Association of Lithium Use and a Higher Serum Concentration of Lithium With the Risk of Declining Renal Function in Older Adults
Bibliographic record
Abstract
OBJECTIVE: Lithium is an important mood disorder treatment; however, the renal risks of its use in older adults are unclear. We wished to determine in older adults (1) whether lithium is associated with increased risk of renal decline compared to valproate and (2) whether this association differs with higher vs lower baseline serum lithium concentrations. METHOD: We conducted a population-based cohort study using linked health care databases (Ontario, Canada). The cohort consisted of older adults (mean age 71 years) accrued 2007-2015; 3,113 lithium users were propensity-score matched 1:1 to 3,113 valproate users. Users with higher (> 0.7 mmol/L) or lower concentration of serum lithium were further examined. The primary outcome was ≥ 30% loss in estimated glomerular filtration rate from baseline. RESULTS: Matched lithium users and valproate users demonstrated similar indicators of baseline health over a median (maximum) follow-up of 3.1 (8.3) years. Lithium was associated with increased risk of renal function loss compared to valproate (674/3,113 [21.7%] vs 584/3,113 [18.8%]; 6.5 vs 5.7 events per 100 person years; hazard ratio = 1.14 [95% CI = 1.02-1.27]). When baseline serum lithium concentrations were > 0.7 mmol/L, the risk of renal decline compared to valproate use was 1.26 (95% CI = 1.06-1.49); when baseline lithium concentrations were ≤ 0.7 mmol/L, the risk was 1.06 (95% CI = 0.92-1.22). CONCLUSION: In older adults, lithium use is associated with a statistically significant increased risk of renal decline compared to valproate use, although the decline is less than previously reported. Further studies should confirm whether this effect is primarily in patients with higher serum lithium concentrations.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".