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
