scholarly journals Long-term irreversible trastuzumab-induced cardiotoxicity for metastatic breast cancer in a patient without cardiac risk factors

2017 ◽  
Vol 2017 (7) ◽  
Author(s):  
Satoru Tanaka ◽  
Ayana Ikari ◽  
Toshikatsu Nitta ◽  
Tetsuya Horiuchi
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Metastatic Breast Cancer ◽  
Metastatic Breast ◽  
Cardiac Risk ◽  
Cardiac Risk Factors

scholarly journals Cardiotoxicity Related to Long-Term Trastuzumab Therapy in Metastatic Breast Cancer: The Potential Role of Treatment Duration and Cardiac Risk Factors

2015 ◽  
Vol 21 (3) ◽  
pp. 318-321 ◽  
Author(s):  
Barbara Pistilli ◽  
Andrea Marcellusi ◽  
Luciano Latini ◽  
Roberto Accardi ◽  
Benedetta Ferretti ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Metastatic Breast Cancer ◽  
Treatment Duration ◽  
Potential Role ◽  
Metastatic Breast ◽  
Cardiac Risk ◽  
Trastuzumab Therapy

scholarly journals Machine-Learning to Discern Interactive Clusters of Risk Factors for Late Recurrence of Metastatic Breast Cancer

2021 ◽  
Author(s):  
Juan Luis Gomez Marti ◽  
Adam Brufsky ◽  
Alan Wells ◽  
Xia Jiang
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Risk Factors ◽  
Metastatic Breast Cancer ◽  
Metastatic Breast ◽  
Large Data ◽  
Late Recurrence ◽  
Individual Risk ◽  
Metastatic Recurrence

Background: Risk of metastatic recurrence of breast cancer after initial diagnosis and treatment depends on the presence of a number of risk factors. Although most univariate risk factors have been identified using classical methods, machine-learning methods are also being conducted to tease out non-obvious contributors to a patient’s individual risk of developing late distant metastasis. Bayesian-network algorithms may predict not only risk factors but also interactions among these risks, which consequently lead to metastatic breast cancer. We proposed to apply a previously developed machine-learning method to predict risk factors of 5-, 10- and 15-year metastasis. Methods: We applied a previously validated algorithm named the Markov Blanket and Interactive risk factor Learner (MBIL) on the electronic health record (EHR)-based Lynn Sage database (LSDB) from the Lynn Sage Comprehensive Breast Cancer at Northwestern Memorial Hospital. This algorithm provided an output of both single and interactive risk factors of 5-, 10-, and 15-year metastasis from LSDB. We individually examined and interpreted the clinical relevance of these interactions based on years to metastasis and the reliance on interactivity between risk factors. Results: We found that with lower alpha values (low interactivity score), the prevalence of variables with an independent influence on long term metastasis was higher (i.e., HER2, TNEG). As the value of alpha increased to 480, stronger interactions were needed to define clusters of factors that increased the risk of metastasis (i.e., ER, smoking, race, alcohol usage). Conclusion: MBIL identified single and interacting risk factors of metastatic breast cancer, many of which were supported by clinical evidence. These results strongly recommend the development of further large data studies with different databases to validate the degree to which some of these variables impact metastatic breast cancer in the long term.

scholarly journals Venous and Arterial Thrombosis Following Abemaciclib Therapy for Metastatic Breast Cancer

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 4063-4063
Author(s):  
Nathan Watson ◽  
Seth A Wander ◽  
Hanny Al-Samkari
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Metastatic Breast Cancer ◽  
Brain Metastases ◽  
Risk Score ◽  
Real World ◽  
Arterial Thrombosis ◽  
Research Funding ◽  
Metastatic Breast

