{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T22:52:15Z","timestamp":1781563935787,"version":"3.54.5"},"reference-count":28,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2022,4,8]],"date-time":"2022-04-08T00:00:00Z","timestamp":1649376000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2022,4,8]],"date-time":"2022-04-08T00:00:00Z","timestamp":1649376000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,4,8]],"date-time":"2022-04-08T00:00:00Z","timestamp":1649376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100005713","name":"Technische Universit\u00e4t M\u00fcnchen","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100005713","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100005713","name":"Technische Universit\u00e4t M\u00fcnchen","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005713","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knee surg. sports traumatol. arthrosc."],"published-print":{"date-parts":[[2023,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>The number of primary total knee arthroplasties (TKA) is expected to rise constantly. For patients and healthcare providers, the early identification of risk factors therefore becomes increasingly fundamental in the context of precision medicine. Others have already investigated the detection of risk factors by conducting literature reviews and applying conventional statistical methods. Since the prediction of events has been moderately accurate, a more comprehensive approach is needed. Machine learning (ML) algorithms have had ample success in many disciplines. However, these methods have not yet had a significant impact in orthopaedic research. The selection of a data source as well as the inclusion of relevant parameters is of utmost importance in this context. In this study, a standardized approach for ML in TKA to predict complications during surgery and an irregular surgery duration using data from two German arthroplasty\u2010specific registries was evaluated.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The dataset is based on two initiatives of the German Society for Orthopaedics and Orthopaedic Surgery. A problem statement and initial parameters were defined. After screening, cleaning and preparation of these datasets, 864 cases of primary TKA (2016\u20132019) were gathered. The XGBoost algorithm was chosen and applied with a hyperparameter search, a cross validation and a loss weighting to cope with class imbalance. For final evaluation, several metrics (accuracy, sensitivity, specificity, AUC) were calculated.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>An accuracy of 92.0%, sensitivity of 34.8%, specificity of 95.8%, and AUC of 78.0% were achieved for predicting complications in primary TKA and 93.4%, 74.0%, 96.3%, and 91.6% for predicting irregular surgery duration, respectively. While traditional statistics (correlation coefficient) could not find any relevant correlation between any two parameters, the feature importance revealed several non\u2010linear outcomes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>In this study, a feasible ML model to predict outcomes of primary TKA with very promising results was built. Complex correlations between parameters were detected, which could not be recognized by conventional statistical analysis. Arthroplasty\u2010specific data were identified as relevant by the ML model and should be included in future clinical applications. Furthermore, an interdisciplinary interpretation as well as evaluation of the results by a data scientist and an orthopaedic surgeon are of paramount importance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of evidence<\/jats:title><jats:p>Level IV.<\/jats:p><\/jats:sec>","DOI":"10.1007\/s00167-022-06957-w","type":"journal-article","created":{"date-parts":[[2022,4,8]],"date-time":"2022-04-08T11:05:23Z","timestamp":1649415923000},"page":"1323-1333","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Prediction of complications and surgery duration in primary TKA with high accuracy using machine learning with arthroplasty\u2010specific data"],"prefix":"10.1002","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0273-0643","authenticated-orcid":false,"given":"Florian","family":"Hinterwimmer","sequence":"first","affiliation":[{"name":"<!--<label>1<\/label>--> Department of Orthopaedics and Sports Orthopaedics, Klinikum Rechts der Isar Technical University of Munich Munich Germany"},{"name":"<!--<label>2<\/label>--> Institute for AI and Informatics in Medicine Technical University of Munich Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Igor","family":"Lazic","sequence":"additional","affiliation":[{"name":"<!--<label>1<\/label>--> Department of Orthopaedics and Sports Orthopaedics, Klinikum Rechts der Isar Technical University of Munich Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Severin","family":"Langer","sequence":"additional","affiliation":[{"name":"<!--<label>1<\/label>--> Department of Orthopaedics and Sports Orthopaedics, Klinikum Rechts der Isar Technical University of Munich Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Suren","sequence":"additional","affiliation":[{"name":"<!--<label>1<\/label>--> Department of Orthopaedics and Sports 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Jena Eisenberg Germany"},{"name":"<!--<label>6<\/label>--> Endoprosthetics Committee of the German Knee Society (DKG) Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fritz","family":"Seidl","sequence":"additional","affiliation":[{"name":"<!--<label>3<\/label>--> Department of Trauma Surgery, Klinikum Rechts der Isar Technical University of Munich Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florian","family":"Pohlig","sequence":"additional","affiliation":[{"name":"<!--<label>1<\/label>--> Department of Orthopaedics and Sports Orthopaedics, Klinikum Rechts der Isar Technical University of Munich Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Rueckert","sequence":"additional","affiliation":[{"name":"<!--<label>2<\/label>--> Institute for AI and Informatics in Medicine Technical University of Munich Munich 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December 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 March 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 April 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers\u2019 bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript. The authors declare that they have no conflict of interest, financial or otherwise.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}