Aims. Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting clinicians in various tasks, such as personalized risk stratification. However, an overview of current applications and critical appraisal to peer-reviewed guidelines is lacking. The objectives of this study are to 1) provide an overview of current ML prediction models in orthopaedic trauma; 2) evaluate the completeness of reporting following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement; and 3) assess the risk of bias following the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool. Methods. A systematic search screening 3,252 studies identified 45 ML-based prediction models in orthopaedic trauma up to January 2023. The TRIPOD statement assessed transparent reporting and the PROBAST tool the risk of bias. Results. A total of 40 studies reported on training and internal validation; four studies performed both development and external validation, and one study performed only external validation. The most commonly reported outcomes were mortality (33%, 15/45) and length of hospital stay (9%, 4/45), and the majority of prediction models were developed in the hip fracture population (60%, 27/45). The overall median completeness for the TRIPOD statement was 62% (interquartile range 30 to 81%). The overall risk of bias in the PROBAST tool was low in 24% (11/45), high in 69% (31/45), and unclear in 7% (3/45) of the studies. High risk of bias was mainly due to analysis domain concerns including small datasets with low number of outcomes, complete-case analysis in case of missing data, and no reporting of performance measures. Conclusion. The results of this study showed that despite a myriad of potential clinically useful applications, a substantial part of ML studies in orthopaedic trauma lack transparent reporting, and are at high risk of bias. These problems must be resolved by following established guidelines to instil confidence in ML models among patients and clinicians. Otherwise, there will remain a sizeable gap between the development of ML prediction models and their clinical application in our day-to-day orthopaedic trauma practice. Cite this article: Bone Jt Open 2024;5(1):9–19

Aims. The aim of this study was to determine the effectiveness of home-based prehabilitation on pre- and postoperative outcomes in participants awaiting total knee (TKA) and hip arthroplasty (THA). Methods. A systematic review with meta-analysis of randomized controlled trials (RCTs) of prehabilitation interventions for TKA and THA. MEDLINE, CINAHL, ProQuest, PubMed, Cochrane Library, and Google Scholar databases were searched from inception to October 2022. Evidence was assessed by the PEDro scale and the Cochrane risk-of-bias (ROB2) tool. Results. A total of 22 RCTs (1,601 patients) were identified with good overall quality and low risk of bias. Prehabilitation significantly improved pain prior to TKA (mean difference (MD) -1.02: p = 0.001), with non-significant improvements for function before (MD -0.48; p = 0.06) and after TKA (MD -0.69; p = 0.25). Small preoperative improvements were observed for pain (MD -0.02; p = 0.87) and function (MD -0.18; p = 0.16) prior to THA, but no post THA effect was found for pain (MD 0.19; p = 0.44) and function (MD 0.14; p = 0.68). A trend favouring usual care for improving quality of life (QoL) prior to TKA (MD 0.61; p = 0.34), but no effect on QoL prior (MD 0.03; p = 0.87) or post THA (MD -0.05; p = 0.83) was found. Prehabilitation significantly reduced hospital length of stay (LOS) for TKA (MD -0.43 days; p < 0.001) but not for THA (MD, -0.24; p = 0.12). Compliance was only reported in 11 studies and was excellent with a mean value of 90.5% (SD 6.82). Conclusion. Prehabilitation interventions improve pain and function prior to TKA and THA and reduce hospital LOS, though it is unclear if these effects enhance outcomes postoperatively. Cite this article: Bone Jt Open 2023;4(5):315–328