{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T01:41:47Z","timestamp":1789177307086,"version":"build-2803163510"},"reference-count":30,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T00:00:00Z","timestamp":1671667200000},"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>Postoperative nausea and vomiting (PONV) is a still highly relevant problem and is known to be a distressing side effect in patients. The aim of this study was to develop a machine learning model to predict PONV up to 24 h with fentanyl-based intravenous patient-controlled analgesia (IV-PCA).<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Methods<\/jats:title>\n<jats:p>From July 2019 and July 2020, data from 2,149 patients who received fentanyl-based IV-PCA for analgesia after non-cardiac surgery under general anesthesia were applied to develop predictive models. The rates of PONV at 1 day after surgery were measured according to patient characteristics as well as anesthetic, surgical, or PCA-related factors. All statistical analyses and computations were performed using the R software.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Results<\/jats:title>\n<jats:p>A total of 2,149 patients were enrolled in this study, 337 of whom (15.7%) experienced PONV. After applying the machine-learning algorithm and Apfel model to the test dataset to predict PONV, we found that the area under the receiver operating characteristic curve using logistic regression was 0.576 (95% confidence interval [CI], 0.520\u20130.633), k-nearest neighbor was 0.597 (95% CI, 0.537\u20130.656), decision tree was 0.561 (95% CI, 0.498\u20130.625), random forest was 0.610 (95% CI, 0.552\u20130.668), gradient boosting machine was 0.580 (95% CI, 0.520\u20130.639), support vector machine was 0.649 (95% CI, 0.592\u20130.707), artificial neural network was 0.686 (95% CI, 0.630\u20130.742), and Apfel model was 0.643 (95% CI, 0.596\u20130.690).<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Conclusions<\/jats:title>\n<jats:p>We developed and validated machine learning models for predicting PONV in the first 24 h. The machine learning model showed better performance than the Apfel model in predicting PONV.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0277957","type":"journal-article","created":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T18:44:01Z","timestamp":1671734641000},"page":"e0277957","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":26,"title":["Machine learning for prediction of postoperative nausea and vomiting in patients with intravenous patient-controlled analgesia"],"prefix":"10.1371","volume":"17","author":[{"given":"Jae-Geum","family":"Shim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyoung-Ho","family":"Ryu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eun-Ah","family":"Cho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin Hee","family":"Ahn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun Byeong","family":"Cha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Goeun","family":"Lim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5616-6649","authenticated-orcid":true,"given":"Sung Hyun","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2022,12,22]]},"reference":[{"issue":"5","key":"pone.0277957.ref001","doi-asserted-by":"crossref","first-page":"811","DOI":"10.3346\/jkms.2005.20.5.811","article-title":"A korean predictive model for postoperative nausea and vomiting","volume":"20","author":"DH Choi","year":"2005","journal-title":"J Korean Med Sci"},{"issue":"2","key":"pone.0277957.ref002","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1213\/ANE.0000000000004833","article-title":"Fourth Consensus Guidelines for the Management of Postoperative Nausea and Vomiting.","volume":"131","author":"TJ Gan","year":"2020","journal-title":"Anesth Analg."},{"issue":"1","key":"pone.0277957.ref003","doi-asserted-by":"crossref","first-page":"39","DOI":"10.3344\/kjp.2015.28.1.39","article-title":"Comparison of ramosetron plus dexamethasone with ramosetron alone on postoperative nausea, vomiting, shivering and pain after thyroid surgery","volume":"28","author":"MJ Lee","year":"2015","journal-title":"Korean J Pain"},{"issue":"4","key":"pone.0277957.ref004","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1016\/j.bpa.2017.07.002","article-title":"Opioid-related side effects: Postoperative ileus, urinary retention, nausea and vomiting, and shivering. 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