Literature DB >> 26175192

A quantitative approach to the interpretation of uroflowmetry in children.

Israel Franco1, Stephen Shei-Dei Yang2, Shang-Jen Chang2, Brandon Nussenblatt3, Jacob A Franco4.   

Abstract

PURPOSE: We hypothesized that by correcting for volume and creating a flow index (FI) we could develop a reproducible and reliable means to estimate flows in children without the use of a flow nomogram. Our second hypothesis was that this volume corrected FI could define objective parameters for the different flow curves that are described in the ICCS document.
METHODS: Uroflowmetry curves of 1,268 healthy children were analyzed. Quadratic equations using nonlinear regression for both sexes were generated for each set of presumed normal voiders (learning data) (NV). The NV test data were used to verify the equations. Linear regression analysis was used to compare the variance between actual and estimated flow rates. A FI (Actual Qavg/Estimated Qavg) was created and ROC analysis for all flow types was performed. Sensitivity and specificity analysis was performed on all voids to validate the accuracy of the FI to predict flow pattern.
RESULTS: Analysis of the FI from the first void to the second confirmed the accuracy and reproducibility in both males and females using various means of analysis. ROC analysis shows that there are very strong AUC's for Bell, plateau, and tower flow patterns. Sensitivity and specificity analysis reveals that defined FI parameters are able to predict the flow patterns.
CONCLUSION: Our predictive formulas allow for direct comparison of one flow to the next in a single patient when the FI is used. Utilizing the FI, we can predict the type of flow pattern removing subjectivity from the analysis of uroflow patterns. Neurourol. Urodynam. 35:836-846, 2016.
© 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Entities:  

Keywords:  average flow rate; children; flow index; nomogram; peak flow; uroflowmetry; volume adjusted flow rates

Mesh:

Year:  2015        PMID: 26175192     DOI: 10.1002/nau.22813

Source DB:  PubMed          Journal:  Neurourol Urodyn        ISSN: 0733-2467            Impact factor:   2.696


  4 in total

1.  Predicting posterior urethral obstruction in boys with lower urinary tract symptoms using deep artificial neural network.

Authors:  S Abdovic; M Cuk; N Cekada; M Milosevic; A Geljic; S Fusic; M Bastic; Z Bahtijarevic
Journal:  World J Urol       Date:  2018-12-04       Impact factor: 4.226

2.  Lower Urinary Tract Dysfunction and Associated Pons Volume in Patients with Wolfram Syndrome.

Authors:  Kyle O Rove; Gino J Vricella; Tamara Hershey; Muang H Thu; Heather M Lugar; Joel Vetter; Bess A Marshall; Paul F Austin
Journal:  J Urol       Date:  2018-06-05       Impact factor: 7.450

Review 3.  The Role of Non-invasive Testing in Evaluation and Diagnosis of Pediatric Lower Urinary Tract Dysfunction.

Authors:  Jason P Van Batavia; Andrew J Combs
Journal:  Curr Urol Rep       Date:  2018-04-06       Impact factor: 3.092

Review 4.  Diagnosis and Management of Bladder Dysfunction in Neurologically Normal Children.

Authors:  Mirgon Fuentes; Juliana Magalhães; Ubirajara Barroso
Journal:  Front Pediatr       Date:  2019-07-25       Impact factor: 3.418

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.