. Author manuscript; available in PMC: 2016 Aug 1.

Published in final edited form as: J Pediatr Urol. 2015 Apr 16;11(4):176.e1–176.e7. doi: 10.1016/j.jpurol.2015.03.006

Table 3.

Characteristics of predictive models.

Predictive model	Sensitivity	Specificity	PPV	NPV	AUROC
Logistic regression
Any VUR^a	86.3%	24.7%	53.7%	64.1%	0.5735
VUR grade >II^b	5.1%	99.1%	70.6%	71.9%	0.6018
VUR grade >III^c	6.0%	99.9%	77.8%	93.2%	0.6742
Neural networks^d
Any VUR	64.2%	59.6%	61.6%	62.2%	0.6852
VUR grade >II	17.8%	97.8%	76.4%	74.5%	0.6726
VUR grade >III	31.6%	99.8%	92.5%	95.0%	0.7863

Logistic model with urothelial thickening, duplication, trabeculation and kidney stones (adjusted for sex, circumcision status in boys and febrile UTI)

Logistic model with any renal bladder ultrasound abnormality, urothelial thickening, duplication, kidney stones (adjusted for age, sex and febrile UTI)

Logistic model with any renal bladder ultrasound abnormality, ureter dilation, urothelial thickening, debris, first or recurrent UTI

All individual RBUS abnormalities, sex, age, circumcision status in boys, febrile UTI, first (vs recurrent) UTI