The acute care diagnostics collaboration: Performance assessment of contrast-enhanced ultrasound compared to abdominal computed tomography and conventional ultrasound in an emergency trauma score bayesian clinical decision scheme

BACKGROUND: Bayes' theorem describes the probability of an event, based on conditions that might be related to the event.[1] We developed the Bayesian Diagnostic Gains (BDG) method as a simple tool for interpreting diagnostic impact.[234567] AIM: We aimed to evaluate the clinical diagnostic impact of contrast-enhanced ultrasound (CEUS) compared to traditional abdominal computed tomography (CT) and standard ultrasound (US) in a Bayesian Clinical Decision Scheme. MATERIALS AND METHODS: Our mathematical method uses Bayesian Diagnostic Gains (BDG) model. For the purposes of our model, the EMTRAS was used as pretest probability and stratified as low risk (0–3 points = 10%), moderate risk (4–6 points = 42%), and high risk (7–12 points = 80%) based on mortality risk. Sensitivity and specificity for US, CT, and CEUS were obtained from pooled data and used to calculate LR- and LR+. Bayesian/Fagan nomogram was used to attain posttest probabilities using baseline probability of an event on the first axis (PRE), with LR on the second axis, and read off the pos-test probability (POST) on the third axis. For the nomogram analysis, the pretest probability (Pre) scoring for the EMTRAS score was obtained using the original EMTRAS data. Posttest probabilities were obtained based on the Bayes/Fagan Nomgram. Relative diagnostic gain (RDG) and absolute diagnostic gain (ADG) were calculated based on the differences deducted from pre- and post-test probabilities. IBM® SPSS® Statistics 20 was used for analysis and modeling. ANOVA was used for association between EMTRAS, CT scan, and CEUS, where P value set at 0.05. RESULTS: Pooled data for Sensitivity (Se), Specificity (Sp), LR+, and LR- were obtained for US (Se = 45.7%, Sp = 91.8%, LR+ = 5.57, and LR- = 0.59), CEUS (Se 91.4%, Sp 100%, LR+ 91, and LR-0.09), and CT (Se = 94.8%, SP = 98.7%, LR+ = 73, and LR- =0.05). ANOVA analysis for LR+ and LR- showed no significant difference (P < 0.8745 and P < 0.9841). Comparison of CT and CEUS did not yield statistically significant differences for LR+ (P < 0.1). CONCLUSION: In this Bayesian model, the diagnostic performance of CEUS was found to be similar to traditional abdominal CT. The greatest diagnostic gain was observed in low pretest positive LR groups.