One-year mortality of colorectal cancer patients: development and validation of a prediction model using linked national electronic data
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 recordAbstract
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 CancerDOI
10.1038/s41416-020-01034-wPubMed ID
32830202Additional Links
https://dx.doi.org/10.1038/s41416-020-01034-wType
ArticleLanguage
enae974a485f413a2113503eed53cd6c53
10.1038/s41416-020-01034-w