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

Abdel‐Qadir, Husam; Thavendiranathan, Paaladinesh; Fung, Kinwah; Amir, Eitan; Austin, Peter C.; Anderson, Geoffrey S.; Lee, Douglas S.

doi:10.1001/jamanetworkopen.2019.11838

Cited by 26 publications

(27 citation statements)

References 43 publications

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“…Not all studies are in agreement with this correlation: a population-based, retrospective, matched cohort study suggests that women patients with early breast cancer may not have a higher prevalence of AF before cancer diagnosis [ 28 ]. However, such observation should be interpreted with caution since the prevalence of CVD and its risk factors is well known to be lower in women population.…”

Section: Atrial Fibrillation In Cancermentioning

confidence: 99%

Atrial Cardiomyopathy and Atrial Fibrillation in Cancer

Ren

Yao

Yue

et al. 2021

Cardiology Research and Practice

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The number of patients with oncologic and cardiologic comorbidities is increasing. A growing number of evidence shows an inextricable link between cancer, atrial fibrillation, and atrial cardiomyopathy. Cancer itself and resultant inflammation, anticancer treatment, and other comorbidities lead to atrial remodeling and fibrosis, which increases the tendency to develop atrial cardiomyopathy and atrial fibrillation. The scarcity of current literature and ambiguous results make its relationship difficult to fully understand. In this review, we will summarize existing evidence of the relationships and interactions among cancer, atrial cardiomyopathy, and atrial fibrillation and discuss the underlying mechanisms, and provide better information for the management of these patients.

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Section: Atrial Fibrillation In Cancermentioning

confidence: 99%

Atrial Cardiomyopathy and Atrial Fibrillation in Cancer

Ren

Yao

Yue

et al. 2021

Cardiology Research and Practice

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“…Besides linear models, the deep learning method is also used to predict complications of this chronic disease [20] but this work considers each complication independently. For breast cancer survivors, relationships between cardiac complications and cancer were also investigated [3], [4], [21] to show the correlation between these two diseases.…”

Section: Related Workmentioning

confidence: 99%

“…In our setting, we focus on profiling the risk of developing cardiac complications in a six-month window after the initial diagnosis of breast cancer, and the positive instances are defined as patients who have cardiac complications in this window. Following previous clinical research [3], [4], we identify six cardiac complications including atrial fibrillation (AF), coronary artery disease (CAD), heart failure (HF), hypertension, peripheral arterial disease (PAD), and stroke. Descriptions, ICD codes, and the corresponding numbers of positive instances of these complications are shown in Table II.…”

Section: A Datasetsmentioning

confidence: 99%

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Cardiac Complication Risk Profiling for Cancer Survivors via Multi-View Multi-Task Learning

Pham¹,

Yin²,

Mehta³

et al. 2021

Preprint

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Complication risk profiling is a key challenge in the healthcare domain due to the complex interaction between heterogeneous entities (e.g., visit, disease, medication) in clinical data. With the availability of real-world clinical data such as electronic health records and insurance claims, many deep learning methods are proposed for complication risk profiling. However, these existing methods face two open challenges. First, data heterogeneity relates to those methods leveraging clinical data from a single view only while the data can be considered from multiple views (e.g., sequence of clinical visits, set of clinical features). Second, generalized prediction relates to most of those methods focusing on single-task learning, whereas each complication onset is predicted independently, leading to suboptimal models. We propose a multi-view multi-task network (MuViTaNet) for predicting the onset of multiple complications to tackle these issues. In particular, MuViTaNet complements patient representation by using a multi-view encoder to effectively extract information by considering clinical data as both sequences of clinical visits and sets of clinical features. In addition, it leverages additional information from both related labeled and unlabeled datasets to generate more generalized representations by using a new multi-task learning scheme for making more accurate predictions. The experimental results show that MuViTaNet outperforms existing methods for profiling the development of cardiac complications in breast cancer survivors. Furthermore, thanks to its multi-view multi-task architecture, MuViTaNet also provides an effective mechanism for interpreting its predictions in multiple perspectives, thereby helping clinicians discover the underlying mechanism triggering the onset and for making better clinical treatments in real-world scenarios.

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“…Nonetheless, while numerous studies have suggested a higher risk of cancer following an AF diagnosis [ 15 ] with a significant prevalence of colorectal cancer [ 16 ], there is a paucity of research in the literature focusing on AF in patients with preexisting BC [ 17 ]. In addition, despite the well-known cardiotoxic side effects of treatments frequently used in BC [ 18 , 19 , 20 ], including radiotherapy [ 10 ], little is known whether cardiotoxic chemotherapy may enhance the link between BC and AF [ 5 , 21 ]. Finally, the higher risk of AF has been more commonly reported in the early stage of the disease [ 22 ], whereas AF associated with metastatic breast cancer (MBC) has been rarely reported.…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer and Atrial Fibrillation

Mauro

Lucà²,

Tetta

et al. 2022

JCM

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This study aims to establish the incidence of atrial fibrillation (AF) in breast cancer (BC) patients, focusing on staging and anti-cancer treatment. A meta-analysis was conducted to investigate the incidence of AF in BC patients and compare this incidence to other cancers. Furthermore, we evaluated the occurrence of AF as an adverse effect of biological therapies vs. non-biological therapies vs. biological therapies + non-biological therapies in BC. Finally, we compared the incidence of AF in early BC and metastatic BC. Thirty studies were included. Twenty-two studies focused on BC, encompassing 166,271 patients. In the BC group, 2.7% of patients developed AF, while in the “all cancer” group, 5.8% of patients developed AF. In addition, there was no difference between different types of therapies (p = 0.61) and between early and metastatic BC (p = 0.57). The type of anti-cancer therapy and the staging of BC does not influence AF’s occurrence in this neoplastic disease.

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

Abstract: This matched cohort study investigates whether early-stage breast cancer and subsequent chemotherapy are associated with the risk of developing atrial fibrillation among women in Ontario, Canada, who were diagnosed with early-stage breast cancer compared with women without breast cancer.

Cited by 26 publications

References 43 publications

Atrial Cardiomyopathy and Atrial Fibrillation in Cancer

Atrial Cardiomyopathy and Atrial Fibrillation in Cancer

Cardiac Complication Risk Profiling for Cancer Survivors via Multi-View Multi-Task Learning

Breast Cancer and Atrial Fibrillation

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