Atorvastatin in the treatment of Lithium-induced nephrogenic diabetes insipidus: the protocol of a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Lithium is the gold-standard treatment for bipolar disorder, is highly effective in treating major depressive disorder, and has anti-suicidal properties. However, clinicians are increasingly avoiding lithium largely due to fears of renal toxicity. Nephrogenic Diabetes Insipidus (NDI) occurs in 15-20% of lithium users and predicts a 2-3 times increased risk of chronic kidney disease (CKD). We recently found that use of statins is associated with lower NDI risk in a cross-sectional study. In this current paper, we describe the methodology of a randomized controlled trial (RCT) to treat lithium-induced NDI using atorvastatin. METHODS: We will conduct a 12-week, double-blind placebo-controlled RCT of atorvastatin for lithium-induced NDI at McGill University, Montreal, Canada. We will recruit 60 current lithium users, aged 18-85, who have indicators of NDI, which we defined as urine osmolality (UOsm) < 600 mOsm/kg after 10-h fluid restriction. We will randomize patients to atorvastatin (20 mg/day) or placebo for 12 weeks. We will examine whether this improves measures of NDI: UOsm and aquaporin (AQP2) excretion at 12-week follow-up, adjusted for baseline. RESULTS: Not applicable. CONCLUSION: The aim of this clinical trial is to provide preliminary data about the efficacy of atorvastatin in treating NDI. If successful, lithium could theoretically be used more safely in patients with a reduced subsequent risk of CKD, hypernatremia, and acute kidney injury (AKI). If future definitive trials confirm this, this could potentially allow more patients to benefit from lithium, while minimizing renal risk. TRIAL REGISTRATION: ClinicalTrials.gov NCT02967653 . Registered in February 2017.
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 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 | 0.025 | 0.026 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".