Artificial intelligence in chronic kidney disease: Early detection, risk prediction, and personalized treatment strategies

Table 3 Survival modeling approaches for kidney failure prediction and comparative performance

Model type	Data inputs	Competing risks handled	Typical performance	Comparator	Key advantage	Ref.
Traditional Cox	Baseline labs	Rarely	C-index 0.65-0.75	KFRE	Simplicity	[39]
KFRE	Static clinical variables	No	C-index 0.70-0.80		Widely adopted	[39]
ML survival (RSF, DeepSurv)	Longitudinal labs	Yes	C-index 0.75-0.88	Cox, KFRE	Dynamic risk	[40]
Multimodal ML	Labs + imaging ± genomics	Yes	+0.03-0.07 C-index gain	Lab-only ML	Rapid progressor ID	[46]

Reported performance ranges are study-level estimates derived from independent validation cohorts. KFRE: Kidney failure risk equation; ML: Machine learning; RSF: Random survival forest.