{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T10:38:09Z","timestamp":1793702289430,"version":"4.5.1"},"reference-count":74,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T00:00:00Z","timestamp":1700006400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Background<\/jats:title>\n<jats:p>Machine learning (ML) has shown promise in improving the risk prediction in non-invasive cardiovascular imaging, including SPECT MPI and coronary CT angiography. However, most algorithms used remain black boxes to clinicians in how they compute their predictions. Furthermore, objective consideration of the multitude of available clinical data, along with the visual and quantitative assessments from CCTA and SPECT, are critical for optimal patient risk stratification. We aim to provide an explainable ML approach to predict MACE using clinical, CCTA, and SPECT data.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Methods<\/jats:title>\n<jats:p>Consecutive patients who underwent clinically indicated CCTA and SPECT myocardial imaging for suspected CAD were included and followed up for MACEs. A MACE was defined as a composite outcome that included all-cause mortality, myocardial infarction, or late revascularization. We employed an Automated Machine Learning (AutoML) approach to predict MACE using clinical, CCTA, and SPECT data. Various mainstream models with different sets of hyperparameters have been explored, and critical predictors of risk are obtained using explainable techniques on the global and patient levels. Ten-fold cross-validation was used in training and evaluating the AutoML model.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Results<\/jats:title>\n<jats:p>A total of 956 patients were included (mean age 61.1 \u00b114.2 years, 54% men, 89% hypertension, 81% diabetes, 84% dyslipidemia). Obstructive CAD on CCTA and ischemia on SPECT were observed in 14% of patients, and 11% experienced MACE. ML prediction\u2019s sensitivity, specificity, and accuracy in predicting a MACE were 69.61%, 99.77%, and 96.54%, respectively. The top 10 global predictive features included 8 CCTA attributes (segment involvement score, number of vessels with severe plaque \u226570, \u226550% stenosis in the left marginal coronary artery, calcified plaque, \u226550% stenosis in the left circumflex coronary artery, plaque type in the left marginal coronary artery, stenosis degree in the second obtuse marginal of the left circumflex artery, and stenosis category in the marginals of the left circumflex artery) and 2 clinical features (past medical history of MI or left bundle branch block, being an ever smoker).<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Conclusion<\/jats:title>\n<jats:p>ML can accurately predict risk of developing a MACE in patients suspected of CAD undergoing SPECT MPI and CCTA. ML feature-ranking can also show, at a sample- as well as at a patient-level, which features are key in making such a prediction.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0291451","type":"journal-article","created":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T18:33:24Z","timestamp":1700073204000},"page":"e0291451","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":16,"title":["Patient-level explainable machine learning to predict major adverse cardiovascular events from SPECT MPI and CCTA imaging"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5481-696X","authenticated-orcid":true,"given":"Fares","family":"Alahdab","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Radwa","family":"El Shawi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed Ibrahim","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yushui","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mouaz","family":"Al-Mallah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2023,11,15]]},"reference":[{"key":"pone.0291451.ref001","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1038\/s41569-021-00527-2","article-title":"Applications of artificial intelligence in cardiovascular imaging","volume":"18","author":"M Sermesant","year":"2021","journal-title":"Nat Rev Cardiol"},{"key":"pone.0291451.ref002","doi-asserted-by":"crossref","DOI":"10.1161\/CIRCIMAGING.117.005614","article-title":"Machine Learning Approaches in Cardiovascular Imaging.","volume":"10","author":"M Henglin","year":"2017","journal-title":"Circ Cardiovasc Imaging"},{"key":"pone.0291451.ref003","doi-asserted-by":"crossref","first-page":"1975","DOI":"10.1093\/eurheartj\/ehy404","article-title":"Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging","volume":"40","author":"SJ Al\u2019Aref","year":"2019","journal-title":"Eur Heart J"},{"key":"pone.0291451.ref004","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1093\/eurheartj\/ehz425","article-title":"2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes","volume":"41","author":"J Knuuti","year":"2020","journal-title":"Eur Heart J"},{"key":"pone.0291451.ref005","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1056\/NEJMoa1805971","article-title":"Coronary CT Angiography and 5-Year Risk of Myocardial Infarction","volume":"379","author":"S-H Investigators","year":"2018","journal-title":"N Engl J Med"},{"key":"pone.0291451.ref006","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.ijcard.2022.04.025","article-title":"Incremental prognostic value of spect over CCTA.","volume":"358","author":"A Javaid","year":"2022","journal-title":"Int J Cardiol"},{"key":"pone.0291451.ref007","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1016\/j.amjmed.2021.12.010","article-title":"Added Prognostic Value of Plaque Burden to Computed Tomography Angiography and Myocardial Perfusion Imaging in Patients with Diabetes","volume":"135","author":"T Alnabelsi","year":"2022","journal-title":"Am J Med"},{"key":"pone.0291451.ref008","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.atherosclerosis.2021.08.032","article-title":"Added prognostic value of plaque burden to computed tomography angiography and myocardial perfusion imaging","volume":"334","author":"AI Ahmed","year":"2021","journal-title":"Atherosclerosis"},{"key":"pone.0291451.ref009","first-page":"40","volume-title":"Automated machine learning: Techniques and frameworks. 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