Evidence map›Paper›PMID 38536518›Full record

ArticleClinical rheumatology2024

Prognostic modeling in early rheumatoid arthritis: reconsidering the predictive role of disease activity scores.

Alix Bird, Lauren Oakden-Rayner, Luke A Smith, Minyan Zeng, Shonket Ray, Susanna Proudman, Lyle J Palmer

Open access · hybridAbstract read
In one paragraph

Article in Clinical rheumatology, 2024. 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
0.5field-weighted citation impact, top 38% of its field
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, 1 citations in OpenAlex.

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

7 authors at 3 institutions in 2 countries.

Alix BirdAustralian Institute of Machine Learning, University of Adelaide, Corner Frome Road and North Terrace, Adelaide, SA, 5000, Australia. alix.bird@adelaide.edu.au.ORCID http://orcid.org/0000-0002-8676-0822
Lauren Oakden-RaynerAustralian Institute of Machine Learning, University of Adelaide, Corner Frome Road and North Terrace, Adelaide, SA, 5000, Australia.
Luke A SmithAustralian Institute of Machine Learning, University of Adelaide, Corner Frome Road and North Terrace, Adelaide, SA, 5000, Australia.
Minyan ZengAustralian Institute of Machine Learning, University of Adelaide, Corner Frome Road and North Terrace, Adelaide, SA, 5000, Australia.
Shonket RayAustralian Institute of Machine Learning, University of Adelaide, Corner Frome Road and North Terrace, Adelaide, SA, 5000, Australia.
Susanna ProudmanDepartment of Rheumatology, Royal Adelaide Hospital, Adelaide, SA, 5000, Australia.
Lyle J PalmerAustralian Institute of Machine Learning, University of Adelaide, Corner Frome Road and North Terrace, Adelaide, SA, 5000, Australia.
Australian Centre for Robotic Vision · AUMachine Intelligence Research Institute · USRoyal Adelaide Hospital · AU

Funding

GSK plc Industry PhD Scholarship
6 · The paper itself

Abstract

objectiveIn this prospective cohort study, we provide several prognostic models to predict functional status as measured by the modified Health Assessment Questionnaire (mHAQ). The early adoption of the treat-to-target strategy in this cohort offered a unique opportunity to identify predictive factors using longitudinal data across 20 years.

methodsA cohort of 397 patients with early RA was used to develop statistical models to predict mHAQ score measured at baseline, 12 months, and 18 months post diagnosis, as well as serially measured mHAQ. Demographic data, clinical measures, autoantibodies, medication use, comorbid conditions, and baseline mHAQ were considered as predictors.

resultsThe discriminative performance of models was comparable to previous work, with an area under the receiver operator curve ranging from 0.64 to 0.88. The most consistent predictive variable was baseline mHAQ. Patient-reported outcomes including early morning stiffness, tender joint count (TJC), fatigue, pain, and patient global assessment were positively predictive of a higher mHAQ at baseline and longitudinally, as was the physician global assessment and C-reactive protein. When considering future function, a higher TJC predicted persistent disability while a higher swollen joint count predicted functional improvements with treatment.

conclusionIn our study of mHAQ prediction in RA patients receiving treat-to-target therapy, patient-reported outcomes were most consistently predictive of function. Patients with high disease activity due predominantly to tenderness scores rather than swelling may benefit from less aggressive treatment escalation and an emphasis on non-pharmacological therapies, allowing for a more personalized approach to treatment. Key Points • Long-term use of the treat-to-target strategy in this patient cohort offers a unique opportunity to develop prognostic models for functional outcomes using extensive longitudinal data. • Patient reported outcomes were more consistent predictors of function than traditional prognostic markers. • Tender joint count and swollen joint count had discordant relationships with future function, adding weight to the possibility that disease activity may better guide treatment when the components are considered separately.

Indexed as

Antirheumatic AgentsArthritis, RheumatoidC-Reactive ProteinHumansMitoxantronePrognosisProspective StudiesSeverity of Illness Index1-hydroxy-5,8-bis(2-((2-hydroxyethyl)amino)ethylamino)-9,10-anthracenedioneAntirheumatic AgentsC-Reactive ProteinMitoxantroneLinear regressionOutcome assessmentPatient reported outcomePrecision medicinePrognosisRheumatoid arthritis

Identifiers

PMID38536518
PMCPMC11018671
OpenAlexW4393225295

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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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.