Optimizing the interpretation of CT for appendicitis: modeling health utilities for clinical practice.
OBJECTIVE
Error in radiology can be reduced by standardizing the interpretation of imaging studies to the optimum sensitivity and specificity. In this report, the authors demonstrate how the optimal interpretation of appendiceal computed tomography (CT) can be determined and how it varies in different clinical scenarios.


METHODS
Utility analysis and receiver operating characteristic (ROC) curve modeling were used to determine the trade-off between false-positive and false-negative test results to determine the optimal operating point on the ROC curve for the interpretation of appendicitis CT. Modeling was based on a previous meta-analysis for the accuracy of CT and on literature estimates of the utilities of various health states. The posttest probability of appendicitis was derived using Bayes's theorem.


RESULTS
At a low prevalence of disease (screening), appendicitis CT should be interpreted at high specificity (97.7%), even at the expense of lower sensitivity (75%). Conversely, at a high probability of disease, high sensitivity (97.4%) is preferred (specificity 77.8%). When the clinical diagnosis of appendicitis is equivocal, CT interpretation should emphasize both sensitivity and specificity (sensitivity 92.3%, specificity 91.5%).


CONCLUSIONS
Radiologists can potentially decrease medical error and improve patient health by varying the interpretation of appendiceal CT on the basis of the clinical probability of appendicitis. This report is an example of how utility analysis can be used to guide radiologists in the interpretation of imaging studies and provide guidance on appropriate targets for the standardization of interpretation.
Blackmore
C Craig
CC
Department of Radiology and Department of Health Services, University of Washington, Seattle, WA 98104, USA. craige@u.washington.edu
Terasawa
Teruhiko
T
D001064:Appendicitis / Q000000981:diagnostic imaging* / Q000453:epidemiology; D010818:Practice Patterns, Physicians' / Q000706:statistics & numerical data; D016016:Proportional Hazards Models; D011785:Quality Assurance, Health Care / Q000379:methods*; D012372:ROC Curve; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods* / Q000706:statistics & numerical data*; D014481:United States