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Cancer Epidemiol Biomarkers Prev. 2014 Nov;23(11):2543-52. doi: 10.1158/1055-9965.EPI-14-0206. Epub 2014 Aug 3.

Development and validation of a risk score predicting risk of colorectal cancer.

Author information

  • 1University of South Australia, Division of Health Science, Adelaide, Australia. steay035@mymail.unisa.edu.au.
  • 2Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria, Australia. Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
  • 3National Centre of Epidemiology and Population Health, Australian National University, Canberra, Australia.
  • 4National Centre of Epidemiology and Population Health, Australian National University, Canberra, Australia. The Sax Institute, Sydney, Australia.
  • 5University of South Australia, Division of Health Science, Adelaide, Australia.

Abstract

BACKGROUND:

Quantifying the risk of colorectal cancer for individuals is likely to be useful for health service provision. Our aim was to develop and externally validate a prediction model to predict 5-year colorectal cancer risk.

METHODS:

We used proportional hazards regression to develop the model based on established personal and lifestyle colorectal cancer risk factors using data from 197,874 individuals from the 45 and Up Study, Australia. We subsequently validated the model using 24,233 participants from the Melbourne Collaborative Cohort Study (MCCS).

RESULTS:

A total of 1,103 and 224 cases of colorectal cancer were diagnosed in the development and validation sample, respectively. Our model, which includes age, sex, BMI, prevalent diabetes, ever having undergone colorectal cancer screening, smoking, and alcohol intake, exhibited a discriminatory accuracy of 0.73 [95% confidence interval (CI), 0.72-0.75] and 0.70 (95% CI, 0.66-0.73) using the development and validation sample, respectively. Calibration was good for both study samples. Stratified models according to colorectal cancer screening history, that additionally included family history, showed discriminatory accuracies of 0.75 (0.73-0.76) and 0.70 (0.67-0.72) for unscreened and screened individuals of the development sample, respectively. In the validation sample, discrimination was 0.68 (0.64-0.73) and 0.72 (0.67-0.76), respectively.

CONCLUSION:

Our model exhibited adequate predictive performance that was maintained in the external population.

IMPACT:

The model may be useful to design more powerful cancer prevention trials. In the group of unscreened individuals, the model may be useful as a preselection tool for population-based screening programs.

PMID:
25087576
DOI:
10.1158/1055-9965.EPI-14-0206
[PubMed - indexed for MEDLINE]
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