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Diagnostic Utility of Alarm Features for Colorectal Cancer: Systematic Review and Meta-analysis.
  1. Alexander Charles Ford (alexf12399{at}
  1. McMaster University, Canada
    1. Sander J O Veldhuyzen van Zanten (vanzanten{at}
    1. University of Alberta, Canada
      1. Christopher C Rodgers ({at}
      1. Dalhousie University, Canada
        1. Nicholas J Talley (talley.nicholas{at}
        1. Mayo Clinic, United States
          1. Nimish Vakil (nvakil{at}
          1. University of Wisconsin School of Medicine and Public Health, United States
            1. Paul Moayyedi (moayyep{at}
            1. McMaster University, Canada


              Objective: Colorectal cancer is the second commonest 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.

              Design: Systematic review and meta-analysis of published literature to assess diagnostic accuracy of alarm features in predicting colorectal cancer. Studies were identified by searching MEDLINE, EMBASE, and CINAHL (up to October 2007).

              Setting: Primary or secondary care-based studies. Patients: Unselected cohorts of adult patients with lower gastrointestinal symptoms.

              Main outcome measures: Accuracy of alarm features or statistical models in predicting presence of colorectal cancer after investigation. Data were pooled to estimate sensitivity, specificity, and positive and negative likelihood ratios. Quality of included studies was assessed according to pre-defined criteria.

              Results: Of 11169 studies identified, 205 were retrieved for evaluation. Fifteen studies were eligible for inclusion, evaluating 19443 patients, with a pooled prevalence of colorectal carcinoma of 6% (95% CI 5-8%). Pooled sensitivity of alarm features was poor (5% to 64%) but specificity was greater than 95% for dark red rectal bleeding and abdominal mass, suggesting 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 utility of dark red rectal bleeding and abdominal mass, and concentrate on maximising specificity when validating statistical models.

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