Real-world impact of antiepileptic drug combinations with versus without perampanel on healthcare resource utilization in patients with epilepsy in the United States
Bibliographic record
Abstract
OBJECTIVES: Combination regimens of antiepileptic drugs (AEDs) with various mechanisms of action (MOA) are commonly used in patients with refractory epilepsy. However, outcomes related to combination AEDs with novel MOA, such as perampanel (PER), are not well described. This study compared healthcare resource utilization (HRU) among recipients of PER-based combinations versus recipients of other non-PER-based combinations. METHODS: This retrospective study used claims data from the Symphony Health's IDV® (Integrated Dataverse) database (August 2012 to July 2018). Patients were aged ≥12 years with epilepsy or non-febrile convulsions, were treated with AED combinations, and had ≥12 and ≥6 months pre- and post-index date, respectively (date of initiation of the second AED in the combination). AEDs were categorized based on MOA: selective non-competitive antagonist of AMPA receptors (i.e., PER), sodium channel blocker (SC), synaptic vesicle protein 2A binding (SV2), and gamma-aminobutyric acid analog (G). Patients were then classified into MOA-based cohorts: PER + SC, PER + SV2, PER + G, SC + SC, SC + SV2, SC + G, SV2 + G, and G + G. HRU outcomes were evaluated during follow-up and compared between PER-based cohorts and non-PER-based cohorts. RESULTS: On average, patients in the PER + SC (N = 3,592), PER + SV2 (N = 2,200), and PER + G (N = 1,313) cohorts were younger and had a lower Quan-Charlson comorbidity index than those in non-PER-based cohorts. PER + SC and PER + SV2 users had significantly fewer all-cause hospitalizations than non-PER-based users (adjusted RR range: 0.66-0.89, all P < 0.05), while PER + G recipients had fewer all-cause hospitalizations than recipients of SV2 + G and G + G (adjusted RR range: 0.92-0.94). Similar trends were observed for epilepsy-related hospitalizations. Across all comparisons, PER-based combinations were associated with significantly lower rates of all-cause clinic/office/outpatient visits relative to non-PER-based combinations (adjusted RR range: 0.69-0.86, all P < 0.05). SIGNIFICANCE: Results showed that patients treated with PER-based combinations had fewer all-cause and epilepsy-related hospitalizations, and fewer all-cause clinic/office/outpatient visits compared with patients treated with most other non-PER-based combinations.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".