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Abstract / Description of output
Purpose
Patients with known coronary artery disease (CAD) comprise a heterogenous population with varied clinical and imaging characteristics. Unsupervised machine learning can identify new risk phenotypes in an unbiased fashion. We use cluster analysis to risk-stratify patients with known CAD undergoing single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI).
Methods
From 37,298 patients in the REFINE SPECT registry, we identified 9,221 patients with known coronary artery disease. Unsupervised machine learning was performed using clinical (23), acquisition (17), and image analysis (24) parameters from 4,774 patients (internal cohort) and validated with 4,447 patients (external cohort). Risk stratification for all-cause mortality was compared to stress total perfusion deficit (
Results
Three clusters were identified, with patients in Cluster 3 having a higher body mass index, more diabetes mellitus and hypertension, and less likely to be male, have dyslipidemia, or undergo exercise stress imaging (p
Conclusions
Our unsupervised cluster analysis in patients with known CAD undergoing SPECT MPI identified three distinct phenotypic clusters and predicted all-cause mortality better than ischemia alone.
Patients with known coronary artery disease (CAD) comprise a heterogenous population with varied clinical and imaging characteristics. Unsupervised machine learning can identify new risk phenotypes in an unbiased fashion. We use cluster analysis to risk-stratify patients with known CAD undergoing single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI).
Methods
From 37,298 patients in the REFINE SPECT registry, we identified 9,221 patients with known coronary artery disease. Unsupervised machine learning was performed using clinical (23), acquisition (17), and image analysis (24) parameters from 4,774 patients (internal cohort) and validated with 4,447 patients (external cohort). Risk stratification for all-cause mortality was compared to stress total perfusion deficit (
Results
Three clusters were identified, with patients in Cluster 3 having a higher body mass index, more diabetes mellitus and hypertension, and less likely to be male, have dyslipidemia, or undergo exercise stress imaging (p
Conclusions
Our unsupervised cluster analysis in patients with known CAD undergoing SPECT MPI identified three distinct phenotypic clusters and predicted all-cause mortality better than ischemia alone.
Original language | English |
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Journal | European Journal of Nuclear Medicine and Molecular Imaging |
Early online date | 17 Apr 2023 |
DOIs | |
Publication status | E-pub ahead of print - 17 Apr 2023 |
Keywords / Materials (for Non-textual outputs)
- Machine learning
- SPECT myocardial perfusion
- Coronary artery disease
- Cluster analysis
- CARDIOVASCULAR RISK
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Incidental coronary calcification on thoracic computed tomography
Williams, M., Mills, N. & Newby, D.
1/02/21 → 31/01/26
Project: Research