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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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Detalles Bibliográficos
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
Descripción
Sumario: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.