Unsupervised learning for automated detection of coronary artery disease subgroups

Alyssa M. Flores, Alejandro Schuler, Anne Verena Eberhard, Jeffrey W. Olin, John P. Cooke, Nicholas J. Leeper, Nigam H. Shah, Elsie G. Ross

Research output: Contribution to journalArticlepeer-review

Abstract

BACKGROUND: The promise of precision population health includes the ability to use robust patient data to tailor prevention and care to specific groups. Advanced analytics may allow for automated detection of clinically informative subgroups that account for clinical, genetic, and environmental variability. This study sought to evaluate whether unsupervised machine learning approaches could interpret heterogeneous and missing clinical data to discover clinically important coronary artery disease subgroups. METHODS AND RESULTS: The Genetic Determinants of Peripheral Arterial Disease study is a prospective cohort that includes individuals with newly diagnosed and/or symptomatic coronary artery disease. We applied generalized low rank modeling and K-means cluster analysis using 155 phenotypic and genetic variables from 1329 participants. Cox proportional hazard models were used to examine associations between clusters and major adverse cardiovascular and cerebrovascular events and all-cause mortality. We then compared performance of risk stratification based on clusters and the American College of Cardiology/American Heart Association pooled cohort equations. Unsupervised analysis identified 4 phenotypically and prognostically distinct clusters. All-cause mortality was highest in cluster 1 (oldest/most comorbid; 26%), whereas major adverse cardiovascular and cerebrovascular event rates were highest in cluster 2 (youngest/multiethnic; 41%). Cluster 4 (middle-aged/ healthiest behaviors) experienced more incident major adverse cardiovascular and cerebrovascular events (30%) than cluster 3 (middle-aged/lowest medication adherence; 23%), despite apparently similar risk factor and lifestyle profiles. In comparison with the pooled cohort equations, cluster membership was more informative for risk assessment of myocardial infarction, stroke, and mortality. CONCLUSIONS: Unsupervised clustering identified 4 unique coronary artery disease subgroups with distinct clinical trajectories. Flexible unsupervised machine learning algorithms offer the ability to meaningfully process heterogeneous patient data and provide sharper insights into disease characterization and risk assessment.

Original languageEnglish (US)
Article numbere021976
JournalJournal of the American Heart Association
Volume10
Issue number23
DOIs
StatePublished - Dec 7 2021

Keywords

  • Cluster analysis
  • Coronary artery disease
  • Machine learning
  • Phenotype discovery

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

Fingerprint

Dive into the research topics of 'Unsupervised learning for automated detection of coronary artery disease subgroups'. Together they form a unique fingerprint.

Cite this