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The value of age and medical history for predicting colorectal cancer and adenomas in people referred for colonoscopy

Barbara-Ann Adelstein1*, Petra Macaskill2, Robin M Turner2, Peter H Katelaris3 and Les Irwig2

Author Affiliations

1 Prince of Wales Clinical School, Faculty of Medicine, University of New South Wales, Sydney, Australia

2 Screening and Test Evaluation Program, Sydney School of Public Health, University of Sydney University, NSW, Australia

3 Gastroenterology Department, Concord Hospital, University of Sydney, NSW, Australia

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BMC Gastroenterology 2011, 11:97  doi:10.1186/1471-230X-11-97

Published: 8 September 2011



Colonoscopy is an invasive and costly procedure with a risk of serious complications. It would therefore be useful to prioritise colonoscopies by identifying people at higher risk of either cancer or premalignant adenomas. The aim of this study is to assess a model that identifies people with colorectal cancer, advanced, large and small adenomas.


Patients seen by gastroenterologists and colorectal surgeons between April 2004 and December 2006 completed a validated, structured self-administered questionnaire prior to colonoscopy. Information was collected on symptoms, demographics and medical history. Multinomial logistic regression was used to simultaneously assess factors associated with findings on colonoscopy of cancer, advanced adenomas and adenomas sized 6 -9 mm, and ≤ 5 mm. The area under the curve of ROC curve was used to assess the incremental gain of adding demographic variables, medical history and symptoms (in that order) to a base model that included only age.


Sociodemographic variables, medical history and symptoms (from 8,204 patients) jointly provide good discrimination between colorectal cancer and no abnormality (AUC 0.83), but discriminate less well between adenomas and no abnormality (AUC advanced adenoma 0.70; other adenomas 0.67). Age is the dominant risk factor for cancer and adenomas of all sizes. Having a colonoscopy within the last 10 years confers protection for cancers and advanced adenomas.


Our models provide guidance about which factors can assist in identifying people at higher risk of disease using easily elicited information. This would allow colonoscopy to be prioritised for those for whom it would be of most benefit.