Acute cardiac care & 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. This phenotypic classification may facilitate personalized management and refined prognostic assessment in this high-risk population.
Study context
- Population
- Patients admitted after out-of-hospital cardiac arrest (OHCA) related to myocardial infarction (MI)
- Participants
- 478 patients
- Exposure
- Three clusters identified by k-means clustering, compared with cluster 1
- Cluster 1n = 260reference
- Cluster 2n = 118
- Cluster 3n = 100
- Outcome
- Adverse in-hospital events:
- All-cause mortality
- Bleeding
- Stent thrombosis
- Follow-up
- In-hospital
Methodological detailsunsupervised machine learning analysis using k-means clustering
- Unsupervised machine learning analysis using k-means clustering
Main finding
As reported in the abstract
- Most frequent in cluster 3
Observational data: associations, not effects.
Further findings
| Outcome | Cluster 1 | Cluster 2 | Cluster 3 | Hazard ratio (95% CI) | |
|---|---|---|---|---|---|
| All-cause mortality · 90 daysCluster 2vs Cluster 1 | 22.5% | 53.0% | 77.2% | 2.97 (2.07–4.26) | |
| All-cause mortality · 90 daysCluster 3vs Cluster 1 | 6.75 (4.74–9.60) | ||||
| 0.10.31310Hazard ratio (log scale) |
- All-cause mortality: p < 0.001 across groups
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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