Standard complete blood count to predict long‐term outcomes in febrile infection–related epilepsy syndrome (FIRES): A multicenter study
Bibliographic record
Abstract
OBJECTIVE: We investigated whether complete blood count (CBC) analyses during intensive care unit stay could predict 12-month outcomes in patients with cryptogenic febrile infection-related epilepsy syndrome (FIRES), a subset of new-onset refractory status epilepticus (NORSE). METHODS: Outcomes at 12 months were classified as "unfavorable" (Glasgow Outcome Score [GOS] 1-3) or "favorable" (GOS 4-5). Demographic, clinical, and serial CBC data were collected across treatment phases: (1) no immunotherapy (before initiation or no treatment), (2) first-line immunotherapy, and (3) second-line immunotherapy. For each treatment phase, predictive models stratified outcomes based on CBC features using decision tree regression, with separate models for adults and children. Model performance was tested using a leave-one-patient-out approach. RESULTS: We studied 63 patients (34 adults, 29 children) from 12 centers. Unfavorable outcomes occurred in 18 adults and 12 children. Children were more likely to receive second-line immunotherapy. We analyzed 1530 CBCs (adults: 997 CBCs, including 539 for unfavorable outcomes; children: 533 CBCs, including 415 for unfavorable outcomes). Subgroup analyses revealed differences in CBC levels according to the outcomes and the treatment received. Adults with unfavorable outcomes notably had higher neutrophil-to-lymphocyte ratios (NLRs) and monocyte-to-lymphocyte ratios (MLRs), whereas children with unfavorable outcomes had higher red cell distribution width. NLRs and MLRs increased when CBCs were collected after the initiation of immunotherapy for both adults and children. The variables of interest differed in the different predictive models but always included the proportion of at least one subtype of leukocyte. Prediction accuracy with our models was higher in children (87% overall, with the best performance in no-treatment and first-line phases) than in adults (83% overall, with the best performance during/after the initiation of second-line). SIGNIFICANCE: Findings suggest the potential for standard CBCs to serve as a rapid, accessible tool for early prognostication in cryptogenic FIRES, particularly in children.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".