The June 2024 Knee Roundup³⁶⁰ looks at: The estimated lifetime risk of revision after primary knee arthroplasty influenced by age, sex, and indication; Should high-risk patients seek out care from high-volume surgeons?; Stability and fracture rates in medial unicondylar knee arthroplasties; Rethinking antibiotic prophylaxis for dental procedures post-arthroplasty; Evaluating DAIR: a viable alternative for acute periprosthetic joint infection; The characteristics and predictors of mortality in periprosthetic fractures around the knee; Patient health-related quality of life deteriorates significantly while waiting six to 12 months for total hip or knee arthroplasty; The importance of looking for diversity in knee implants.

Aims

The primary aim was to assess change in health-related quality of life (HRQoL) of patients as they waited from six to 12 months for a total hip (THA) or total or partial knee arthroplasty (KA). Secondary aims were to assess change in joint-specific function, mental health, quality of sleep, number living in a state worse than death (WTD), wellbeing, and patient satisfaction with their healthcare.

The October 2023 Knee Roundup³⁶⁰ looks at: Cementless total knee arthroplasty is associated with more revisions within a year; Kinematically and mechanically aligned total knee arthroplasties: long-term follow-up; Aspirin thromboprophylaxis following primary total knee arthroplasty is associated with a lower rate of early periprosthetic joint infection compared with other agents; The impact of a revision arthroplasty network on patient outcomes; Re-revision knee arthroplasty in a tertiary centre: how does infection impact on outcomes?; Does the knee joint have its own microbiome?; Revision knee surgery provision in Scotland; Aspirin is a safe and effective thromboembolic prophylaxis after total knee arthroplasty: a systematic review and meta-analysis; Patellar resurfacing and kneeling ability after total knee arthroplasty: a systematic review.

Aims. 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. Methods. 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). Results. Predictive performance of the best models per outcome ranged from 0.71 for HOOS-PS to 0.84 for EQ-VAS (HA sample). ML statistically significantly outperformed LR and pre-surgery PROM scores in two out of six cases. Conclusion. MCIDs can be predicted with reasonable performance. ML was able to outperform traditional methods, although only in a minority of cases. Cite this article: Bone Joint Res 2023;12(9):512–521

Aims. 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). Methods. 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. Results. There was a significant correlation between CFS and the Oxford score (THA r = −0.838; p < 0.001, KA r = −0.867; p < 0.001), EQ-5D index (THA r = −0.663, p =< 0.001; KA r = −0.681; p =< 0.001), and EQ-VAS (THA r = −0.414; p < 0.001, KA r = −0.386; p < 0.001). Confounding variables (demographics and waiting time) where adjusted for using multiple regression analysis. For each 8.5 (THA, 95% CI 7.1 to 10.0; p < 0.001) and 9.9 (KA, 95% CI 8.4 to 11.4; p < 0.001) point change in the Oxford score, there was an associated change in level of the CFS. For each 0.16 (THA, 95% CI 0.10 to 0.22; p < 0.001) and 0.20 (KA, 95% CI 0.12 to 0.27; p < 0.001) utility change in EQ-5D, there was an associated change in level of the CFS. EQ-VAS (THA, B = −11.5; p < 0.001, KA B = −7.9; p = 0.005) was also associated with CFS. Conclusion. JSF and HRQoL in patients awaiting THA or KA for more than six months, were independently associated with level of clinical frailty. With further prospective studies, clinical frailty may prove to be a useful metric to assist in the prioritization of arthroplasty waiting lists. Cite this article: Bone Jt Open 2023;4(4):241–249

