Modifiable Factors Associated with Allopurinol Adherence and Outcomes Among Patients with Gout in an Integrated Healthcare System
Bibliographic record
Abstract
OBJECTIVE: To identify modifiable patient and provider factors associated with allopurinol adherence and the achievement of a serum urate acid (SUA) goal in gout. METHODS: We identified a retrospective cohort of patients with gout, newly treated with allopurinol. All patient data came from administrative datasets at a large integrated health delivery system. Patients were ≥ 18 years old at time of initial allopurinol dispensing, and had 12 months or more of membership and drug eligibility prior to the index date. Allopurinol adherence was defined as a proportion of days covered ≥ 0.80, evaluated during the first 12 months of observation after the initial dispensing. Multivariable logistic regression was used to examine factors associated with allopurinol nonadherence and attaining an SUA concentration < 6.0 mg/dl. RESULTS: We identified 13,341 patients with gout with incident allopurinol use (mean age 60 yrs, 78% men). Of these, 9581 patients (72%) had SUA measured both at baseline and during followup. Only 3078 patients (32%) attained an SUA target of < 6.0 mg/dl during followup. Potentially modifiable factors associated with treatment adherence and obtaining the SUA goal in the multivariable analysis included concomitant diuretic use, prescriber specialty, and allopurinol dosing practices. Adherent patients were 2.5-fold more likely than nonadherent patients to achieve an SUA < 6.0 mg/dl during observation. CONCLUSION: Among patients with gout initiating allopurinol in our study, 68% did not reach the SUA goal and 57% of patients were nonadherent. Modifiable factors, including allopurinol dose escalation, treatment adherence, rheumatology referral, and concomitant medication use, could be important factors to consider in efforts aimed at optimizing gout treatment outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".