Diagnostic utility of alarm features for colorectal cancer: systematic review and meta-analysis

Ford, A. C.; van Zanten, S. J. O. Veldhuyzen; Rodgers, C. C.; TaIIey, N. J.; Vakil, N. B.; Moayyedi, P.
November 2008
Gut;Nov2008, Vol. 57 Issue 11, p1545
Academic Journal
Objective: Colorectal cancer is the second most common cause of cancer death in Europe and North America. Alarm features are used to prioritise access to urgent investigation, but there is little information concerning their utility in the diagnosis of colorectal cancer. Methods: A systematic review and meta-analysis of the published literature was carried out to assess the diagnostic accuracy of alarm features in predicting colorectal cancer. Primary or secondary care-based studies in unselected cohorts of adult patients with lower gastrointestinal symptoms were identified by searching MEDLINE, EMBASE and CINAHL (up to October 2007). The main outcome measures were accuracy of alarm features or statistical models in predicting the presence of colorectal cancer after investigation. Data were pooled to estimate sensitivity, specificity, and positive and negative likelihood ratios. The quality of the included studies was assessed according to predefined criteria. Results: Of 11169 studies identified, 205 were retrieved for evaluation. Fifteen studies were eligible for inclusion, evaluating 19 443 patients, with a pooled prevalence of colorectal carcinoma of 6% (95% Cl 5% to 8%). Pooled sensitivity of alarm features was poor (5% to 64%) but specificity was >95% for dark red rectal bleeding and abdominal mass, suggesting that the presence of either rules the diagnosis of colorectal cancer in. Statistical models had a sensitivity of 90%, but poor specificity. Conclusions: Most alarm features had poor sensitivity and specificity for the diagnosis of colorectal carcinoma, whilst statistical models performed better in terms of sensitivity. Future studies should examine the utility of dark red rectal bleeding and abdominal mass, and concentrate on maximising specificity when validating statistical models.


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