Personalized Behavioral Modifications Algorithm and Software Based on Analysis and Computer Simulation of Voiding Patterns in Patients with Lower Urinary Tract Symptoms

7.项目摘要/摘要 下尿路症状(LUT)，如尿急、尿频、夜尿和大小便失禁 在美国，大约四分之三的40岁以上的人口与 生活质量下降。膀胱日记是一种简单、廉价的工具，可以捕捉真实生活中的症状和 排尿行为。基于膀胱日记的简单建议已经被证明有一个 对LUT有深远影响，包括大小便失禁。我们正在努力走得更远，发展个性化。 给病人的建议，而不是一般的“少喝水，每隔2小时排便，睡前限制液体” 建议，这可能不会改善所有患者的LUTS。膀胱日记提供排出液体的时间 摄入量，这是尿量的主要驱动力，但摄入量和排出量之间的时间间隔不是 直截了当。为了填补这一知识空白，我们计划开发一个简约的机械模型，基于 目前对体内排尿和液体变化的生理学和神经调节的了解 描述患有和不患有下尿路结石的人的排尿模式。我们将使用在以下方面收集的膀胱日记数据 两项研究：下尿路功能障碍症状研究网络(Lurn)和建立 大小便失禁流行(EPI)。数学模型将考虑：1)摄入时间和数量；2) 向膀胱交付的变化；以及3)膀胱感觉和排泄的变化。这种模式可以 为未来的研究人员评估排尿变量和LUTS的机制研究服务。基于 模型，我们计划开发一种算法和软件来提供个性化的修改建议 为了最大限度地减少“不良尿路事件”(即尿漏或夜间排尿过多)，应尽量减少尿液的摄入模式。 将进行摄取模式的优化，以最大限度地减少模拟的不良泌尿事件的数量 对于给定的模拟患者，基于从真实患者的膀胱日记开发的模型。目标是 就是开发一种用于评估膀胱日记的精准医学工具，以便临床医生能够 提供个性化的行为矫正建议。这项工作将是第一个系统化的 基于膀胱日记数据的液体摄入和排尿行为的数学模型将有助于 了解个体排尿模式、疾病机制和治疗机会。

7. Project Summary/Abstract Lower urinary tract symptoms (LUTS), such as urinary urgency, frequency, nocturia, and incontinence affect approximately three-quarters of the population over age 40 in the United States and are associated with decreased quality of life. Bladder Diaries are a simple, inexpensive tool to capture real-life symptoms and voiding behavior. Simple recommendations based on the Bladder Diaries have already been shown to have a profound impact on LUTS, including incontinence. We are striving to go further and develop individualized advice for patients, rather than a generic “drink less, void every 2 hours, and restrict fluids before bed” recommendation, which may not improve LUTS in all patients. Bladder Diaries provide the timing of fluid intake, which is the primary driver of urinary output, but the time lapse between intake and output is not straightforward. To fill this knowledge gap, we plan to develop a parsimonious mechanistic model based on current understanding of the physiology and neural regulation of urination and fluid shifts in the body that describes voiding patterns in people with and without LUTS. We will be using Bladder Diary data collected in two studies: Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN) and Establishing Prevalence of Incontinence (EPI). The mathematical model will consider: 1) ingestion time and amount; 2) variability in delivery to the bladder; and 3) variation in bladder sensation and excretion. This model could serve future researchers in evaluation of voiding variables and in mechanistic studies of LUTS. Based on the model, we plan to develop an algorithm and software to provide individualized recommendations to modify intake patterns in order to minimize “adverse urinary events” (i.e., leaking or excessive night voids). Optimization of the intake patterns will be performed to minimize number of simulated adverse urinary events for a given simulated patient based on the model developed from the Bladder Diary of a real patient. The goal is to develop a “precision medicine” tool for assessment of Bladder Diaries so that clinicians will be able to provide individualized behavior modification recommendations. This work would represent the first systematic mathematical model of fluid intake and voiding behavior based on Bladder Diary data that would help understanding individual voiding patterns, mechanisms of disease, and opportunities for treatment.