Machine Learning to Predict Abnormal Myocardial Perfusion from Pre-test Features

作者：R. H. Miller, M. T. Hauser, Tali Sharir, A. J. Einstein, M. Fish, T. D. Ruddy, Philipp A. Kaufmann, A. Sinusas, E. Miller, T. Bateman, S. Dorbala, M. D. Di Carli, Cathleen Huang, Joanna X. Liang, Donghee Han, D. Dey, D. Berman, P. Slomka · 发表于：Journal of Nuclear Cardiology · 年份：2022 · DOI：10.1007/s12350-022-03012-6 · 被引用次数：22 · 研究领域：Medicine

Accurately predicting which patients will have abnormal perfusion on MPI based on pre-test clinical information may help physicians make test selection decisions. We developed and validated a machine learning (ML) model for predicting abnormal perfusion using pre-test features. We included consecutive patients who underwent SPECT MPI, with 20,418 patients from a multi-center (5 sites) international registry in the training population and 9019 patients (from 2 separate sites) in the external testing population. The ML (extreme gradient boosting) model utilized 30 pre-test features to predict the presence of abnormal myocardial perfusion by expert visual interpretation. In external testing, the ML model had higher prediction performance for abnormal perfusion (area under receiver-operating characteristic curve [AUC] 0.762, 95% CI 0.750–0.774) compared to the clinical CAD consortium (AUC 0.689) basic CAD consortium (AUC 0.657), and updated Diamond-Forrester models (AUC 0.658, p < 0.001 for all). Calibration (validation of the continuous risk prediction) was superior for the ML model (Brier score 0.149) compared to the other models (Brier score 0.165 to 0.198, all p < 0.001). ML can predict abnormal myocardial perfusion using readily available pre-test information. This model could be used to help guide physician decisions regarding non-invasive test selection.