| Title: | AI-based cluster analysis enables outcomes prediction among patients with increased LVM |
| Journal: | Frontiers in Cardiovascular Medicine |
| Published: | 2 Sep 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39285853/ |
| DOI: | https://doi.org/10.3389/fcvm.2024.1357305 |
Publication 13039
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Abstract
Background: The traditional classification of left ventricular hypertrophy (LVH), which relies on left ventricular geometry, fails to correlate with outcomes among patients with increased LV mass (LVM).</p>
Objectives: To identify unique clinical phenotypes of increased LVM patients using unsupervised cluster analysis, and to explore their association with clinical outcomes.</p>
Methods: Among the UK Biobank participants, increased LVM was defined as LVM index ≥72 g/m2 for men, and LVM index ≥55 g/m2 for women. Baseline demographic, clinical, and laboratory data were collected from the database. Using Ward's minimum variance method, patients were clustered based on 27 variables. The primary outcome was a composite of all-cause mortality with heart failure (HF) admissions, ventricular arrhythmia, and atrial fibrillation (AF). Cox proportional hazard model and Kaplan-Meier survival analysis were applied.</p>
Results: Increased LVM was found in 4,255 individuals, with an average age of 64 ± 7 years. Of these patients, 2,447 (58%) were women. Through cluster analysis, four distinct subgroups were identified. Over a median follow-up period of 5 years (IQR: 4-6), 100 patients (2%) died, 118 (2.8%) were admissioned due to HF, 29 (0.7%) were admissioned due to VA, and 208 (5%) were admissioned due to AF. Univariate Cox analysis demonstrated significantly elevated risks of major events for patients in the 2nd (HR = 1.6; 95% CI 1.2-2.16; p < .001), 3rd (HR = 2.04; 95% CI 1.49-2.78; p < .001), and 4th (HR = 2.64; 95% CI 1.92-3.62; p < .001) clusters compared to the 1st cluster. Further exploration of each cluster revealed unique clinical phenotypes: Cluster 2 comprised mostly overweight women with a high prevalence of chronic lung disease; Cluster 3 consisted mostly of men with a heightened burden of comorbidities; and Cluster 4, mostly men, exhibited the most abnormal cardiac measures.</p>
Conclusions: Unsupervised cluster analysis identified four outcomes-correlated clusters among patients with increased LVM. This phenotypic classification holds promise in offering valuable insights regarding clinical course and outcomes of patients with increased LVM.</p>
5 Authors
- Ranel Loutati
- Yotam Kolben
- David Luria
- Offer Amir
- Yitschak Biton
1 Application
| Application ID | Title |
|---|---|
| 96272 | A Deep Learning model for the classification of cardiac amyloidosis among patients with Left Ventricular Hypertrophy. |
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