Key result
Unsupervised machine learning clustering identified distinct patient subsets with significantly different rates of death, reinfarction, and major bleeding at 1 year after acute coronary syndrome.
Why the study?
ACS characterization and management remain challenging, and it is unclear whether currently available clinical and procedural features suffice to inform adequate decision making.
Can unsupervised machine learning identify specific patient subsets with different clinical outcomes among patients discharged after an acute coronary syndrome?
Observational (n=23,270)
Yes
Can unsupervised machine learning identify specific patient subsets with different clinical outcomes among patients discharged after an acute coronary syndrome?
Unsupervised machine learning can identify distinct clinical phenotypes in ACS patients that are associated with significantly different 1-year cardiovascular outcomes, potentially improving risk stratification.
Characterization and management of patients admitted for acute coronary syndromes (ACS) remain challenging, and it is unclear whether currently available clinical and procedural features can suffice to inform adequate decision making. We aimed to explore the presence of specific subsets among patients with ACS. The details on patients discharged after ACS were obtained by querying an extensive multicenter registry and detailing patient features, as well as management details. The clinical outcomes included fatal and nonfatal cardiovascular events at 1-year follow-up. After missing data imputation, 2 unsupervised machine learning approaches (k-means and Clustering Large Applications [CLARA]) were used to generate separate clusters with different features. Bivariate- and multivariable-adjusted analyses were performed to compare the different clusters for clinical outcomes. A total of 23,270 patients were included, with 12,930 cases (56%) of ST-elevation myocardial infarction (STEMI). K-means clustering identified 2 main clusters: a first 1 including 21,998 patients (95%) and a second 1 including 1,282 subjects (5%), with equal distribution for STEMI. CLARA generated 2 main clusters: a first 1 including 11,268 patients (48%) and a second 1 with 12,002 subjects (52%). Notably, the STEMI distribution was significantly different in the CLARA-generated clusters. The clinical outcomes were significantly different across clusters, irrespective of the originating algorithm, including death reinfarction and major bleeding, as well as their composite. In conclusion, unsupervised machine learning can be leveraged to explore the patterns in ACS, potentially highlighting specific patient subsets to improve risk stratification and management.
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Mohammadi et al. (2023) conducted an observational in Acute Coronary Syndrome (n=23,270). Unsupervised machine learning clusters (k-means and CLARA) vs. Between-cluster comparison was evaluated on Fatal and nonfatal cardiovascular events. Unsupervised machine learning clustering identified distinct patient subsets with significantly different rates of death, reinfarction, and major bleeding at 1 year after acute coronary syndrome.
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