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. 2022 Jul 25;8(4):1885–1894. doi: 10.3390/tomography8040159

Table 4.

Diagnostic performance and characteristics of BMI in predicting poor collaterals.

Characteristics All Cases ICA M1 Proximal M2
Value 95% CI Value 95% CI Value 95% CI Value 95% CI
Poor Collaterals from Good Collateral
AUC 0.560 0.401–0.720 0.813 0.465–1.000 0.550 0.320–0.781 0.465 0.199–0.730
p-value 0.446 0.149 0.663 0.790
Cut point ≤35.0 ≤35.0 ≤35.0 ≤35.0
Sensitivity 96.4% 81.7–99.9% 100% 39.8–100% 100% 75.3–100% 90.9% 58.7–99.8%
Specificity 30.8% 14.3–51.8% 75.0% 19.4–99.4% 23.1% 5.0–53.8% 22.2% 2.8–60.0%
DA 64.8% 50.6–77.3% 87.5% 47.3–99.7% 61.5% 40.6–79.8% 60.0% 36.1–80.9%
YI 27.2% 8.2–46.2% 75.0% 32.6–100% 23.1% 0.2–46.0% 13.1% 18.9–45.2%
PPV 60.0% 44.3–74.3% 80.0% 28.4–99.5% 56.5% 34.5–76.8% 58.8% 32.9–81.6%
NPV 88.9% 51.8–99.7% 100% 29.2–100% 100% 29.2–100% 66.7% 9.4–99.2%

AUC: Area under the curve; CI: Confidence interval. DA: Diagnostic accuracy. YI: Youden’s Index. PPV: Positive Predictive value. NPV: Negative Predictive value.