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Modeling risks of cardiovascular and cancer mortality following a diagnosis of loco-regional breast cancer
BACKGROUND: Many women with breast cancer also have a high likelihood of cardiovascular mortality, and while there are several cardiovascular risk prediction models, none have been validated in a cohort of breast cancer patients. We first compared the performance of commonly-used cardiovascular mode...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8474887/ https://www.ncbi.nlm.nih.gov/pubmed/34579765 http://dx.doi.org/10.1186/s13058-021-01469-w |
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author | Leoce, Nicole M. Jin, Zhezhen Kehm, Rebecca D. Roh, Janise M. Laurent, Cecile A. Kushi, Lawrence H. Terry, Mary Beth |
author_facet | Leoce, Nicole M. Jin, Zhezhen Kehm, Rebecca D. Roh, Janise M. Laurent, Cecile A. Kushi, Lawrence H. Terry, Mary Beth |
author_sort | Leoce, Nicole M. |
collection | PubMed |
description | BACKGROUND: Many women with breast cancer also have a high likelihood of cardiovascular mortality, and while there are several cardiovascular risk prediction models, none have been validated in a cohort of breast cancer patients. We first compared the performance of commonly-used cardiovascular models, and then derived a new model where breast cancer and cardiovascular mortality were modeled simultaneously, to account for the competing risk endpoints and commonality of risk factors between the two events. METHODS: We included 20,462 women diagnosed with stage I–III breast cancer between 2000 and 2010 in Kaiser Permanente Northern California (KPNC) with follow-up through April 30, 2015, and examined the performance of the Framingham, CORE and SCOREOP cardiovascular risk models by area under the receiver operating characteristic curve (AUC), and observed-to -expected (O/E) ratio. We developed a multi-state model based on cause-specific hazards (CSH) to jointly model the causes of mortality. RESULTS: The extended models including breast cancer characteristics (grade, tumor size, nodal involvement) with CVD risk factors had better discrimination at 5-years with AUCs of 0.85 (95% CI 0.83, 0.86) for cardiovascular death and 0.80 (95% CI 0.78, 0.87) for breast cancer death compared with the existing cardiovascular models evaluated at 5 years AUCs ranging 0.71–0.78. Five-year calibration for breast and cardiovascular mortality from our multi-state model was also excellent (O/E = 1.01, 95% CI 0.91–1.11). CONCLUSION: A model incorporating cardiovascular risk factors, breast cancer characteristics, and competing events, outperformed traditional models of cardiovascular disease by simultaneously estimating cancer and cardiovascular mortality risks. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13058-021-01469-w. |
format | Online Article Text |
id | pubmed-8474887 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-84748872021-09-28 Modeling risks of cardiovascular and cancer mortality following a diagnosis of loco-regional breast cancer Leoce, Nicole M. Jin, Zhezhen Kehm, Rebecca D. Roh, Janise M. Laurent, Cecile A. Kushi, Lawrence H. Terry, Mary Beth Breast Cancer Res Research Article BACKGROUND: Many women with breast cancer also have a high likelihood of cardiovascular mortality, and while there are several cardiovascular risk prediction models, none have been validated in a cohort of breast cancer patients. We first compared the performance of commonly-used cardiovascular models, and then derived a new model where breast cancer and cardiovascular mortality were modeled simultaneously, to account for the competing risk endpoints and commonality of risk factors between the two events. METHODS: We included 20,462 women diagnosed with stage I–III breast cancer between 2000 and 2010 in Kaiser Permanente Northern California (KPNC) with follow-up through April 30, 2015, and examined the performance of the Framingham, CORE and SCOREOP cardiovascular risk models by area under the receiver operating characteristic curve (AUC), and observed-to -expected (O/E) ratio. We developed a multi-state model based on cause-specific hazards (CSH) to jointly model the causes of mortality. RESULTS: The extended models including breast cancer characteristics (grade, tumor size, nodal involvement) with CVD risk factors had better discrimination at 5-years with AUCs of 0.85 (95% CI 0.83, 0.86) for cardiovascular death and 0.80 (95% CI 0.78, 0.87) for breast cancer death compared with the existing cardiovascular models evaluated at 5 years AUCs ranging 0.71–0.78. Five-year calibration for breast and cardiovascular mortality from our multi-state model was also excellent (O/E = 1.01, 95% CI 0.91–1.11). CONCLUSION: A model incorporating cardiovascular risk factors, breast cancer characteristics, and competing events, outperformed traditional models of cardiovascular disease by simultaneously estimating cancer and cardiovascular mortality risks. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13058-021-01469-w. BioMed Central 2021-09-27 2021 /pmc/articles/PMC8474887/ /pubmed/34579765 http://dx.doi.org/10.1186/s13058-021-01469-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Leoce, Nicole M. Jin, Zhezhen Kehm, Rebecca D. Roh, Janise M. Laurent, Cecile A. Kushi, Lawrence H. Terry, Mary Beth Modeling risks of cardiovascular and cancer mortality following a diagnosis of loco-regional breast cancer |
title | Modeling risks of cardiovascular and cancer mortality following a diagnosis of loco-regional breast cancer |
title_full | Modeling risks of cardiovascular and cancer mortality following a diagnosis of loco-regional breast cancer |
title_fullStr | Modeling risks of cardiovascular and cancer mortality following a diagnosis of loco-regional breast cancer |
title_full_unstemmed | Modeling risks of cardiovascular and cancer mortality following a diagnosis of loco-regional breast cancer |
title_short | Modeling risks of cardiovascular and cancer mortality following a diagnosis of loco-regional breast cancer |
title_sort | modeling risks of cardiovascular and cancer mortality following a diagnosis of loco-regional breast cancer |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8474887/ https://www.ncbi.nlm.nih.gov/pubmed/34579765 http://dx.doi.org/10.1186/s13058-021-01469-w |
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