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Drug-Drug Interactions and the Clinical Tolerability of Colchicine Among Patients With COVID-19

2024· article· en· W4402306656 on OpenAlexaboutno aff
Lama Alfehaid, Subrina Farah, Azza Omer, Brittany Weber, Omar S. Alkhezi, Yahya M. K. Tawfik, Amil M. Shah, Peter Libby, Leo F. Buckley

Bibliographic record

VenueJAMA Network Open · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammasome and immune disorders
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood Institute
KeywordsMedicinePlaceboTolerabilityInternal medicineAdverse effectDrugColchicinePharmacologyPathology

Abstract

fetched live from OpenAlex

Importance: Colchicine has many drug-drug interactions with commonly prescribed medications. Only pharmacokinetic studies have provided data on colchicine drug-drug interactions. Objective: To evaluate the clinical tolerability of colchicine according to the presence or absence of a drug-drug interaction. Design, Setting, and Participants: A secondary analysis of the COLCORONA trial was performed. The COLCORONA trial was a randomized, double-blind, placebo-controlled trial conducted in Brazil, Canada, Greece, South Africa, Spain, and the US between March 23, 2020, and January 20, 2021. The COLCORONA trial included ambulatory patients with COVID-19 with at least 1 high-risk characteristic and compared the effects of colchicine (0.5 mg twice daily for 3 days, then 0.5 mg daily thereafter) with placebo for 27 days. Data analysis was performed from February 24, 2023, to June 20, 2024. Exposure: In this secondary analysis, baseline medications that had interactions with colchicine were identified using a previously published expert classification. Main Outcomes and Measures: The primary outcome for this analysis was the composite of serious and nonserious treatment-related and treatment-unrelated gastrointestinal adverse events. The secondary outcomes were other adverse events and the composite of death or hospital admission due to COVID-19 infection. Logistic regression models adjusted for age, sex, estimated glomerular filtration rate, diabetes, heart failure, and myocardial infarction were assessed for effect modification of the association between the randomization arm and the outcomes of interest by drug-drug interaction status. Results: The cohort included 2205 participants in the colchicine arm and 2227 in the placebo arm (median age, 54 [IQR, 47-61] years; 2389 [54%] women). The most common colchicine drug-drug interactions were rosuvastatin (12%) and atorvastatin (10%). In fully adjusted models, the odds of any gastrointestinal adverse event were 1.80 (95% CI, 1.51-2.15) times higher in the colchicine arm than the placebo arm among people without a drug-drug interaction and 1.68 (95% CI, 1.24-2.26) times higher in the colchicine arm than the placebo arm among people with a drug-drug interaction (P = .69 for interaction). Drug-drug interaction status did not significantly modify the effect of colchicine on the composite of COVID-19 hospitalization or death (odds ratio, 0.91; 95% CI, 0.59-1.40 for drug-drug interaction and 0.84; 95% CI, 0.60-1.19 for no drug-drug interaction; P = .80 for interaction). Conclusions and Relevance: In this secondary analysis of the COLCORONA trial, operational classification of drug interactions system class 3 or 4 drug-drug interactions did not appear to significantly increase the risk of colchicine-related adverse effects. Trial Registration: ClinicalTrials.gov Identifier: NCT04322682.

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)0.000
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 score0.312
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.012
GPT teacher head0.310
Teacher spread0.298 · 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

Citations10
Published2024
Admission routes1
Has abstractyes

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