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

基于下尿路症状患者排尿模式分析和计算机模拟的个性化行为矫正算法和软件

• 批准号: 10004605
• 负责人: Victor P. Andreev
• 金额: $ 22.08万
• 依托单位: ARBOR RESEARCH COLLABORATIVE FOR HEALTH
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10004605
• 关键词: Adverse event; Affect; Age; Algorithmic Software; Algorithms; Assessment tool; Beds; Behavior; Behavior Therapy; Belief; Bladder; Classification; Clinical; Clinical Trials; Communication; Computer Simulation; Data; Diagnosis; Differential Equation; Disease; Esthesia; Evaluation; Event; Excretory function; Fluid Shifts; Frequencies; Functional disorder; Future; Goals; Hour; Incontinence; Increased frequency of micturition; Individual; Ingestion; Intake; Intestines; Kidney; Knowledge; Life; Liquid substance; Modeling; Modification; Movement; Nocturia; Output; Patient Self-Report; Patients; Pattern; Phenotype; Physicians; Physiology; Population; Prevalence; Process; Production; Quality of life; Recommendation; Regulation; Renal tubule structure; Research; Research Personnel; Stomach; Symptoms; Time; Tissues; Travel; United States; Urination; Urine; Variant; Water; Work; base; care seeking; diaries; improved; interstitial; lower urinary tract symptoms; mathematical model; micturition urgency; mobile application; neuroregulation; precision medicine; prevent; tool; urinary

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.

7.项目总结/摘要 下尿路症状（LUTS），如尿急、尿频、尿失禁和尿失禁影响 在美国，大约四分之三的40岁以上的人口， 生活质量下降。膀胱日记是一种简单、廉价的工具，可以捕捉现实生活中的症状， 排泄行为基于膀胱日记的简单建议已经被证明具有 对LUTS的深远影响，包括失禁。我们正在努力走得更远，开发个性化的 给病人的建议，而不是一般的“少喝酒，每2小时排尿一次，睡前限制液体” 这可能不会改善所有患者的LUTS。膀胱日记提供了液体的时间 摄入，这是尿输出的主要驱动力，但摄入和输出之间的时间间隔不是 直截了当。为了填补这一知识空白，我们计划开发一个基于 目前对排尿和体内液体转移的生理学和神经调节的理解， 描述了有和没有下尿路症状的人的排尿模式。我们将使用收集的膀胱日记数据， 两项研究：下尿路功能障碍的症状研究网络（LURN）和建立 免疫缺陷综合征（EPI）。数学模型将考虑：1）摄入时间和量; 2） 递送至膀胱的可变性;和3）膀胱感觉和排泄的可变性。该模型能够 为今后的研究人员提供排尿变量的评价和LUTS的机制研究。基于 模型，我们计划开发一种算法和软件，以提供个性化的建议，以修改 摄入模式以最小化“不利的泌尿事件”（即，泄漏或夜间排尿过多）。 将对摄入模式进行优化，以最大限度地减少模拟泌尿系统不良事件的数量 基于从真实的患者的膀胱日记开发的模型，目标 是开发一种用于评估膀胱日记的“精确医学”工具，以便临床医生能够 提供个性化的行为修正建议。这项工作将是第一个系统的 基于膀胱日记数据的液体摄入和排泄行为的数学模型， 了解个人排尿模式、疾病机制和治疗机会。