Subgrouping patients with ischemic heart disease by means of the Markov cluster algorithm
Amalie D Haue1,2, Peter C Holm1, Karina Banasik1
1Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
Insights
Unsupervised clustering identified 31 distinct patient subgroups in ischemic heart disease (IHD). These clusters reveal varied risks for new ischemic events and mortality, aiding personalized treatment strategies.
Area of Science:
- Cardiology
- Computational Biology
- Genetics
Background:
- Ischemic heart disease (IHD) presents diverse clinical trajectories.
- Identifying distinct patient subgroups is crucial for tailored management.
- Unsupervised clustering offers a method to uncover hidden patterns in complex diseases.
Purpose of the Study:
- To apply unsupervised clustering to identify clinically relevant multimorbidity clusters in IHD patients.
- To characterize these clusters based on clinical features, outcomes, and genetic predispositions.
- To stratify IHD patients into subgroups with similar characteristics and prognoses.
Main Methods:
- Utilized a cohort of 72,249 IHD patients undergoing coronary angiography (CAG) or CCTA.
- Applied the Markov Cluster algorithm to patient diagnosis codes (n=3046) to form clusters.
- Characterized clusters using Cox regressions for ischemic events and mortality, and enrichment analysis for phenotypes and lab results.
Main Results:
- Identified 31 distinct patient clusters (C1-31).
- Seven clusters showed significantly altered risks for new ischemic events.
- 18 and 23 clusters exhibited modified risks for non-IHD and all-cause mortality, respectively.
- Significant differences in laboratory test results and enriched cardiovascular/inflammatory diseases were observed across clusters.
- Increased polygenic risk scores were noted in 15 clusters.
Conclusions:
- Unsupervised clustering effectively stratifies ischemic heart disease patients into subgroups.
- These subgroups exhibit distinct clinical features and associated outcomes.
- This stratification aids in understanding IHD heterogeneity and personalizing patient care.
Background:
Ischemic heart disease (IHD) is heterogeneous with respect to onset, burden of symptoms, and disease progression. We hypothesized that unsupervised clustering analysis could facilitate identification of distinct and clinically relevant multimorbidity clusters.
Methods:
We included IHD patients who underwent coronary angiography (CAG) or coronary computed tomography angiography (CCTA) between 2004 and 2016 and used the earliest procedure as the index date. Patient health records were obtained from the Danish National Patient Registry, the Danish National Prescription Registry, and two in-hospital laboratory database systems. Genetic data were obtained from the Copenhagen Hospital Biobank. Using registered pre-index diagnosis codes (n = 3046), patients were clustered by application of the Markov Cluster algorithm. Multimorbidity clusters were then characterized using Cox regressions (new ischemic events, non-IHD mortality, and all-cause mortality) and enrichment analysis to explore both risks and phenotypical characteristics.
Results:
In a cohort of 72,249 patients with IHD (mean age 63.9 years, 63.1% males), 31 distinct clusters (C1-31, 67,136 patients) are identified. Comparing each cluster to the 30 others, seven clusters (9,590 patients) have significantly higher or lower risk of new ischemic events (five and two clusters, respectively). A total of 18 clusters (35,982 patients) have higher or lower risk of death from non-IHD causes (12 and six clusters, respectively), and 23 clusters have a statistically significant higher or lower risk for all-cause mortality. Cardiovascular or inflammatory diseases are commonly enriched in clusters (13). Distributions for 24 laboratory test results differ significantly across clusters. Polygenic risk scores are increased in a total of 15 clusters (48.4%).
Conclusions:
Based on prior disease profiles, unsupervised clustering robustly stratify patients with IHD in subgroups with similar clinical features and outcomes.
More Related Videos
05:16Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
Published on: June 10, 2025
08:51Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
Published on: September 20, 2024
Related Concept Videos
01:17Ischemic Heart Disease: Overview
Atherosclerosis, the primary malefactor, orchestrates this dangerous condition. It manifests as the accumulation of fatty deposits, akin to insidious plaques, within arterial walls. As time elapses, these plaques metamorphose, hardening and...
01:30Acute Coronary Syndrome III: Diagnostic Studies
01:30Heart Failure IV: Classification and Diagnostic Evaluation
01:30Coronary Artery Disease I: Introduction
01:30Coronary Artery Disease III: Clinical Manifestations
01:27Angina II: Classification