Budgetary impact of treating acute bipolar mania in hospitalized patients with quetiapine: an economic analysis of clinical trials
Bibliographic record
Abstract
OBJECTIVE: To present a tool that allows estimation of the budget impact of treatments for acute mania in bipolar I disorder from a US healthcare payer perspective. METHODS: Using discrete event simulation, the course of individuals is simulated beginning with hospitalization. Discharge depends on symptom level measured by the Young Mania Rating Scale (YMRS). The treatment effect is determined using time-dependent regression equations derived from trial data, and decision rules obtained from clinical experts. Outcomes include: time to response and symptom resolution; proportion of subjects reaching each outcome; number of adverse events. Costs were obtained from hospital discharge databases, the National Medicare Physician Fee Schedule and RedBook. Different scenarios are examined, each describing the proportion of subjects on the various treatments (lithium, divalproex sodium, olanzapine, risperidone, and quetiapine--monotherapy and in combination with lithium). Analyses are intention-to-treat over 100 days, corresponding to follow-up in mania trials. Despite its flexibility and structural adaptability, the model has some important limitations related to the characteristics of the clinical trials. These include focus on inpatient management of acute mania, use of the YMRS as the model driver, polypharmacy restricted to two-drug regimens, no explicit consideration of titration and dose changes, and relatively short time horizon. RESULTS: Scenarios with a greater proportion of quetiapine users (5% vs. 40% and 100%) result in a smaller impact on the healthcare budget (6912, 6277, and 5525 dollars per patient, respectively) and improvements in patient outcomes (e.g., 43%, 47%, and 54% responding at day 21; 74%, 77%, and 80% remitting by day 84). Sensitivity analyses showed that the budget impact is influenced by drug prices, discharge criteria and side-effect management. CONCLUSION: Results suggest that increased use of quetiapine for bipolar mania in the US is economically justified and improves health outcomes. In addition, this model illustrates that discrete event simulation is a useful and versatile tool for budget impact analyses.
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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 | 0.024 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".