{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T06:22:35Z","timestamp":1790144555681,"version":"4.0.1"},"reference-count":31,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T00:00:00Z","timestamp":1753920000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T00:00:00Z","timestamp":1753920000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","award":["TL1TR004420"],"award-info":[{"award-number":["TL1TR004420"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Microsurgery"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Postoperative free flap monitoring is crucial yet taxing, requiring frequent and often subjective assessments to detect early signs of compromise. The present study aims to develop a machine learning model to predict the risk of flap take\u2010back reoperation due to arterial and\/or venous compromise, as a basis for real\u2010time risk monitoring and alerts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective cohort study utilized patient data from a New York City hospital system from 2019 to 2024. Adult patients undergoing free flap reconstruction were included. Data from electronic medical records (EMRs) included demographic and clinical variables. The primary outcome was flap takeback, defined as urgent or emergent microvascular exploration or revision surgery during the same admission. A random forest model was developed and trained on the data with oversampling to balance the training set. Model performance was evaluated using AUROC, sensitivity, specificity, accuracy, and precision.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The study included 458 patient encounters, with a flap takeback rate of 6.1%. The final model achieved a train AUROC of 0.99 and a test AUROC of 0.86. Sensitivity and specificity on the test set were 75% and 78%, respectively, with 78% accuracy. Key predictors included skin integrity, pulse, and diastolic blood pressure.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The machine learning model accurately predicts free flap takeback, offering a proactive approach to postoperative monitoring. Integrating this model into EMR platforms can provide real\u2010time early warning systems (EWS), enhancing early detection and intervention for flap compromise. Future research should validate the model across diverse settings.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/micr.70100","type":"journal-article","created":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T12:28:21Z","timestamp":1753964901000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Machine Learning\u2010Based Flap Takeback Prediction Modeling: Theory for a Real\u2010Time, Patient\u2010Specific Postoperative Flap Monitoring and Alert System"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0327-2613","authenticated-orcid":false,"given":"Olachi O.","family":"Oleru","sequence":"first","affiliation":[{"name":"Division of Plastic and Reconstructive Surgery Icahn School of Medicine at Mount Sinai  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kim\u2010Anh\u2010Nhi","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Icahn School of Medicine at Mount Sinai Institute for Health Care Delivery Science  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Taub","sequence":"additional","affiliation":[{"name":"Division of Plastic and Reconstructive Surgery Icahn School of Medicine at Mount Sinai  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arash","family":"Kia","sequence":"additional","affiliation":[{"name":"Icahn School of Medicine at Mount Sinai Institute for Health Care Delivery Science  New York New York USA"},{"name":"Department of Anesthesiology, Perioperative and Pain Medicine Icahn School of Medicine at Mount Sinai  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,7,31]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.5662\/wjm.v13.i5.373"},{"key":"e_1_2_9_3_1","unstructured":"Apache Spark.2018.\u201cMLlib: Main Guide\u2014Spark 2.3.0 Documentation.\u201dhttps:\/\/spark.apache.org\/."},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1245\/s10434\u2010022\u201013"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1177\/019459989310800207"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1097\/00006534\u2010199"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1097\/01.prs.0000264077.07779.50"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.g7594"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.12968\/jowc.2001.10.1.26037"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamaoto.2016.2981"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhsa.2021.07.024"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/lary.31367"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1097\/SLA.0000000000001457"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/lary.28508"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010022\u201009954\u20108"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1097\/PRS.0000000000010307"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1097\/PRS.0000000000006010"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1055\/s\u20100040\u20101"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1097\/SAP.0000000000003269"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1002\/micr.30332"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1055\/s\u20100039\u20101"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1097\/CCM.0000000000006243"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1055\/s\u20100041\u20101"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.3348\/kjr.2004.5.1.11"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1097\/GOX.0000000000004637"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1055\/s\u20100040\u20101"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.12998\/wjcc.v10.i12.3729"},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.1097\/SAP.0000000000003449"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1002\/sam.11348"},{"key":"e_1_2_9_30_1","doi-asserted-by":"publisher","DOI":"10.1002\/hed.27690"},{"key":"e_1_2_9_31_1","doi-asserted-by":"publisher","DOI":"10.62347\/ZXJV8062"},{"key":"e_1_2_9_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bjps.2017.05.041"}],"container-title":["Microsurgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/micr.70100","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/micr.70100","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/micr.70100","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T17:27:57Z","timestamp":1786987677000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/micr.70100"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,31]]},"references-count":31,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["10.1002\/micr.70100"],"URL":"https:\/\/doi.org\/10.1002\/micr.70100","archive":["Portico"],"relation":{},"ISSN":["0738-1085","1098-2752"],"issn-type":[{"value":"0738-1085","type":"print"},{"value":"1098-2752","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,31]]},"assertion":[{"value":"2025-02-03","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-24","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-31","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70100"}}