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Record W4229038231 · doi:10.1093/ndt/gfac071.033

MO502: Use of Lithium, Valproate and the Risk of Acute or Chronic Kidney Disease: An Observational Study From Routine Care Data

2022· article· en· W4229038231 on OpenAlexaff
Alessandro Bosi, Laura Ceriani, Edouard L. Fu, Björn Runesson, Catherine M. Clase, Zheng Chang, Mikael Landén, Carl‐Gustaf Elinder, Juan Jesús Carrero, Rino Bellocco

Bibliographic record

VenueNephrology Dialysis Transplantation · 2022
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsMcMaster UniversityImpact
Fundersnot available
KeywordsMedicineKidney diseaseHazard ratioInternal medicineRenal functionLithium (medication)Proportional hazards modelBipolar disorderObservational studyCohort studyConfidence intervalAcute kidney injuryConfoundingIntensive care medicine

Abstract

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Abstract BACKGROUND AND AIMS Lithium is an established treatment for bipolar disorder and treatment-resistant depression. Despite awareness of potential kidney damage, there is a lack of research evidence to inform on the existence and magnitude of the risk. Observational studies to date show conflicting findings, possibly explained by inadequate control populations, prevalent user bias and a lack of information on kidney function or serum lithium levels. METHOD We conducted a cohort study to compare kidney outcomes in adults who started lithium or valproate therapy in Stockholm, Sweden, during 2007–18. Within lithium users, we also compared outcomes by average serum lithium concentrations during the first year of therapy. Kidney outcomes were CKD progression (composite of >30% eGFR decline and kidney failure) and AKI (by diagnosis or KDIGO transient creatinine elevations). Propensity score weighted Cox regression was used to estimate hazard ratios with [95% confidence intervals (95% CI)] and balance 46 identified confounders. Complete collection of repeated dispensations at Swedish pharmacies allowed modelling of the time-dependent risk associated with cumulative lithium exposure. Sensitivity analyses included restricting to patients with a diagnosis of bipolar disorder, apply a 1-year lag, use of alternative weighting methods and evaluation of eGFR monitoring rates to ascertain surveillance bias between groups. RESULTS We included 16 645 individuals, of whom 5308 initiated lithium and 5638 valproate therapy. Their median age was 45 years (57% women) and median eGFR was 99 mL/min/1.73 m2. A total of 179 CKD progression events and 234 AKI were identified during a median of follow-up of 4.3 and 4.2 years, respectively. After propensity score weighting, the adjusted hazard ratio for the risk of CKD progression was 1.12 (95% CI 0.86–1.47) and for AKI 0.88 (95% CI 0.7–1.09). There was a weak, non-statistically significant association between cumulative exposure to lithium and the risk of CKD progression that was not observed for cumulative use of valproate. Results were consistent among patients with a bipolar disorder diagnosis and robust to 1-year lag or alternative weighing methods. A total of 3913 lithium users were on therapy for at least one year and underwent routine serum lithium monitoring. Compared with patients with average serum lithium levels <1.0 mEq/L, those with serum lithium ≥1.0 mEq/L (n = 270) were at a higher risk of both CKD progression (HR: 2.18; 1.28–3.71) and AKI (HR: 1.37; 0.81–2.34). CONCLUSION In this analysis of patients from routine clinical practice, the risk of kidney outcomes associated with lithium therapy did not differ from that of valproate. Modest risk magnitudes need to be offset with the effectiveness and anti-suicidal benefits of lithium. However, elevated serum lithium levels strongly predicting kidney risk, emphasizing the need for close monitoring and lithium dose-adjustment.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.061
GPT teacher head0.310
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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