Abstract
Importance
Extracellular matrix proteins and enzymes involved in degradation have been found to be associated with tissue fibrosis and ureteropelvic junction obstruction (UPJO). In this study we developed a promising urinary biomarker model which can identify reduced renal function in UPJ obstruction patients. This can potentially serve as a non-invasive way to enhance surgical decision making for patients and urologists.
Objective
We sought to develop a predictive model to identify UPJO patients at risk for reduced renal function.
Design
Prospective cohort study
Setting
Pre-operative urine samples were collected in a prospectively enrolled UPJO biomarker registry at our institution. Urinary MMP-2, MMP-7, TIMP-2, and NGAL were measured as well as clinical characteristics including hydronephrosis grade, differential renal function, t1/2, and UPJO etiology.
Participants
Children who underwent pyeloplasty for UPJO
Main outcome measurement
Primary outcome was reduced renal function defined as MAG3 function <40%. Multivariable logistic regression was applied to identify the independent predictive biomarkers in the original Training cohort. Model validation and generalizability were evaluated in a new UPJO Testing cohort.
Results
We included 71 patients with UPJO in the original training cohort and 39 in the validation cohort. Median age was 3.3 years (70% male). By univariate analysis, reduced renal function was associated with higher MMP-2 (p = 0.064), MMP-7 (p = 0.047), NGAL (p = 0.001), and lower TIMP-2 (p = 0.033). Combining MMP-7 with TIMP-2, the multivariable logistic regression model predicted reduced renal function with good performance (AUC = 0.830; 95% CI: 0.722–0.938). The independent testing dataset validated the results with good predictive performance (AUC = 0.738).
Conclusions and relevance
Combination of urinary MMP-7 and TIMP-2 can identify reduced renal function in UPJO patients. With the high sensitivity cutoffs, patients can be categorized into high risk (aggressive management) versus lower risk (observation).
Publisher
Public Library of Science (PLoS)
Cited by
4 articles.
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