Personalized Remote Patient Management in Heart Failure : From Implanted Hemodynamic Monitoring Toward an Artificial Intelligence-Supported Adaptive Care Cycle.

PURPOSE OF REVIEW: Implanted pulmonary artery pressure (PAP) monitoring provides an early window on imminent worsening heart failure (HF). Randomized trials demonstrated that PAP-guided management significantly reduces HF hospitalizations, but substantial residual risk persists despite recent major advances in HF pharmacotherapy. We review lessons from PAP-guided remote patient management and examine how artificial intelligence (AI) could support more individualized eligibility assessment and multimodal longitudinal surveillance within a patient-centered adaptive care cycle to further improve clinical outcomes. RECENT FINDINGS: HF is increasingly recognized as a systemic disease characterized, across HF phenotypes, by multiple cardiac and non-cardiac comorbidities. CHAMPION, GUIDE-HF, and MONITOR-HF did not systematically incorporate comorbidity-related risk into patient selection, while PAP-guided treatment focused predominantly on diuretic adjustment, and monitoring strategies were not adapted to individual disease trajectories. AI could help integrate multidimensional phenotyping to estimate individual risk and expected benefit pre-implant, use comprehensive longitudinal monitoring data to support personalized, actionable clinical decision-making, and enable dynamic adaptation of monitoring intensity tailored to the disease trajectory. This may improve clinical outcomes, reduce healthcare professional workload, enhance patient experience and treatment adherence, and increase cost-effectiveness.