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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.


The Bone & Joint Journal
Vol. 106-B, Issue 10 | Pages 1044 - 1049
1 Oct 2024
Abelleyra Lastoria DA Ogbolu C Olatigbe O Beni R Iftikhar A Hing CB

Aims

To determine whether obesity and malnutrition have a synergistic effect on outcomes from skeletal trauma or elective orthopaedic surgery.

Methods

Electronic databases including MEDLINE, Global Health, Embase, Web of Science, ScienceDirect, and PEDRo were searched up to 14 April 2024, as well as conference proceedings and the reference lists of included studies. Studies were appraised using tools according to study design, including the Oxford Levels of Evidence, the Institute of Health Economics case series quality appraisal checklist, and the CLARITY checklist for cohort studies. Studies were eligible if they reported the effects of combined malnutrition and obesity on outcomes from skeletal trauma or elective orthopaedic surgery.


Bone & Joint Open
Vol. 2, Issue 10 | Pages 785 - 795
1 Oct 2021
Matar HE Porter PJ Porter ML

Aims

Metal allergy in knee arthroplasty patients is a controversial topic. We aimed to conduct a scoping review to clarify the management of metal allergy in primary and revision total knee arthroplasty (TKA).

Methods

Studies were identified by searching electronic databases: Cochrane Central Register of Controlled Trials, Ovid MEDLINE, and Embase, from their inception to November 2020, for studies evaluating TKA patients with metal hypersensitivity/allergy. All studies reporting on diagnosing or managing metal hypersensitivity in TKA were included. Data were extracted and summarized based on study design, study population, interventions and outcomes. A practical guide is then formulated based on the available evidence.


The Bone & Joint Journal
Vol. 103-B, Issue 9 | Pages 1449 - 1456
1 Sep 2021
Kazarian GS Lieberman EG Hansen EJ Nunley RM Barrack RL

Aims

The goal of the current systematic review was to assess the impact of implant placement accuracy on outcomes following total knee arthroplasty (TKA).

Methods

A systematic review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines using the Ovid Medline, Embase, Cochrane Central, and Web of Science databases in order to assess the impact of the patient-reported outcomes measures (PROMs) and implant placement accuracy on outcomes following TKA. Studies assessing the impact of implant alignment, rotation, size, overhang, or condylar offset were included. Study quality was assessed, evidence was graded (one-star: no evidence, two-star: limited evidence, three-star: moderate evidence, four-star: strong evidence), and recommendations were made based on the available evidence.


The Bone & Joint Journal
Vol. 103-B, Issue 1 | Pages 7 - 15
1 Jan 2021
Farhan-Alanie MM Burnand HG Whitehouse MR

Aims

This study aimed to compare the effect of antibiotic-loaded bone cement (ALBC) versus plain bone cement (PBC) on revision rates for periprosthetic joint infection (PJI) and all-cause revisions following primary elective total hip arthroplasty (THA) and total knee arthroplasty (TKA).

Methods

MEDLINE, Embase, Web of Science, and Cochrane databases were systematically searched for studies comparing ALBC versus PBC, reporting on revision rates for PJI or all-cause revision following primary elective THA or TKA. A random-effects meta-analysis was performed. The study was registered in the International Prospective Register of Systematic Reviews (PROSPERO ID CRD42018107691).


The Bone & Joint Journal
Vol. 101-B, Issue 1 | Pages 7 - 14
1 Jan 2019
Sorel JC Veltman ES Honig A Poolman RW

Aims

We performed a meta-analysis investigating the association between preoperative psychological distress and postoperative pain and function after total knee arthroplasty (TKA).

Materials and Methods

Pubmed/Medline, Embase, PsycINFO, and the Cochrane library were searched for studies on the influence of preoperative psychological distress on postoperative pain and physical function after TKA. Two blinded reviewers screened for eligibility and assessed the risk of bias and the quality of evidence. We used random effects models to pool data for the meta-analysis.