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Clinical phenotypes among patients that underwent cardiac resynchronization therapy using unsupervised learning
Giovane Leal de Azevedo Junior1, Noah Painter2, Zhuo He3
1Hospital Universitário Antônio Pedro - Ebserh, Universidade Federal Fluminense, Niterói, Brazil.
Nuclear Medicine Communications
|October 8, 2025
Summary
Cardiac resynchronization therapy (CRT) response varies. Unsupervised learning identified patient phenotypes, revealing that substantial cardiac fibrosis predicts poor CRT response, suggesting tailored selection criteria can improve outcomes.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Cardiac resynchronization therapy (CRT) is effective for heart failure with left ventricular dyssynchrony (LVdys).
- However, 30-40% of patients do not respond to CRT.
- Identifying non-responders is crucial for optimizing treatment.
Purpose of the Study:
- To utilize unsupervised learning to identify patient phenotypes with improved CRT response rates.
- To characterize patient groups based on clinical and imaging data for better treatment selection.
Main Methods:
- Unsupervised learning integrating gated single-photon emission computed tomography (SPECT) was employed.
- Hierarchical clustering analysis grouped 217 patients using 49 pretreatment variables.
- Cardiac fibrosis and LVdys were quantified using gated SPECT phase analysis.
Main Results:
- Three patient phenotypes were identified.
- Two phenotypes showed high CRT response rates (86.2% and 87.0%) with distinct characteristics (LVdys, fibrosis, heart dilation).
- A third phenotype with moderate LVdys, substantial fibrosis, and dilated heart exhibited poor CRT response (55.9%).
Conclusions:
- Phase analysis of gated SPECT aids in characterizing CRT responders by assessing dyssynchrony, fibrosis, and remodeling.
- Patients with significant cardiac fibrosis benefit less from CRT.
- Customized CRT selection criteria using gated SPECT can enhance patient response rates.
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