Table 4. Evaluation of logistic regression with covariates (Wald test) and leave-one-out cross-validation (LOOCV).

Model: $l={\beta }_0+{\beta }_{11}{x}_1+{\beta }_{12}{x}_1^2+{\beta }_2{x}_2+{\beta }_3{x}_3+{\beta }_4{x}_4+{\beta }_5{x}_5+{\beta }_6{x}_6+{\beta }_7{x}_7+{\beta }_8{x}_8+{\beta }_9{x}_9$, where $x_1:$ average MAP; $x_2$ : average HR; $x_3:$ length of surgery (min); $x_4:$ days to AIS discharge (days); $x_5:$ age; $x_6:$ AIS admission D (‘yes’,’no’); $x_7:$ AIS admission C (‘yes’,’no’); $x_8:$ AIS admission B (‘yes’,’no’); $x_9:$ AIS admission A (‘yes’,’no’); (AIS admission E was set as the reference level for AIS admission variable and is part of the intercept) (final n = 93)
LOOCV: average observed accuracy = 0.688; average kappa statistic = 0.362
Predictor		Coef. estimate (logit)		Std. error		z-Value		p-Value
Intercept		${\beta }_0$= –1.530		121.8		–0.013		0.99
Average MAP ($x_1$)		${\beta }_{11}$= 7.398		3.112		2.377		0.017*
Average MAP ($x_1^2$)		${\beta }_{12}$= –8.053		3.530		–2.281		0.022*
Average HR ($x_2$)		${\beta }_2$= –2.087		0.0245		–0.851		0.394
Length of surgery ($x_3$)		${\beta }_3$= 0.0011		0.0015		0.728		0.466
Days to AIS discharge ($x_4$)		${\beta }_4$= 0.0037		0.0109		0.344		0.730
Age ($x_5$)		${\beta }_5$= 0.0082		0.013		0.634		0.526
AIS admission D ($x_6$)		${\beta }_6$= 1.454		1.218		0.012		0.990
AIS admission C ($x_7$)		${\beta }_7$= 1.645		1.218		0.014		0.989
AIS admission B ($x_8$)		${\beta }_8$= 1.585		1.218		0.013		0.989
AIS admission A ($x_9$)		${\beta }_9$= 1.527		1.218		0.013		0.990
Correlation matrix (Spearman)
	Average MAP	Average HR	Length of surgery		Days to AIS discharge		Age		AIS admission
Average MAP	1
Average HR	–0.126	1
Length of surgery	–0.152	0.101	1
Days to AIS discharge	0.088	–0.059	0.165		1
Age	0.006	–0.245	0.011		0.022		1
AIS admission	0.024	0.003	–0.01		0.258		–0.13		1

*p < 0.05.