{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T23:32:16Z","timestamp":1786577536367,"version":"build-2736575974"},"reference-count":69,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2024,9,21]],"date-time":"2024-09-21T00:00:00Z","timestamp":1726876800000},"content-version":"vor","delay-in-days":366,"URL":"http:\/\/www.sagepub.com\/licence-information-for-chorus"}],"funder":[{"DOI":"10.13039\/100013130","name":"Center for Neuroscience and Regenerative Medicine","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100013130","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Clinical Research Operations Group"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["The American Surgeon\u2122"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>A steadily rising opioid pandemic has left the US suffering significant social, economic, and health crises. Machine learning (ML) domains have been utilized to predict prolonged postoperative opioid (PPO) use. This systematic review aims to compile all up-to-date studies addressing such algorithms\u2019 use in clinical practice.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We searched PubMed\/MEDLINE, EMBASE, CINAHL, and Web of Science using the keywords \u201cmachine learning,\u201d \u201copioid,\u201d and \u201cprediction.\u201d The results were limited to human studies with full-text availability in English. We included all peer-reviewed journal articles that addressed an ML model to predict PPO use by adult patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Fifteen studies were included with a sample size ranging from 381 to 112898, primarily orthopedic-surgery-related. Most authors define a prolonged misuse of opioids if it extends beyond 90 days postoperatively. Input variables ranged from 9 to 23 and were primarily preoperative. Most studies developed and tested at least two algorithms and then enhanced the best-performing model for use retrospectively on electronic medical records. The best-performing models were decision-tree-based boosting algorithms in 5 studies with AUC ranging from .81 to .66 and Brier scores ranging from .073 to .13, followed second by logistic regression classifiers in 5 studies. The topmost contributing variable was preoperative opioid use, followed by depression and antidepressant use, age, and use of instrumentation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>ML algorithms have demonstrated promising potential as a decision-supportive tool in predicting prolonged opioid use in post-surgical patients. Further validation studies would allow for their confident incorporation into daily clinical practice.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/00031348231198112","type":"journal-article","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T06:35:48Z","timestamp":1695278148000},"page":"140-151","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":11,"title":["Machine Learning Algorithms Predict Long-Term Postoperative Opioid Misuse: A Systematic Review"],"prefix":"10.1177","volume":"90","author":[{"given":"Omar S.","family":"Emam","sequence":"first","affiliation":[{"name":"Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah S.","family":"Eldaly","sequence":"additional","affiliation":[{"name":"Department of General Surgery, Houston Methodist Hospital, Houston, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francisco R.","family":"Avila","sequence":"additional","affiliation":[{"name":"Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ricardo A.","family":"Torres-Guzman","sequence":"additional","affiliation":[{"name":"Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karla C.","family":"Maita","sequence":"additional","affiliation":[{"name":"Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John P.","family":"Garcia","sequence":"additional","affiliation":[{"name":"Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sally","family":"Anne Brown","sequence":"additional","affiliation":[{"name":"Department of Administration, Mayo Clinic, Jacksonville, FL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Clifton R.","family":"Haider","sequence":"additional","affiliation":[{"name":"Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio J.","family":"Forte","sequence":"additional","affiliation":[{"name":"Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2023,9,21]]},"reference":[{"key":"bibr1-00031348231198112","doi-asserted-by":"publisher","DOI":"10.1093\/epirev\/mxaa012"},{"key":"bibr2-00031348231198112","doi-asserted-by":"publisher","DOI":"10.36076\/ppj.2008\/11\/S63"},{"key":"bibr3-00031348231198112","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2021.101198"},{"key":"bibr4-00031348231198112","unstructured":"Ahmad FBRL, Sutton P. 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