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Bone & Joint Open
Vol. 5, Issue 11 | Pages 992 - 998
6 Nov 2024
Wignadasan W Magan A Kayani B Fontalis A Chambers A Rajput V Haddad FS

Aims. While residual fixed flexion deformity (FFD) in unicompartmental knee arthroplasty (UKA) has been associated with worse functional outcomes, limited evidence exists regarding FFD changes. The objective of this study was to quantify FFD changes in patients with medial unicompartmental knee arthritis undergoing UKA, and investigate any correlation with clinical outcomes. Methods. This study included 136 patients undergoing robotic arm-assisted medial UKA between January 2018 and December 2022. The study included 75 males (55.1%) and 61 (44.9%) females, with a mean age of 67.1 years (45 to 90). Patients were divided into three study groups based on the degree of preoperative FFD: ≤ 5°, 5° to ≤ 10°, and > 10°. Intraoperative optical motion capture technology was used to assess pre- and postoperative FFD. Clinical FFD was measured pre- and postoperatively at six weeks and one year following surgery. Preoperative and one-year postoperative Oxford Knee Scores (OKS) were collected. Results. Overall, the median preoperative navigated (NAV) FFD measured 6.0° (IQR 3.1 to 8), while the median postoperative NAV FFD was 3.0° (IQR 1° to 4.4°), representing a mean correction of 49.2%. The median preoperative clinical FFD was 5° (IQR 0° to 9.75°) for the entire cohort, which decreased to 3.0° (IQR 0° to 5°) and 2° (IQR 0° to 3°) at six weeks and one year postoperatively, respectively. A statistically significant improvement in PROMs compared with baseline was evident in all groups (p < 0.001). Regression analyses showed that participants who experienced a larger FFD correction, showed greater improvement in PROMs (β = 0.609, p = 0.049; 95% CI 0.002 to 1.216). Conclusion. This study found that UKA was associated with an approximately 50% improvement in preoperative FFD across all three examined groups. Participants with greater correction of FFD also demonstrated larger OKS gains. These findings could prove a useful augment to clinical decision-making regarding candidacy for UKA and anticipated improvements in FFD


Bone & Joint Open
Vol. 5, Issue 7 | Pages 550 - 559
5 Jul 2024
Ronaldson SJ Cook E Mitchell A Fairhurst CM Reed M Martin BC Torgerson DJ

Aims

To assess the cost-effectiveness of a two-layer compression bandage versus a standard wool and crepe bandage following total knee arthroplasty, using patient-level data from the Knee Replacement Bandage Study (KReBS).

Methods

A cost-utility analysis was undertaken alongside KReBS, a pragmatic, two-arm, open label, parallel-group, randomized controlled trial, in terms of the cost per quality-adjusted life year (QALY). Overall, 2,330 participants scheduled for total knee arthroplasty (TKA) were randomized to either a two-layer compression bandage or a standard wool and crepe bandage. Costs were estimated over a 12-month period from the UK NHS perspective, and health outcomes were reported as QALYs based on participants’ EuroQol five-dimesion five-level questionnaire responses. Multiple imputation was used to deal with missing data and sensitivity analyses included a complete case analysis and testing of costing assumptions, with a secondary analysis exploring the inclusion of productivity losses.


Bone & Joint Open
Vol. 3, Issue 5 | Pages 383 - 389
1 May 2022
Motesharei A Batailler C De Massari D Vincent G Chen AF Lustig S

Aims

No predictive model has been published to forecast operating time for total knee arthroplasty (TKA). The aims of this study were to design and validate a predictive model to estimate operating time for robotic-assisted TKA based on demographic data, and evaluate the added predictive power of CT scan-based predictors and their impact on the accuracy of the predictive model.

Methods

A retrospective study was conducted on 1,061 TKAs performed from January 2016 to December 2019 with an image-based robotic-assisted system. Demographic data included age, sex, height, and weight. The femoral and tibial mechanical axis and the osteophyte volume were calculated from CT scans. These inputs were used to develop a predictive model aimed to predict operating time based on demographic data only, and demographic and 3D patient anatomy data.