Table 5. Evaluation of logistic regression covariates only (Wald test) and leave-one-out cross-validation (LOOCV).

Model: $l={\beta }_0+{\beta }_2{x}_2+{\beta }_3{x}_3+{\beta }_4{x}_4+{\beta }_5{x}_5+{\beta }_6{x}_6+{\beta }_7{x}_7+{\beta }_8{x}_8+{\beta }_9{x}_9$, where $x_2$ : 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 = 0.612; average kappa statistic = 0.17
Predictor	Coef. estimate (logit)	Std. error	z-Value	p-Value
Intercept	${\beta }_0$= –1.585	138.2	–0.011	0.991
Average HR ($x_2$)	${\beta }_2$= –0.0209	0.029	–0.911	0.362
Length of surgery ($x_3$)	${\beta }_3$= 0.0016	0.00141	1.151	0.250
Days to AIS discharge ($x_4$)	${\beta }_4$= 0.0105	0.0106	0.993	0.320
Age ($x_5$)	${\beta }_5$= 0.0052	0.012	0.424	0.672
AIS admission D ($x_6$)	${\beta }_6$= 1.511	1.382	0.011	0.991
AIS admission C ($x_7$)	${\beta }_7$= 1.715	1.382	0.012	0.991
AIS admission B ($x_8$)	${\beta }_8$= 1.643	1.382	0.012	0.990
AIS admission A ($x_9$)	${\beta }_9$= 1.574	1.382	0.011	0.991