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Bone & Joint Open
Vol. 2, Issue 10 | Pages 806 - 812
1 Oct 2021
Gerritsen M Khawar A Scheper H van der Wal R Schoones J de Boer M Nelissen R Pijls B

Aims

The aim of this meta-analysis is to assess the association between exchange of modular parts in debridement, antibiotics, and implant retention (DAIR) procedure and outcomes for hip and knee periprosthetic joint infection (PJI).

Methods

We conducted a systematic search on PubMed, Embase, Web of Science, and Cochrane library from inception until May 2021. Random effects meta-analyses and meta-regression was used to estimate, on a study level, the success rate of DAIR related to component exchange. Risk of bias was appraised using the (AQUILA) checklist.


The Bone & Joint Journal
Vol. 104-B, Issue 12 | Pages 1292 - 1303
1 Dec 2022
Polisetty TS Jain S Pang M Karnuta JM Vigdorchik JM Nawabi DH Wyles CC Ramkumar PN

Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered.

Cite this article: Bone Joint J 2022;104-B(12):1292–1303.


Bone & Joint Open
Vol. 3, Issue 3 | Pages 245 - 251
16 Mar 2022
Lester D Barber C Sowers CB Cyrus JW Vap AR Golladay GJ Patel NK

Aims

Return to sport following undergoing total (TKA) and unicompartmental knee arthroplasty (UKA) has been researched with meta-analyses and systematic reviews of varying quality. The aim of this study is to create an umbrella review to consolidate the data into consensus guidelines for returning to sports following TKA and UKA.

Methods

Systematic reviews and meta-analyses written between 2010 and 2020 were systematically searched. Studies were independently screened by two reviewers and methodology quality was assessed. Variables for analysis included objective classification of which sports are safe to participate in postoperatively, time to return to sport, prognostic indicators of returning, and reasons patients do not.