The impact of artificial intelligence-driven risk prediction on clinical decisions in heart failure patients: the design of an international vignette study
Rattachement africain : nl, gr, es, pe, cz, Tanzanie, pt. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Abstract Background/introduction Heart failure (HF), affecting over 64 million people worldwide, is a complex syndrome that requires personalized treatment. [1] The use of risk prediction models may help guide tailored care and is recommended by international guidelines. [2] However, clinical utility and model impact on clinical decision-making in HF care remain largely uninvestigated. [3,4] The AI4HF consortium (Trustworthy Artificial intelligence for Personalized Risk Assessment in Chronic Heart Failure) is developing HF risk prediction models for use at the ED, at hospital discharge and for decisions regarding referral to specialized advanced HF teams. These models, with their outcomes and explainability shown in a web-based interface, will offer a valuable opportunity to evaluate the impact of AI-driven risk predictions on HF decision making. Purpose We present the design of a clinical vignette study to evaluate the impact of the use of predictive models on the clinical decision making in three different hospital settings. Methods and Results Questionnaires distributed in preparation for the vignette study (filled in by 24 cardiologists and residents from the eight participating hospitals) revealed that clinicians anticipate risk-scores to be helpful in the three clinical settings (see figure 1). Therefore, we consider it valuable to evaluate the models in all three settings. During the study, eighty clinicians from eight hospitals will evaluate clinical vignettes based on real-world data extracted from electronic health records (EHR) from patients presenting at the ED, hospitalized for acute HF, or visiting the outpatient clinic with non-ischemic dilated cardiomyopathy. For the proposed study, 375 vignettes will be created and clinicians will be asked to take clinical decisions and indicate their decision certainty and trust in the risk prediction model. Multiple AI4HF models are available for each clinical setting, that differ in their predicted outcome or training population. Each clinician will assess 25 vignettes, first without access to a model, and subsequently reassess the same vignette with one of the models present alongside the vignette. Finally, clinicians reassess the same vignette while they have access to follow-up data from the subsequent period (up to five years if available) serving as a reference standard. As an outcome, we will assess whether the use of a model leads to changes in clinical decisions, and study associations between those changes and clinician-, vignette- and model-related factors. Besides, we will use binomial tests to evaluate whether the use of each model improves agreement with the prospective decisions. Conclusion This study will provide insight into the impact of an AI-based risk prediction tool on clinical decision making of HF patients. Together with patient focus group results, outcomes of this study will guide implementation of AI for heart failure.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- The impact of artificial intelligence-driven risk prediction on clinical decisions in heart failure patients: the design of an international vignette study
- Date Crossref
- 01/01/2026
- Éditeur
- Oxford University Press (OUP)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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University Medical Center Utrecht pays non établi dans la noticeÉtablissement de santé
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Amsterdam University Medical Centers pays non établi dans la noticeÉtablissement de santé
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Centre for Research and Technology Hellas pays non établi dans la noticeStructure de recherche
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Vall d'Hebron Institut de Recerca pays non établi dans la noticeStructure de recherche
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Instituto Nacional de Salud del Niño pays non établi dans la noticeÉtablissement de santé
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St. Antonius Ziekenhuis pays non établi dans la noticeOrganisme public
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St. Anne's University Hospital Brno pays non établi dans la noticeÉtablissement de santé
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Muhimbili University of Health and Allied Sciences Dar es Salaam, Tanzanie (code pays fourni par la source)Université ou école supérieure
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Administração Regional de Saúde de Lisboa e Vale do Tejo pays non établi dans la noticeOrganisme public
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Centro Hospitalar de Lisboa Ocidental pays non établi dans la noticeÉtablissement de santé
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Catholic University of Health and Allied Sciences Mwanza, Tanzanie (code pays fourni par la source)Université ou école supérieure
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Amsterdam UMC pays non établi dans la noticeInstitution
University Medical Center Utrecht, Amsterdam University Medical Centers et Centre for Research and Technology Hellas, avec 9 autres affiliations. Pays d’affiliation : Tanzanie.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.