According to the ESH/ESC CV risk matrix, 47, 43, 32, 10, and 16 patients were classified as CV risk category 1, 2, 3, 4, and 5, respectively. We examined the relationships between the five ASI parameters and the ESH/ESC CV risk score using multivariate logistic regression models. Individual models of each of the predictors (PWA-I, mean PPT, mean RRPO, PR-I, and Spo2-I) accounted for between 60% and 70% of the models’ discriminatory power (c index). The performance of these variables in predicting CV risk was variable, but data suggested that each variable provided a strong association with a shift in risk class that was statistically significant for all variables (mean PPT, P < .0001; mean RRPO, P = .02; PR-I, P = .01; Spo2-I, P = .0001, respectively), except PWA-I (P = .08). Correlation analysis showed that the five ASI variables interrelated with Spearman correlation varying from r2 = 0.04 to 0.18. A multivariate model using all five ASI variables was validated using 200 bootstrap replications with backward stepwise term elimination. Indeed, each of the five variables contributed significantly to the model fit and none could be eliminated effectively. This full model predicted risk class with a discrimination index of 79% (χ2(5) = 59.5, P < .0001). The individual risk odds are illustrated in Figure 4. Finally, the diagnostic sensitivity and specificity of the computed composite ASI CV risk score for separation of the ESH/ESC matrix-derived CV risk class 1 to 3 (average to moderate risk) from class 4 and 5 (high and very high risk) were determined. The overall agreements between the two risk assessment tools in an analysis including all 148 patients and 49 validation group subjects were 81% and 78%, with a Cohen κ of 0.51 and 0.48, respectively.