A population‐based assessment of the potential interaction between serotonin‐specific reuptake inhibitors and digoxin
Bibliographic record
Abstract
AIM: In vitro evidence suggests that some serotonin-specific reuptake inhibitors (SSRIs) inhibit P-glycoprotein, a multidrug efflux pump responsible for the elimination of several drugs including digoxin. We sought to determine if some SSRIs cause digoxin toxicity in the clinical setting. METHODS: Population-based nested case-control study set in Ontario, Canada from 1994 to 2001. We studied all patients 66 years or older treated with digoxin. Prescription and hospital admission records were analysed to determine the relationship between the initiation of SSRI therapy and hospital admission for digoxin toxicity in the subsequent 30 days. RESULTS: Among 245 305 older patients treated with digoxin, we identified 3144 cases of digoxin toxicity. After adjusting for potential confounders, we observed an increased risk of digoxin toxicity following initiation of paroxetine [odds ratio (OR) 2.8; 95% confidence interval (CI) 1.6, 4.7], fluoxetine (OR 2.9; 95% CI 1.5, 5.4), sertraline (OR 3.0; 95% CI 1.9, 4.7), and fluvoxamine (OR 3.0; 95% CI 1.5, 5.7). However, an elevated risk was also seen with tricyclic antidepressants (OR 1.5; 95% CI 1.0, 2.4) and benzodiazepines (OR 2.1; 95% CI 1.7, 2.5), drugs classes having no known pharmacokinetic interaction with digoxin. There was no statistical difference in the risk of digoxin toxicity among any of the agents tested. CONCLUSIONS: We found no major discrepancy in the risk of digoxin toxicity after initiation of various SSRI antidepressants, suggesting that the inhibition of P-glycoprotein by sertraline and paroxetine observed in vitro is unlikely to be of major clinical significance.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".