicians with early warning signals of worsening physiology. CTIs enable a comprehensive, non-invasive assessment of cardiac chamber performance. This is especially relevant across the full spectrum of heart failure, including both HFrEF and HFpEF. The ability to deliver precise cardiac timing data outside of traditional clinical settings makes it a transformative tool for proactive, physiology-based heart failure management.
Primary graft dysfunction (PGD), defined as ventricular dysfunction occurring within 24 h following heart transplantation, is a life-threatening complication following heart transplantation. It affects approximately 8–10% of recipients and decreases the 1-year survival from 95% to 75%. The convergence of machine-based learning, multi-omics biomarker discovery, and advanced organ preservation technologies provides a unique opportunity to transform PGD from poorly predictable into a computable, potentially preventable complication. This focused review combines the rapidly evolving evidence at the intersection of artificial intelligence (AI) and PGD biology, offering a novel conceptual framework, which we named the “AI-enhanced PGD prevention pipeline”. This algorithm maps computational tools to every transplant step: donor evaluation, organ preservation and transportation, intraoperative management, and early postoperative surveillance. We critically appraise the evolution from traditional scoring systems (RADIAL, PREDICTA) through contemporary machine learning (ML) models, examine the integration of emerging molecular biomarkers into computational architectures, and evaluate how AI-guided organ preservation platforms may close the loop between prediction and prevention. Moreover, we propose a research agenda to advance this field toward prospective validation and clinical deployment.
