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Record W2917564178 · doi:10.1177/2054358119830706

Outcomes Following Macrolide Use in Kidney Transplant Recipients

2019· article· en· W2917564178 on OpenAlexaffabout
Rachel Jeong, Robert R. Quinn, Krista L. Lentine, Anita Lloyd, Pietro Ravani, Brenda R. Hemmelgarn, Branko Braam, Amit X. Garg, Kevin Wen, Anita Wong-Chan, Sita Gourishankar, Ngan N. Lam

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2019
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsWestern UniversityUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsMedicineAzithromycinClarithromycinErythromycinRenal functionTacrolimusInternal medicineKidney transplantationCreatinineDialysisKidney diseaseMacrolide AntibioticsCalcineurinTransplantationAntibioticsMicrobiology

Abstract

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Background: Calcineurin inhibitors (CNI; cyclosporine, tacrolimus) are critical for kidney transplant immunosuppression, but have multiple potential drug interactions, such as with macrolide antibiotics. Macrolide antibiotics (clarithromycin, erythromycin, and azithromycin) are often used to treat atypical infections. Clarithromycin and erythromycin inhibit CNI metabolism and increase the risk of CNI nephrotoxicity, while azithromycin does not. Objective: To determine the frequency of CNI-macrolide co-prescriptions, the proportion who receive post-prescription monitoring, and the risk of adverse drug events in kidney transplant recipients. Design: Retrospective cohort study. Setting: We used linked health care databases in Alberta, Canada. Patients: We included 293 adult kidney transplant recipients from 2008-2015 who were co-prescribed a CNI and macrolide. Measurements: The primary outcome was a composite of all-cause hospitalization, acute kidney injury (creatinine increase ≥0.3 mg/dL or 1.5 times baseline), or death within 30 days of the macrolide prescription. Methods: We identified CNI-macrolide co-prescriptions and compared outcomes in those who received clarithromycin/erythromycin versus azithromycin. We used a linear mixed-effects model to examine the mean change in serum creatinine and estimated glomerular filtration rate (eGFR). Results: Of the 293 recipients who were co-prescribed a CNI and a macrolide, 38% (n = 112) were prescribed clarithromycin/erythromycin while 62% (n = 181) were prescribed azithromycin. Compared with azithromycin users, clarithromycin/erythromycin users were less likely to have outpatient serum creatinine monitoring post-prescription (56% vs 69%, P = .03). There was no significant difference in the primary outcome between the 2 groups (17% vs 11%, P = .11); however, the risk of all-cause hospitalization was higher in the clarithromycin/erythromycin group (10% vs 3%, P = .02). The mean decrement in eGFR was significantly greater in the clarithromycin/erythromycin versus azithromycin group (−5.4 vs −1.9 mL/min/1.73 m 2 , P < .05). Limitations: We did not have CNI levels to correlate with the timing of CNI-macrolide co-prescriptions. We also did not have information regarding the indications for macrolide prescriptions. Conclusion: Clarithromycin and erythromycin were frequently co-prescribed in kidney transplant recipients on CNIs despite known drug interactions. Clarithromycin/erythromycin use was associated with a higher risk of hospitalization compared with azithromycin users. Safer prescribing practices in kidney transplant recipients are warranted.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.056
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.022
GPT teacher head0.295
Teacher spread0.273 · 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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Citations16
Published2019
Admission routes2
Has abstractyes

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