ArticleEuropean heart journal. Digital health2025
Developing an artificial intelligence-based decision engine for disease-modifying therapy in heart failure: a pilot study.
Article in European heart journal. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04699253 (Usability and Feasibility of a Personalized, Web-based Education and Self-management Approach for Patients With Chronic Heart Failure Across Four European Sites), which is not on this map. Cited by 2 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Usability and Feasibility of a Personalized, Web-based Education and Self-management Approach for Patients With Chronic Heart Failure Across Four European Sites
Who cites it
2 citing papers in PubMed.
- Cardiovascular Disease, Sleep-Disordered Breathing, and Artificial Intelligence: From Neutral Trials to Precision Sleep Cardiology.Reviews in cardiovascular medicine · 2026Review
- AI-assisted heart failure management: A review of clinical applications, case studies, and future directions.Global cardiology science & practice · 2025Review
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: Heart failure is an escalating burden on global healthcare systems. Modernizing heart failure care is inevitable, with eHealth products poised to play an important role. However, eHealth devices that can initiate and adjust heart failure medication are currently lacking. Consequently, this study aimed to develop an artificial intelligence-based decision engine to provide guideline-based recommendations for disease-modifying medication in heart failure patients. Methods and results: We developed the decision engine by converting the European Society of Cardiology (ESC) heart failure guidelines into Business Process Model and Notation, a visual modelling language suitable for developing complex decision engines. A safety evaluation, based on clinical parameters, was conducted to ascertain the system's applicability to specific cases. The decision engine renders specific decisions concerning disease- modifying therapy for heart failure patients. We defined 72 virtual heart failure patient scenarios, encompassing a broad spectrum of baseline characteristics and background medication. All recommendations offered by the engine were evaluated by an independent heart failure specialist. All but three recommendations (94%) were identical to the treatment decisions by the heart failure specialist, and all (100%) were in line with the 2021 ESC heart failure guidelines. Conclusion: The decision engine offers guideline-based recommendations for disease-modifying therapy, positioning it as a tool to enhance self-care among heart failure patients. To validate our results, the decision engine is being prospectively tested in real-world patients in a multicentre clinical trial (NCT04699253).
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