Aims. To determine whether obesity and malnutrition have a synergistic effect on outcomes from skeletal trauma or elective orthopaedic surgery. Methods. Electronic databases including MEDLINE, Global Health, Embase, Web of Science, ScienceDirect, and PEDRo were searched up to 14 April 2024, as well as conference proceedings and the reference lists of included studies. Studies were appraised using tools according to study design, including the Oxford Levels of Evidence, the Institute of Health Economics case series quality appraisal checklist, and the CLARITY checklist for cohort studies. Studies were eligible if they reported the effects of combined malnutrition and obesity on outcomes from skeletal trauma or elective orthopaedic surgery. Results. A total of eight studies (106,319 patients) were included. These carried moderate to high risk of bias. Combined obesity and malnutrition did not lead to worse outcomes in patients undergoing total shoulder arthroplasty or repair of proximal humeral fractures (two retrospective cohort studies). Three studies (two retrospective cohort studies, one case series) found that malnourishment and obesity had a synergistic effect and led to poor outcomes in total hip or knee arthroplasty, including longer length of stay and higher complication rates. One retrospective cohort study pertaining to posterior lumbar fusion found that malnourished obese patients had higher odds of developing surgical site infection and sepsis, as well as higher odds of requiring a revision procedure. Conclusion. Combined malnutrition and obesity have a synergistic effect and lead to poor outcomes in lower limb procedures. Appropriate preoperative optimization and postoperative care are required to improve outcomes in this group of patients. Cite this article: Bone Joint J 2024;106-B(10):1044–1049

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.

Aims

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.

Aims

Sarcopenia is characterized by a generalized progressive loss of skeletal muscle mass, strength, and physical performance. This systematic review primarily evaluated the effects of sarcopenia on postoperative functional recovery and mortality in patients undergoing orthopaedic surgery, and secondarily assessed the methods used to diagnose and define sarcopenia in the orthopaedic literature.