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One-year mortality of colorectal cancer patients: development and validation of a prediction model using linked national electronic data

BACKGROUND: The existing literature does not provide a prediction model for mortality of all colorectal cancer patients using contemporary national hospital data. We developed and validated such a model to predict colorectal cancer death within 90, 180 and 365 days after diagnosis. METHODS: Cohort s...

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Autores principales: Cowling, Thomas E., Bellot, Alexis, Boyle, Jemma, Walker, Kate, Kuryba, Angela, Galbraith, Sarah, Aggarwal, Ajay, Braun, Michael, Sharples, Linda D., van der Meulen, Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7652941/
https://www.ncbi.nlm.nih.gov/pubmed/32830202
http://dx.doi.org/10.1038/s41416-020-01034-w
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author Cowling, Thomas E.
Bellot, Alexis
Boyle, Jemma
Walker, Kate
Kuryba, Angela
Galbraith, Sarah
Aggarwal, Ajay
Braun, Michael
Sharples, Linda D.
van der Meulen, Jan
author_facet Cowling, Thomas E.
Bellot, Alexis
Boyle, Jemma
Walker, Kate
Kuryba, Angela
Galbraith, Sarah
Aggarwal, Ajay
Braun, Michael
Sharples, Linda D.
van der Meulen, Jan
author_sort Cowling, Thomas E.
collection PubMed
description BACKGROUND: The existing literature does not provide a prediction model for mortality of all colorectal cancer patients using contemporary national hospital data. We developed and validated such a model to predict colorectal cancer death within 90, 180 and 365 days after diagnosis. METHODS: Cohort study using linked national cancer and death records. The development population included 27,480 patients diagnosed in England in 2015. The test populations were diagnosed in England in 2016 (n = 26,411) and Wales in 2015–2016 (n = 3814). Predictors were age, gender, socioeconomic status, referral source, performance status, tumour site, TNM stage and treatment intent. Cox regression models were assessed using Brier scores, c-indices and calibration plots. RESULTS: In the development population, 7.4, 11.7 and 17.9% of patients died from colorectal cancer within 90, 180 and 365 days after diagnosis. T4 versus T1 tumour stage had the largest adjusted association with the outcome (HR 4.67; 95% CI: 3.59–6.09). C-indices were 0.873–0.890 (England) and 0.856–0.873 (Wales) in the test populations, indicating excellent separation of predicted risks by outcome status. Models were generally well calibrated. CONCLUSIONS: The model was valid for predicting short-term colorectal cancer mortality. It can provide personalised information to support clinical practice and research.
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spelling pubmed-76529412021-08-24 One-year mortality of colorectal cancer patients: development and validation of a prediction model using linked national electronic data Cowling, Thomas E. Bellot, Alexis Boyle, Jemma Walker, Kate Kuryba, Angela Galbraith, Sarah Aggarwal, Ajay Braun, Michael Sharples, Linda D. van der Meulen, Jan Br J Cancer Article BACKGROUND: The existing literature does not provide a prediction model for mortality of all colorectal cancer patients using contemporary national hospital data. We developed and validated such a model to predict colorectal cancer death within 90, 180 and 365 days after diagnosis. METHODS: Cohort study using linked national cancer and death records. The development population included 27,480 patients diagnosed in England in 2015. The test populations were diagnosed in England in 2016 (n = 26,411) and Wales in 2015–2016 (n = 3814). Predictors were age, gender, socioeconomic status, referral source, performance status, tumour site, TNM stage and treatment intent. Cox regression models were assessed using Brier scores, c-indices and calibration plots. RESULTS: In the development population, 7.4, 11.7 and 17.9% of patients died from colorectal cancer within 90, 180 and 365 days after diagnosis. T4 versus T1 tumour stage had the largest adjusted association with the outcome (HR 4.67; 95% CI: 3.59–6.09). C-indices were 0.873–0.890 (England) and 0.856–0.873 (Wales) in the test populations, indicating excellent separation of predicted risks by outcome status. Models were generally well calibrated. CONCLUSIONS: The model was valid for predicting short-term colorectal cancer mortality. It can provide personalised information to support clinical practice and research. Nature Publishing Group UK 2020-08-24 2020-11-10 /pmc/articles/PMC7652941/ /pubmed/32830202 http://dx.doi.org/10.1038/s41416-020-01034-w Text en © The Author(s), under exclusive licence to Cancer Research UK 2020 https://creativecommons.org/licenses/by/4.0/Note 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
Cowling, Thomas E.
Bellot, Alexis
Boyle, Jemma
Walker, Kate
Kuryba, Angela
Galbraith, Sarah
Aggarwal, Ajay
Braun, Michael
Sharples, Linda D.
van der Meulen, Jan
One-year mortality of colorectal cancer patients: development and validation of a prediction model using linked national electronic data
title One-year mortality of colorectal cancer patients: development and validation of a prediction model using linked national electronic data
title_full One-year mortality of colorectal cancer patients: development and validation of a prediction model using linked national electronic data
title_fullStr One-year mortality of colorectal cancer patients: development and validation of a prediction model using linked national electronic data
title_full_unstemmed One-year mortality of colorectal cancer patients: development and validation of a prediction model using linked national electronic data
title_short One-year mortality of colorectal cancer patients: development and validation of a prediction model using linked national electronic data
title_sort one-year mortality of colorectal cancer patients: development and validation of a prediction model using linked national electronic data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7652941/
https://www.ncbi.nlm.nih.gov/pubmed/32830202
http://dx.doi.org/10.1038/s41416-020-01034-w
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