The risk of hyponatremia induced by SSRIs and SNRIs antidepressants: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: To systematically evaluate the risk differences of hyponatremia induced by selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs), stratify risks among individual drugs, and provide evidence-based guidance for clinical medication safety. METHODS: A systematic search was conducted across the Cochrane Library, PubMed, and Web of Science databases. Study quality was assessed using the Newcastle-Ottawa Scale (NOS), and the certainty of evidence was evaluated using the GRADE framework. A meta-analysis was performed to compare the event rates and odds ratios (ORs) of hyponatremia between SSRIs and SNRIs, followed by subgroup analysis and bias assessment. RESULTS: A total of 38 observational studies (including 30 cohort studies and 8 case-control studies) were included in this study. The overall event rate of hyponatremia with antidepressants was 6.03% (P < 0.001), with rates of 5.98% for SSRIs and 6.13% for SNRIs. Both drug classes significantly increased the risk of hyponatremia (SSRIs: OR = 2.158; SNRIs: OR = 2.270, P < 0.001), with SNRIs demonstrating a higher risk in clinically relevant hyponatremia (OR = 2.227, P < 0.001). Risk stratification among individual drugs revealed that fluoxetine (SSRIs) and venlafaxine (SNRIs) had the highest risk, while sertraline and duloxetine were associated with lower risks. CONCLUSION: Both SSRIs and SNRIs significantly increase the risk of hyponatremia, with SNRIs posing a slightly higher risk. Clinicians should consider individual patient characteristics when selecting lower-risk medications and enhance serum sodium monitoring in high-risk populations.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.043 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".