Aims. 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. Methods. A total of 11 databases were examined using the COre, Standard, Ideal (COSI) protocol. Bibliographic searches (no chronological or linguistic restriction) were conducted using, among other methods, the Patient, Intervention, Comparison, Outcome (PICO) template. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for flow diagram and checklist. Final reading of the complete texts was conducted in English, French, and Spanish. Classification of papers was completed within five levels of evidence (LE). Results. There were a total of 621 hits (526 COre; 95 Standard, Ideal) for screening identification, and 107 records were screened. Overall 67 full-text articles were assessed for eligibility, and 21 articles were included for the study question. A total of 46 full-text articles were excluded with reasons. No studies could be included in quantitative synthesis (meta-analyses), and there were many confounding variables including surgeons’ experience, prosthesis models used, and surgical technique. Conclusion. Patients with hip fracture and with a viral infection in the upper respiratory tract or without major clinical symptoms should be operated on as soon as possible (LE: I-III). There is no evidence that patients with coronavirus disease 2019 (COVID-19) should be treated differently. In relation to pneumonia, its prevention is a major issue. Antibiotics should be administered if surgery is delayed by > 72 hours or if bacterial infection is present in the lower respiratory tract (LE: III-V). In patients with hip fracture and urinary tract infection (UTI), delaying surgery may provoke further complications (LE: I). However, diabetic or immunocompromised patients may benefit from immediate antibiotic treatment. Cite this article: Bone Joint Res 2020;9(12):884–893

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 systematic review and meta-analysis was to gather epidemiological information on selected musculoskeletal injuries and to provide pooled injury-specific incidence rates. Methods. PubMed (National Library of Medicine) and Scopus (Elsevier) databases were searched. Articles were eligible for inclusion if they reported incidence rate (or count with population at risk), contained data on adult population, and were written in English language. The number of cases and population at risk were collected, and the pooled incidence rates (per 100,000 person-years) with 95% confidence intervals (CIs) were calculated by using either a fixed or random effects model. Results. The screening of titles yielded 206 articles eligible for inclusion in the study. Of these, 173 (84%) articles provided sufficient information to be included in the pooled incidence rates. Incidences of fractures were investigated in 154 studies, and the most common fractures in the whole adult population based on the pooled incidence rates were distal radius fractures (212.0, 95% CI 178.1 to 252.4 per 100,000 person-years), finger fractures (117.1, 95% CI 105.3 to 130.2 per 100,000 person-years), and hip fractures (112.9, 95% CI 82.2 to 154.9 per 100,000 person-years). The most common sprains and dislocations were ankle sprains (429.4, 95% CI 243.0 to 759.0 per 100,000 person-years) and first-time patellar dislocations (32.8, 95% CI 21.6 to 49.7 per 100,000 person-years). The most common injuries were anterior cruciate ligament (17.5, 95% CI 6.0 to 50.2 per 100,000 person-years) and Achilles (13.7, 95% CI 9.6 to 19.5 per 100,000 person-years) ruptures. Conclusion. The presented pooled incidence estimates serve as important references in assessing the global economic and social burden of musculoskeletal injuries. Cite this article: Bone Joint Res 2022;11(11):814–825

A balanced inflammatory response is important for successful fracture healing. The response of osteoporotic fracture healing is deranged and an altered inflammatory response can be one underlying cause. The objectives of this review were to compare the inflammatory responses between normal and osteoporotic fractures and to examine the potential effects on different healing outcomes. A systematic literature search was conducted with relevant keywords in PubMed, Embase, and Web of Science independently. Original preclinical studies and clinical studies involving the investigation of inflammatory response in fracture healing in ovariectomized (OVX) animals or osteoporotic/elderly patients with available full text and written in English were included. In total, 14 articles were selected. Various inflammatory factors were reported; of those tumour necrosis factor-α (TNF-α) and interleukin (IL)-6 are two commonly studied markers. Preclinical studies showed that OVX animals generally demonstrated higher systemic inflammatory response and poorer healing outcomes compared to normal controls (SHAM). However, it is inconclusive if the local inflammatory response is higher or lower in OVX animals. As for clinical studies, they mainly examine the temporal changes of the inflammatory stage or perform comparison between osteoporotic/fragility fracture patients and normal subjects without fracture. Our review of these studies emphasizes the lack of understanding that inflammation plays in the altered fracture healing response of osteoporotic/elderly patients. Taken together, it is clear that additional studies, preclinical and clinical, are required to dissect the regulatory role of inflammatory response in osteoporotic fracture healing.

Cite this article: Bone Joint Res 2020;9(7):368–385.