Serotonin reuptake inhibitors and mortality in epilepsy: A linked primary‐care cohort study
Bibliographic record
Abstract
OBJECTIVE: Preliminary evidence suggests that serotonin reuptake inhibitor (SRI) use may increase postictal respiratory drive and prevent death. We sought to determine whether SRIs are associated with improved all-cause and possible seizure-specific mortality in patients with epilepsy. METHODS: Patients with epilepsy and a random 10:1 sample without epilepsy were extracted from The ClinicAl research using LInked Bespoke studies and Electronic health Records (CALIBER) resource. The hazard ratio (HR) of all-cause and possible seizure-specific mortality, treating SRI use as a time-varying covariate, was determined using the date of a second SRI prescription as exposure and in discrete 6-month periods over the entire duration of follow-up. We used Cox regression and competing risk models with Firth correction to calculate the HR. We controlled for age, sex, depression, comorbidity, (Charlson comorbidity index) and socioeconomic status (Index of Multiple Deprivation). RESULTS: We identified 2,718,952 eligible patients in CALIBER, of whom 16,379 (0.60%) had epilepsy. Median age and follow-up were 44 (interquartile range [IQR] 29-61]) and 6.4 years (IQR 2.4-10.4 years), respectively, and 53% were female. A total of 2,178 patients (13%) had at least two SRI prescriptions. Hazard of all-cause mortality was significantly elevated following a second prescription for an SRI (HR 1.64 95% confidence interval [95% CI] 1.44-1.86; p < 0.001). The HR was similar in 163,778 age, sex, and general practitioner (GP) practice-matched controls without epilepsy. Exposure to an SRI was not associated with seizure-related death (HR 1.08, 95% CI 0.59-1.97; 0.796). SIGNIFICANCE: There is no evidence in this large population-based cohort that SRIs protect against all-cause mortality or seizure-specific mortality. Rather, SRI use was associated with increased mortality, irrespective of epilepsy, which is probably due to various factors associated with the use of antidepressants. Larger studies with systematically collected clinical data are needed to shed further light on these findings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".