Adding Renal Scan Data Improves the Accuracy of a Computational Model to Predict Vesicoureteral Reflux Resolution

Kenneth G. Nepple, Matthew J. Knudson, James (Christopher) Austin, Moshe Wald, Antoine A. Makhlouf, Craig S. Niederberger, Christopher S. Cooper

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1648-1652
Number of pages5
JournalJournal of Urology
Volume180
Issue number4 SUPPL.
DOIs
StatePublished - Oct 2008
Externally publishedYes

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Vesico-Ureteral Reflux
Kidney
Logistic Models
Internet
Cicatrix
Medical Records
Neurons

Keywords

  • decision support techniques
  • kidney
  • radionuclide imaging
  • vesico-ureteral reflux

ASJC Scopus subject areas

  • Urology

Cite this

Nepple, K. G., Knudson, M. J., Austin, J. C., Wald, M., Makhlouf, A. A., Niederberger, C. S., & Cooper, C. S. (2008). Adding Renal Scan Data Improves the Accuracy of a Computational Model to Predict Vesicoureteral Reflux Resolution. Journal of Urology, 180(4 SUPPL.), 1648-1652. https://doi.org/10.1016/j.juro.2008.03.109

Adding Renal Scan Data Improves the Accuracy of a Computational Model to Predict Vesicoureteral Reflux Resolution. / Nepple, Kenneth G.; Knudson, Matthew J.; Austin, James (Christopher); Wald, Moshe; Makhlouf, Antoine A.; Niederberger, Craig S.; Cooper, Christopher S.

In: Journal of Urology, Vol. 180, No. 4 SUPPL., 10.2008, p. 1648-1652.

Research output: Contribution to journalArticle

Nepple, KG, Knudson, MJ, Austin, JC, Wald, M, Makhlouf, AA, Niederberger, CS & Cooper, CS 2008, 'Adding Renal Scan Data Improves the Accuracy of a Computational Model to Predict Vesicoureteral Reflux Resolution', Journal of Urology, vol. 180, no. 4 SUPPL., pp. 1648-1652. https://doi.org/10.1016/j.juro.2008.03.109
Nepple, Kenneth G. ; Knudson, Matthew J. ; Austin, James (Christopher) ; Wald, Moshe ; Makhlouf, Antoine A. ; Niederberger, Craig S. ; Cooper, Christopher S. / Adding Renal Scan Data Improves the Accuracy of a Computational Model to Predict Vesicoureteral Reflux Resolution. In: Journal of Urology. 2008 ; Vol. 180, No. 4 SUPPL. pp. 1648-1652.
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abstract = "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.",
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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.

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