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American Heart Association

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Final ID: MDP697

Longitudinal Trajectory-Based Classification of Heart Failure with preserved Ejection Fraction: A Machine Learning Approach

Abstract Body (Do not enter title and authors here): Background
Heart failure (HF) with preserved ejection fraction (HFpEF) is a complex syndrome characterized by heterogeneous pathophysiology and dynamic changes in cardiac structure and function. Current research predominantly focuses on left ventricular ejection fraction (LVEF) or left ventricular structures with predefined categories. This study hypothesized that applying unsupervised methodologies to longitudinal echocardiography will reveal distinct trajectory patterns in cardiac structure and function related to clinical outcomes in HFpEF patients.

Methods
This retrospective cohort study, conducted at UC Davis Medical Center (2014 to 2022), consisted of adult patients with HFpEF. The inclusion criteria included ICD HF codes, a left ventricular ejection fraction (LVEF) ≥50%, and a Brain natriuretic peptide (BNP) ≥100 pg/ml or N-terminal-pro BNP (NT-proBNP) ≥225 pg/ml. Longitudinal data were analyzed using unsupervised machine learning, including UMAP for dimensionality reduction and Gaussian Mixture Models for clustering. Primary and secondary outcomes included all-cause mortality and changes in BNP levels, respectively.

Results
The study included 1,626 patients. The cohort had a median age of 69 years, was 57.5% female, and 51.8% white. Three distinct echocardiographic trajectory patterns emerged (p<.05) among the HFpEF cohort. Trajectory #1 (n=568) exhibited a high-stable pattern with the highest mortality rates, Trajectory #2 (n=512) showed a high-decreasing pattern with high mortality rates, and Trajectory #3 (n=546) had a low-increasing pattern with the lowest mortality rates. Trajectories varied significantly in baseline characteristics and longitudinal echocardiographic changes, with Trajectory #3 having a lower baseline left ventricular mass index (p<.001) and a higher mean arterial pressure (p=.001) compared to the other trajectories. Kaplan-Meier survival analysis indicated significant differences in survival among the trajectories (p=.004).

Conclusion
The study utilized unsupervised analysis of longitudinal echocardiographic data to identify three distinct HFpEF trajectories, each associated with unique clinical features and outcomes. These findings underscore the heterogeneity of HFpEF and highlight the potential trajectory-based patient stratification to improve targeted intervention strategies. Future validation in larger, multicenter cohorts is warranted to enhance HFpEF management and patient prognosis.
  • Romero, Erick  ( UC Davis Medical Center , Sacramento , California , United States )
  • Patel, Dev  ( UC Davis Medical Center , Sacramento , California , United States )
  • Cadeiras, Martin  ( UC Davis Medical Center , Sacramento , California , United States )
  • Author Disclosures:
    Erick Romero: DO NOT have relevant financial relationships | Dev Patel: DO NOT have relevant financial relationships | Martin Cadeiras: No Answer
Meeting Info:

Scientific Sessions 2024

2024

Chicago, Illinois

Session Info:

Deep Heart Learning: Technological Advances in Heart Failure

Saturday, 11/16/2024 , 02:50PM - 04:15PM

Moderated Digital Poster Session

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