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Record W4407059246 · doi:10.1016/j.eclinm.2025.103096

Risk of aortic aneurysm or dissection following use of fluoroquinolones: a retrospective multinational network cohort study

2025· article· en· W4407059246 on OpenAlexaff
Jack Janetzki, Jung Ho Kim, Evan Minty, Jung Ah Lee, Daniel R. Morales, Rohan Khera, Chungsoo Kim, Thamir M. Alshammari, Scott L. DuVall, Michael E. Matheny, Thomas Falconer, Seonji Kim, Phan Thanh Phuc, Phung‐Anh Nguyen, Min‐Huei Hsu, Jason C. Hsu, Rae Woong Park, Kenneth K. C. Man, Sarah Seager, Mui Van Zandt, James P. Gilbert, Patrick Ryan, Martijn J. Schuemie, Marc A. Suchard, George Hripcsak, Nicole Pratt, Seng Chan You

Bibliographic record

VenueEClinicalMedicine · 2025
Typearticle
Languageen
FieldMedicine
TopicUrinary Tract Infections Management
Canadian institutionsUniversity of Calgary
FundersDaiichi Sankyo EuropeYonsei University College of MedicineAstraZenecaNational Health and Medical Research CouncilNational Institutes of HealthInnovation and Technology CommissionYonsei UniversityEuropean CommissionMedical Research CouncilNational Center for Advancing Translational SciencesWellcome TrustNational Heart, Lung, and Blood InstituteAustralian GovernmentU.S. National Library of MedicineYale UniversityU.S. Department of Veterans AffairsAlnylam PharmaceuticalsParexelJanssen PharmaceuticalsCelgeneDepartment of Veterans' Affairs, Australian GovernmentBristol-Myers SquibbNovo NordiskNational Institute for Health and Care Research
KeywordsMedicineRetrospective cohort studyMultinational corporationAortic dissectionAneurysmCohortAortic aneurysmGeneral surgerySurgeryInternal medicineAorta

Abstract

fetched live from OpenAlex

Background: Fluoroquinolones (FQs) are commonly used to treat urinary tract infections (UTIs), but some studies have suggested they may increase the risk of aortic aneurysm or dissection (AA/AD). However, no large-scale international study has thoroughly assessed this risk. Methods: A retrospective cohort study was conducted using a large, distributed network analysis across 14 databases from 5 countries (United States, South Korea, Japan, Taiwan, and Australia). The study included 13,588,837 patients aged 35 or older who initiated systemic fluoroquinolones (FQs) or comparable antibiotics (trimethoprim with or without sulfamethoxazole [TMP] or cephalosporins [CPHs]) for UTI treatment in the outpatient setting between JAN 01, 2010 and DEC 31, 2019. Patients were included if at the index date they had at least 365 days of prior observation and were not hospitalised for any reason on or within 7 days prior to the index date. The primary outcome was AA/AD occurrence within 60 days of exposure, with secondary outcomes examining AA and AD separately. Cox proportional hazards models with 1:1 propensity score (PS) matching were used to estimate the risk, with results calibrated using negative control outcomes. Analyses were subjected to pre-defined study diagnostics, and only those passing all diagnostics were reported. Hazard ratios (HRs) were pooled using Bayesian random-effects meta-analysis. Findings: Among analyses that passed diagnostics there were 1,954,798 and 1,195,962 propensity-matched pairs for the FQ versus TMP and FQ versus CPH comparisons respectively. For the 60-day follow-up there was no difference in risk of AA/AD between FQ and TMP (absolute rate difference [ARD], 0.21 per 1000 person-year; calibrated HR, 0.91 [95% CI 0.73-1.10]). There was no significant difference in risk for FQ versus CPH (ARD, 0.11 per 1000 person-year; calibrated HR, 1.01 [95% CI 0.82-1.25]). Interpretation: This large-scale study used a rigorous design with objective diagnostics to address bias and confounding. There was no increased risk of AA/AD associated with FQ compared to TMP or CPH in patients treated for UTI in the outpatient setting. As we only examined FQ used to treat UTIs in the outpatient setting, the results may not be generalisable to other indications with different severity. Funding: Yonsei University College of Medicine, Government-wide R&D Fund project for infectious disease research (GFID), Republic of Korea, National Health and Medical Research Council (NHMRC) Australian Government. Department of Veterans Affairs (VA) Informatics and Computing Infrastructure (VINCI), Department of Veterans Affairs, the United States Government.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation 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 score0.005
Threshold uncertainty score0.717

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.361
Teacher spread0.331 · 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 teacher head, 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".

Quick stats

Citations6
Published2025
Admission routes1
Has abstractyes

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