RESUMEN
Cite this article: Bone Joint Res 2022;11(12):890-892.
RESUMEN
INTRODUCTION: The COVID-19 pandemic has led to unprecedented delays for those awaiting elective hip and knee arthroplasty. Current demand far exceeds available resource, and therefore it is integral that healthcare resource is fairly rationed to those who need it most. We therefore set out to determine if pre-operative health-related quality of life assessment (HRQoL) could be used to triage arthroplasty waiting lists. METHODS: Data regarding demographics, perioperative variables and patient reported outcome measures (PROMs) (pre-operative and 1-year post-operative EuroQOL five dimension (EQ-5D-3L) and Oxford hip and knee scores (OHS/OKS) were retrospectively extracted from electronic patient health records at a large university teaching hospital. Patients were split into two equal groups based on pre-operative EQ-5D TTO scores and compared (Group1 [worse HRQoL] = -0.239 to 0.487; Group2 [better HRQoL] = 0.516-1 [best]). RESULTS: 513 patients were included. Patients in Group1 had significantly greater improvement in post-operative EQ-5D-3L scores compared to Group2 (Median 0.67 vs. 0.19; p < 0.0001), as well as greater improvement in OHS/OKS (Mean 22.4 vs. 16.4; p < 0.0001). Those in Group2 were significantly less likely to achieve the EQ-5D-3L minimum clinically important difference (MCID) attainment (OR 0.13, 95%CI 0.07-0.23; p < 0.0001) with a trend towards lower OHS/OKS MCID attainment (OR 0.66, 95%CI 0.37-1.19; p = 0.168). There was no clinically significant difference in length of stay (Median 3-days both groups), and no statistically significant difference in adverse events (30 days and 1 year readmission/reoperation). CONCLUSIONS: A pre-operative EQ-5D-3L cut-off of ≤0.487 for hip and knee arthroplasty prioritisation may help to maximise clinical utility and cost-effectiveness in a limited resource setting post COVID-19.
RESUMEN
BACKGROUND: Hip and knee osteoarthritis is substantially prevalent worldwide, with large numbers of older adults undergoing joint replacement (arthroplasty) every year. A backlog of elective surgery due to the COVID-19 pandemic, and an aging population, has led to substantial issues with access to timely arthroplasty surgery. A potential method to improve the efficiency of arthroplasty services is by increasing the percentage of patients who are listed for surgery from primary care referrals. The use of artificial intelligence (AI) techniques, specifically machine learning, provides a potential unexplored solution to correctly and rapidly select suitable patients for arthroplasty surgery. OBJECTIVE: This study has 2 objectives: (1) develop a cohort of patients with referrals by general practitioners regarding assessment of suitability for hip or knee replacement from National Health Service (NHS) Grampian data via the Grampian Data Safe Haven and (2) determine the demographic, clinical, and imaging characteristics that influence the selection of patients to undergo hip or knee arthroplasty, and develop a tested and validated patient-specific predictive model to guide arthroplasty referral pathways. METHODS: The AI to Revolutionise the Patient Care Pathway in Hip and Knee Arthroplasty (ARCHERY) project will be delivered through 2 linked work packages conducted within the Grampian Data Safe Haven and Safe Haven Artificial Intelligence Platform. The data set will include a cohort of individuals aged ≥16 years with referrals for the consideration of elective primary hip or knee replacement from January 2015 to January 2022. Linked pseudo-anonymized NHS Grampian health care data will be acquired including patient demographics, medication records, laboratory data, theatre records, text from clinical letters, and radiological images and reports. Following the creation of the data set, machine learning techniques will be used to develop pattern classification and probabilistic prediction models based on radiological images. Supplemental demographic and clinical data will be used to improve the predictive capabilities of the models. The sample size is predicted to be approximately 2000 patients-a sufficient size for satisfactory assessment of the primary outcome. Cross-validation will be used for development, testing, and internal validation. Evaluation will be performed through standard techniques, such as the C statistic (area under curve) metric, calibration characteristics (Brier score), and a confusion matrix. RESULTS: The study was funded by the Chief Scientist Office Scotland as part of a Clinical Research Fellowship that runs from August 2021 to August 2024. Approval from the North Node Privacy Advisory Committee was confirmed on October 13, 2021. Data collection started in May 2022, with the results expected to be published in the first quarter of 2024. ISRCTN registration has been completed. CONCLUSIONS: This project provides a first step toward delivering an automated solution for arthroplasty selection using routinely collected health care data. Following appropriate external validation and clinical testing, this project could substantially improve the proportion of referred patients that are selected to undergo surgery, with a subsequent reduction in waiting time for arthroplasty appointments. TRIAL REGISTRATION: ISRCTN Registry ISRCTN18398037; https://www.isrctn.com/ISRCTN18398037. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/37092.
