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Record W3000938649 · doi:10.1161/jaha.119.014708

Changes in Cardiovascular Biomarkers With Breast Cancer Therapy and Associations With Cardiac Dysfunction

2020· article· en· W3000938649 on OpenAlexaff
Biniyam G. Demissei, Rebecca A. Hubbard, Liyong Zhang, Amanda M. Smith, Karyn Sheline, Caitlin McDonald, Vivek Narayan, Susan M. Domchek, Angela DeMichele, Payal D. Shah, Amy S. Clark, Kevin R. Fox, Jennifer M. Matro, Angela R. Bradbury, Hayley Knollman, Kelly Getz, Saro H. Armenian, James L. Januzzi, W.H. Wilson Tang, Peter Liu, Bonnie Ky

Bibliographic record

VenueJournal of the American Heart Association · 2020
Typearticle
Languageen
FieldMedicine
TopicChemotherapy-induced cardiotoxicity and mitigation
Canadian institutionsUniversity of Ottawa
FundersNational Heart, Lung, and Blood Institute
KeywordsMedicineEjection fractionHazard ratioInternal medicineAnthracyclineTrastuzumabProportional hazards modelCardiologyBreast cancerProspective cohort studyNatriuretic peptideCohort studyTroponinCancerOncologyHeart failureMyocardial infarctionConfidence interval

Abstract

fetched live from OpenAlex

Background We examined the longitudinal associations between changes in cardiovascular biomarkers and cancer therapy-related cardiac dysfunction (CTRCD) in patients with breast cancer treated with cardotoxic cancer therapy. Methods and Results Repeated measures of high-sensitivity cardiac troponin T (hs-cTnT), NT-proBNP (N-terminal pro-B-type natriuretic peptide), myeloperoxidase, placental growth factor, and growth differentiation factor 15 were assessed longitudinally in a prospective cohort of 323 patients treated with anthracyclines and/or trastuzumab followed over a maximum of 3.7 years with serial echocardiograms. CTRCD was defined as a ≥10% decline in left ventricular ejection fraction to a value <50%. Associations between changes in biomarkers and left ventricular ejection fraction were evaluated in repeated-measures linear regression models. Cox regression models assessed the associations between biomarkers and CTRCD. Early increases in all biomarkers occurred with anthracycline-based regimens. hs-cTnT levels >14 ng/L at anthracycline completion were associated with a 2-fold increased CTRCD risk (hazard ratio, 2.01; 95% CI, 1.00-4.06). There was a modest association between changes in NT-proBNP and left ventricular ejection fraction in the overall cohort; this was most pronounced with sequential anthracycline and trastuzumab (1.1% left ventricular ejection fraction decline [95% CI, -1.8 to -0.4] with each NT-proBNP doubling). Increases in NT-proBNP were also associated with CTRCD (hazard ratio per doubling, 1.56; 95% CI, 1.32-1.84). Increases in myeloperoxidase were associated with CTRCD in patients who received sequential anthracycline and trastuzumab (hazard ratio per doubling, 1.28; 95% CI, 1.04-1.58). Conclusions Cardiovascular biomarkers may play an important role in CTRCD risk prediction in patients with breast cancer who receive cardiotoxic cancer therapy, particularly in those treated with sequential anthracycline and trastuzumab therapy. Clinical Trial Registration URL: https://www.clinicaltrials.gov/. Unique identifier: NCT01173341.

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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.014
GPT teacher head0.242
Teacher spread0.228 · 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".

Quick stats

Citations163
Published2020
Admission routes1
Has abstractyes

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