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225.5: Quantiferon®-monitor as a biomarker of immunosuppression and predictor of infection: A scoping review.

2024· article· en· W4402801023 on OpenAlexaboutno aff
Bradley J. Gardiner, Roy F. Chemaly, Camille N. Kotton

Bibliographic record

VenueTransplantation · 2024
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvancements in Transdermal Drug Delivery
Canadian institutionsnot available
Fundersnot available
KeywordsImmunosuppressionQuantiFERONBiomarkerMedicineImmunologyBiologyTuberculosisPathologyLatent tuberculosis

Abstract

fetched live from OpenAlex

Introduction: Quantiferon®-Monitor (QFM) is a novel immune function assay that measures plasma interferon- γ levels after stimulation with innate and adaptive immune antigens. Clinical studies of this promising biomarker are emerging, exploring its role as a measure of the net state of immunosuppression and predictor of infections. The aim of this study is to synthesize the currently available published evidence regarding the use of QFM in transplant recipients, inform the feasibility of a systematic review, and identify knowledge gaps for future studies. Methods: We performed a systematic literature search that considered all primary study types. Studies had to involve human participants aged ≥18 years who received a solid organ or bone marrow transplant, with QFM testing performed at least once before/after transplant. Data were collected regarding study type, patient population, QFM testing and results, outcomes assessed, and other key findings. Results: Our search identified 13 published studies, of which 4 were available as conference abstracts only. The 9 manuscripts published from 2014-2023 all describe observational studies, including 7 prospective cohorts and 2 cross-sectional studies. Five studies were performed in Australia, 2 in Europe, 1 in Canada and 1 in Jordan. In total, QFM was assessed in 580 transplant recipients, 505 solid organ (223 liver, 151 kidney, 130 lung and 1 small bowel) and 75 allogeneic bone marrow transplant recipients. Most studies measured QFM longitudinally with multiple measurements over the first 6-12 months post-transplant. There was significant heterogeneity amongst values obtained and analyses performed. The main outcomes assessed were associations with immunosuppression (n=6), infections (n=8) and rejection (n=3) or graft versus host disease (n=2). Four studies identified relationships between higher immunosuppression doses and lower QFM values, 7 studies described increased risks of infection in patients testing low, and one study identified a link with QFM results and rejection. Conclusion: Global immune assays such as QFM have the potential to provide a measure of the net state of immunosuppression and identify overly immunosuppressed patients at high risk for infections. This could inform personalized interventions such as reductions in immunosuppression, more intensive clinical monitoring, or targeted antimicrobial prophylaxis. The current evidence is limited to observational studies, with significant heterogeneity in methodology that makes direct comparison difficult, rendering a meta-analysis/systematic review challenging. Ultimately, these assays could improve our ability to predict and prevent infections in transplant recipients, reduce hospitalizations, minimize adverse events of immunosuppression, improve quality of life and increase survival. Carefully planned interventional trials are needed to further evaluate these novel biomarkers.

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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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.098
GPT teacher head0.484
Teacher spread0.386 · 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 designBench or experimental
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

Citations0
Published2024
Admission routes1
Has abstractyes

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