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. Patients with metabolic syndrome (MetS) are known to be at increased risk of postoperative complications, but it is unclear whether MetS is also associated with complications after total hip arthroplasty (THA) or total knee arthroplasty (TKA). Here, we perform a systematic review and meta-analysis linking MetS to postoperative complications in THA and TKA. Methods. The PubMed, OVID, and ScienceDirect databases were comprehensively searched and studies were selected and analyzed according to the guidelines of the Meta-analysis of Observational Studies in Epidemiology (MOOSE). We assessed the methodological quality of each study using the Newcastle-Ottawa Scale (NOS), and we evaluated the quality of evidence using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE). Data were extracted and meta-analyzed or qualitatively synthesized for several outcomes. Results. Ten cohort studies involving 1,352,685 patients were included. Qualitative analysis suggested that MetS was associated with a higher incidence of cardiovascular events, and meta-analysis showed that MetS increased the risk of all-cause complications (risk ratio (RR) 1.55, 95% confidence interval (CI) 1.28 to 1.89), surgical site infection (SSI; RR 2.99, 95% CI 1.30 to 6.90), urinary tract infection (UTI; RR 2.58, 95% CI 1.03 to 6.43), and 30-day readmission (RR 1.45, 95% CI 1.33 to 1.59). There was insufficient evidence for assessing an association between MetS and venous thromboembolism events, pulmonary or gastrointestinal complications, or mortality. Conclusion. Patients with MetS undergoing THA and TKA are at increased risk of all-cause complications, cardiovascular complications, SSI, UTI, and 30-day readmission. Surgeons should be aware of the increased risk of these complications in MetS, and presurgical protocols for these complications should give special consideration to MetS patients. Cite this article:Bone Joint Res. 2020;9(3):120–129

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Open lower limb fracture is a life-changing injury affecting 11.5 per 100,000 adults each year, and causes significant morbidity and resource demand on trauma infrastructures. This study aims to identify what, and how, outcomes have been reported for people following open lower limb fracture over ten years.

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Biofilm-related infection is a major complication that occurs in orthopaedic surgery. Various treatments are available but efficacy to eradicate infections varies significantly. A systematic review was performed to evaluate therapeutic interventions combating biofilm-related infections on in vivo animal models.

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The aim of this meta-analysis is to assess the association between exchange of modular parts in debridement, antibiotics, and implant retention (DAIR) procedure and outcomes for hip and knee periprosthetic joint infection (PJI).

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A systematic literature review focusing on how long before surgery concurrent viral or bacterial infections (respiratory and urinary infections) should be treated in hip fracture patients, and if there is evidence for delaying this surgery.

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The effect of the gut microbiota (GM) and its metabolite on bone health is termed the gut-bone axis. Multiple studies have elucidated the mechanisms but findings vary greatly. A systematic review was performed to analyze current animal models and explore the effect of GM on bone.