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World J Urol. 2009 Jun;27(3):379-83. doi: 10.1007/s00345-008-0355-z. Epub 2008 Nov 26.

The value of newborn urinary proteome analysis in the evaluation and management of ureteropelvic junction obstruction: a cost-effectiveness study.

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Department of Urology, Children's Hospital and Medical College of Wisconsin, Milwaukee, WI 53226, USA.



The cornerstone of management in newborns with ureteropelvic junction obstruction (UPJO) is serial imaging over time. Surgery is undertaken for disease progression. A marker of disease progression would select out those likely to progress for early surgery and diminish the intensity of imaging and follow-up in the remainder. Recently, urinary proteome analysis in the newborn has been reported to fulfill this aim. The objective of this study is to quantitatively evaluate the effect of this matrix of protein biomarkers on the overall cost-effectiveness (C-E) of UPJO evaluation and management.


A Markov process decision tree model (Tree Age Pro software, Boston, MA) is created to compare the current strategy (watchful waiting) to one incorporating a urine proteome analysis at birth as a marker of disease progression. The analysis includes the costs of surgery, imaging and office visits based on hospital charge data. We analyze a total of 53 variables.


The incorporation of this marker of progression results on the average, in an incremental C-E gain of 8,000 US dollars per quality adjusted life year (QALY) per patient compared to the current strategy of watchful waiting. The results are not sensitive to variation of any of the probabilities including costs and quality of life parameters used for the base-case analysis.


The incorporation of urinary proteome analysis in the initial evaluation of UPJO significantly reduces costs and increases the QALYs in this patient population. The test increases the odds of detecting UPJO progression from 1:3 to 1:1, while improving the overall C-E. These findings justify continued research in this area which in addition may have important applications in evaluating treatment outcomes.

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