ArticleEuropean heart journal. Digital health2024
Real-world evaluation of an algorithmic machine-learning-guided testing approach in stable chest pain: a multinational, multicohort study.
Article in European heart journal. Digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
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Who cites it
8 citing papers in PubMed.
- Artificial Intelligence-enhanced Electrocardiography for Heart Failure Screening and Risk Stratification.Current heart failure reports · 2026Review
- Evaluating the effectiveness of the SHEL model in reducing occupational exposure risks among pre-hospital emergency personnel: a retrospective cohort study (2018-2024).Frontiers in public health · 2026Article
- Automated transformation of unstructured cardiovascular diagnostic reports into structured datasets using sequentially deployed large language models.European heart journal. Digital health · 2025Article
- Hearts, Data, and Artificial Intelligence Wizardry: From Imitation to Innovation in Cardiovascular Care.Biomedicines · 2025Review
- Machine learning-based scoring system to predict cardiogenic shock in acute coronary syndrome.European heart journal. Digital health · 2025Article
- Cardiovascular care with digital twin technology in the era of generative artificial intelligence.European heart journal · 2024Review
- The power of data-driven ASSISTance in personalized testing for coronary artery disease.European heart journal. Digital health · 2024Article
- Automated Transformation of Unstructured Cardiovascular Diagnostic Reports into Structured Datasets Using Sequentially Deployed Large Language Models.medRxiv : the preprint server for health sciences · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Aims: An algorithmic strategy for anatomical vs. functional testing in suspected coronary artery disease (CAD) (Anatomical vs. Stress teSting decIsion Support Tool; ASSIST) is associated with better outcomes than random selection. However, in the real world, this decision is rarely random. We explored the agreement between a provider-driven vs. simulated algorithmic approach to cardiac testing and its association with outcomes across multinational cohorts. Methods and results: In two cohorts of functional vs. anatomical testing in a US hospital health system [Yale; 2013-2023; Conclusion: In cohorts where historical practices largely favour functional testing, alignment with an algorithmic approach to cardiac testing defined by ASSIST was associated with a lower risk of adverse outcomes. This highlights the potential utility of a data-driven approach in the diagnostic management of CAD.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.