A double‐blind, randomized, placebo‐controlled pilot trial of atorvastatin for nephrogenic diabetes insipidus in lithium users
Bibliographic record
Abstract
OBJECTIVE: Lithium remains an important treatment for mood disorders but is associated with kidney disease. Nephrogenic diabetes insipidus (NDI) is associated with up to 3-fold risk of incident chronic kidney disease among lithium users. There are limited randomized controlled trials (RCT) for treatments of lithium-induced NDI, and existing therapies can be poorly tolerated. Therefore, novel treatments are needed for lithium-induced NDI. METHOD: We conducted a 12-week double-blind pilot RCT to assess the feasibility and efficacy of 20 mg/d atorvastatin vs placebo in the treatment of NDI in chronic lithium users. Patients, recruited between September 2017 and October 2018, were aged 18 to 85, currently on a stable dose of lithium, and determined to have NDI. RESULTS: Urinary osmolality (UOsm) at 12 weeks adjusted for baseline was not statistically different between groups (+39.6 mOsm/kg [95% CI, -35.3, 114.5] in atorvastatin compared to placebo groups). Secondary outcomes of fluid intake and aquaporin-2 excretions at 12 weeks adjusted for baseline were -0.13 L [95% CI, -0.54, 0.28] and 98.68 [95% CI, -190.34, 387.70], respectively. A moderate effect size was observed for improvements in baseline UOsm by ≥100 mOsm/kg at 12 weeks in patients who received atorvastatin compared to placebo (38.45% (10/26) vs 22.58% (7/31); Cohen's d = 0.66). CONCLUSION: Among lithium users with NDI, atorvastatin 20 mg/d did not significantly improve urinary osmolality compared to placebo over a 12-week period. Larger confirmatory trials with longer follow-up periods may help to further assess the effects of statins on NDI, especially within patients with more severe NDI.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".