The international journal of medical robotics + computer assisted surgery : MRCAS · 2016
A prognostic model for predicting urinary incontinence after robot-assisted radical prostatectomy.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
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История · было → стало
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Honda Masashi, Kawamoto Bunya, Morizane Shuichi, Hikita Katsuya, Muraoka Kuniyasu, Sejima Takehiro, Takenaka Atsushi
Аннотация
BACKGROUND: The aim of this study is to develop a novel prognostic model for estimating the risk of postoperative urinary incontinence (UI) after robot-assisted radical prostatectomy (RARP).
METHODS: Participants comprised 131 men who underwent RARP at our hospital from 2011 to 2013. Cox regression analyses were performed to evaluate associations between UI and preoperative factors including filling cystometry and pressure-flow study results and magnetic resonance imaging.
RESULTS: Logistic regression analyses revealed significant associations between membranous urethral length or levator thickness and UI at 3 and 6 months. Stratification produced high (membranous urethral length < 9.5 mm or levator thickness < 9.0 mm) and low (membranous urethral length ≥ 9.5 mm and levator thickness ≥ 9.0 mm) UI risk groups. These inter-group differences in UI rate were significant.
CONCLUSIONS: We developed a novel prognostic model based on preoperative patient data that can be used for patient counselling.
METHODS: Participants comprised 131 men who underwent RARP at our hospital from 2011 to 2013. Cox regression analyses were performed to evaluate associations between UI and preoperative factors including filling cystometry and pressure-flow study results and magnetic resonance imaging.
RESULTS: Logistic regression analyses revealed significant associations between membranous urethral length or levator thickness and UI at 3 and 6 months. Stratification produced high (membranous urethral length < 9.5 mm or levator thickness < 9.0 mm) and low (membranous urethral length ≥ 9.5 mm and levator thickness ≥ 9.0 mm) UI risk groups. These inter-group differences in UI rate were significant.
CONCLUSIONS: We developed a novel prognostic model based on preoperative patient data that can be used for patient counselling.