Citalopram and escitalopram: adverse cardiac outcomes in medically ill inpatients
Bibliographic record
Abstract
Serotonin selective reuptake inhibitors (SSRIs) have widely replaced older tricyclic antidepressants (TCAs) in the treatment of depression and anxiety over the years, given their safer cardiac profile [Glassman, 1998].Even at therapeutic doses, TCAs commonly cause a slowing of intraventricular conduction through their sodiumchannel blocking properties, leading to prolonged PR, QRS and QT intervals [Pacher and Kecskemeti, 2004].Similarly, in vitro and animal studies have shown that citalopram and escitalopram, through their cardiotoxic metabolite, didesmethylcitalopram, can delay ventricular repolarization, prolong QT, and increase the risk of torsade de pointes by directly blocking potassium-hERG channels in cardiomyocytes [Overa, 1989;Witchel et al. 2002].In 2011-2012, the US Food and Drug Administration (FDA) released a safety communication regarding citalopram and escitalopram and their increased risk for QTc prolongation and cardiac outcomes, especially in elderly and patients with comorbid medical illnesses [FDA, 2012].The new FDA recommendations state that citalopram should not be used at doses greater than 40 mg per day in healthy adults, and not exceed 20 mg per day in the elderly, patients with hepatic impairment, or patients taking a CYP2C19 inhibitor.Health Canada released a similar warning for escitalopram in 2012, stating that doses greater than 10 mg should be avoided in these latter high-risk populations [Health Canada, 2012].A retrospective cross-sectional study in elderly surgical patients published in 2014 showed no association between citalopram and escitalopram and cardiac outcomes in this vulnerable population [van Haelst et al. 2014].However, this association has never been assessed in a sample of acutely medically ill inpatients, another highly vulnerable population.We performed a retrospective cohort study, including 275 medically ill inpatients, randomly
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".