A multi-center study of pediatric uroflowmetry data using patterning software

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• 作者：Akihiro Kanematsu^a ; ^c ; ^{aqui@hyo-med.ac.jp} ; ^{aqui@kuhp.kyoto-u.ac.jp} ; Shiro Tanaka^b ; ^{shiro@kuhp.kyoto-u.ac.jp} ; Kazuyoshi Johnin^d ; ^{johnin@belle.shiga-med.ac.jp} ; Shina Kawai^e ; ^{ANB23482@jichi.ac.jp} ; Shigeru Nakamura^e ; ^{s-nakamu@jichi.ac.jp} ; Masaaki Imamura^a ; ^{m69ima@kuhp.kyoto-u.ac.jp} ; Koji Yoshimura^a ; ^{ky7527@kuhp.kyoto-u.ac.jp} ; Yoshihide Higuchi^c ; ^{higuchiy@hyo-med.ac.jp} ; Shingo Yamamoto^c ; ^{shingoy@hyo-med.ac.jp} ; Yusaku Okada^d ; ^{saki@belle.shiga-med.ac.jp} ; Hideo Nakai^e ; ^{hnakai@jichi.ac.jp} ; Osamu Ogawa^a ; ^{ogawao@kuhp.kyoto-u.ac.jp}
• 关键词：Uroflowmetry ; Children ; Enuresis ; Daytime wetting ; Epidemiology ; Patterning ; Software
• 刊名：Journal of Pediatric Urology
• 出版年：2013
• 出版时间：February, 2013
• 年：2013
• 卷：9
• 期：1
• 页码：57-61
• 全文大小：286 K

文摘

Objective

We created software for patterning uroflowmetry (UFM) curves, and validated its utility.

Patients and Methods

The software patterns a given UFM curve upon four parameters: sex, voided volume, maximal flow rate, and amplitude of fluctuation. Using the software, 6 urologists from 4 institutes assessed 30 test curves. Further, 329 UFM curves obtained from children presenting to 3 institutes for daytime and/or nighttime wetting were assessed. Clinical presentation was divided into 3 groups: group A, daytime incontinence; group B, non-monosymptomatic nocturnal enuresis without daytime wetting; and group C, monosymptomatic nocturnal enuresis.

Results

Using the software, inter-rater agreement ranged from 0.85 to 1.00 (mean, 0.93?¡À?0.04). It could pattern 310 out of 329 clinical curves. In each institute, the tower pattern was prevalent according to severity of daytime symptoms, although not significantly. The merged data showed that the percent tower pattern significantly correlated with presence of daytime symptoms (groups A, B, and C, 29.7 % , 27.0 % , and 16.3 % , respectively; p?<?0.05). No correlation with daytime symptoms was noted for fluctuated (staccato and interrupted) and plateau patterns.

Conclusion

The software creates a common platform for evaluating pediatric UFM, enabling extraction of common and biased features of different cohorts, and their integration into one single cohort.