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Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging.
Williams, Michelle C; Bednarski, Bryan P; Pieszko, Konrad; Miller, Robert J H; Kwiecinski, Jacek; Shanbhag, Aakash; Liang, Joanna X; Huang, Cathleen; Sharir, Tali; Dorbala, Sharmila; Di Carli, Marcelo F; Einstein, Andrew J; Sinusas, Albert J; Miller, Edward J; Bateman, Timothy M; Fish, Mathews B; Ruddy, Terrence D; Acampa, Wanda; Hauser, M Timothy; Kaufmann, Philipp A; Dey, Damini; Berman, Daniel S; Slomka, Piotr J.
Afiliação
  • Williams MC; Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA.
  • Bednarski BP; British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK.
  • Pieszko K; Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA.
  • Miller RJH; Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA.
  • Kwiecinski J; Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA.
  • Shanbhag A; Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada.
  • Liang JX; Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA.
  • Huang C; Department of Interventional Cardiology and Angiology, Institute of Cardiology, Warsaw, Poland.
  • Sharir T; Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA.
  • Dorbala S; Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA.
  • Di Carli MF; Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA.
  • Einstein AJ; Department of Nuclear Cardiology, Assuta Medical Centers, Tel Aviv, and Ben Gurion University of the Negev, Beer Sheba, Israel.
  • Sinusas AJ; Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Brigham and Women's Hospital, Boston, MA, USA.
  • Miller EJ; Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Brigham and Women's Hospital, Boston, MA, USA.
  • Bateman TM; Division of Cardiology, Department of Medicine, and Department of Radiology, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, NY, USA.
  • Fish MB; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA.
  • Ruddy TD; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA.
  • Acampa W; Cardiovascular Imaging Technologies LLC, Kansas City, MO, USA.
  • Hauser MT; Oregon Heart and Vascular Institute, Sacred Heart Medical Center, Springfield, OR, USA.
  • Kaufmann PA; Division of Cardiology, University of Ottawa Heart Institute, Ottawa, ON, Canada.
  • Dey D; Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy.
  • Berman DS; Department of Nuclear Cardiology, Oklahoma Heart Hospital, Oklahoma City, OK, USA.
  • Slomka PJ; Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Zurich, Switzerland.
Eur J Nucl Med Mol Imaging ; 50(9): 2656-2668, 2023 07.
Article em En | MEDLINE | ID: mdl-37067586
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 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%). 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 < 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). 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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doença da Artéria Coronariana / Imagem de Perfusão do Miocárdio Tipo de estudo: Prognostic_studies Limite: Female / Humans / Male Idioma: En Revista: Eur J Nucl Med Mol Imaging Assunto da revista: MEDICINA NUCLEAR Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doença da Artéria Coronariana / Imagem de Perfusão do Miocárdio Tipo de estudo: Prognostic_studies Limite: Female / Humans / Male Idioma: En Revista: Eur J Nucl Med Mol Imaging Assunto da revista: MEDICINA NUCLEAR Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos