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Record W4223552761 · doi:10.1186/s13075-022-02724-x

The impact of tofacitinib on fatigue, sleep, and health-related quality of life in patients with rheumatoid arthritis: a post hoc analysis of data from Phase 3 trials

2022· article· en· W4223552761 on OpenAlexaff
Susan J. Bartlett, Clifton O. Bingham, Ronald van Vollenhoven, Christopher W. Murray, David Gruben, David Gold, David Cella

Bibliographic record

VenueArthritis Research & Therapy · 2022
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsPfizer (Canada)McGill University
FundersPfizer
KeywordsMedicineTofacitinibJanus kinase inhibitorInternal medicinePlaceboAdalimumabRheumatoid arthritisQuality of life (healthcare)PopulationPost-hoc analysisMinimal clinically important differencePhysical therapyClinical trialRheumatologyRandomized controlled trialAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Fatigue, a common symptom of rheumatoid arthritis (RA), is detrimental to health-related quality of life (HRQoL). We evaluated the impact of tofacitinib on fatigue, sleep, and HRQoL and explored associations between fatigue, related patient-reported outcomes (PROs), and disease activity in RA patients. METHODS: This post hoc analysis pooled data from three Phase 3 studies of tofacitinib (ORAL Scan; ORAL Standard; ORAL Sync) in RA patients. Patients received tofacitinib 5 or 10 mg twice daily, placebo, or adalimumab (active control; ORAL Standard only, not powered for superiority) with conventional synthetic disease-modifying antirheumatic drugs. Assessed through Month (M)12 were changes from baseline in disease activity, Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F), Medical Outcomes Study Sleep scale (MOS-SS), and Short Form-36 Health Survey (SF-36) composite/domain scores, and proportions of patients reporting improvements from baseline in FACIT-F total and SF-36 domain scores ≥ minimum clinically important differences (MCIDs) or ≥ population normative values. Pearson correlations examined associations among PROs at M6. Treatment comparisons were exploratory, with p < 0.05 considered nominally significant. RESULTS: Generally, active treatment led to significant improvements from baseline in FACIT-F total, and MOS-SS and SF-36 composite/domain scores vs placebo, observed by M1 and maintained through M6 (last placebo-controlled time point). Through M6, more patients achieved improvements from baseline ≥ MCID and achieved scores ≥ population normative values in FACIT-F total and SF-36 domain scores with tofacitinib vs placebo. Through M12, some nominally significant improvements with tofacitinib vs adalimumab were observed. With active treatment at M6, FACIT-F scores were moderately (0.40-0.59) to highly (≥ 0.60) correlated with SF-36 composite/domain scores (particularly vitality), moderately correlated with most MOS-SS domain scores, and highly correlated with MOS-SS Sleep Problems Index I scores. Disease activity correlations were moderate with FACIT-F scores and low (0.20-0.39) to moderate with SF-36 general health domain/composite scores. CONCLUSION: Tofacitinib and adalimumab generally conferred significant, clinically meaningful improvements in fatigue, sleep, and HRQoL (including vitality) vs placebo through M6, with improvements maintained to M12. M6 correlations between FACIT-F, PROs of sleep, HRQoL, and disease activity underscore the interrelatedness of multiple PROs and disease activity in RA. TRIAL REGISTRATION: ClinicalTrials.gov NCT00847613 (registered: February 19, 2009); NCT00853385 (registered: March 2, 2009); NCT00856544 (registered: March 5, 2009).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.034
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.181

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0340.020
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.018
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.185
GPT teacher head0.455
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations13
Published2022
Admission routes1
Has abstractyes

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