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Risk stratification of emergency department patients with Crohn's disease could reduce computed tomography use by nearly half.
Govani SM, Guentner AS, Waljee AK, Higgins PD. Risk stratification of emergency department patients with Crohn's disease could reduce computed tomography use by nearly half. Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association. 2014 Oct 1; 12(10):1702-7.e3.
BACKGROUND and AIMS:
Computed tomography (CT) is a useful tool for assessing disease activity and excluding complications in patients with Crohn's disease (CD). However, excessive radiation increases risk for malignancy. We aimed to identify automatable algorithms with high negative predictive values for significant CT findings in patients with CD who present at the emergency department.
We conducted a retrospective review of a tertiary center's medical records to identify adults diagnosed with CD who presented from 2000 through 2011. Logistic regression was used to model complications (perforations, abscesses, or other serious findings) and inflammation.
There were 1095 visits made by 613 individuals that included a CT scan within 24 hours of arrival. The average number of CT scans was 1.8 (range, 1-31). Complications of CD were observed in 16.8% of CT scans, inflammation in 54.5%, and new/worse findings in 67.2%. On the basis of 10-fold cross-validation, the area under the receiver operating characteristic curve value for the complications model was 0.80 (95% confidence interval, 0.74-0.86) and for the inflammation model was 0.71 (95% confidence interval, 0.68-0.74). Scanning only patients with model-predicted complications would reduce scans by 43.0%, with a miss rate of 0.8% (4 of 491).
Patients presenting to the emergency department with CD are frequently assessed by CT. However, no significant findings are observed in 32.8%, and only 17% have complications from CD. We created models to identify patients not likely to have significant findings from CT with high negative predictive values; these could aid physicians in avoiding CT scans for many patients. Studies are needed to validate these models beyond a single center.