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
Prolonged waits for hip and knee arthroplasty have raised questions about the equity of current approaches to waiting list prioritization for those awaiting surgery. We therefore set out to understand key stakeholder (patient and surgeon) preferences for the prioritization of patients awaiting such surgery, in order to guide future waiting list redesign. A combined qualitative/quantitative approach was used. This comprised a Delphi study to first inform which factors patients and surgeons designate as important for prioritization of patients on hip and knee arthroplasty waiting lists, followed by a discrete choice experiment (DCE) to determine how the factors should be weighed against each other. Coefficient values for each included DCE attribute were used to construct a ‘priority score’ (weighted benefit score) that could be used to rank individual patients waiting for surgery based on their respective characteristics.Aims
Methods
While internet search engines have been the primary information source for patients’ questions, artificial intelligence large language models like ChatGPT are trending towards becoming the new primary source. The purpose of this study was to determine if ChatGPT can answer patient questions about total hip (THA) and knee arthroplasty (TKA) with consistent accuracy, comprehensiveness, and easy readability. We posed the 20 most Google-searched questions about THA and TKA, plus ten additional postoperative questions, to ChatGPT. Each question was asked twice to evaluate for consistency in quality. Following each response, we responded with, “Please explain so it is easier to understand,” to evaluate ChatGPT’s ability to reduce response reading grade level, measured as Flesch-Kincaid Grade Level (FKGL). Five resident physicians rated the 120 responses on 1 to 5 accuracy and comprehensiveness scales. Additionally, they answered a “yes” or “no” question regarding acceptability. Mean scores were calculated for each question, and responses were deemed acceptable if ≥ four raters answered “yes.”Aims
Methods
The aims were to assess whether preoperative joint-specific function (JSF) and health-related quality of life (HRQoL) were associated with level of clinical frailty in patients waiting for a primary total hip arthroplasty (THA) or knee arthroplasty (KA). Patients waiting for a THA (n = 100) or KA (n = 100) for more than six months were prospectively recruited from the study centre. Overall,162 patients responded to the questionnaire (81 THA; 81 KA). Patient demographics, Oxford score, EuroQol five-dimension (EQ-5D) score, EuroQol visual analogue score (EQ-VAS), Rockwood Clinical Frailty Score (CFS), and time spent on the waiting list were collected.Aims
Methods
This study aimed to investigate the estimated change in primary and revision arthroplasty rate in the Netherlands and Denmark for hips, knees, and shoulders during the COVID-19 pandemic in 2020 (COVID-period). Additional points of focus included the comparison of patient characteristics and hospital type (2019 vs COVID-period), and the estimated loss of quality-adjusted life years (QALYs) and impact on waiting lists. All hip, knee, and shoulder arthroplasties (2014 to 2020) from the Dutch Arthroplasty Register, and hip and knee arthroplasties from the Danish Hip and Knee Arthroplasty Registries, were included. The expected number of arthroplasties per month in 2020 was estimated using Poisson regression, taking into account changes in age and sex distribution of the general Dutch/Danish population over time, calculating observed/expected (O/E) ratios. Country-specific proportions of patient characteristics and hospital type were calculated per indication category (osteoarthritis/other elective/acute). Waiting list outcomes including QALYs were estimated by modelling virtual waiting lists including 0%, 5% and 10% extra capacity.Aims
Methods
This study aimed to evaluate whether an enhanced recovery protocol (ERP) for arthroplasty established during the COVID-19 pandemic at a safety net hospital can be associated with a decrease in hospital length of stay (LOS) and an increase in same-day discharges (SDDs) without increasing acute adverse events. A retrospective review of 124 consecutive primary arthroplasty procedures performed after resuming elective procedures on 11 May 2020 were compared to the previous 124 consecutive patients treated prior to 17 March 2020, at a single urban safety net hospital. Revision arthroplasty and patients with < 90-day follow-up were excluded. The primary outcome measures were hospital LOS and the number of SDDs. Secondary outcome measures included 90-day complications, 90-day readmissions, and 30day emergency department (ED) visits.Aims
Methods
We studied the outcomes of hip and knee arthroplasties in a high-volume arthroplasty centre to determine if patients with morbid obesity (BMI ≥ 40 kg/m2) had unacceptably worse outcomes as compared to those with BMI < 40 kg/m2. In a two-year period, 4,711 patients had either total hip arthroplasty (THA; n = 2,370), total knee arthroplasty (TKA; n = 2,109), or unicompartmental knee arthroplasty (UKA; n = 232). Of these patients, 392 (8.3%) had morbid obesity. We compared duration of operation, anaesthetic time, length of stay (LOS), LOS > three days, out of hours attendance, emergency department attendance, readmission to hospital, return to theatre, and venous thromboembolism up to 90 days. Readmission for wound infection was recorded to one year. Oxford scores were recorded preoperatively and at one year postoperatively.Aims
Methods
Patient function after arthroplasty should ideally quickly improve.
It is not known which peri-operative function assessments predict
length of stay (LOS) and short-term functional recovery. The objective
of this study was to identify peri-operative functions assessments
predictive of hospital LOS and short-term function after hospital discharge
in hip or knee arthroplasty patients. In total, 108 patients were assessed peri-operatively with the
timed-up-and-go (TUG), Iowa level of assistance scale, post-operative
quality of recovery scale, readiness for hospital discharge scale,
and the Western Ontario and McMaster Osteoarthritis Index (WOMAC).
The older Americans resources and services activities of daily living
(ADL) questionnaire (OARS) was used to assess function two weeks
after discharge. Objectives
Methods
