Prognostic Value of Combined Clinical and Myocardial Perfusion Imaging Data Using Machine Learning

OBJECTIVES This study evaluated the added predictive value of combining clinical information and, myocardial. Perfusion single photon emission computed tomography (SPECT imaging (MPI) data using machine learning (ML) predict major adverse cardiac events (MACE). BACKGROUND Traditionally, prognostication by MPI has relied on visual or quantitative analysis of images without objective consideration of the clinical data. ML permits a large number of variables to be considered in combination and at a level of complexity beyond the human clinical reader. METHODS A total of 2,619 consecutive patients (48% men; 62 +/- 13 years of age) who underwent exercise (38%) or Pharmacological stress (62%) with high-speed SPECT MPI were monitored for MACE. Twenty eight clinical variables, 17 stress test variables, and 25 imaging variables (including total perfusion deficit [TPD]) were recorded. Areas under the receiver operating characteristic curve (AUC) for MACE prediction were compared among: 1) ML with all available data (ML-combined); 2) ML with only imaging data (ML-imaging); 3) 5-P Mt Kate visual diagnosis (Physician [MD] diagnosis); and 4) automated quantitative imaging analysis (stress TPD and ischemic TPD). ML involved automated variable selection by information gain ranking, model building with a boosted ensemble algorithm, and 10 fold stratified cross validation. RESULTS During follow-up (3.2 +/- 0.6 years), 239 patients (9.1%) had MACE. MACE prediction was significantly higher for ML combined than ML imaging (AUG: 0.81 vs. 0.78; p < 0.01). ML combined also had higher predictive accuracy compared with MD diagnosis, automated stress TPD, and automated ischemic TPD (AUC: 0.81 vs. 0.65 vs. 0.73 vs. 0.71, respectively; p < 0.01 for all) Risk reclassification for ML combined compared with visual MD diagnosis was 26% (p < 0.001). CONCLUSIONS ML combined with both clinical and imaging data variables was found to have high predictive accuracy for 3-year risk of MACE and was superior to existing visual or automated perfusion assessments. ML could allow integration of clinical and imaging data for personalized MACE risk computations in patients undergoing SPECT MPI. (C) 2018 by the American College of Cardiology Foundation.