{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T03:10:41Z","timestamp":1771643441030,"version":"3.50.1"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,4,24]],"date-time":"2022-04-24T00:00:00Z","timestamp":1650758400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,4,24]],"date-time":"2022-04-24T00:00:00Z","timestamp":1650758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>The machine learning algorithm (MLA) was implemented to establish an optimal model to predict the no reflow (NR) process and in-hospital death that occurred in ST-elevation myocardial infarction (STEMI) patients who underwent primary percutaneous coronary intervention (pPCI).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>The data were obtained retrospectively from 854 STEMI patients who underwent pPCI. MLA was applied to predict the potential NR phenomenon and confirm the in-hospital mortality. A random sampling method was used to split the data into the training (66.7%) and testing (33.3%) sets. The final results were an average of 10 repeated procedures. The area under the curve (AUC) and the associated 95% confidence intervals (CIs) of the receiver operator characteristic were measured.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>A random forest algorithm (RAN) had optimal discrimination for the NR phenomenon with an AUC of 0.7891 (95% CI: 0.7093\u20130.8688) compared with 0.6437 (95% CI: 0.5506\u20130.7368) for the decision tree (CTREE), 0.7488 (95% CI: 0.6613\u20130.8363) for the support vector machine (SVM), and 0.681 (95% CI: 0.5767\u20130.7854) for the neural network algorithm (NNET). The optimal RAN AUC for in-hospital mortality was 0.9273 (95% CI: 0.8819\u20130.9728), for SVM, 0.8935 (95% CI: 0.826\u20130.9611); NNET, 0.7756 (95% CI: 0.6559\u20130.8952); and CTREE, 0.7885 (95% CI: 0.6738\u20130.9033).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>The MLA had a relatively higher performance when evaluating the NR risk and in-hospital mortality in patients with STEMI who underwent pPCI and could be utilized in clinical decision making.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-022-01853-2","type":"journal-article","created":{"date-parts":[[2022,4,24]],"date-time":"2022-04-24T15:02:23Z","timestamp":1650812543000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Machine learning to predict no reflow and in-hospital mortality in patients with ST-segment elevation myocardial infarction that underwent primary percutaneous coronary intervention"],"prefix":"10.1186","volume":"22","author":[{"given":"Lianxiang","family":"Deng","sequence":"first","affiliation":[]},{"given":"Xianming","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Xiaolin","family":"Su","sequence":"additional","affiliation":[]},{"given":"Mei","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Daizheng","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Xiaocong","family":"Zeng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,24]]},"reference":[{"issue":"2","key":"1853_CR1","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1093\/eurheartj\/ehx393","volume":"39","author":"B Ibanez","year":"2018","unstructured":"Ibanez B, James S, Agewall S, Antunes MJ, Bucciarelli-Ducci C, Bueno H, et al. 2017 ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation: The Task Force for the management of acute myocardial infarction in patients presenting with ST-segment elevation of the European Society of Cardiology (ESC). 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