Abstract Introduction: Over the past several years, inhibitors of cyclin-dependent kinases 4 and 6 (CDK 4/6) have revolutionized the treatment of hormone receptor (HR)-positive breast cancer. However, evidence suggests an increased risk of venous thromboembolism (VTE) with use of these agents. Recent studies additionally suggest higher VTE rates in real-world populations receiving palbociclib as compared with the highly selected population of published clinical trials. Such study in real-world patients has not been performed for abemaciclib, a newer CDK 4/6 inhibitor with unique pharmacokinetic and pharmacodynamic properties. This study evaluated rates and predictors of thrombosis in patients receiving abemaciclib for metastatic breast cancer. Methods: We conducted a multicenter observational cohort study of patients with metastatic breast cancer receiving abemaciclib at 5 affiliated hospitals. A research patient data repository was queried to identify all patients receiving abemaciclib and manual chart review was used to extract all data. Patient demographics, concurrent medications, labs, Khorana risk score, tumor characteristics, and relevant venous and arterial thrombotic risk factors (including age, BMI, prior thrombosis, recent surgery, hereditary thrombophilia, systemic inflammatory diseases, presence of brain metastases, hypertension, hyperlipidemia, diabetes mellitus, atrial fibrillation, heart failure, and atherosclerosis) were collected for all patients. The primary endpoint was thrombosis during abemaciclib treatment or within 30 days of discontinuation. Multivariable logistic models assessed predictors of VTE and a multivariable Cox proportional hazards model was used to compare mortality in patients developing VTE with those who did not. Data are presented as median (IQR) or number (%). Results: Patient Cohort and Thrombosis Risk Factors. 364 patients were included in the analysis. 360 (98.9%) patients were female, with median (interquartile range) age of 61 (53-71) years. 320 (88.7%) were post-menopausal and 291 (79.9%) were concurrently on endocrine therapy (of which 19 (5.2%) were on tamoxifen). At the time of abemaciclib initiation, 51 (14.0%) were receiving long-term anticoagulation and 47 (12.9%) were receiving aspirin. Khorana scores were between 0-3 with 339 (93.1%) patients having a score of 0 or 1. 267 (73.4%) and 46 (12.6%) were diagnosed with invasive ductal and lobular carcinoma, respectfully. Brain metastases were present in 71 (19.5%) patients. Venous and arterial thrombosis risk factors for this cohort are highlighted in TABLE 1. The median duration of abemaciclib therapy was 5.5 (2.8-13.0) months and median duration of follow-up was 12.7 (6.2-22.1) months. Thrombotic Events. 27 patients (7.4%) developed one or more thrombotic event (17 VTE, 9 arterial thrombosis, 1 both). Events are described in TABLE 2. Risk Factors for VTE. In a multivariable logistic model including age, race, BMI, receipt of long-term anticoagulation, receipt of aspirin, brain metastases, Khorana risk score, receipt of tamoxifen, prior VTE, systemic autoimmune disease, and known thrombophilia, HER2 positivity was predictive of VTE during or after abemaciclib treatment (odds ratio 5.20, 95% CI 1.29-20.93, P=0.020). Association of VTE with Mortality. In a multivariable Cox model controlling for age, race, HER2 status, receipt of long-term anticoagulation, receipt of aspirin, brain metastases, Khorana risk score, receipt of tamoxifen, prior VTE, systemic autoimmune disease, and known thrombophilia, patients developing VTE during abemaciclib therapy had a significantly higher risk of death (hazard ratio, 2.04, 95% CI, 1.03-4.01, P=0.040), FIGURE 1. Median survival in patients developing a VTE vs. those who did not was 9.6 months vs. 25.8 months, respectively. Conclusions: In this study, we provide the first real-world data describing risk of venous and arterial thrombosis in a large cohort of patients with metastatic breast cancer treated with the CDK 4/6 inhibitor abemaciclib. As the role of abemaciclib continues to expand both within and beyond the metastatic disease setting, understanding the VTE risk of this agent has become critical. Thrombosis occurred in 7.4%, and in multivariable models controlling for relevant covariates, HER2 positivity predicted for development of VTE, and patients developing VTE had an approximate 2-fold risk of mortality. Figure 1 Figure 1. Disclosures Al-Samkari: Moderna: Consultancy; Amgen: Research Funding; Novartis: Consultancy; Rigel: Consultancy; Argenx: Consultancy; Dova/Sobi: Consultancy, Research Funding; Agios: Consultancy, Research Funding.

scholarly journals Machine Learning to Discern Interactive Clusters of Risk Factors for Late Recurrence of Metastatic Breast Cancer

Cancers ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 253
Author(s):  
Juan Luis Gomez Marti ◽  
Adam Brufsky ◽  
Alan Wells ◽  
Xia Jiang
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Risk Factors ◽  
Metastatic Breast Cancer ◽  
Metastatic Breast ◽  
Large Data ◽  
Late Recurrence ◽  
Individual Risk ◽  
Metastatic Recurrence

