Predictive value of specific ultrasound findings when used as a screening test for abnormalities on VCUG.
BACKGROUND
Renal and bladder ultrasound (RBUS) is often used as an initial screening test for children after urinary tract infection (UTI), and the 2011 AAP guidelines specifically recommend RBUS be performed first, with voiding cystourethrogram (VCUG) to be performed only if the ultrasound is abnormal. It is uncertain whether specific RBUS findings, alone or in combination, might make RBUS more useful as a predictor of VCUG abnormalities.


AIMS
To evaluate the association of specific RBUS with VCUG findings, and determine whether predictive models that accurately predict patients at high risk of VCUG abnormalities, based on RBUS findings, can be constructed.


METHODS
and study sample: A total of 3995 patients were identified with VCUG and RBUS performed on the same day. The RBUS and VCUG reports were reviewed and the findings were classified. Analysis was limited to patients aged 0-60 months with no prior postnatal genitourinary imaging and no history of prenatal hydronephrosis.


ANALYSIS
The associations between large numbers of specific RBUS findings with abnormalities seen on VCUG were investigated. Both multivariate logistic models and a neural network machine learning algorithms were constructed to evaluate the predictive power of RBUS for VCUG abnormalities (including VUR or bladder/urethral findings). Sensitivity, specificity, predictive values and area under receiving operating curves (AUROC) of RBUS for VCUG abnormalities were determined.


RESULTS
A total of 2259 patients with UTI as the indication for imaging were identified. The RBUS was reported as "normal" in 75.0%. On VCUG, any VUR was identified in 41.7%, VUR grade > II in 20.9%, and VUR grade > III in 2.8%. Many individual RBUS findings were significantly associated with VUR on VCUG. Despite these strong univariate associations, multivariate modeling didn't result in a predictive model that was highly accurate. Multivariate logistic regression built via stepwise selection had: AUROC = 0.57, sensitivity = 86% and specificity = 25% for any VUR; AUROC = 0.60, sensitivity = 5% and specificity = 99% for VUR grade > II; and AUROC = 0.67, sensitivity = 6% and specificity = 99% for VUR grade > III. The best predictive model constructed via neural networks had: AUROC = 0.69, sensitivity = 64% and specificity = 60% for any VUR; AUROC = 0.67, sensitivity = 18% and specificity = 98% for VUR grade > II; and AUROC = 0.79, sensitivity = 32% and specificity = 100% for VUR grade > III.


CONCLUSIONS
Even with the state-of-the-art predictive models, abnormal findings on RBUS provide a poor screening test for genitourinary abnormalities. Renal bladder ultrasound and VCUG should be considered complementary, as they provide important, but different, information.