Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging.
Cardiovascular risk
Cluster analysis
Coronary artery disease
Machine learning
SPECT myocardial perfusion
Journal
European journal of nuclear medicine and molecular imaging
ISSN: 1619-7089
Titre abrégé: Eur J Nucl Med Mol Imaging
Pays: Germany
ID NLM: 101140988
Informations de publication
Date de publication:
07 2023
07 2023
Historique:
received:
05
01
2023
accepted:
29
03
2023
medline:
5
7
2023
pubmed:
18
4
2023
entrez:
17
4
2023
Statut:
ppublish
Résumé
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). From 37,298 patients in the REFINE SPECT registry, we identified 9221 patients with known coronary artery disease. Unsupervised machine learning was performed using clinical (23), acquisition (17), and image analysis (24) parameters from 4774 patients (internal cohort) and validated with 4447 patients (external cohort). Risk stratification for all-cause mortality was compared to stress total perfusion deficit (< 5%, 5-10%, ≥10%). 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 < 0.001 for all). In the external cohort, during median follow-up of 2.6 [0.14, 3.3] years, all-cause mortality occurred in 312 patients (7%). Cluster analysis provided better risk stratification for all-cause mortality (Cluster 3: hazard ratio (HR) 5.9, 95% confidence interval (CI) 4.0, 8.6, p < 0.001; Cluster 2: HR 3.3, 95% CI 2.5, 4.5, p < 0.001; Cluster 1, reference) compared to stress total perfusion deficit (≥10%: HR 1.9, 95% CI 1.5, 2.5 p < 0.001; < 5%: reference). 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.
Identifiants
pubmed: 37067586
doi: 10.1007/s00259-023-06218-z
pii: 10.1007/s00259-023-06218-z
pmc: PMC10317876
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2656-2668Subventions
Organisme : NHLBI NIH HHS
ID : R01HL089765
Pays : United States
Organisme : NHLBI NIH HHS
ID : R35HL16119
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01HL089765
Pays : United States
Organisme : NHLBI NIH HHS
ID : R35HL16119
Pays : United States
Organisme : British Heart Foundation
ID : FS/ICRF/20/26002
Pays : United Kingdom
Informations de copyright
© 2023. The Author(s).
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