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ORIGINAL RESEARCH article

Front. Cardiovasc. Med.
Sec. General Cardiovascular Medicine
Volume 11 - 2024 | doi: 10.3389/fcvm.2024.1357305
This article is part of the Research Topic Modalities, Applications and Impact of Artificial Intelligence in Cardiology View all articles

AI-based Cluster Analysis Enables Outcomes Prediction Among Patients with Increased LVM

Provisionally accepted
Ranel Loutati Ranel Loutati 1,2Yotam Kolben Yotam Kolben 1,2David Luria David Luria 1,2*Offer Amir Offer Amir 1,2*Yitschak Biton Yitschak Biton 1,2*
  • 1 Heart Institute, Hadassah Medical Center, Jerusalem, Jerusalem, Israel
  • 2 Hebrew University of Jerusalem, Jerusalem, Jerusalem, Israel

The final, formatted version of the article will be published soon.

    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). Objectives: To identify unique clinical phenotypes of increased LVM patients using unsupervised cluster analysis, and to explore their association with clinical outcomes. 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. 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 2 nd (HR = 1.6; 95% CI 1.2-2.16; p<.001), 3 rd (HR = 2.04; 95% CI 1.49-2.78; p<.001), and 4 th (HR = 2.64; 95% CI 1.92-3.62; p<.001) clusters compared to the 1 st 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. 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.

    Keywords: artificial intelligence, Cluster analysis, Left venticular hypertrophy, cardiovascular outcome assessment, Unsupervised learning

    Received: 17 Dec 2023; Accepted: 04 Jun 2024.

    Copyright: © 2024 Loutati, Kolben, Luria, Amir and Biton. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    David Luria, Heart Institute, Hadassah Medical Center, Jerusalem, 9112001, Jerusalem, Israel
    Offer Amir, Heart Institute, Hadassah Medical Center, Jerusalem, 9112001, Jerusalem, Israel
    Yitschak Biton, Heart Institute, Hadassah Medical Center, Jerusalem, 9112001, Jerusalem, Israel

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.