Selected article for: "clinical information and gender age"

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
Title: One-year mortality of colorectal cancer patients: development and validation of a prediction model using linked national electronic data.
  • Cord-id: 75m8q3e3
  • Document date: 2020_8_24
  • ID: 75m8q3e3
    Snippet: 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)
    Document: 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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