Does lower urine-specific gravity predict decline in renal function and hypernatremia in older adults exposed to psychotropic medications? An exploratory analysis
Bibliographic record
Abstract
BACKGROUND: Exposure to psychotropic agents, including lithium, antipsychotics and antidepressants, has been associated with nephrogenic diabetes insipidus (NDI). This is especially concerning in older adults already at risk of developing chronic kidney disease (CKD) and hypernatremia with advanced aging. This study investigates whether commonly performed random urine-specific gravity (USG) tests can predict adverse NDI outcomes (CKD and hypernatremia) in psychotropic-exposed older adults. METHODS: This was a retrospective longitudinal study of 173 geriatric psychiatry patients (age ≥65 years) exposed to psychotropic medications. Our main continuous outcome was 'decrease in estimated glomerular filtration rate (eGFR) >10 mL/min/1.73 m(2)' over 5-year follow-up. Hypernatremia and acute kidney injury (AKI) were secondary outcomes. Whether baseline USG <1.010 predicted outcomes was assessed in bivariate and multivariate analyses. RESULTS: USG <1.010 predicted hypernatremia episodes (sodium concentration ≥150 mmol/L-28.1 versus 12%, χ(2) = 4.7, P = 0.03). USG <1.010 [odds ratio 2.36 (95% confidence interval 0.93-6.0), P = 0.07], baseline eGFR and typical antipsychotic use independently predicted decrease in eGFR >10 mL/min/1.73 m(2). Patients with a single baseline sodium concentration of ≥140 mmol/L and USG <1.010 have a 26.3% incidence of AKI and a 57.9% incidence of hypernatremia over the ensuing 5 years. CONCLUSIONS: In psychotropic-exposed older adults, there appears to be a clinically important association between low USG and developing both hypernatremia and CKD. USG may be a useful surrogate measure for NDI-related outcomes in large administrative database studies, where ideal measures such as 24-h urine volume may not be available.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".