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Record W2105534196 · doi:10.1136/bmj.f880

Use of high potency statins and rates of admission for acute kidney injury: multicenter, retrospective observational analysis of administrative databases

2013· review· en· W2105534196 on OpenAlexafffundabout
Colin R. Dormuth, Brenda R. Hemmelgarn, J. Michael Paterson, Matthew T. James, Gary Teare, Colette B. Raymond, Jean‐Philippe Lafrance, Adrian R. Levy, Amit X. Garg, Pierre Ernst

Bibliographic record

VenueBMJ · 2013
Typereview
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsWestern UniversityDalhousie UniversityUniversité de MontréalUniversity of ManitobaMcGill UniversityInstitute for Clinical Evaluative SciencesUniversity of CalgaryJewish General HospitalManitoba HealthSaskatchewan Health Quality CouncilUniversity of British Columbia
FundersCanadian Institutes of Health ResearchHealth Canada
KeywordsMedicineAcute kidney injuryKidney diseaseAtorvastatinRetrospective cohort studyStatinInternal medicinePotencyRosuvastatinObservational studyDatabaseConfoundingPopulationPropensity score matchingEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVE: To quantify an association between acute kidney injury and use of high potency statins versus low potency statins. DESIGN: Retrospective observational analysis of administrative databases, using nine population based cohort studies and meta-analysis. We performed as treated analyses in each database with a nested case-control design. Rate ratios for different durations of current and past statin exposure to high potency or low potency statins were estimated using conditional logistic regression. Ratios were adjusted for confounding by high dimensional propensity scores. Meta-analytic methods estimated overall effects across participating sites. SETTING: Seven Canadian provinces and two databases in the United Kingdom and the United States. PARTICIPANTS: 2,067,639 patients aged 40 years or older and newly treated with statins between 1 January 1997 and 30 April 2008. Each person hospitalized for acute kidney injury was matched with ten controls. INTERVENTION: A dispensing event was new if no cholesterol lowering drug or niacin prescription was dispensed in the previous year. High potency statin treatment was defined as ≥ 10 mg rosuvastatin, ≥ 20 mg atorvastatin, and ≥ 40 mg simvastatin; all other statin treatments were defined as low potency. Statin potency groups were further divided into cohorts with or without chronic kidney disease. MAIN OUTCOME MEASURE: Relative hospitalization rates for acute kidney injury. RESULTS: Of more than two million statin users (2,008,003 with non-chronic kidney disease; 59,636 with chronic kidney disease), patients with similar propensity scores were comparable on measured characteristics. Within 120 days of current treatment, there were 4691 hospitalizations for acute kidney injury in patients with non-chronic kidney injury, and 1896 hospitalizations in those with chronic kidney injury. In patients with non-chronic kidney disease, current users of high potency statins were 34% more likely to be hospitalized with acute kidney injury within 120 days after starting treatment (fixed effect rate ratio 1.34, 95% confidence interval 1.25 to 1.43). Users of high potency statins with chronic kidney disease did not have as large an increase in admission rate (1.10, 0.99 to 1.23). χ(2) tests for heterogeneity confirmed that the observed association was robust across participating sites. CONCLUSIONS: Use of high potency statins is associated with an increased rate of diagnosis for acute kidney injury in hospital admissions compared with low potency statins. The effect seems to be strongest in the first 120 days after initiation of statin treatment.

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)0.011
metaresearch head score (Gemma)0.033
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: Review · Consensus signal: none
Teacher disagreement score0.051
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0040.012
Science and technology studies0.0010.000
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 head0.238
GPT teacher head0.469
Teacher spread0.231 · 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
GenreReview

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".

Quick stats

Citations244
Published2013
Admission routes3
Has abstractyes

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