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Record W3006686680 · doi:10.1093/jcag/gwz047.185

A186 HEPATIC STEATOSIS PREDICTS FIBROSIS IN LONG-TERM METHOTREXATE USE

2020· article· en· W3006686680 on OpenAlexaff
Marcel Tomaszewski, Monica Dahiya, Alireza Mohajerani, Hanaa Punja, Hin Hin Ko, Alnoor Ramji

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2020
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsCanadian Society of Intestinal ResearchUniversity of British Columbia
Fundersnot available
KeywordsSteatosisMedicineInternal medicineTransient elastographyGastroenterologyPsoriasisFatty liverFibrosisRheumatoid arthritisMethotrexateHepatic fibrosisPsoriatic arthritisPopulationRetrospective cohort studyDiseaseDermatologyLiver fibrosis

Abstract

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Abstract Background Methotrexate (MTX) is effective for dermatologic and rheumatologic conditions such as psoriasis (Ps), psoriatic arthritis (PsO) and rheumatoid arthritis (RA). Long-term MTX use may be complicated by hepatic fibrosis, although patient, disease factors and the mechanism remain unclear. Transient elastography (TE) is a non-invasive measure of hepatic fibrosis that is often used as surveillance in this patient population. Patients with Ps and PsO have higher rates of non-alcoholic fatty liver disease. The controlled attenuation parameter (CAP) measurement is a non-invasive test that correlates with histologic degree of steatosis. To our knowledge, no studies have evaluated hepatic steatosis via CAP scores in MTX use. Aims To determine the prevalence of steatosis and significant fibrosis (F≥stage 2) in persons on MTX therapy and to determine the predictive factors for these events. Methods A single centred retrospective cohort study was performed. Patients on >6 months of MTX for a dermatologic or rheumatologic disease who had undergone TE from January 2015 to September 2019 were included. Demographic variables, laboratory investigations, TE and CAP scores were collected. Multivariate analysis was performed to determine predictors of steatosis and fibrosis. Results A total of 177 patients on methotrexate were included. Ps was the most frequent diagnosis (n=52) followed by RA (n=50) and PsO (n=38). Steatosis (CAP≥245 dB/m) was present in 73.9% of patients. Patients with steatosis had significantly more fibrosis and a higher BMI than those without steatosis (CAP<245 dB/m). Higher CAP score was correlated with increased lifetime dose of methotrexate by Pearson correlation analysis (r=0.48, p=0.001) (n=85 patients). Multivariate regression analysis revealed that diabetes mellitus (OR 10.5, 95% CI 1.38–80.60), hypertension (OR 4.97, 95% CI 1.66–14.84), and BMI> 30 (OR 10.1, 95% CI 1.88–37.14) were predictors of steatosis (CAP≥245 dB/m). Predictors of METAVIR≥F2 (TE≥8.0 kPa) by multivariate regression analysis included CAP score of ≥270 (OR 8.36, 95% CI 1.88–37.14), diabetes mellitus (OR 2.85, 95% CI 1.09–7.48), hypertension (OR 5.4, 95% CI 2.23–13.0), dyslipidemia (OR 3.71, 95% CI 1.50–9.18) and alcohol use (OR 3.06, 95% CI 1.2–7.49). Conclusions In patients on MTX for rheumatologic and dermatologic diseases, hepatic steatosis was common and predicted significant fibrosis. Additionally, increasing MTX exposure is correlated with steatosis. Features of the metabolic syndrome including diabetes, hypertension or obesity were predictors of both steatosis and fibrosis (F≥2). Further study is needed to evaluate if steatosis is a mechanism by which fibrosis occurs in patients on MTX, or if it due to other patient factors. Funding Agencies None

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)0.000
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.244
Teacher spread0.226 · 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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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