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Record W4200095518 · doi:10.1093/ofid/ofab466.1591

1399. Clarithromycin–Rifampin-based Treatment for Non-tuberculous Mycobacterial Infections in Immunocompromised Patients Who Require Concomitant CYP-Metabolized Medications

2021· article· en· W4200095518 on OpenAlexaff
Isabel H Gonzalez-Bocco, Muneerah M Aleissa, Matthew P. Cheng, Jennifer Manne‐Goehler, Francisco M. Marty

Bibliographic record

VenueOpen Forum Infectious Diseases · 2021
Typearticle
Languageen
FieldMedicine
TopicMycobacterium research and diagnosis
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineClarithromycinAzithromycinRegimenRifabutinInternal medicineRifamycinAdverse effectConcomitantDiscontinuationAntibioticsPharmacologyMicrobiologyBiology

Abstract

fetched live from OpenAlex

Abstract Background Non-tuberculous mycobacteria (NTM) are causes of pulmonary and extrapulmonary disease that frequently affect immunocompromised hosts (ICH). Current treatment guidelines recommend a macrolide-based, multi-drug regimen that includes rifampin. Rifampin is a potent cytochrome P450 (CYP) 3A inducer, which often results in drug-drug interactions in ICH receiving multiple CYP substrates. One way to mitigate rifampin’s CYP induction is to utilize clarithromycin, a CYP inhibitor, as the accompanying macrolide. We evaluated the incidence of NTM treatment-related adverse events (AEs) in patients who received a clarithromycin-based regimen compared to patients who received an azithromycin-based regimen. Methods We conducted a retrospective review of NTM infection in 30 immunocompromised adults. All participants had a positive culture for a NTM and had received a rifamycin (rifampin or rifabutin) with a macrolide (azithromycin or clarithromycin) for treatment at Brigham and Women’s Hospital between 01/01/2011-10/18/2020 or Dana-Farber Cancer Institute between 06/03/2015-07/01/2020. The primary outcome was the incidence of NTM treatment-related AEs in patients who received a clarithromycin-based regimen compared to those who received an azithromycin-based regimen. Results There were no significant differences in the reasons for discontinuation of NTM treatment or 90-day mortality between groups. The number of AEs possibly related to NTM treatment were similar in patients who received a clarithromycin-based regimen and those who received an azithromycin-based one (10/13 vs. 14/17; p=0.73). The most common AE was liver function test abnormalities (Table 1). Additionally, the proportion of patients requiring dose adjustments for interacting medications and patients with out-of-range tacrolimus levels were similar between the two groups (23.1% vs. 29.4%; p=0.76 and 8.0% vs. 6.0%; p=1.00, respectively). Table 1: Adverse events Conclusion A clarithromycin-based regimen for NTM treatment was safe and well tolerated in our patient population. This combination provides a good alternative for patients requiring medications that are CYP substrates, or those who cannot tolerate azithromycin. Disclosures Matthew Cheng, MD, GEn1E Lifesciences (Advisor or Review Panel member)Kanvas Biosciences (Board Member, Shareholder)nplex biosciences (Advisor or Review Panel member)

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)0.003
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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.017
GPT teacher head0.326
Teacher spread0.308 · 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
Published2021
Admission routes1
Has abstractyes

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