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Independent validation of the PREDICT breast cancer prognosis prediction tool in 45,789 patients using Scottish Cancer Registry data
BACKGROUND: PREDICT is a widely used online prognostication and treatment benefit tool for patients with early stage breast cancer. The aim of this study was to conduct an independent validation exercise of the most up-to-date version of the PREDICT algorithm (version 2) using real-world outcomes fr...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6189179/ https://www.ncbi.nlm.nih.gov/pubmed/30220705 http://dx.doi.org/10.1038/s41416-018-0256-x |
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author | Gray, Ewan Marti, Joachim Brewster, David H. Wyatt, Jeremy C. Hall, Peter S. |
author_facet | Gray, Ewan Marti, Joachim Brewster, David H. Wyatt, Jeremy C. Hall, Peter S. |
author_sort | Gray, Ewan |
collection | PubMed |
description | BACKGROUND: PREDICT is a widely used online prognostication and treatment benefit tool for patients with early stage breast cancer. The aim of this study was to conduct an independent validation exercise of the most up-to-date version of the PREDICT algorithm (version 2) using real-world outcomes from the Scottish population of women with breast cancer. METHODS: Patient data were obtained for all Scottish Cancer Registry (SCR) records with a diagnosis of primary invasive breast cancer diagnosed in the period between January 2001 and December 2015. Prognostic scores were calculated using the PREDICT version 2 algorithm. External validity was assessed by statistical analysis of discrimination and calibration. Discrimination was assessed by area under the receiver-operator curve (AUC). Calibration was assessed by comparing the predicted number of deaths to the observed number of deaths across relevant sub-groups. RESULTS: A total of 45,789 eligible cases were selected from 61,437 individual records. AUC statistics ranged from 0.74 to 0.77. Calibration results showed relatively close agreement between predicted and observed deaths. The 5-year complete follow-up sample reported some overestimation (11.5%), while the 10-year complete follow-up sample displayed more limited overestimation (1.7%). CONCLUSIONS: Validation results suggest that the PREDICT tool remains essentially relevant for contemporary patients with early stage breast cancer. |
format | Online Article Text |
id | pubmed-6189179 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-61891792019-09-17 Independent validation of the PREDICT breast cancer prognosis prediction tool in 45,789 patients using Scottish Cancer Registry data Gray, Ewan Marti, Joachim Brewster, David H. Wyatt, Jeremy C. Hall, Peter S. Br J Cancer Article BACKGROUND: PREDICT is a widely used online prognostication and treatment benefit tool for patients with early stage breast cancer. The aim of this study was to conduct an independent validation exercise of the most up-to-date version of the PREDICT algorithm (version 2) using real-world outcomes from the Scottish population of women with breast cancer. METHODS: Patient data were obtained for all Scottish Cancer Registry (SCR) records with a diagnosis of primary invasive breast cancer diagnosed in the period between January 2001 and December 2015. Prognostic scores were calculated using the PREDICT version 2 algorithm. External validity was assessed by statistical analysis of discrimination and calibration. Discrimination was assessed by area under the receiver-operator curve (AUC). Calibration was assessed by comparing the predicted number of deaths to the observed number of deaths across relevant sub-groups. RESULTS: A total of 45,789 eligible cases were selected from 61,437 individual records. AUC statistics ranged from 0.74 to 0.77. Calibration results showed relatively close agreement between predicted and observed deaths. The 5-year complete follow-up sample reported some overestimation (11.5%), while the 10-year complete follow-up sample displayed more limited overestimation (1.7%). CONCLUSIONS: Validation results suggest that the PREDICT tool remains essentially relevant for contemporary patients with early stage breast cancer. Nature Publishing Group UK 2018-09-17 2018-10-02 /pmc/articles/PMC6189179/ /pubmed/30220705 http://dx.doi.org/10.1038/s41416-018-0256-x Text en © Cancer Research UK 2018 https://creativecommons.org/licenses/by/4.0/ This work is published under the standard license to publish agreement. After 12 months the work will become freely available and the license terms will switch to a Creative Commons Attribution 4.0 International (CC BY 4.0). |
spellingShingle | Article Gray, Ewan Marti, Joachim Brewster, David H. Wyatt, Jeremy C. Hall, Peter S. Independent validation of the PREDICT breast cancer prognosis prediction tool in 45,789 patients using Scottish Cancer Registry data |
title | Independent validation of the PREDICT breast cancer prognosis prediction tool in 45,789 patients using Scottish Cancer Registry data |
title_full | Independent validation of the PREDICT breast cancer prognosis prediction tool in 45,789 patients using Scottish Cancer Registry data |
title_fullStr | Independent validation of the PREDICT breast cancer prognosis prediction tool in 45,789 patients using Scottish Cancer Registry data |
title_full_unstemmed | Independent validation of the PREDICT breast cancer prognosis prediction tool in 45,789 patients using Scottish Cancer Registry data |
title_short | Independent validation of the PREDICT breast cancer prognosis prediction tool in 45,789 patients using Scottish Cancer Registry data |
title_sort | independent validation of the predict breast cancer prognosis prediction tool in 45,789 patients using scottish cancer registry data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6189179/ https://www.ncbi.nlm.nih.gov/pubmed/30220705 http://dx.doi.org/10.1038/s41416-018-0256-x |
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