Machine learning predicts per-vessel early coronary revascularization after fast myocardial perfusion SPECT: results from multicentre REFINE SPECT registry

• Published in: European heart journal cardiovascular imaging 2020-05, Vol.21 (5), p.549-559
• Main Authors: Hu, Lien-Hsin, Betancur, Julian, Sharir, Tali, Einstein, Andrew J, Bokhari, Sabahat, Fish, Mathews B, Ruddy, Terrence D, Kaufmann, Philipp A, Sinusas, Albert J, Miller, Edward J, Bateman, Timothy M, Dorbala, Sharmila, Di Carli, Marcelo, Germano, Guido, Commandeur, Frederic, Liang, Joanna X, Otaki, Yuka, Tamarappoo, Balaji K, Dey, Damini, Berman, Daniel S, Slomka, Piotr J
• Format: Article
• Language: English
• Subjects: Original
• ISSN: 2047-2404; 2047-2412
• DOI: 10.1093/ehjci/jez177

Abstract Aims To optimize per-vessel prediction of early coronary revascularization (ECR) within 90 days after fast single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) using machine learning (ML) and introduce a method for a patient-specific explanation of ML results in a clinical setting. Methods and results A total of 1980 patients with suspected coronary artery disease (CAD) underwent stress/rest 99mTc-sestamibi/tetrofosmin MPI with new-generation SPECT scanners were included. All patients had invasive coronary angiography within 6 months after SPECT MPI. ML utilized 18 clinical, 9 stress test, and 28 imaging variables to predict per-vessel and per-patient ECR with 10-fold cross-validation. Area under the receiver operator characteristics curve (AUC) of ML was compared with standard quantitative analysis [total perfusion deficit (TPD)] and expert interpretation. ECR was performed in 958 patients (48%). Per-vessel, the AUC of ECR prediction by ML (AUC 0.79, 95% confidence interval (CI) [0.77, 0.80]) was higher than by regional stress TPD (0.71, [0.70, 0.73]), combined-view stress TPD (AUC 0.71, 95% CI [0.69, 0.72]), or ischaemic TPD (AUC 0.72, 95% CI [0.71, 0.74]), all P