Aims. The primary aim was to assess whether preoperative health-related quality of life (HRQoL) was associated with postoperative mortality following total hip arthroplasty (THA) and knee arthroplasty (KA). Secondary aims were to assess whether patient demographics/comorbidities and/or joint-specific function were associated with postoperative mortality. Methods. Patients undergoing THA (n = 717) and KA (n = 742) during a one-year period were identified retrospectively from an arthroplasty register. Patient demographics, comorbidities, Oxford score, and EuroQol five-dimension (EQ-5D) were recorded preoperatively. Patients were followed up for a minimum of seven years and their mortality status was obtained. Cox regression analysis was used to adjust for confounding. Results. During the study period, 111 patients (15.5%) undergoing THA and 135 patients (18.2%) undergoing KA had died at a mean follow-up of 7.5 years (7 to 8). When adjusting for confounding, the preoperative EQ-5D was associated with postoperative mortality, and for each 0.1 difference in the utility there was an associated change in mortality risk of 6.7% (p = 0.048) after THA, and 6.8% (p = 0.047) after KA. Comorbidities of connective tissue disease (p ≤ 0.026) and diabetes (p ≤ 0.028) were associated with mortality after THA, whereas MI (p ≤ 0.041), diabetes (p ≤ 0.009), and pain in other joints (p ≤ 0.050) were associated with mortality following KA. The preoperative Oxford score was associated with mortality, and for each one-point change in the score there was an associated change in mortality risk of 2.7% (p = 0.025) after THA and 4.3% (p = 0.003) after KA. Conclusion. Worse preoperative HRQoL and joint specific function were associated with an increased risk of postoperative mortality. Both HRQoL and joint-specific function decline with longer waiting times to surgery for THA and KA and therefore may result in an increased postoperative mortality risk than would have been expected if surgery had been undertaken earlier. Cite this article: Bone Jt Open 2022;3(12):933–940

Aims

The primary aim of this study was to assess whether patients waiting six months or more for a total hip (THA) or knee (KA) arthroplasty had a deterioration in their health-related quality of life (HRQoL). Secondary aims were to assess changes in frailty and the number of patients living in a state considered to be worse than death (WTD), and factors associated with changes in HRQoL and frailty.

Aims

The aim of this study was to determine satisfaction rates after hip and knee arthroplasty in patients who did not respond to postoperative patient-reported outcome measures (PROMs), characteristics of non-responders, and contact preferences to maximize response rates.

Aims. The aim of this study was to determine the long-term mortality rate, and to identify factors associated with this, following primary and revision knee arthroplasty (KA). Methods. Data from the Scottish Arthroplasty Project (1998 to 2019) were retrospectively analyzed. Patient mortality data were linked from the National Records of Scotland. Analyses were performed separately for the primary and revised KA cohorts. The standardized mortality ratio (SMR) with 95% confidence intervals (CIs) was calculated for the population at risk. Multivariable Cox proportional hazards were used to identify predictors and estimate relative mortality risks. Results. At a median 7.4 years (interquartile range (IQR) 4.0 to 11.6) follow-up, 27.8% of primary (n = 27,474/98,778) and 31.3% of revision (n = 2,611/8,343) KA patients had died. Both primary and revision cohorts had lower mortality rates than the general population (SMR 0.74 (95% CI 0.73 to 0.74); p < 0.001; SMR 0.83 (95% CI 0.80 to 0.86); p < 0.001, respectively), which persisted for 12 and eighteight years after surgery, respectively. Factors associated with increased risk of mortality after primary KA included male sex (hazard ratio (HR) 1.40 (95% CI 1.36 to 1.45)), increasing socioeconomic deprivation (HR 1.43 (95% CI 1.36 to 1.50)), inflammatory polyarthropathy (HR 1.79 (95% CI 1.68 to 1.90)), greater number of comorbidities (HR 1.59 (95% CI 1.51 to 1.68)), and periprosthetic joint infection (PJI) requiring revision (HR 1.92 (95% CI 1.57 to 2.36)) when adjusting for age. Similarly, male sex (HR 1.36 (95% CI 1.24 to 1.49)), increasing socioeconomic deprivation (HR 1.31 (95% CI 1.12 to 1.52)), inflammatory polyarthropathy (HR 1.24 (95% CI 1.12 to 1.37)), greater number of comorbidities (HR 1.64 (95% CI 1.33 to 2.01)), and revision for PJI (HR 1.35 (95% 1.18 to 1.55)) were independently associated with an increased risk of mortality following revision KA when adjusting for age. Conclusion. The SMR of patients undergoing primary and revision KA was lower than that of the general population and remained so for several years post-surgery. However, approximately one in four patients undergoing primary and one in three patients undergoing revision KA died within tenten years of surgery. Several patient and surgical factors, including PJI, were associated with the risk of mortality within ten years of primary and revision surgery. Cite this article: Bone Joint J 2022;104-B(1):45–52

