Emerging Infectious Disease ISSN: 1080-6059

Evaluating Detection and Diagnostic Decision Support Systems for Bioterrorism Response

Dena M. Bravata*†

, Vandana Sundaram*†‡, Kathryn M. McDonald*†, Wendy M. Smith*†, Herbert Szeto*†§, Mark D. Schleinitz¶#, and Douglas K. Owens*†‡

Author affiliations: *University of California San Francisco-Stanford Evidence-based Practice Center, Stanford, California, USA; †Stanford University School of Medicine, Stanford, California, USA; ‡VA Palo Alto Healthcare System, Palo Alto, California, USA; §Kaiser Permanente, Redwood City, California, USA; ¶Rhode Island Hospital, Providence, Rhode Island, USA; #Brown University School of Medicine, Providence, Rhode Island, USA

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Figure 1

Effect of sensitivity, specificity, and pretest probability on posttest probability of anthrax’s being present. Upper curves show the posttest probability of anthrax’s being present after a positive detection or diagnostic test result. Lower curves show the posttest probability of anthrax’s being present after a negative detection or diagnostic test result. Separate curves are drawn for two diagnostic tests described in the text: one with 99% sensitivity and 99% specificity (thick) and another w

Figure 1. Effect of sensitivity, specificity, and pretest probability on posttest probability of anthrax’s being present. Upper curves show the posttest probability of anthrax’s being present after a positive detection or diagnostic test result. Lower curves show the posttest probability of anthrax’s being present after a negative detection or diagnostic test result. Separate curves are drawn for two diagnostic tests described in the text: one with 99% sensitivity and 99% specificity (thick) and another with 96% sensitivity and 94% specificity (thin). The arrow marks a pretest probability of disease of 0.0014, which relates to the example described in the text.

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