Evaluation of the Canadian Clinical Practice Guidelines Risk Prediction Tool for Acute Aortic Syndrome: The RIPP Score.

Abstract

Acute aortic syndrome (AAS) is a rare clinical syndrome with a high mortality rate. The Canadian clinical practice guideline for the diagnosis of AAS was developed in order to reduce the frequency of misdiagnoses. As part of the guideline, a clinical decision aid was developed to facilitate clinician decision-making (RIPP score). The aim of this study is to validate the diagnostic accuracy of this tool and assess its performance in comparison to other risk prediction tools that have been developed. This was a historical case-control study. Consecutive cases and controls were recruited from three academic emergency departments from 2002-2020. Cases were identified through an admission, discharge, or death certificated diagnosis of acute aortic syndrome. Controls were identified through presenting complaint of chest, abdominal, flank, back pain, and/or perfusion deficit. We compared the clinical decision tools' C statistic and used the DeLong method to test for the significance of these differences and report sensitivity and specificity with 95% confidence intervals. We collected data on 379 cases of acute aortic syndrome and 1340 potential eligible controls; 379 patients were randomly selected from the final population. The RIPP score had a sensitivity of 99.7% (98.54-99.99). This higher sensitivity resulted in a lower specificity (53%) compared to the other clinical decision aids (63-86%). The DeLong comparison of the C statistics found that the RIPP score had a higher C statistic than the ADDRS (-0.0423 (95% confidence interval -0.07-0.02); P < 0.0009) and the AORTAs score (-0.05 (-0.07 to -0.02); P = 0.0002), no difference compared to the Lovy decision tool (0.02 (95% CI -0.01-0.05 P < 0.25)) and decreased compared to the Von Kodolitsch decision tool (0.04 (95% CI 0.01-0.07 P < 0.008)). The Canadian clinical practice guideline's AAS clinical decision aid is a highly sensitive tool that uses readily available clinical information. It has the potential to improve diagnosis of AAS in the emergency department.