Evidence map›Paper›PMID 39991690›Full record

ArticleRheumatology advances in practice2025

A novel classifier of radiographic knee osteoarthritis for use on knee DXA images is predictive of joint replacement in UK Biobank.

Rhona A Beynon, Fiona R Saunders, Raja Ebsim, Benjamin G Faber, Mijin Jung, Jennifer S Gregory, Claudia Lindner, Richard M Aspden, Nicholas C Harvey, Timothy Cootes and 1 more

Abstract read
In one paragraph

Article in Rheumatology advances in practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

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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

11 authors.

Rhona A BeynonMusculoskeletal Research Unit, Bristol Medical School, University of Bristol, Bristol, UK.ORCID https://orcid.org/0000-0001-5494-9519
Fiona R SaundersCentre for Arthritis and Musculoskeletal Health, University of Aberdeen, Aberdeen, UK.
Raja EbsimDivision of Informatics, Imaging and Data Sciences, School of Health Sciences, University of Manchester, Manchester, UK.
Benjamin G FaberMusculoskeletal Research Unit, Bristol Medical School, University of Bristol, Bristol, UK.ORCID https://orcid.org/0000-0003-4102-0190
Mijin JungMusculoskeletal Research Unit, Bristol Medical School, University of Bristol, Bristol, UK.
Jennifer S GregoryCentre for Arthritis and Musculoskeletal Health, University of Aberdeen, Aberdeen, UK.
Claudia LindnerDivision of Informatics, Imaging and Data Sciences, School of Health Sciences, University of Manchester, Manchester, UK.ORCID https://orcid.org/0000-0001-9396-3436
Richard M AspdenCentre for Arthritis and Musculoskeletal Health, University of Aberdeen, Aberdeen, UK.ORCID https://orcid.org/0000-0002-0693-1194
Nicholas C HarveyMedical Research Council Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK.ORCID https://orcid.org/0000-0002-8194-2512
Timothy CootesDivision of Informatics, Imaging and Data Sciences, School of Health Sciences, University of Manchester, Manchester, UK.
Jonathan H TobiasMusculoskeletal Research Unit, Bristol Medical School, University of Bristol, Bristol, UK.

Funding

Wellcome Trust 223267
6 · The paper itself

Abstract

Objectives: DXA scans may offer a novel means of evaluating radiographic knee OA (rKOA) in large population studies and through opportunistic screening. We aimed to develop and apply a semi-automated method for assessing rKOA using ≈20 000 knee DXA images from UK Biobank (UKB) and assess its face validity by checking for expected relationships with clinical outcomes. Methods: Right knee DXA scans were manually annotated for osteophytes to derive corresponding grades. Joint space narrowing (JSN) grades in the medial joint compartment were determined from automatically measured minimum joint space width. Overall rKOA grade (0-4) was determined by combining osteophyte and JSN grades. Logistic regression was employed to investigate the associations of osteophyte, JSN and rKOA grades with knee pain and hospital-diagnosed KOA. Cox proportional hazards modelling was used to examine the associations of these variables with risk of subsequent total knee replacement (TKR). Results: Of the 19 595 participants included (mean age 63.7 years), 19.5% had rKOA grade ≥1 (26.1% female, 12.5% male). Grade ≥1 osteophytes and grade ≥1 JSN were associated with knee pain, hospital-diagnosed KOA and TKR. Higher rKOA grades were linked to stronger associations with these clinical outcomes, with the most pronounced effects observed for TKR. Hazard ratios for the association of rKOA grades with TKR were 3.28, 8.75 and 28.63 for grades 1, 2 and 3-4, respectively. Conclusions: Our DXA-derived measure of rKOA demonstrated a progressive relationship with clinical outcomes. These findings support the use of DXA for classifying rKOA in large epidemiological studies and in future population-based screening.

Indexed as

DXAknee osteoarthritisradiographic osteoarthritis

Identifiers

PMID39991690
PMCPMC11846665

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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.