Risk of Severe Exacerbation Associated with Gabapentinoid Use in Patients with Chronic Obstructive Pulmonary Disease: A Population-Based Cohort Study
Bibliographic record
Abstract
Evidence on the risk of adverse respiratory outcomes associated with gabapentinoids in patients with chronic obstructive pulmonary disease (COPD) remains limited. Thus, we aimed to assess the risk of severe COPD exacerbation associated with gabapentinoids. We assembled a base cohort of patients aged ≥ 55 years newly diagnosed with COPD between 1993 and 2021 using the UK's Clinical Practice Research Datalink, linked to the Hospital Episode Statistics, and Office for National Statistics datasets. Using a time-conditional propensity score (TCPS)-matched, new-user design, patients prescribed gabapentinoids with an indication of epilepsy, neuropathic pain, or other chronic pain were matched 1:1 with non-users with the same indication on age, sex, calendar year, COPD duration, and TCPS. Cox proportional hazards models were used to estimate the hazard ratios (HR) and 95% confidence intervals (CIs) of severe exacerbation associated with gabapentinoid use compared to non-use in the overall cohort, and by indication. The study cohort comprised 29,882 gabapentinoid users, including 1,256 with epilepsy, 19,155 patients with neuropathic pain, and 9,471 with other chronic pain matched 1:1 with non-users. Compared with non-use, gabapentinoid use was associated with an increased risk of severe exacerbation in the overall cohort (HR 1.43; 95% CI: 1.35-1.52), and among patients with epilepsy (HR 1.39; 95% CI: 1.11-1.74), neuropathic pain (HR 1.43; 95% CI: 1.32-1.54), and other chronic pain (HR 1.45; 95% CI: 1.31-1.60). These findings suggest that gabapentinoid use is associated with an increased risk of severe exacerbation among patients with COPD, consistent among patients with neuropathic pain, epilepsy, and other chronic pain.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".