Association Between Hospital Volume and Mortality in Status Epilepticus: A National Cohort Study
Bibliographic record
Abstract
OBJECTIVES: In various medical and surgical conditions, research has found that centers with higher patient volumes have better outcomes. This relationship has not previously been explored for status epilepticus. This study sought to examine whether centers that see higher volumes of patients with status epilepticus have lower in-hospital mortality than low-volume centers. DESIGN: Cohort study, using 2010-2015 data from the nationwide Case Mix Programme database of the U.K.'s Intensive Care National Audit and Research Centre. SETTING: Greater than 90% of ICUs in United Kingdom, Wales, and Northern Ireland. PATIENTS: Twenty-thousand nine-hundred twenty-two adult critical care admissions with a primary or secondary diagnosis of status epilepticus or prolonged seizure. INTERVENTIONS: Annual hospital status epilepticus admission volume. MEASUREMENTS AND MAIN RESULTS: We used multiple logistic regression to evaluate the association between hospital annual status epilepticus admission volume and in-hospital mortality. Hospital volume was modeled as a nonlinear variable using restricted cubic splines, and generalized estimating equations with robust SEs were used to account for clustering by institution. There were 2,462 in-hospital deaths (11.8%). There was no significant association between treatment volume and in-hospital mortality for status epilepticus (p = 0.54). This conclusion was unchanged across a number of subgroup and sensitivity analyses, although we lacked data on seizure duration and medication use. Secondary analyses suggest that many high-risk patients were already transferred from low- to high-volume centers. CONCLUSIONS: We find no evidence that higher volume centers are associated with lower mortality in status epilepticus overall. It is likely that national guidelines and local pathways in the United Kingdom allow efficient patient transfer from smaller centers like district general hospitals to provide satisfactory patient care in status epilepticus. Future research using more granular data should explore this association for the subgroup of patients with refractory and superrefractory status epilepticus.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".