Selective Serotonin Re-Uptake Inhibitors and Hyponatremia in Acutely Medically-Ill Inpatients
Bibliographic record
Abstract
BACKGROUND: Selective Serotonin Reuptake Inhibitors (SSRIs) have become the mainstay of treatment for depression, anxiety, and many other conditions. However, they have been associated with an increased risk of hyponatremia. Little is known about the risk of SSRI-associated hyponatremia in certain potentially at-risk populations, such as patients with acute medical illnesses. OBJECTIVE: The main objective of this study was to examine the effect of SSRIs on serum sodium levels in medically-ill inpatients. METHOD: We performed a retrospective cohort study of 239 medically-ill inpatients assessed by the psychiatric consultation-liaison team of a large Canadian academic hospital between 2008 and 2014. We grouped patients based on whether they were exposed to an SSRI, a non-SSRI antidepressant, or no antidepressant at all. Our primary outcome was the maximum decrease in serum sodium level observed within 30 days of antidepressant exposure in the inpatient setting. Our secondary outcome was the incidence of hyponatremia (serum sodium level <135 mEq/L) or severe hyponatremia (sodium level <130 mEq/L) within the same time frame. RESULTS: The maximum decrease in sodium serum level from baseline did not differ between the 3 groups studied (SSRIs - 3.31 mEq/L vs non-SSRI antidepressants -3.41 mEq/L vs no antidepressants -3.13 mEq/L, F (2) = 0.79, p= 0.92). The incidence of hyponatremia and severe hyponatremia did not differ between groups either. This remained the case after controlling for covariates. CONCLUSION: SSRIs do not appear to be associated with an increased risk of hyponatremia in medically-ill inpatients. Clinicians should not avoid prescribing SSRIs in this population based solely on the assumption of hyponatremia risk.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".