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. 2021 Jun 19;10(13):e020597. doi: 10.1161/JAHA.120.020597

Table 3.

Multivariate Logistic Regression Analyses for All Significant Univariate Variables (P≤0.05) Predicting Stress‐Induced Myocardial Ischemia in Patients With MB

OR (95% CI for OR) P Value R 2 HL Test P Value
Univariate analysis
MLD MB (end‐systole) 0.207 (0.036–1.192) 0.078 0.075 0.531
MLD MB (end‐diastole) 0.029 ( 0.003–0.244) 0.001 0.330 0.763
%DS MB (end‐systole) 1.035 (0.964–1.090) 0.191 0.040 0.581
%DS MB (end‐diastole) 1.222 (1.096–1.363) <0.001 0.446 0.270
Conventional‐FFR ADO 0.004 (0.000001–1328.866) 0.392 0.018 0.867
Conventional‐FFR DOBmax 0.033 (0.000002–594.865) 0.495 0.011 0.169
Diastolic‐FFR ADO 0.127 (0.0001–138.290) 0.563 0.008 0.708
Diastolic‐FFR DOBmax* 0.803 (0.701–0.919) 0.002 0.393 0.200
Model 1. Backward method with %DS MB (end‐diastole) and:
Conventional‐FFR ADO
%DS MB (end‐diastole) 1.227 (1.095–1.376) <0.001 0.468 0.795
Conventional‐FFR DOBmax
%DS MB (end‐diastole) 1.222 (1.096–1.363) <0.001 0.446 0.270
Diastolic‐FFR ADO
%DS MB (end‐diastole) 1.226 (1.093–1.374) <0.001 0.461 0.656
Diastolic‐FFR DOBmax* , 0.851 (0.750–0.967) 0.013 0.586 0.436
%DS MB (end‐diastole) 1.201 (1.062–1.358) 0.004 0.586 0.436
Model 2. Backward method with %DS MB (end‐diastole), conventional‐FFR ADO, and diastolic‐FFR DOBmax
Diastolic‐FFR DOBmax* 0.870 (0.767–0.986) 0.030 0.567 0.891
%DS MB (end‐diastole) 1.208 (1.065–1.370) 0.003 0.567 0.891

Dependent variable: stress‐induced wall‐motion abnormalities in the left anterior descending coronary artery territory. Multivariate logistic regression analyses were adjusted for all variables with P≤0.05 in univariate analysis. ADO indicates adenosine; DOBmax, peak dobutamine dose; DS, diameter stenosis; FFR, fractional flow reserve; HL, Hosmer and Lemeshow test; MB, myocardial bridging; MLD, minimal luminal diameter; OR, odds ratio; and R 2, Nagelkerke R square.

*

Because of small values of diastolic‐FFR DOBmax, in model, this variable is multiplied by 100 to obtain OR with 95% CI that can be evaluated.

Only variable in the model.