Cardiac arrest & resuscitation
Phenotypic clustering of myocardial infarction complicated by out-of-hospital cardiac arrest using unsupervised machine learning
Authors' conclusion
Unsupervised machine learning identified three phenotypes among patients with MI-related OHCA associated with distinct outcomes.
Study context
- Population
- Patients admitted after out-of-hospital cardiac arrest (OHCA) related to myocardial infarction (MI)
- Participants
- 478 patients
- Exposure
- Clusters 2 and 3 vs cluster 1
- Cluster 1n = 260reference
- Cluster 2n = 118
- Cluster 3n = 100
- Outcome
- All-cause mortality
- Follow-up
- 90 days
Methodological detailsunsupervised machine learning, k-means clustering
- Unsupervised machine learning
- K-means clustering
Main finding
As reported in the abstract
Each group vs cluster 1Hazard ratio (95% CI)
Cluster 2
2.97(2.07–4.26)
Cluster 3
6.75(4.74–9.60)
Observational data: associations, not effects.
Safety
In-hospital adverse events (all-cause mortality, bleeding, stent thrombosis)
- Most frequent in cluster 3
Original publication
Phenotypic clustering of myocardial infarction complicated by out-of-hospital cardiac arrest using unsupervised machine learning. Resuscitation. Published 5 March 2026. doi:10.
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