SSRI/SNRI ANTI-DEPRESSANT INDUCED INTERSTITIAL LUNG DISEASE: A CASE SERIES AND CASE- CONTROL STUDY
Bibliographic record
Abstract
SSRI and SNRI anti-depressants are widely prescribed in the elderly population. For unknown reasons, the incidence of interstitial lung disease (ILD) is increasing in western populations. There are published case reports and references on the Pneumotox web site (www.pneumotox.com) linking SSRIs/SNRIs to development of ILD and/or airway involvement (ILD/AWI). A case of venlafaxine induced ILD/AWI led us to explored this association in more detail. We report a series of 5 cases and a case control study examining the association between SSRI/SNRI usage and presence of ILD/AWI in an elderly population. Participants were all 296 elderly people followed in a primary care geriatric practice. A chart audit of the electronic medical record was done to identify cases and controls. The case definition included chronic respiratory symptoms and presence of ILD/AWI on CT or CXR. SSRI/SNRI usage was standardized to 10mg of citalopram and person-month (p-m) exposure was calculated. There were 24 cases identified and 272 controls. Their mean ages were 90.5 and 88.6 (ns) respectively. There were 16/24 cases exposed to SSRI/SNRI and 97/175 controls. The Odds Ratio was 3.61, 95% CI 1.49–8.74, p 0.007. The mean p-m exposure to SSRI/SNRI was 129.3 months for cases and 27.1 for controls (P<0.001). We conclude that SSRIs and SNRIs were significantly associated with the risk of ILD/AWI. Because of their wide spread usage, further studies should be done to validate these findings. Prescribers should be cautious to monitor for development of insidious pulmonary symptoms and signs when these drugs are prescribed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".