Machine learning based prediction of inpatient falls during post-acute rehabilitation after total hip and knee arthroplasty – a multicenter study of 11 clinics (2016

26dkou383 10.3205/26dkou383 urn:nbn:de:0183-26dkou3833 Meeting Abstract Machine learning based prediction of inpatient falls during post-acute rehabilitation after total hip and knee arthroplasty – a multicenter study of 11 clinics (2016–2026) Gillani Gillani Amna Ahmed AA

Department of Orthopaedics and Sports Orthopaedics, School of Medicine and Health, TUM University Hospital, Technical University of Munich, München, Deutschland Institute for AI and Informatics in Medicine, München, Deutschland

author Smits Serena Smits Serena Ricardo R

author Lazic Lazic Igor I

Department of Orthopaedics and Sports Orthopaedics, School of Medicine and Health, TUM University Hospital, Technical University of Munich, München, Deutschland

author von Eisenhart-Rothe von Eisenhart-Rothe Rüdiger R

Department of Orthopaedics and Sports Orthopaedics, School of Medicine and Health, TUM University Hospital, Technical University of Munich, München, Deutschland

author Hinterwimmer Hinterwimmer Florian F

author Valle Valle Christina C

Department of Orthopaedics and Sports Orthopaedics, School of Medicine and Health, TUM University Hospital, Technical University of Munich, München, Deutschland Medical Park Chiemsee, Bernau am Chiemsee, Deutschland

author German Medical Science GMS Publishing House

Düsseldorf

610 20261020 engl This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). M0659 383 Deutsche Gesellschaft für Orthopädie und Unfallchirurgie Deutsche Gesellschaft für Orthopädie und Orthopädische Chirurgie Deutsche Gesellschaft für Unfallchirurgie Berufsverband für Orthopädie und Unfallchirurgie Deutscher Kongress für Orthopädie und Unfallchirurgie (DKOU 2026) Grundlagenforschung | Digitalisierung und KI Berlin 20261020 20261023 DKOU26_Abs_2256 TextObjectives and questions: Inpatient falls are a relevant adverse event during post-acute rehabilitation after total hip arthroplasty (THA) and total knee arthroplasty (TKA) and can lead to dislocation, periprosthetic fractures, delayed recovery, and prolonged length of stay. Falls in patient rooms or bathrooms are particularly concerning, as patients may remain undiscovered. Descriptive analyses suggest that falls tend to cluster in specific environments and may be preceded by potentially modifiable behaviors such as not using the nurse call system before mobilizing. However, tools to identify characteristics of high-risk fall situations at scale remain limited. We aimed to identify factors associated with room and bathroom falls specifically, using machine learning (ML) models trained on routinely collected fall reports from inpatient rehabilitation settings, with external validation on held-out clinical sites.Material and methods: We performed a multicenter retrospective study using standardized fall reports from 11 rehabilitation clinics (2016–2026) for THA/TKA patients. Predictors included temporal factors (hour, weekday/weekend), lighting conditions, patient orientation (familiarity with call system and ward), mobility aids (crutches, rollator, walker, none), and footwear (barefoot, socks, slippers, proper shoes). The outcome was falls in patient rooms/bathrooms versus other locations. Nine clinics (n=1,116) served as the internal development set; two clinics (n=338, n=341) were held out for external validation. We trained Logistic Regression, Random Forest, XGBoost and CatBoost models using stratified 5-fold cross-validation. Performance was assessed using AUROC, F1, precision, sensitivity, and specificity. Interpretability was assessed using SHAP values.Results: Among 1,795 fall events, 58% occurred in rooms/bathrooms. At the time of the event, most patients were using crutches (53.4%) or a rollator (22.2%), while mobilization without an assistive device was uncommon (0.3%). 70.9% had not used the call system beforehand. CatBoost achieved internal cross-validation AUC of 0.832 (±0.032), F1 0.763, sensitivity 0.702, and specificity 0.802. External validation demonstrated robust generalizability: AUC 0.856 and 0.843 at the two held-out sites respectively. SHAP analysis identified nighttime hours (midnight–6 AM) as the strongest predictor, followed by inadequate footwear (barefoot/socks). Proper shoes were protective.Discussion and conclusions: Using fall reports from 11 rehabilitation clinics over 10 years, ML models identified reproducible patterns associated with room/bathroom falls after THA/TKA, with robust external validation (AUC 0.856 and 0.843 at two held-out sites). Nighttime hours (midnight–6 AM) and inadequate footwear (barefoot/socks) were the strongest predictors. These findings support prevention strategies that combine behavioral advice (“wear proper shoes”, “call for assistance before getting up in the morning”) with improved accessibility of the call system and targeted monitoring during high-risk hours. 0 0 0 0