Assessment of Access Barriers to Rifaximin Among Patients with Hepatic Encephalopathy Using Adjudicated Claims Data
Bibliographic record
Abstract
INTRODUCTION: Continuous treatment with rifaximin 550 mg (hereafter rifaximin) is associated with lower hospitalization rates in patients with hepatic encephalopathy (HE); however, access barriers may exist. This study assessed gaps in rifaximin access and the impact of treatment gaps, particularly those resulting from claim rejections, on hospitalizations and healthcare costs among patients with HE in the United States. METHODS: Plus database linked with Longitudinal Access and Adjudicated Data (2015-2022) were used to identify adults with HE who had ≥ 1 paid rifaximin prescription fill. Rifaximin treatment gaps were assessed during the 12-month period from the first observed attempt at receiving rifaximin (index date). Adjusted number of overt HE (OHE) hospitalizations and healthcare costs were compared over the 6 months following the index date between Cohort 1, who had no gap due to claim rejection and had < 7 days of treatment gap due to other reasons, and Cohort 2, who had ≥ 1 rejection gap or had ≥ 7 days of non-rejection gap. RESULTS: During the year following the index date, 94.7% of the 1711 patients experienced a treatment gap, including 34.8% with initiation gaps from first attempt at receiving rifaximin to first paid claim (77.7% of initiation gaps due to rejected claims) and 72.0% with gaps in access during active treatment (14.8% of active treatment gaps due to rejected claims). Compared with Cohort 1 (n = 432; mean age 56.3 years), Cohort 2 (n = 679; mean age 54.8 years) had 1.55 times the incidence rate of OHE hospitalizations [adjusted incidence rate ratio: 1.55 (95% confidence interval: 1.10-2.20)] and incurred US$1579 more in healthcare-associated costs per-patient-per-month (all p < 0.05). CONCLUSION: Prescription claim rejections frequently led to delays in rifaximin initiation and gaps in access during active treatment. Access barriers to rifaximin were associated with increased hospitalizations and healthcare costs in patients with HE.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".