Background: Risk of metastatic recurrence of breast cancer after initial diagnosis and treatment depends on the presence of a number of risk factors. Although most univariate risk factors have been identified using classical methods, machine-learning methods are also being used to tease out non-obvious contributors to a patient’s individual risk of developing late distant metastasis. Bayesian-network algorithms can identify not only risk factors but also interactions among these risks, which consequently may increase the risk of developing metastatic breast cancer. We proposed to apply a previously developed machine-learning method to discern risk factors of 5-, 10- and 15-year metastases. Methods: We applied a previously validated algorithm named the Markov Blanket and Interactive Risk Factor Learner (MBIL) to the electronic health record (EHR)-based Lynn Sage Database (LSDB) from the Lynn Sage Comprehensive Breast Center at Northwestern Memorial Hospital. This algorithm provided an output of both single and interactive risk factors of 5-, 10-, and 15-year metastases from the LSDB. We individually examined and interpreted the clinical relevance of these interactions based on years to metastasis and reliance on interactivity between risk factors. Results: We found that, with lower alpha values (low interactivity score), the prevalence of variables with an independent influence on long-term metastasis was higher (i.e., HER2, TNEG). As the value of alpha increased to 480, stronger interactions were needed to define clusters of factors that increased the risk of metastasis (i.e., ER, smoking, race, alcohol usage). Conclusion: MBIL identified single and interacting risk factors of metastatic breast cancer, many of which were supported by clinical evidence. These results strongly recommend the development of further large data studies with different databases to validate the degree to which some of these variables impact metastatic breast cancer in the long term.

Conventional cardiac risk factors associated with trastuzumab-induced cardiotoxicity in breast cancer: Systematic review and meta-analysis

2021 ◽  
pp. 100723
Author(s):  
George Koulaouzidis ◽  
Amanda E. Yung ◽  
Diana E. Yung ◽  
Karolina Skonieczna-Żydecka ◽  
Wojciech Marlicz ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Systematic Review ◽  
Meta Analysis ◽  
Cardiac Risk ◽  
Factors Associated ◽  
Cardiac Risk Factors

Cardioprotection with ICRF-187 (Cardioxane) in patients with advanced breast cancer having cardiac risk factors for doxorubicin cardiotoxicity, treated with the FDC regimen

1995 ◽  
Vol 3 (3) ◽  
pp. 176-182 ◽  
Author(s):  
Svetislav Jelić ◽  
Siniša Radulović ◽  
Zora Nešković-Konstantinović ◽  
Miroslav Kreačić ◽  
Zorana Ristović ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Advanced Breast Cancer ◽  
Cardiac Risk ◽  
Advanced Breast ◽  
Cardiac Risk Factors ◽  
Doxorubicin Cardiotoxicity

scholarly journals Post-surgical depressive symptoms and long-term survival in non-metastatic breast cancer patients at 11-year follow-up

2017 ◽  
Vol 44 ◽  
pp. 16-21 ◽  
Author(s):  
Michael H. Antoni ◽  
Jamie M. Jacobs ◽  
Laura C. Bouchard ◽  
Suzanne C. Lechner ◽  
Devika R. Jutagir ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Metastatic Breast Cancer ◽  
Depressive Symptoms ◽  
Cancer Patients ◽  
Metastatic Breast ◽  
Breast Cancer Patients ◽  
Term Survival ◽  
Long Term Survival

scholarly journals Factors Associated with Metastatic Breast Cancer in Patients Who Show Long-Term Stable Disease Status

2017 ◽  
Vol 5 (1) ◽  
pp. 1-7
Author(s):  
Young Hoon Noh ◽  
Yun Gyoung Kim ◽  
Ji Hyun Kim ◽  
Hyang Suk Choi ◽  
Seok Joon Lee ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Metastatic Breast Cancer ◽  
Stable Disease ◽  
Metastatic Breast ◽  
Disease Status ◽  
Factors Associated

scholarly journals Cerebral metastases in metastatic breast cancer: disease-specific risk factors and survival

2011 ◽  
Vol 22 (7) ◽  
pp. 1571-1581 ◽  
Author(s):  
F. Heitz ◽  
J. Rochon ◽  
P. Harter ◽  
H.-J. Lueck ◽  
A. Fisseler-Eckhoff ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Metastatic Breast Cancer ◽  
Metastatic Breast ◽  
Cerebral Metastases ◽  
Cancer Disease ◽  
Specific Risk ◽  
Disease Specific
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