The purpose of this study was to determine the association between prior sleeve gastrectomy in patients undergoing primary total hip and knee arthroplasty, and 90-day complications, incidence of revision arthroplasty, and patient-reported outcome scores at final follow-up. This is a retrospective, single-centre analysis. Patients undergoing primary hip or knee arthroplasty with a prior sleeve gastrectomy were eligible for inclusion (n = 80 patients). A morbidly obese control group was established from the same institutional registry using a 1:2 match, for cases:controls with arthroplasty based on propensity score using age, sex, pre-sleeve gastrectomy BMI, Current Procedural Terminology code to identify anatomical location, and presurgical haemoglobin A1C. Outcomes included 90-day complications, incidence of revision arthroplasty, and patient-reported outcome scores at final follow-up. Multivariable logistic regressions evaluated associations of underlying preoperative demographic and treatment characteristics with outcomes.Aims
Methods
The overall aim of this study was to determine the impact of deprivation with regard to quality of life, demographics, joint-specific function, attendances for unscheduled care, opioid and antidepressant use, having surgery elsewhere, and waiting times for surgery on patients awaiting total hip arthroplasty (THA) and total knee arthroplasty (TKA). Postal surveys were sent to 1,001 patients on the waiting list for THA or TKA in a single Northern Ireland NHS Trust, which consisted of the EuroQol five-dimension five-level questionnaire (EQ-5D-5L), visual analogue scores (EQ-VAS), and Oxford Hip and Knee Scores. Electronic records determined prescriptions since addition to the waiting list and out-of-hour GP and emergency department attendances. Deprivation quintiles were determined by the Northern Ireland Multiple Deprivation Measure 2017 using postcodes of home addresses.Aims
Methods
A substantial fraction of patients undergoing knee arthroplasty (KA) or hip arthroplasty (HA) do not achieve an improvement as high as the minimal clinically important difference (MCID), i.e. do not achieve a meaningful improvement. Using three patient-reported outcome measures (PROMs), our aim was: 1) to assess machine learning (ML), the simple pre-surgery PROM score, and logistic-regression (LR)-derived performance in their prediction of whether patients undergoing HA or KA achieve an improvement as high or higher than a calculated MCID; and 2) to test whether ML is able to outperform LR or pre-surgery PROM scores in predictive performance. MCIDs were derived using the change difference method in a sample of 1,843 HA and 1,546 KA patients. An artificial neural network, a gradient boosting machine, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, elastic net, random forest, LR, and pre-surgery PROM scores were applied to predict MCID for the following PROMs: EuroQol five-dimension, five-level questionnaire (EQ-5D-5L), EQ visual analogue scale (EQ-VAS), Hip disability and Osteoarthritis Outcome Score-Physical Function Short-form (HOOS-PS), and Knee injury and Osteoarthritis Outcome Score-Physical Function Short-form (KOOS-PS).Aims
Methods
