Advertisement for orthosearch.org.uk
Results 1 - 6 of 6
Results per page:
The Bone & Joint Journal
Vol. 106-B, Issue 6 | Pages 532 - 539
1 Jun 2024
Lei T Wang Y Li M Hua L

Aims

Intra-articular (IA) injection may be used when treating hip osteoarthritis (OA). Common injections include steroids, hyaluronic acid (HA), local anaesthetic, and platelet-rich plasma (PRP). Network meta-analysis allows for comparisons between two or more treatment groups and uses direct and indirect comparisons between interventions. This network meta-analysis aims to compare the efficacy of various IA injections used in the management of hip OA with a follow-up of up to six months.

Methods

This systematic review and network meta-analysis used a Bayesian random-effects model to evaluate the direct and indirect comparisons among all treatment options. PubMed, Web of Science, Clinicaltrial.gov, EMBASE, MEDLINE, and the Cochrane Library were searched from inception to February 2023. Randomized controlled trials (RCTs) which evaluate the efficacy of HA, PRP, local anaesthetic, steroid, steroid+anaesthetic, HA+PRP, and physiological saline injection as a placebo, for patients with hip OA were included.


The Bone & Joint Journal
Vol. 104-B, Issue 5 | Pages 604 - 612
1 May 2022
MacDessi SJ Wood JA Diwan A Harris IA

Aims

Intraoperative pressure sensors allow surgeons to quantify soft-tissue balance during total knee arthroplasty (TKA). The aim of this study was to determine whether using sensors to achieve soft-tissue balance was more effective than manual balancing in improving outcomes in TKA.

Methods

A multicentre randomized trial compared the outcomes of sensor balancing (SB) with manual balancing (MB) in 250 patients (285 TKAs). The primary outcome measure was the mean difference in the four Knee injury and Osteoarthritis Outcome Score subscales (ΔKOOS4) in the two groups, comparing the preoperative and two-year scores. Secondary outcomes included intraoperative balance data, additional patient-reported outcome measures (PROMs), and functional measures.


The Bone & Joint Journal
Vol. 106-B, Issue 11 | Pages 1216 - 1222
1 Nov 2024
Castagno S Gompels B Strangmark E Robertson-Waters E Birch M van der Schaar M McCaskie AW

Aims

Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials.

Methods

A systematic review was conducted in accordance with PRISMA guidelines. We searched MEDLINE and Embase on 5 May 2024 for studies utilizing ML to predict OA progression. Titles and abstracts were independently screened, followed by full-text reviews for studies that met the eligibility criteria. Key information was extracted and synthesized for analysis, including types of data (such as clinical, radiological, or biochemical), definitions of OA progression, ML algorithms, validation methods, and outcome measures.


The Bone & Joint Journal
Vol. 103-B, Issue 10 | Pages 1561 - 1570
1 Oct 2021
Blyth MJG Banger MS Doonan J Jones BG MacLean AD Rowe PJ

Aims

The aim of this study was to compare the clinical outcomes of robotic arm-assisted bi-unicompartmental knee arthroplasty (bi-UKA) with conventional mechanically aligned total knee arthroplasty (TKA) during the first six weeks and at one year postoperatively.

Methods

A per protocol analysis of 76 patients, 43 of whom underwent TKA and 34 of whom underwent bi-UKA, was performed from a prospective, single-centre, randomized controlled trial. Diaries kept by the patients recorded pain, function, and the use of analgesics daily throughout the first week and weekly between the second and sixth weeks. Patient-reported outcome measures (PROMs) were compared preoperatively, and at three months and one year postoperatively. Data were also compared longitudinally and a subgroup analysis was conducted, stratified by preoperative PROM status.


The Bone & Joint Journal
Vol. 102-B, Issue 7 | Pages 941 - 949
1 Jul 2020
Price AJ Kang S Cook JA Dakin H Blom A Arden N Fitzpatrick R Beard DJ

Aims

To calculate how the likelihood of obtaining measurable benefit from hip or knee arthroplasty varies with preoperative patient-reported scores.

Methods

Existing UK data from 222,933 knee and 209,760 hip arthroplasty patients were used to model an individual’s probability of gaining meaningful improvement after surgery based on their preoperative Oxford Knee or Hip Score (OKS/OHS). A clinically meaningful improvement after arthroplasty was defined as ≥ 8 point improvement in OHS, and ≥ 7 in OKS.


The Bone & Joint Journal
Vol. 102-B, Issue 4 | Pages 434 - 441
1 Apr 2020
Hamilton DF Burnett R Patton JT MacPherson GJ Simpson AHRW Howie CR Gaston P

Aims

There are comparatively few randomized studies evaluating knee arthroplasty prostheses, and fewer still that report longer-term functional outcomes. The aim of this study was to evaluate mid-term outcomes of an existing implant trial cohort to document changing patient function over time following total knee arthroplasty using longitudinal analytical techniques and to determine whether implant design chosen at time of surgery influenced these outcomes.

Methods

A mid-term follow-up of the remaining 125 patients from a randomized cohort of total knee arthroplasty patients (initially comprising 212 recruited patients), comparing modern (Triathlon) and traditional (Kinemax) prostheses was undertaken. Functional outcomes were assessed with the Oxford Knee Score (OKS), knee range of movement, pain numerical rating scales, lower limb power output, timed functional assessment battery, and satisfaction survey. Data were linked to earlier assessment timepoints, and analyzed by repeated measures analysis of variance (ANOVA) mixed models, incorporating longitudinal change over all assessment timepoints.