Table 4:

Results of discrimination and calibration metrics for the 50 bootstrap samples.

Discrimination and calibration metrics
Model	Stage 1+	Stage 2+	Dialysis
	Median (95% CI)	Median (95% CI)	Median (95% CI)
AUC
Logistic regression	0.758 (0.758–0.758)	0.715 (0.714–0.716)	0.825 (0.823–0.827)
Lasso regression	0.758 (0.757–0.758)	0.714 (0.713–0.715)	0.824 (0.822–0.826)
Random forest	0.746 (0.744–0.748)	0.721 (0.720–0.721)	0.823 (0.818–0.828)
NRI
Lasso vs LR	0.461 (0.460–0.463)	0.348 (0.344–0.351)	0.549(0.538–0.559)
RF vs Lasso	0.378 (0.377–0.379)	0.271 (0.267–0.275)	0.306
RF vs LR	0.419 (0.417–0.420)	0.332 (0.329–0.336)	0.409 (0.399–0.420)
IDI
Lasso vs LR	0.004 (0.004–0.004)	0.001 (0.001–0.001)	0.007 (0.006–0.007)
RF vs Lasso	0.022 (0.022–0.022)	0.004 (0.004–0.004)	–0.021 (−0.022 to −0.021)
RF vs LR	0.026 (0.026–0.026)	0.005 (0.005–0.005)	−0.015 (−0.016 to −0.014)
Brier
LR	0.067 (0.067–0.067)	0.010 (0.009–0.010)	0.001 (0.001–0.001)
RF	0.068 (0.068–0.068)	0.010 (0.009–0.010)	0.001 (0.001–0.001)
Lasso	0.068 (0.068–0.068)	0.010 (0.009–0.010)	0.001 (0.001–0.001)

Area under the receiver operating characteristic curve (AUC) values are represented for each model by median and 95% confidence intervals (95% CIs) for the stage 1+, 2+, and dialysis outcomes. The continuous net reclassification index (NRI) and integrated discrimination improvement (IDI) values are reported as the improvement of the second model vs the first model (model A vs model B being positive is interpreted as model B having a superior classification). The Brier score is represented for each model with medians and 95% CIs.

LR = logistic regression, RF = random forest.