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Association of Early-Stage Breast Cancer and Subsequent Chemotherapy With Risk of Atrial Fibrillation

2019· article· en· W2974166734 on OpenAlexafffundabout
Husam Abdel‐Qadir, Paaladinesh Thavendiranathan, Kinwah Fung, Eitan Amir, Peter C. Austin, Geoffrey S. Anderson, Douglas S. Lee

Bibliographic record

VenueJAMA Network Open · 2019
Typearticle
Languageen
FieldMedicine
TopicChemotherapy-induced cardiotoxicity and mitigation
Canadian institutionsPrincess Margaret Cancer CentreInstitute for Clinical Evaluative SciencesWomen's College HospitalUniversity of TorontoUniversity Health Network
FundersCanadian Institutes of Health Research
KeywordsMedicineBreast cancerInternal medicineAtrial fibrillationCancer registryHazard ratioCancerIncidence (geometry)PopulationRetrospective cohort studyStage (stratigraphy)Cumulative incidenceOncologyCohortConfidence interval

Abstract

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Importance: Several types of cancer have been linked to a higher risk of developing atrial fibrillation (AF). Fewer data exist regarding early-stage breast cancer (EBC), for which cardio-oncology concerns are more pertinent. Objective: To investigate the association of EBC and subsequent chemotherapy with the risk of developing AF. Design, Setting, and Participants: This was a population-based, retrospective, matched cohort study conducted in Toronto, Ontario, Canada, of 68 113 women diagnosed with EBC between April 2007 and December 2016 who were matched 1:3 to a cancer-free control group based on birth year and receipt of breast imaging. Prevalence of AF before the index date (date of EBC diagnosis) was compared between the cohorts using the McNemar test. Cumulative incidence function curves were used to describe the AF incidence. To study preexisting AF, participants were matched before exclusion for prior AF. For the remaining analyses, we excluded women with prior AF before matching. An analysis was conducted beginning 1 year after the index date (ie, excluding AF diagnoses in year 1), which we stratified by chemotherapy exposure. Multivariable cause-specific regression was used to determine the hazard ratio (HR) associated with EBC relative to the controls and the association of chemotherapy with AF in patients with EBC. Exposures: Breast cancer and chemotherapy. Main Outcomes and Measures: Incidence of AF. Results: A total of 68 113 women with EBC and 204 330 cancer-free controls were included in the study; both groups had a mean (SD) age of 60 (13) years. Of the women with EBC, 44.3% were diagnosed as having stage I breast cancer; 38.7%, stage II; and 13.4%, stage III; cancer stage information was missing for 3.6% of the patients. No difference was observed in preexisting AF prevalence (5.3% in the EBC cohort vs 5.2% in controls; P = .21). At 10 years after the index date, the AF incidence was 7.4% (95% CI, 7.1%-7.7%) for women with EBC and 6.8% (95% CI, 6.7%-7.0%) for the controls (P < .001). The adjusted cause-specific HR was significantly elevated at year 1 (HR, 2.16; 95% CI, 1.94-2.41) and after year 5 (HR, 1.20; 95% CI, 1.11-1.30) but not during years 2 through 5. Analyses beginning 1 year after diagnosis showed attenuated differences that remained statistically significant: the cumulative incidence of AF at 9-year follow-up was 7.0% (95% CI, 6.7%-7.3%) for patients with EBC and 6.5% (95% CI, 6.3%-6.7%) for the cancer-free controls. The rate of AF was higher in women who received chemotherapy (adjusted HR, 1.23; 95% CI, 1.13-1.35) but was not associated with exposure to anthracyclines or trastuzumab. Conclusions and Relevance: This study's findings suggest that patients with EBC may not have a higher prevalence of AF before cancer diagnosis. A higher rate of AF was observed in the first year and after 5 years following the EBC diagnosis. The rate of AF was higher in patients who received chemotherapy but appeared to not be associated with specific cardiotoxic agents. These findings suggest that the early and late periods of increased AF risk in EBC survivors warrant focused research to better understand the underlying causes and subsequent implications.

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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)0.001
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 score0.017
Threshold uncertainty score0.321

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 head0.010
GPT teacher head0.263
Teacher spread0.253 · 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

Citations45
Published2019
Admission routes3
Has abstractyes

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