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Absolute and Relative Risks of Kidney Outcomes Associated With Lithium vs Valproate Use in Sweden

2023· article· en· W4383482677 on OpenAlexaff
Alessandro Bosi, Catherine M. Clase, Laura Ceriani, Arvid Sjölander, Edouard L. Fu, Björn Runesson, Zheng Chang, Mikael Landén, Rino Bellocco, Carl‐Gustaf Elinder, Juan Jesús Carrero

Bibliographic record

VenueJAMA Network Open · 2023
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsMcMaster UniversityImpact
FundersVetenskapsrådet
KeywordsMedicineRenal functionLithium (medication)CreatinineKidney diseaseAlbuminuriaInternal medicineAcute kidney injuryCohortRelative riskCohort studyBipolar disorderConfidence interval

Abstract

fetched live from OpenAlex

Importance: Among patients with bipolar disorder, discordant findings have been published on the nephrotoxic effects of lithium therapy. Objective: To quantify absolute and relative risks of chronic kidney disease (CKD) progression and acute kidney injury (AKI) in people who initiated lithium compared with valproate therapy and to investigate the association between cumulative use and elevated lithium levels and kidney outcomes. Design, Setting, and Participants: This cohort study had a new-user active-comparator design and used inverse probability of treatment weights to minimize confounding. Included patients initiated therapy with lithium or valproate from January 1, 2007, to December 31, 2018, and had a median follow-up of 4.5 years (IQR, 1.9-8.0 years). Data analysis began in September 2021, using routine health care data from the period 2006 to 2019 from the Stockholm Creatinine Measurements project, a recurrent health care use cohort of all adult residents in Stockholm, Sweden. Exposures: New use of lithium vs new use of valproate and high (>1.0 mmol/L) vs low serum lithium levels. Main Outcomes and Measures: Progression of CKD (composite of >30% decrease relative to baseline estimated glomerular filtration rate [eGFR] and kidney failure), AKI (by diagnosis or transient creatinine elevations), new albuminuria, and annual eGFR decrease. Outcomes by attained lithium levels were also compared in lithium users. Results: The study included 10 946 people (median [IQR] age, 45 [32-59] years; 6227 female [56.9%]), of whom 5308 initiated lithium therapy and 5638 valproate therapy. During follow-up, 421 CKD progression events and 770 AKI events were identified. Compared with patients who received valproate, those who received lithium did not have increased risk of CKD (hazard ratio [HR], 1.11 [95% CI, 0.86-1.45]) or AKI (HR, 0.88 [95% CI, 0.70-1.10]). Absolute 10-year CKD risks were low and similar: 8.4% in the lithium group and 8.2% in the valproate group. No difference in the risk of developing albuminuria or the annual rate of eGFR decrease was found between groups. Among more than 35 000 routine lithium tests, only 3% of results were in the toxic range (>1.0 mmol/L). Lithium values greater than 1.0 mmol/L, compared with lithium values of 1.0 mmol/L or less, were associated with increased risk of CKD progression (HR, 2.86; 95% CI, 0.97-8.45) and AKI (HR, 3.51; 95% CI, 1.41-8.76). Conclusions and Relevance: In this cohort study, compared with new use of valproate, new use of lithium was meaningfully associated with adverse kidney outcomes, with low absolute risks that did not differ between therapies. However, elevated serum lithium levels were associated with future kidney risks, particularly AKI, 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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.096
GPT teacher head0.348
Teacher spread0.252 · 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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Citations33
Published2023
Admission routes1
Has abstractyes

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