Aims. The aim of this study was to assess the quality of life of patients on the waiting list for a total hip (THA) or knee arthroplasty (KA) during the COVID-19 pandemic. Secondary aims were to assess whether length of time on the waiting list influenced quality of life and rate of deferral of surgery. Methods. During the study period (August and September 2020) 843 patients (THA n = 394, KA n = 449) from ten centres in the UK reported their EuroQol five dimension (EQ-5D) scores and completed a waiting list questionnaire (2020 group). Patient demographic details, procedure, and date when listed were recorded. Patients scoring less than zero for their EQ-5D score were defined to be in a health state “worse than death” (WTD). Data from a retrospective cohort (January 2014 to September 2017) were used as the control group. Results. The 2020 group had a significantly worse EQ-5D score compared to the control group for both THA (p < 0.001) and KA (p < 0.001). Over one-third (35.0%, n = 138/394) of patients waiting for a THA and nearly a quarter (22.3%, n = 100/449) for KA were in a health state WTD, which was significantly greater than the control group (odds ratio 2.30 (95% confidence interval (CI) 1.83 to 2.93) and 2.08 (95% CI 1.61 to 2.70), respectively; p < 0.001). Over 80% (n = 680/843) of the 2020 group felt that their quality of life had deteriorated while waiting. Each additional month spent on the waiting list was independently associated with a decrease in quality of life (EQ-5D: -0.0135, p = 0.004). There were 117 (13.9%) patients who wished to defer their surgery and the main reason for this was health concerns for themselves and or their family (99.1%, n = 116/117). Conclusion. Over one-third of patients waiting for THA and nearly one-quarter waiting for a KA were in a state WTD, which was approaching double that observed prior to the pandemic. Increasing length of time on the waiting list was associated with decreasing quality of life. Level of evidence: Level III retrospective case control study. Cite this article: Bone Joint J 2021;103-B(4):672–680

Aims

The aim of this study was to give estimates of the incidence of component incompatibility in hip and knee arthroplasty and to test the effect of an online, real-time compatibility check.

Infection is a leading indication for revision arthroplasty. Established criteria used to diagnose prosthetic joint infection (PJI) include a range of laboratory tests. Leucocyte esterase (LE) is widely used on a colorimetric reagent strip for the diagnosis of urinary tract infections. This inexpensive test may be used for the diagnosis or exclusion of PJI. Aspirates from 30 total hip arthroplasties (THAs) and 79 knee arthroplasties (KA) were analysed for LE activity. Semi-quantitative reagent strip readings of 15, 70, 125 and 500 white blood cells (WBC) were validated against a manual synovial white cell count (WCC). A receiver operating characteristic (ROC) curve was constructed to determine the optimal cut-off point for the semi-quantitative results. Based on established criteria, six THAs and 15 KAs were classified as infected. The optimal cut-off point for the diagnosis of PJI was 97 WBC. The closest semi-quantitative reading for a positive result was 125 WBC, achieving a sensitivity of 81% and a specificity of 93%. The positive and negative predictive values of the LE test strip were 74% and 95% respectively. The LE reagent strip had a high specificity and negative predictive value. A negative result may exclude PJI and negate the need for further diagnostic tests. Cite this article: Bone Joint J 2015;97-B:1232–6