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

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    Authors
    Cowling, T. E.
    Bellot, A.
    Boyle, J.
    Walker, K.
    Kuryba, A.
    Galbraith, S.
    Aggarwal, A.
    Braun, Michael S
    Sharples, L. D.
    van der Meulen, J.
    Affiliation
    Clinical Effectiveness Unit, Royal College of Surgeons of England, London, UK.
    Issue Date
    2020
    
    Metadata
    Show full item record
    Abstract
    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.
    Citation
    Cowling TE, Bellot A, Boyle J, Walker K, Kuryba A, Galbraith S, et al. One-year mortality of colorectal cancer patients: development and validation of a prediction model using linked national electronic data. British Journal of Cancer. 2020.
    Journal
    British Journal of Cancer
    URI
    http://hdl.handle.net/10541/623238
    DOI
    10.1038/s41416-020-01034-w
    PubMed ID
    32830202
    Additional Links
    https://dx.doi.org/10.1038/s41416-020-01034-w
    Type
    Article
    Language
    en
    ae974a485f413a2113503eed53cd6c53
    10.1038/s41416-020-01034-w
    Scopus Count
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    All Christie Publications

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