Observational studyEBioMedicine2024
Clinical phenotypes among patients with normal cardiac perfusion using unsupervised learning: a retrospective observational study.
Observational study in EBioMedicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- Risk prediction models for contrast-induced acute kidney injury in patients with acute coronary syndromes: a systematic review and meta-analysis.Frontiers in medicine · 2025Pooled it
- Cardiovascular Phenotypes Across Major Cancer Types: Distribution and Association with All-Cause Mortality in a Retrospective Cohort Study.Diagnostics (Basel, Switzerland) · 2026Article
- Prehospital Risk Stratification Using Unsupervised Machine Learning in STEMI.European journal of clinical investigation · 2026Observational
- Multi-algorithm consensus classification identifies three distinct acute liver failure subtypes with differential treatment responses: a multi-database cohort study.Journal of advanced research · 2026Article
- Unsupervised clustering identifies distinct phenotypes in acute myocardial infarction: insights from the FAST-MI 2015 registry.Frontiers in artificial intelligence · 2026Article
- Artificial Intelligence in Nuclear Cardiology.Journal of clinical medicine · 2025Review
- Respiratory Rehabilitation Index (R2I): Unsupervised Clustering Approach to Identify COPD Subgroups Associated with Rehabilitation Outcomes.Diagnostics (Basel, Switzerland) · 2025Article
- Unsupervised Machine Learning in Identification of Septic Shock Phenotypes and Their In-Hospital Outcomes: A Multicenter Cohort Study.Journal of clinical medicine · 2025Article
- Predicting major cardiac and cerebrovascular events in acute coronary syndrome patients using the thyroid hormone sensitivity index.Frontiers in endocrinology · 2025Article
- Kynurenic Acid Is a Predictive Prognostic Metabolic Marker in ST-Elevation Myocardial Infarction.Cardiovascular therapeutics · 2025Article
- MedAlmighty: enhancing disease diagnosis with large vision model distillation.Frontiers in artificial intelligence · 2025Article
- Article
Corrections and comments
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Authors and funding
24 authors at 12 institutions in 7 countries.
Funding
Abstract
backgroundMyocardial perfusion imaging (MPI) is one of the most common cardiac scans and is used for diagnosis of coronary artery disease and assessment of cardiovascular risk. However, the large majority of MPI patients have normal results. We evaluated whether unsupervised machine learning could identify unique phenotypes among patients with normal scans and whether those phenotypes were associated with risk of death or myocardial infarction.
methodsPatients from a large international multicenter MPI registry (10 sites) with normal perfusion by expert visual interpretation were included in this cohort analysis. The training population included 9849 patients, and external testing population 12,528 patients. Unsupervised cluster analysis was performed, with separate training and external testing cohorts, to identify clusters, with four distinct phenotypes. We evaluated the clinical and imaging features of clusters and their associations with death or myocardial infarction.
findingsPatients in Clusters 1 and 2 almost exclusively underwent exercise stress, while patients in Clusters 3 and 4 mostly required pharmacologic stress. In external testing, the risk for Cluster 4 patients (20.2% of population, unadjusted hazard ratio [HR] 6.17, 95% confidence interval [CI] 4.64-8.20) was higher than the risk associated with pharmacologic stress (HR 3.03, 95% CI 2.53-3.63), or previous myocardial infarction (HR 1.82, 95% CI 1.40-2.36).
interpretationUnsupervised learning identified four distinct phenotypes of patients with normal perfusion scans, with a significant proportion of patients at very high risk of myocardial infarction or death. Our results suggest a potential role for patient phenotyping to improve risk stratification of patients with normal imaging results.
fundingThis work was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R35HL161195 to PS]. The REFINE SPECT database was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R01HL089765 to PS]. MCW was supported by the British Heart Foundation [FS/ICRF/20/26002].
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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.