Evidence map›Paper›PMID 41909003›Full record

ReviewIndian journal of orthopaedics2026

Impact of Glucagon-Like Peptide-1 Receptor Agonists on Knee Arthroplasty Outcomes.

Haroon Zaffar, Mohammed Araiz Imran, Farwah Rushd

Abstract readReview
In one paragraph

Review in Indian journal of orthopaedics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Haroon ZaffarEast Lancashire NHS Trust, Blackburn, England, UK.ORCID https://orcid.org/0009-0008-3034-1734
Mohammed Araiz ImranRoyal Alexandra Hospital, Paisley, Scotland, UK.
Farwah RushdUniversity of Glasgow, Glasgow, Scotland, UK.ORCID 0009-0004-1850-1628

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are increasingly used for managing both obesity and diabetes mellitus, two major risk factors for adverse outcomes following total knee arthroplasty (TKA). Despite this, their potential role in pre-operative optimisation remains uncertain. The aim of this systematic review and meta-analysis was to assess whether the use of GLP-1 RAs is associated with improved post-operative outcomes in patients undergoing primary TKA. Methods: A comprehensive literature search was conducted across MEDLINE, PubMed, Embase, and CENTRAL from inception to 1st June 2025. Eligible studies included adults (aged ≥ 18 years) undergoing primary TKA, comparing outcomes between GLP-1 RA users and non-users. Primary outcomes included surgical and medical complications. Secondary outcomes were hospital-related outcomes, including hospital readmissions and length of stay. Risk of bias and certainty of evidence were assessed using ROBINS-I and GRADE, respectively. Results: Six retrospective cohort studies met the inclusion criteria which consisted of 20,074 GLP-1 RA users and 55,332 controls. Meta-analysis showed a statistically significant reduction in the odds of 90-day readmissions in comparison with control groups (OR 0.76, 95% CI 0.61-0.94; Conclusions: GLP-1 RA use was associated with reduced 90-day readmissions following TKA. However, significant heterogeneity limits the clinical applicability of these findings. Further prospective studies are warranted. Supplementary Information: The online version contains supplementary material available at 10.1007/s43465-025-01646-5.

Indexed as

90-Day readmissionGlucagon-like peptide-1 receptor agonistsPost-operative complicationsRevisionTotal knee arthroplasty

Identifiers

PMID41909003
PMCPMC13031487

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.