Table 2.

Frailty classification performance using EEG at different periods using gradient boosting tree (GBT). “[]” indicates 95% confidence interval from bootstrapping 1000 times.

Period	Method + Features	AUC*	Cohen’s kappa+
Fried Classification
pre-surgery	GBT + Covariates only	0.78 [0.75–0.82]	0.31 [0.25–0.40]
	GBT + EEG features only	0.43 [0.33–0.53]	−0.08 [−0.30–0.03]
	GBT + Covariates and EEG features	0.82 [0.77–0.84]	0.38 [0.28–0.45]
baseline	GBT + Covariates only	0.69 [0.68–0.73]	0.42 [0.34–0.46]
	GBT + EEG features only	0.66 [0.53–0.69]	0.17 [−0.03–0.31]
	GBT + Covariates and EEG features	0.73 [0.68–0.77]	0.21 [0.12–0.35]
post-surgery	GBT + Covariates only	0.74 [0.69–0.77]	0.36 [0.23–0.40]
	GBT + EEG features only	0.55 [0.36–0.58]	0.00 [−0.25–0.15]
	GBT + Covariates and EEG features	0.76 [0.71–0.79]	0.40 [0.23–0.47]
CFS Classification
pre-surgery	GBT + Covariates only	0.81 [0.77–0.82]	0.52 [0.46–0.52]
	GBT + EEG features only	0.41 [0.36–0.56]	−0.06 [−0.23–0.13]
	GBT + Covariates and EEG features	0.81 [0.74–0.80]	0.44 [0.35–0.55]
baseline	GBT + Covariates only	0.75 [0.74–0.76]	0.42 [0.42–0.55]
	GBT + EEG features only	0.60 [0.48–0.65]	0.15 [−0.16–0.21]
	GBT + Covariates and EEG features	0.74 [0.69–0.81]	0.49 [0.25–0.55]
post-surgery	GBT + Covariates only	0.72 [0.71–0.74]	0.47 [0.43–0.58]
	GBT + EEG features only	0.44 [0.42–0.63]	0.03 [−0.20–0.14]
	GBT + Covariates and EEG features	0.66 [0.64–0.69]	0.29 [0.19–0.43]

^*AUC ranges from 0 to 1, where 0.5 is random guess, 1 is perfect agreement, and 0 is perfectly opposite.

⁺Cohen’s kappa ranges from −1 to 1, where 0 is random guess, 1 is perfect agreement, and 0 is perfectly opposite. Cohen’s kappa considers data imbalance. Cohen’s kappa is usually used for inter-rater agreement, but can also be used to evaluate human-model agreement assuming model is another rater.