Acute aortic dissection (AD), is an often under diagnosed lethal cardiovascular catastrophes. The objective of this study was to assess the diagnostic utility of a Bayesian clinical decision support scheme (DSS) that integrated the Aortic Dissection Detection Risk Score (ADD-RS) combined D-Dimer testing. Pretest probability (Pre) scoring for the ADDRS was obtained using derived precalculated DerSimonian-Laird random-effects models. Sensitivity, specificity, positive and negative likelihood ratios for D-Dimer testing where obtained from a meta-analysis. Posttest probability (Post) was obtained from Bayesian statistical modeling integrating Low, Intermediate and High pretest for the ADD Score and LRs for D-Dimer testing. Relative (RDG) and Absolute (ADG) diagnostic gains were calculated based on the differences deducted from pre and posttest probabilities (ADG= Post-Pre), (RDG= 100x ADG/Pre). IBM SPSS Statistics 20 was used for analysis and modeling. ADD risk score categorized low risk pretest probability (ADD-RS of 0) as 4.3%, intermediate risk 36.5% (ADD-RS of 1) and high risk 59.2% (ADD-RS of 2 or 3). Pool Meta-analysis D-Dimer data demonstrated a sensitivity (0.97, 95% CI 0.94 to 0.99), specificity (0.56, 95% CI 0.51 to 0.60) negative LR (0.06, 95% CI 0.03 to 0.12), positive LR (2.43, 95% CI 1.89 to 3.12). Bayes modeling for negative likelihood ratios demonstrated posttest probabilities score of 0.24% for low risk (ADG = 4.06% and RDG= 94.42%), 3.4% for intermediate risk (ADG = 33.1% and RDG= 90.68%) and 7.9% for high risk (ADG = 51.3% and RDG= 86.65%) The integration of the ADD Risk Score and D-Dimer testing in a DSS yielded important rule-out diagnostic relative and absolute gains, mostly evidenced in the ADD-RS low and intermediate pretest probability category. We propose the use of the DSS as a triage tool to limit utilization of CT scan as a primary rule-out tool.