Personalized Remote Patient Management in Heart Failure : From Implanted Hemodynamic Monitoring Toward an Artificial Intelligence-Supported Adaptive Care Cycle.
6 affiliations
- 1Comprehensive Heart Failure Centre, University and University Hospital Würzburg, Germany, and Department of Medicine 1, Cardiology, University Hospital Würzburg, Am Schwarzenberg 15, 97078, Würzburg, Germany. [email protected].
- 2Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, the Netherlands.
- 3Institute of Health Informatics, University College London, London, UK.
- 4The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK.
- 5Comprehensive Heart Failure Centre, University and University Hospital Würzburg, Germany, and Department of Medicine 1, Cardiology, University Hospital Würzburg, Am Schwarzenberg 15, 97078, Würzburg, Germany.
- 6Comprehensive Heart Failure Centre, Department of Clinical Research & Epidemiology, Würzburg, Germany.
Abstract
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.
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BibTeX
@article{angermann2026personalized,
title = {{Personalized Remote Patient Management in Heart Failure : From Implanted Hemodynamic Monitoring Toward an Artificial Intelligence-Supported Adaptive Care Cycle.}},
author = {Christiane E Angermann and Folkert W Asselbergs and Georg Ertl and Stefan Störk},
journal = {Current heart failure reports},
year = {2026},
month = oct,
doi = {10.1007/s11897-026-00783-8},
url = {https://europepmc.org/article/MED/42895671},
note = {CC BY},
}
RIS
TY - JOUR
TI - Personalized Remote Patient Management in Heart Failure : From Implanted Hemodynamic Monitoring Toward an Artificial Intelligence-Supported Adaptive Care Cycle.
AU - Christiane E Angermann
AU - Folkert W Asselbergs
AU - Georg Ertl
AU - Stefan Störk
PY - 2026
DA - 2026/10/19
JO - Current heart failure reports
DO - 10.1007/s11897-026-00783-8
UR - https://europepmc.org/article/MED/42895671
AB - 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.
SN - 1546-9530
SN - 1546-9549
ER -