Using data from the Hip Fracture Evaluation with Alternatives of Total Hip Arthroplasty versus Hemiarthroplasty (HEALTH) trial, we sought to determine if a difference in functional outcomes exists between monopolar and bipolar hemiarthroplasty (HA). This study is a secondary analysis of patients aged 50 years or older with a displaced femoral neck fracture who were enrolled in the HEALTH trial and underwent monopolar and bipolar HA. Scores from the Western Ontario and McMaster University Arthritis Index (WOMAC) and 12-Item Short Form Health Survey (SF-12) Physical Component Summary (PCS) and (MCS) were compared between the two HA groups using a propensity score-weighted analysis.Aims
Methods
To develop prediction models using machine-learning (ML) algorithms for 90-day and one-year mortality prediction in femoral neck fracture (FNF) patients aged 50 years or older based on the Hip fracture Evaluation with Alternatives of Total Hip arthroplasty versus Hemiarthroplasty (HEALTH) and Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trials. This study included 2,388 patients from the HEALTH and FAITH trials, with 90-day and one-year mortality proportions of 3.0% (71/2,388) and 6.4% (153/2,388), respectively. The mean age was 75.9 years (SD 10.8) and 65.9% of patients (1,574/2,388) were female. The algorithms included patient and injury characteristics. Six algorithms were developed, internally validated and evaluated across discrimination (c-statistic; discriminative ability between those with risk of mortality and those without), calibration (observed outcome compared to the predicted probability), and the Brier score (composite of discrimination and calibration).Aims
Methods
The aim of this study was to explore the functional results in a fitter subgroup of participants in the Hip Fracture Evaluation with Alternatives of Total Hip Arthroplasty versus Hemiarthroplasty (HEALTH) trial to determine whether there was an advantage of total hip arthroplasty (THA) versus hemiarthroplasty (HA) in this population. We performed a post hoc exploratory analysis of a fitter cohort of patients from the HEALTH trial. Participants were aged over 50 years and had sustained a low-energy displaced femoral neck fracture (FNF). The fittest participant cohort was defined as participants aged 70 years or younger, classified as American Society of Anesthesiologists grade I or II, independent walkers prior to fracture, and living at home prior to fracture. Multilevel models were used to estimate the effect of THA versus HA on functional outcomes. In addition, a sensitivity analysis of the definition of the fittest participant cohort was performed.Aims
Methods
To evaluate the impact of negative pressure wound therapy (NPWT) on the odds of having deep infections and health-related quality of life (HRQoL) following open fractures. Patients from the Fluid Lavage in Open Fracture Wounds (FLOW) trial with Gustilo-Anderson grade II or III open fractures within the lower limb were included in this secondary analysis. Using mixed effects logistic regression, we assessed the impact of NPWT on deep wound infection requiring surgical intervention within 12 months post-injury. Using multilevel model analyses, we evaluated the impact of NPWT on the Physical Component Summary (PCS) of the 12-Item Short-Form Health Survey (SF-12) at 12 months post-injury.Aims
Methods
Artificial intelligence and machine-learning analytics have gained extensive popularity in recent years due to their clinically relevant applications. A wide range of proof-of-concept studies have demonstrated the ability of these analyses to personalize risk prediction, detect implant specifics from imaging, and monitor and assess patient movement and recovery. Though these applications are exciting and could potentially influence practice, it is imperative to understand when these analyses are indicated and where the data are derived from, prior to investing resources and confidence into the results and conclusions. In this article, we review the current benefits and potential limitations of machine-learning for the orthopaedic surgeon with a specific emphasis on data quality.
Using tibial shaft fracture participants from a large, multicentre randomized controlled trial, we investigated if patient and surgical factors were associated with health-related quality of life (HRQoL) at one year post-surgery. The Study to Prospectively Evaluate Reamed Intramedullary Nails in Patients with Tibial Fractures (SPRINT) trial examined adults with an open or closed tibial shaft fracture who were treated with either reamed or unreamed intramedullary nails. HRQoL was assessed at hospital discharge (for pre-injury level) and at 12 months post-fracture using the Short Musculoskeletal Functional Assessment (SMFA) Dysfunction, SMFA Bother, 36-Item Short Form 36 (SF-36) Physical, and SF-36 Mental Component scores. We used multiple linear regression analysis to determine if baseline and surgical factors, as well as post-intervention procedures within one year of fracture, were associated with these HRQoL outcomes. Significance was set at p < 0.01. We hypothesize that, irrespective of the four measures used, prognosis is guided by both modifiable and non-modifiable factors and that patients do not return to their pre-injury level of function, nor HRQoL.Aims
Methods
