Confident diagnosis of appendicitis when the appendix is borderline (6 to 7 mm) in size can be challenging. This retrospective study assessed computed tomography (CT) findings that are most predictive of appendicitis when the appendix is borderline in diameter. Three radiologists conducted separate, blind retrospective reviews of 105 contrast-enhanced CTs with borderline appendices. Presence or absence of appendicitis was confirmed by chart review of clinical or surgical outcomes. Logistic regression was used to determine the odds ratio (OR) and the receiver operating characteristic for CT features predictive of appendicitis. Absence of intraluminal air (OR = 5.11, p < 0.001), wall hyperemia (OR = 3.92, p = 0.002), wall thickening (OR = 29.7, p < 0.001), and fat stranding (OR = 3.85, p = 0.003) were significant findings in univariate logistic regression. Using a multivariate model, we found that the absence of intraluminal air (OR = 6.04, p = 0.002) and wall thickening (OR = 24.6, p < 0.001) remained statistically significant and were unaffected by adjustment for gender and pediatric age. The area under the curve was significantly greater for the multivariate model than the initial, clinical CT impressions (p = 0.024). The combination of wall thickening and absence of intraluminal air was 92.6 % (95 % CI 75.7–99.1) sensitive and 82.4 % (95 % CI 65.5–93.2) specific for appendicitis. Wall thickening and the absence of intraluminal air are prominent predictors of appendicitis and, if present together, these features may aid in identifying appendicitis on CT when the appendix is borderline in size.
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We would like to thank Dr. David Bingham and Dr. Dan Eisenberg at Stanford University Hospital for their advice on this study. The first author received financial support from Stanford Medical School’s Goodrich Medical Scholars Research Fund to conduct this research. All data were identified using the Stanford Translational Research Integrated Database Environment (STRIDE) Data Capture Tool. STRIDE is a research and development project at Stanford University to create a standards-based informatics platform supporting clinical and translational research. Study data were collected and managed using the Research Electronic Data Capture (REDCap) tools hosted at the Stanford Center for Clinical Informatics. REDCap is a secure, web-based application designed to support data capture for research studies, providing the following: (1) an intuitive interface for validated data entry; (2) audit trails for tracking data manipulation and export procedures; (3) automated export procedures for seamless data downloads to common statistical packages; and (4) procedures for importing data from external sources. STRIDE and REDCap are supported by the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through grant UL1 RR025744. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
Conflict of interest
The authors have no relevant conflicts of interest to disclose.
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