Lithium toxicity following co-prescription of lithium and ACEI/ARBs: A population-based cohort study
Bibliographic record
Abstract
Guidelines caution against co-prescribing angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs) together with lithium, as this may increase lithium levels leading to toxicity. We conducted a population-based retrospective cohort study using administrative health data in Ontario, Canada, to evaluate the 90-day risk of any hospital encounter with lithium toxicity, all-cause mortality, and all-cause hospitalization in chronic lithium users newly prescribed an ACEI or ARB between 2002 and 2021. Modified Poisson regression was used to estimate risk ratios (RR). ACEI/ARB use versus non-use was not associated with a higher 90-day risk of lithium toxicity (2.20% vs. 1.75%, risk ratio [RR] 1.25, 95% confidence interval [CI] 0.86-1.84), and was associated with a lower risk of 90-day all-cause mortality (0.75% vs. 2.05%, RR 0.36, 95% CI 0.22-0.61). While there are potential concerns about confounding in this analysis, these findings suggest that warnings in guidelines and drug monographs against using ACEIs and ARBs with lithium may be unwarranted.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".