Hyponatremia Associated with the Use of Common Antidepressants in the <i>All of Us</i> Research Program
Bibliographic record
Abstract
Selective serotonin reuptake inhibitor (SSRI), serotonin-norepinephrine reuptake inhibitor (SNRI), and norepinephrine-dopamine reuptake inhibitor (NRI) antidepressants can cause hyponatremia through syndrome of inappropriate antidiuretic hormone secretion (SIADH). This study assesses the differential risks of hyponatremia associated with commonly prescribed SSRIs (fluoxetine, paroxetine, sertraline, citalopram, escitalopram), SNRIs (duloxetine, venlafaxine) and NRI (bupropion), as well as omeprazole as a reference, with a retrospective observational cohort study in the All of Us Research Program, a national multicenter research cohort containing de-identified electronic health records (EHR). Participants who had been prescribed monotherapy with any of eight common antidepressants were included, with each drug considered as a separate arm indexed with a start date. Events were defined as the first occurrence of a low plasma sodium measurement or a clinical diagnosis recorded for either hyponatremia or SIADH. Those who did not have events were censored at their last plasma sodium measurement. A total of 17,439 individuals were exposed to one of the eight antidepressants as monotherapy. The overall incidences for hyponatremia were 0.87% in the first 30 days and 10.5% in the first 3 years in the antidepressant arms. Compared to sertraline, duloxetine (hazard ratio [HR] = 1.37 [1.19-1.58]) and escitalopram (HR = 1.16 [1.01-1.33]) were associated with the highest overall risk of hyponatremia, and bupropion (HR = 0.83 [0.73-0.94]) and paroxetine (HR = 0.78 [0.65-0.93]) were associated with the lowest risk. The risks were unchanged after adjusting for comorbidity and polypharmacy. Such information could help guide providers in managing patients and their risks of hyponatremia when on common antidepressants.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".