TY - JOUR
T1 - Adding Renal Scan Data Improves the Accuracy of a Computational Model to Predict Vesicoureteral Reflux Resolution
AU - Nepple, Kenneth G.
AU - Knudson, Matthew J.
AU - Austin, J. Christopher
AU - Wald, Moshe
AU - Makhlouf, Antoine A.
AU - Niederberger, Craig S.
AU - Cooper, Christopher S.
N1 - Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2008/10
Y1 - 2008/10
N2 - Purpose: We previously developed a computational model to predict vesicoureteral reflux resolution 1 and 2 years after diagnosis. Previous studies suggest that an abnormal renal scan may be a predictor of the failure of vesicoureteral reflux to resolve. We investigated whether the addition of renal scan data would improve the accuracy of our computational model. Materials and Methods: Medical records and renal scans were reviewed on 161 children, including 127 girls and 34 boys, with primary reflux between 1988 and 2004. In addition to the 9 input variables from our prior model, we added renal scan data on decreased relative renal function (40% or less in the refluxing kidney) and renal scars. Resolution outcome was evaluated 1 and 2 years after diagnosis. Data sets were prepared for 1 and 2-year outcomes, and randomized into a modeling set of 111 and a cross-validation set of 50. The model was constructed using neUROn++. Results: A logistic regression model had the best fit with an ROC area of 0.945 for predicting reflux resolution in the 2-year model. This was improved compared to our previous model without renal scan data. A prognostic calculator using this model can be deployed for availability on the Internet, allowing input variables to be entered and calculating the odds of resolution. Conclusions: This computational model uses multiple variables, including renal scan data, to improve individualized prediction of early reflux resolution with almost 95% accuracy. The prognostic calculator is a useful tool for predicting individualized vesicoureteral reflux resolution.
AB - Purpose: We previously developed a computational model to predict vesicoureteral reflux resolution 1 and 2 years after diagnosis. Previous studies suggest that an abnormal renal scan may be a predictor of the failure of vesicoureteral reflux to resolve. We investigated whether the addition of renal scan data would improve the accuracy of our computational model. Materials and Methods: Medical records and renal scans were reviewed on 161 children, including 127 girls and 34 boys, with primary reflux between 1988 and 2004. In addition to the 9 input variables from our prior model, we added renal scan data on decreased relative renal function (40% or less in the refluxing kidney) and renal scars. Resolution outcome was evaluated 1 and 2 years after diagnosis. Data sets were prepared for 1 and 2-year outcomes, and randomized into a modeling set of 111 and a cross-validation set of 50. The model was constructed using neUROn++. Results: A logistic regression model had the best fit with an ROC area of 0.945 for predicting reflux resolution in the 2-year model. This was improved compared to our previous model without renal scan data. A prognostic calculator using this model can be deployed for availability on the Internet, allowing input variables to be entered and calculating the odds of resolution. Conclusions: This computational model uses multiple variables, including renal scan data, to improve individualized prediction of early reflux resolution with almost 95% accuracy. The prognostic calculator is a useful tool for predicting individualized vesicoureteral reflux resolution.
KW - decision support techniques
KW - kidney
KW - radionuclide imaging
KW - vesico-ureteral reflux
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U2 - 10.1016/j.juro.2008.03.109
DO - 10.1016/j.juro.2008.03.109
M3 - Article
C2 - 18715584
AN - SCOPUS:51049100161
SN - 0022-5347
VL - 180
SP - 1648
EP - 1652
JO - Journal of Urology
JF - Journal of Urology
IS - 4 SUPPL.
ER -