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Independent validation of the PREDICT breast cancer prognosis prediction tool in 45,789 patients using Scottish Cancer Registry data

Independent validation of the PREDICT breast cancer prognosis prediction tool in 45,789 patients using Scottish Cancer Registry data
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 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.

0007-0920
808-814
Gray, Ewan
640f76cd-997b-4a8d-8690-081ffdcd7d01
Marti, Joachim
55e9017a-66c0-4f11-9d38-97cbccac95f4
Brewster, David H.
0ee02f74-62c5-480c-9956-2cf86b4abeb2
Wyatt, Jeremy C.
8361be5a-fca9-4acf-b3d2-7ce04126f468
Hall, Peter S.
d45f9300-0dd4-4ad7-bd9d-f23873f80616
the SATURNE Advisory Group
Gray, Ewan
640f76cd-997b-4a8d-8690-081ffdcd7d01
Marti, Joachim
55e9017a-66c0-4f11-9d38-97cbccac95f4
Brewster, David H.
0ee02f74-62c5-480c-9956-2cf86b4abeb2
Wyatt, Jeremy C.
8361be5a-fca9-4acf-b3d2-7ce04126f468
Hall, Peter S.
d45f9300-0dd4-4ad7-bd9d-f23873f80616

Gray, Ewan, Marti, Joachim, Brewster, David H., Wyatt, Jeremy C. and Hall, Peter S. , the SATURNE Advisory Group (2018) Independent validation of the PREDICT breast cancer prognosis prediction tool in 45,789 patients using Scottish Cancer Registry data. British Journal of Cancer, 119, 808-814. (doi:10.1038/s41416-018-0256-x).

Record type: Article

Abstract

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.

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More information

Accepted/In Press date: 16 August 2018
e-pub ahead of print date: 17 September 2018

Identifiers

Local EPrints ID: 426909
URI: https://eprints.soton.ac.uk/id/eprint/426909
ISSN: 0007-0920
PURE UUID: 304f4152-0dff-44e3-b0c3-1cbcb18550c3
ORCID for Jeremy C. Wyatt: ORCID iD orcid.org/0000-0001-7008-1473

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Date deposited: 14 Dec 2018 17:30
Last modified: 20 Jul 2019 00:33

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Contributors

Author: Ewan Gray
Author: Joachim Marti
Author: David H. Brewster
Author: Jeremy C. Wyatt ORCID iD
Author: Peter S. Hall

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