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Record W3191008658 · doi:10.1093/cvr/cvab262

Using multimarker screening to identify biomarkers associated with cardiovascular death in patients with atrial fibrillation

2021· article· en· W3191008658 on OpenAlexaff
Tymon Pol, Ziad Hijazi, Johan Lindbäck, Jonas Oldgren, John H. Alexander, Stuart J. Connolly, John W. Eikelboom, Michael D. Ezekowitz, Christopher B. Granger, Renato D. Lópes, Salim Yusuf, Agneta Siegbahn, Lars Wallentin

Bibliographic record

VenueCardiovascular Research · 2021
Typearticle
Languageen
FieldImmunology and Microbiology
TopicIL-33, ST2, and ILC Pathways
Canadian institutionsPopulation Health Research Institute
FundersDuke Clinical Research InstituteScience for Life LaboratoryUppsala UniversitetStiftelsen för Strategisk ForskningHjärt-LungfondenPfizer
KeywordsMedicineInternal medicineHazard ratioCohortAtrial fibrillationGDF15BiomarkerProportional hazards modelNatriuretic peptideGastroenterologyCardiologyEndocrinologyOncologyHeart failureConfidence intervalBiology

Abstract

fetched live from OpenAlex

AIMS: Atrial fibrillation (AF) is associated with higher mortality. Biomarkers may improve the understanding of key pathophysiologic processes in AF that lead to death. Using a new multiplex analytic technique, we explored the association between 268 biomarkers and cardiovascular (CV) death in anticoagulated patients with AF. METHODS AND RESULTS: A case-cohort design with 1.8- to 1.9-year follow-up. The identification cohort included 517 cases and 4057 randomly selected patients from ARISTOTLE. The validation cohort included 277 cases and 1042 randomly selected controls from RE-LY. Plasma collected at randomization was analysed with conventional immunoassays and the OLINK proximity extension assay panels: CVDII, CVDIII, and Inflammation. Association between biomarkers and CV death was evaluated using Random Survival Forest, Boruta, and adjusted Cox-regression analyses. The biomarkers most strongly and consistently associated with CV death were as follows (hazard ratio for inter-quartile comparison [95% CI]): N-terminal pro-B-type natriuretic peptide [NT-proBNP; 1.63 (1.37-1.93)], cardiac troponin T [cTnT-hs; 1.60 (1.35-1.88)], interleukin-6 [IL-6; 1.29 (1.13-1.47)], growth differentiation factor-15 [GDF-15; 1.30 (1.10-1.53)], fibroblast growth factor 23 [FGF-23; 1.21 (1.10-1.33)], urokinase receptor [uPAR; 1.38 (1.16-1.64)], trefoil factor 3 [TFF3; 1.27 (1.10-1.46)], tumour necrosis factor receptor 1 [TNFR1; 1.21 (1.01-1.45)], TNF-related apoptosis-inducing ligand receptor 2 [TRAILR2; 1.18 (1.04-1.34)], and cathepsin L1 [CTSL1; 1.22 (1.07-1.39)]. CONCLUSION: In this comprehensive screening of 268 biomarkers in anticoagulated patients with AF, the underlying mechanisms most strongly associated with CV death were cardiorenal dysfunction (NT-proBNP, cTnT-hs, CTSL1, TFF3), oxidative stress (GDF-15), inflammation (IL-6, GDF-15), calcium balance, vascular and renal dysfunction (FGF-23), fibrinolysis (suPAR), and apoptosis (TNFR1, TRAILR2). These findings provide novel insights into pathophysiologic aspects associated with CV death in AF. CLINICALTRIALS.GOV IDENTIFIER: NCT00412984 and NCT00262600.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.084
GPT teacher head0.329
Teacher spread0.245 · 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.

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

Citations47
Published2021
Admission routes1
Has abstractyes

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