Integrating clinical and image-based parameters for prediction of early post-prostatectomy incontinence recovery: simplified nomogram approach.

IF 3.4 2区 医学 Q2 ONCOLOGY
I-Hung Shao, Sy-Yuan Chen, Hung-Yi Chen, Ting-Wen Sheng, Ying-Hsu Chang, Chung-Yi Liu, Liang-Kang Huang, Hung-Chen Kan, Po-Hung Lin, Kai-Jie Yu, Cheng-Keng Chuang, See-Tong Pang, Chun-Te Wu
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引用次数: 0

Abstract

Purpose: This study aimed to develop a novel model that combines both clinical and image-based parameters to predict early recovery of urinary incontinence after robotic-assisted radical prostatectomy (RARP) more easily and precisely.

Materials and methods: We retrospectively enrolled data from patients who underwent RARP performed by a single surgeon. Clinical parameters were collected through medical chart review. All patients received cystography one week after RARP to evaluate the anastomosis healing condition. All cystography images were analyzed by a single radiologist who was blinded to the clinical status of the patients. Multivariate analysis was performed to select significant predictors for early post-prostatectomy incontinence (PPI) recovery, defined as being pad-free within four weeks after surgery.

Results: A total of 293 patients were enrolled in this study. Among them, 26.7% experienced immediate dryness after surgery, while 47.6% achieved being pad-free within one month. The overall continence rate was over 90% six months after surgery. In univariate analysis, factors associated with early PPI recovery were BMI, T stage, NVB preservation, surgical margin status, downward bladder neck, and bladder neck angle on cystography. BMI, NVB preservation, and downward bladder neck remained significant in multivariate analysis (p-values = 0.041, 0.027, and 0.023, respectively). A nomogram model was established based on these three predictors.

Conclusion: This is the first model to combine preoperative clinical factors, peri-surgical factors, and postoperative image-based factors to predict PPI recovery after RARP. This model can assist clinicians in taking optimal actions for PPI and also reduce patient anxiety.

整合临床和图像参数,预测前列腺切除术后尿失禁早期恢复情况:简化提名图法。
目的:本研究旨在开发一种结合临床和图像参数的新型模型,以更简便、更精确地预测机器人辅助前列腺癌根治术(RARP)后尿失禁的早期恢复情况:我们回顾性地收集了由一名外科医生实施的前列腺癌根治术患者的数据。通过病历审查收集临床参数。所有患者在 RARP 术后一周接受膀胱造影检查,以评估吻合口愈合情况。所有膀胱造影图像均由一名放射科医生进行分析,该医生对患者的临床状况是盲人。多变量分析筛选出前列腺切除术后尿失禁(PPI)早期恢复的重要预测因素,即术后四周内无尿垫:共有 293 名患者参与了这项研究。其中,26.7%的患者在术后立即出现干涩,47.6%的患者在一个月内实现无尿垫。术后六个月的总体尿失禁率超过 90%。在单变量分析中,与 PPI 早期恢复相关的因素有体重指数、T 期、NVB 保留、手术边缘状态、膀胱颈向下和膀胱造影显示的膀胱颈角度。在多变量分析中,BMI、NVB 保留率和膀胱颈向下仍具有显著性(P 值分别为 0.041、0.027 和 0.023)。根据这三个预测因素建立了一个提名图模型:这是首个结合术前临床因素、围手术期因素和术后图像因素来预测 RARP 术后 PPI 恢复情况的模型。该模型可帮助临床医生采取最佳的 PPI 治疗措施,同时减轻患者的焦虑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BMC Cancer
BMC Cancer 医学-肿瘤学
CiteScore
6.00
自引率
2.60%
发文量
1204
审稿时长
6.8 months
期刊介绍: BMC Cancer is an open access, peer-reviewed journal that considers articles on all aspects of cancer research, including the pathophysiology, prevention, diagnosis and treatment of cancers. The journal welcomes submissions concerning molecular and cellular biology, genetics, epidemiology, and clinical trials.
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