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OP0222 TRAJECTORIES OF ANTIMALARIAL ADHERENCE AMONG NEWLY DIAGNOSED RHEUMATOID ARTHRITIS AND SYSTEMIC LUPUS ERYTHEMATOSUS PATIENTS: A POPULATION-BASED COHORT STUDY

2023· article· en· W4379523116 on OpenAlexaffabout
Md. Rashedul Hoque, J. Antonio Aviña‐Zubieta, Diane Lacaille, Mary A. De Vera, Yi Qian, Lawrence C. McCandless, John M. Esdaile, Hui Xie

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsUniversity of British ColumbiaResearch CanadaSimon Fraser University
FundersErasmus Universitair Medisch Centrum RotterdamErasmus Medisch CentrumAmsterdam University Medical CentersErasmus+Vanderbilt University Medical Center
KeywordsMedicineRheumatoid arthritisInternal medicineCohortLogistic regressionComorbidityHydroxychloroquinePopulationQuartileDiseasePhysical therapyConfidence intervalEnvironmental health

Abstract

fetched live from OpenAlex

Background Adherence to antimalarial regimens are suboptimal in rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) patients. Also, adherence is dynamic in nature, and varies over the time. Computing a single adherence level over a period may not explain the adherence trajectories of antimalarial in RA and SLE patients over time. Objectives To identify the groups of patients with similar patterns or trajectories of antimalarial adherence over time and evaluate the baseline determinants of the group membership of adherence trajectories. Methods All patients with incident RA/SLE and incident antimalarial use in British Columbia, Canada, between January 1997 and March 2021, were identified using previously published definitions and administrative health data. Patients were followed up for 12 months from the index date, the time when subjects met RA/SLE criteria and were on antimalarials. We calculated a measure of adherence, the proportion of days covered (PDC) for all patients each month. Then, we used group-based trajectory model (GBTM) analysis on monthly PDC values to identify the latent groups of antimalarial adherence trajectories. The number of groups was selected using the AIC and minimum percentage criterion. Finally, we used an ordered logistic regression to evaluate the baseline determinants of the group membership of adherence trajectories. Baseline determinants of adherence for assessment included sociodemographic factors (neighborhood income quartile, region, age, sex), disease-related factors (disease type (RA vs. SLE), disease duration at index date, hypertension, angina, COPD, modified Charlson comorbidity index), healthcare system factors (hospital visits, physician and specialist visits), and medication use factors (glucocorticoids, immunosuppressives, biologics, Cox-2 selective NSAIDs). Results We identified 27,510 patients with incident antimalarial use (23,997 RA and 3,513 SLE patients, mean ± SD age 56.8 ± 15.5 years, 74.8% female). Using GBTM analysis, we identified four groups for antimalarial medication adherence trajectories, representing an ordered pattern of antimalarial adherence from worse to better. Those trajectory groups were - Group 1: quick deterioration (19%), Group 2: moderate deterioration (15.7%), Group 3: slow deterioration (18.4%), and Group 4: consistent high adherence (46.8%) (Figure 1). Significant determinants of the group membership of adherence trajectories from the ordinal logistic regression model are shown in Table 1. The odds of better adherence were higher for those who, at baseline, were older, had higher income, had SLE compared with RA, had hypertension, had rheumatologist visits, and used glucocorticoids, immunosuppressives or Cox-2 selective NSAIDs. Conclusion Among incident RA/SLE incident antimalarial users from a population-based cohort, 53.2% did not continuously adhere to the antimalarial regimen in the first year of treatment. We identified four distinct antimalarial adherence trajectory groups in this study. Sociodemographic, disease-related, healthcare system, and medication use factors associated with better adherence trajectories could help inform strategies to improve antimalarial adherence among RA and SLE patients. REFERENCES: NIL. Acknowledgements: NIL. Disclosure of Interests None Declared.

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)0.001
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 score0.339
Threshold uncertainty score0.673

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.273
Teacher spread0.259 · 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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Citations0
Published2023
Admission routes2
Has abstractyes

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