RESUMEN
BACKGROUND: COVID-19 has had a detrimental impact on access to hip and knee arthroplasty surgery. We set out to examine whether this had a subsequent impact on preoperative opioid prescribing rates for those awaiting surgery. METHODS: Data regarding patient demographics and opioid utilisation were collected from the electronic health records of included patients at a large university teaching hospital. Patients on the outpatient waiting list for primary hip and knee arthroplasty as of September 2020 (COVID-19 group) were compared with historical controls (Controls) who had previously undergone surgery. A sample size calculation indicated 452 patients were required to detect a 15% difference in opioid prescription rates between groups. RESULTS: A total of 548 patients (58.2% female) were included, 260 in the COVID-19 group and 288 in the Controls. Baseline demographics were similar between the groups. For those with data available, the proportion of patients on any opioid at follow-up in the COVID-19 group was significantly higher: 55.0% (143/260) compared with 41.2% (112/272) in the Controls (p=0.002). This remained significant when adjusted for confounding (age, gender, Scottish Index of Multiple Deprivation, procedure and wait time). The proportion of patients on a strong opioid was similar (4.2% (11/260) vs 4.8% (13/272)) for COVID-19 and Controls, respectively. The median waiting time from referral to follow-up was significantly longer in the COVID-19 group compared with the Controls (455 days vs 365 days; p<0.0001). CONCLUSION: The work provides evidence of potential for an emerging opioid problem associated with the influence of COVID-19 on elective arthroplasty services. Viable alternatives to opioid analgesia for those with end-stage arthritis should be explored, and prolonged waiting times for surgery ought to be avoided in the recovery from COVID-19 to prevent more widespread opioid use.
RESUMEN
AIMS: The primary aim was to assess the independent influence of coronavirus disease (COVID-19) on 30-day mortality for patients with a hip fracture. The secondary aims were to determine whether: 1) there were clinical predictors of COVID-19 status; and 2) whether social lockdown influenced the incidence and epidemiology of hip fractures. METHODS: A national multicentre retrospective study was conducted of all patients presenting to six trauma centres or units with a hip fracture over a 46-day period (23 days pre- and 23 days post-lockdown). Patient demographics, type of residence, place of injury, presentation blood tests, Nottingham Hip Fracture Score, time to surgery, operation, American Society of Anesthesiologists (ASA) grade, anaesthetic, length of stay, COVID-19 status, and 30-day mortality were recorded. RESULTS: Of 317 patients with acute hip fracture, 27 (8.5%) had a positive COVID-19 test. Only seven (26%) had suggestive symptoms on admission. COVID-19-positive patients had a significantly lower 30-day survival compared to those without COVID-19 (64.5%, 95% confidence interval (CI) 45.7 to 83.3 vs 91.7%, 95% CI 88.2 to 94.8; p < 0.001). COVID-19 was independently associated with increased 30-day mortality risk adjusting for: 1) age, sex, type of residence (hazard ratio (HR) 2.93; p = 0.008); 2) Nottingham Hip Fracture Score (HR 3.52; p = 0.001); and 3) ASA (HR 3.45; p = 0.004). Presentation platelet count predicted subsequent COVID-19 status; a value of < 217 × 109/l was associated with 68% area under the curve (95% CI 58 to 77; p = 0.002) and a sensitivity and specificity of 63%. A similar number of patients presented with hip fracture in the 23 days pre-lockdown (n = 160) and 23 days post-lockdown (n = 157) with no significant (all p ≥ 0.130) difference in patient demographics, residence, place of injury, Nottingham Hip Fracture Score, time to surgery, ASA, or management. CONCLUSION: COVID-19 was independently associated with an increased 30-day mortality rate for patients with a hip fracture. Notably, most patients with hip fracture and COVID-19 lacked suggestive symptoms at presentation. Platelet count was an indicator of risk of COVID-19 infection. These findings have implications for the management of hip fractures, in particular the need for COVID-19 testing. Cite this article: Bone Joint J 2020;102-B(9):1219-1228.