Prevalence of impaired kidney function in patients with long‐term lithium treatment: A systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVES: Although lithium renal effects have been extensively investigated, prevalence rates of chronic kidney disease (CKD) in lithium-treated patients vary. Our aim was to provide prevalence estimates and related moderators. METHODS: We performed a systematic review in PubMed/Embase until November 01, 2021, conducting a random effects meta-analysis of studies evaluating CKD prevalence rates in lithium-treated patients calculating overall prevalence ±95% confidence intervals (CIs). Meta-regression analyses included sex, age, body mass index, smoking, hypertension, diabetes, cardiovascular disease, lithium-treatment dose, duration, and blood levels. Subgroup analyses included sample size, diagnoses, and study design. Pooled odds ratios (OR) were estimated for studies including patients receiving nonlithium treatment. Study quality was assessed using the Newcastle-Ottawa scale. RESULTS: Five, nine, and six trials were rated as high, fair, and low quality, respectively. In 20 studies (n = 25,907 patients), we estimated an overall prevalence of 25.5% (95% CI = 19.8-32.2) of impaired kidney function; despite lack of differences (p = 0.18), prevalence rates were higher in elderly samples than mixed samples of elderly and nonelderly (35.6%, 95% CI = 21.4-52.9, k = 2, n = 3,161 vs. 25.1%, 95% CI = 19.1-31.3, k = 18, n = 22,746). Prevalence rates were associated with longer lithium treatment duration (p = 0.04). Cross-sectional studies provided lower rates than retrospective studies (14.5%, 95% CI = 13.5-15.5, k = 6, n = 4,758 vs. 29.5%, 95% CI = 22.1-38.0, k = 12, n = 17,988, p < 0.001). Compared with 722,529 patients receiving nonlithium treatment, the OR of impaired kidney function in 14,187 lithium-treated patients was 2.09 (95% CI = 1.24-3.51, k = 8, p = 0.005). CONCLUSIONS: One-fourth of patients receiving long-term lithium may develop impaired kidney function, although research suffers from substantial heterogeneity between studies. This risk may be twofold higher compared with nonlithium treatment and may increase for a longer lithium treatment duration.
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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".