Table 2:

Optimal threshold, sensitivity, specificity, and accuracy for TTP, rCBF, ATD_diff, MTT_diff, and the final model in identifying infarcted voxels

Variable	Threshold	Sensitivity	Specificity	Accuracy	AUC
TTP	28.82 seconds	65.3%	77.9%	76.5%	0.76
rCBF	22.10	60.0%	72.9%	71.5%	0.73
ATD_diff	0.87 seconds	68.1%	80.2%	78.9%	0.80
MTT_diff	1.38 seconds	56.2%	74.5%	72.5%	0.69
Final model^{^a}	0.109^{^b}	74.2%	80.0%	79.4%	0.84

↵a The final model consisted of TTP, rCBF, ATD_diff, and MTT_diff as the independent variables and the presence of infarct as the outcome.
↵b Equation of the final model: logit(P) = −3.9170 + 0.0601 × TTP − 0.0095 × rCBF + 0.4629 × ATD_diff + 0.0989 × MTT_diff where logit(P) = estimated log odds of infarction for a given voxel. If logit(P) is greater than the optimal threshold of 0.109, the voxel is classified as infarct.