Antipsychotic Use and Bloodstream Infections Among Adult Patients With Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
OBJECTIVE: Mounting evidence suggests that antipsychotics may have immunomodulatory effects, but their impact on disseminated infections remains unknown. This study thus sought to estimate the effect of antipsychotic treatment on the occurrence of bloodstream infection during long-term follow-up in adult patients with chronic obstructive pulmonary disease. METHODS: This retrospective cohort study, with new user and active comparator design, included adult patients seen from January 2008 to June 2018 in a tertiary teaching hospital in Buenos Aires, Argentina. New users of antipsychotic drugs were compared to new users of any benzodiazepine. The primary outcome of interest was incident bloodstream infection at 1 year of follow-up. Propensity score methods and a Cox proportional hazards model were used to adjust for baseline confounding. RESULTS: A total of 923 patients were included in the present analysis. Mean (SD) age was 75.0 (9.8) years, and 51.9% of patients were female. The cumulative incidence of bloodstream infections at 1 year was 6.0% and 2.3% in the antipsychotic and benzodiazepine groups, respectively. Antipsychotic use was associated with a higher risk of bloodstream infections during the first year of follow-up (hazard ratio [HR] = 2.41; 95% CI, 1.13 to 5.14) compared to benzodiazepine use. Antipsychotics with high dopamine receptor affinity presented greater risk than less selective agents (HR = 5.20; 95% CI, 1.53 to 17.67). CONCLUSIONS: Antipsychotic use is associated with bloodstream infections during the first year of follow-up in adult patients with chronic obstructive pulmonary disease. Further studies are warranted to confirm our findings and evaluate this effect in a broader population of patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".