{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T19:07:34Z","timestamp":1792782454109,"version":"4.5.0"},"reference-count":47,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T00:00:00Z","timestamp":1688601600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T00:00:00Z","timestamp":1688601600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T00:00:00Z","timestamp":1688601600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Foderaro-Quattrone Musculoskeletal-Orthopaedic Surgery Research Innovation Fund"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knee surg. sports traumatol. arthrosc."],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Identifying predictive factors for all\u2010cause reoperation after anterior cruciate ligament reconstruction could inform clinical decision making and improve risk mitigation. The primary purposes of this study are to (1) determine the incidence of all\u2010cause reoperation after anterior cruciate ligament reconstruction, (2) identify predictors of reoperation after anterior cruciate ligament reconstruction using machine learning methodology, and (3) compare the predictive capacity of the machine learning methods to that of traditional logistic regression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A longitudinal geographical database was utilized to identify patients with a diagnosis of new anterior cruciate ligament injury. Eight machine learning models were appraised on their ability to predict all\u2010cause reoperation after anterior cruciate ligament reconstruction. Model performance was evaluated via area under the receiver operating characteristics curve. To explore modeling interpretability and radiomic feature influence on the predictions, we utilized a game\u2010theory\u2010based method through SHapley Additive exPlanations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 1400 patients underwent anterior cruciate ligament reconstruction with a mean postoperative follow\u2010up of 9 years. Two\u2010hundred and eighteen (16%) patients experienced a reoperation after anterior cruciate ligament reconstruction, of which 6% of these were revision ACL reconstruction. SHapley Additive exPlanations plots identified the following risk factors as predictive for all\u2010cause reoperation: diagnosis of systemic inflammatory disease, distal tear location, concomitant medial collateral ligament repair, higher visual analog scale pain score prior to surgery, hamstring autograft, tibial fixation via radial expansion device, younger age at initial injury, and concomitant meniscal repair. Pertinent negatives, when compared to previous studies, included sex and timing of surgery. XGBoost was the best\u2010performing model (area under the receiver operating characteristics curve of 0.77) and outperformed logistic regression in this regard.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>All\u2010cause reoperation after anterior cruciate ligament reconstruction occurred at a rate of 16%. Machine learning models outperformed traditional statistics and identified diagnosis of systemic inflammatory disease, distal tear location, concomitant medial collateral ligament repair, higher visual analog scale pain score prior to surgery, hamstring autograft, tibial fixation via radial expansion device, younger age at initial injury, and concomitant meniscal repair as predictive risk factors for reoperation. Pertinent negatives, when compared to previous studies, included sex and timing of surgery. These models will allow surgeons to tabulate individualized risk for future reoperation for patients undergoing anterior cruciate ligament reconstruction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Level of evidence<\/jats:title>\n                    <jats:p>III.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00167-023-07497-7","type":"journal-article","created":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T12:01:42Z","timestamp":1688644902000},"page":"4099-4108","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Machine learning can accurately predict risk factors for all\u2010cause reoperation after ACLR: creating a clinical tool to improve patient counseling and outcomes"],"prefix":"10.1002","volume":"31","author":[{"given":"Quinn J.","family":"Johnson","sequence":"first","affiliation":[{"name":"<!--<label>2<\/label>--> Mayo Clinic Alix School of Medicine Mayo Clinic 200 First St SW 55905 Rochester MN USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed S.","family":"Jabal","sequence":"additional","affiliation":[{"name":"<!--<label>3<\/label>--> Department of Diagnostic Radiology Mayo Clinic Rochester MN USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexandra M.","family":"Arguello","sequence":"additional","affiliation":[{"name":"<!--<label>1<\/label>--> Department of Orthopedic Surgery Mayo Clinic Rochester MN USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yining","family":"Lu","sequence":"additional","affiliation":[{"name":"<!--<label>1<\/label>--> Department of Orthopedic Surgery Mayo Clinic Rochester MN USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin","family":"Jurgensmeier","sequence":"additional","affiliation":[{"name":"<!--<label>1<\/label>--> Department of Orthopedic Surgery Mayo Clinic Rochester MN USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bruce A.","family":"Levy","sequence":"additional","affiliation":[{"name":"<!--<label>1<\/label>--> Department of Orthopedic Surgery Mayo Clinic Rochester MN USA"},{"name":"<!--<label>2<\/label>--> Mayo Clinic Alix School of Medicine Mayo Clinic 200 First St SW 55905 Rochester MN USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher L.","family":"Camp","sequence":"additional","affiliation":[{"name":"<!--<label>1<\/label>--> Department of Orthopedic Surgery Mayo Clinic Rochester MN USA"},{"name":"<!--<label>2<\/label>--> Mayo Clinic Alix School of Medicine Mayo Clinic 200 First St SW 55905 Rochester MN USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3248-8007","authenticated-orcid":false,"given":"Aaron J.","family":"Krych","sequence":"additional","affiliation":[{"name":"<!--<label>1<\/label>--> Department of Orthopedic Surgery Mayo Clinic 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All competing interests are listed at the end of the publication.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The research conducted in this study was compliant with all relevant national regulations.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was not required due to the retrospective and chart review nature of the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}