Urinary Continence Index for Prediction of Urinary Incontinence in Older Women

预测老年女性尿失禁的尿失禁指数

• 批准号: 8237238
• 负责人: ANANIAS C DIOKNO
• 金额: $ 22.36万
• 依托单位: WILLIAM BEAUMONT HOSPITAL RESEARCH INST
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-15 至 2014-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8237238
• 关键词: Adult; Affect; Aging; Award; Behavior Therapy; Bladder Control; Chronic; Clinic Visits; Clinical; Clinical Research; Collaborations; Complement; Control Groups; Data; Data Set; Databases; Development; Documentation; Early treatment; Elderly woman; Epidemiology; Equipment and supply inventories; Family; Female; Funding; Future; Household; Institutes; Intervention; Interview; Knowledge; Longitudinal Studies; Mails; Medical; Mental Depression; Methods; Michigan; National Institute on Aging; Positioning Attribute; Prevention; Prevention strategy; Preventive Intervention; Productivity; Quality of life; Randomized; Recruitment Activity; Reporting; Research; Research Personnel; Risk; Risk Factors; Sensitivity and Specificity; Social isolation; Staging; Statistical Models; Techniques; Testing; Time; United States National Institutes of Health; Universities; Urinary Incontinence; Urologist; Validation; Woman; cost effective; data mining; economic cost; follow-up; indexing; information gathering; male; men; novel; older women; population based; prevent; prospective; psychologic; simulation; social; tool; treatment program; treatment strategy

DESCRIPTION (provided by applicant): Many studies collect a wealth of information that, although not integral to achieving their primary purpose, proves to be extremely valuable in answering subsequent research questions. Even though extracting and analyzing existing data may be efficient and cost effective, identifying meaningful relationships within the volumes of data often contained in existing datasets can be challenging. Developing reliable and effective statistical models to aid in mining data enhances the ability to utilize the data to answer novel research questions with significant potential to impact troubling conditions such as urinary incontinence (UI). Beaumont, Royal Oak investigator and urologist, Dr. Ananias Diokno, has accumulated a wealth of data over the past two decades from in depth population-based studies of UI. Dr. Diokno's significant productivity in UI research contributed to earning a coveted NIH MERIT award. These data are now contained in two databases. The first contains information gathered during the original Medical, Epidemiologic, and Social Aspects of Aging (MESA) project. Funded by the National Institutes on Aging (NIA), this multi-stage longitudinal study of senior men and women began in 1983 and focused on factors related to the epidemiology of UI (MESA 1 database). The second database (MESA 2 database) contains data collected during a subsequent collaborative study with Dr. Carolyn Sampselle at the University of Michigan. The aim of this multiphase prospective, randomized controlled "MESA 2" trial was to prevent UI in older women. Continent women 55 years and older were recruited from mailings and clinic visits and were randomized to a behavior modification program (BMP) treatment or control group. Many of the interview questions and other data collected during this prevention trial were the same, or similar, to those collected in the original MESA 1 database study. In this proposed project, Dr. Diokno and his team at Beaumont, Royal Oak, MI will build upon established collaborations with internationally recognized data mining experts Dr. Mohammad Siadat and colleagues at Oakland University in nearby Rochester, Michigan, and Dr. Sampselle at the University of Michigan. Dr. Diokno's domain knowledge and possession of the MESA data provides a unique opportunity to create a robust "Urinary Continence Index" to identify women who are likely to become incontinent over time. Since UI is more prevalent in women than men and the underlying causes differ, this project will focus on developing a UI index for use in women. However, the statistical modeling methods developed might be applied in future projects using male MESA subjects' data or other data to identify predictive indices for UI or perhaps other chronic and costly conditions. We propose a novel application of data mining strategies to identify salient predictors of UI in women and create and test a predictive index. We are in an extraordinary position to further utilize MESA data and take a major step towards developing a predictive index for widespread use. Since UI is a costly condition contributing to social isolation and poor quality of life, establishing a clinically useful index would create significant opportunities for focused prevention and early intervention strategies. PUBLIC HEALTH RELEVANCE: Urinary incontinence (UI) is a costly condition that causes social isolation and poorer quality of life. Data collected from years of productive population-based UI research are now contained in two databases (MESA 1 and 2). In collaboration with data mining experts, we propose to develop a urinary continence index that will help predict UI in older women in order to promote early prevention and treatment strategies.

描述（由申请人提供）：许多研究收集了大量信息，尽管这些信息对于实现其主要目的来说不是不可或缺的，但事实证明对于回答后续研究问题非常有价值。尽管提取和分析现有数据可能高效且具有成本效益，但在现有数据集中通常包含的大量数据中识别有意义的关系可能具有挑战性。开发可靠且有效的统计模型来帮助挖掘数据，可以增强利用数据回答新研究问题的能力，这些问题极有可能影响尿失禁 (UI) 等令人不安的疾病。博蒙特皇家橡树研究员兼泌尿科医生 Ananias Diokno 博士在过去二十年中通过基于人群的 UI 深入研究积累了丰富的数据。 Diokno 博士在 UI 研究方面的卓越成果为他赢得了令人垂涎的 NIH MERIT 奖做出了贡献。这些数据现在包含在两个数据库中。第一个包含在最初的老龄化医学、流行病学和社会方面 (MESA) 项目期间收集的信息。这项针对老年男性和女性的多阶段纵向研究由美国国家老龄化研究所 (NIA) 资助，于 1983 年开始，重点关注与 UI 流行病学相关的因素（MESA 1 数据库）。第二个数据库（MESA 2 数据库）包含在随后与密歇根大学 Carolyn Sampselle 博士合作研究期间收集的数据。这项多阶段前瞻性随机对照“MESA 2”试验的目的是预防老年女性 UI。通过邮寄和临床访问招募 55 岁及以上的大陆女性，并随机分配到行为矫正计划 (BMP) 治疗组或对照组。在此预防试验期间收集的许多访谈问题和其他数据与原始 MESA 1 数据库研究中收集的数据相同或相似。在这个拟议的项目中，Diokno 博士和他在密歇根州罗亚尔奥克博蒙特的团队将与国际知名的数据挖掘专家 Mohammad Siadat 博士及其密歇根州附近的奥克兰大学的同事以及密歇根大学的 Sampselle 博士建立合作关系。 Diokno 博士的领域知识和对 MESA 数据的掌握为创建强大的“尿失禁指数”提供了独特的机会，以识别随着时间的推移可能会失禁的女性。由于UI在女性中比男性更普遍，并且根本原因也不同，因此该项目将重点开发用于女性的UI指数。然而，所开发的统计建模方法可能会应用于未来的项目中，使用男性 MESA 受试者的数据或其他数据来确定 UI 或其他慢性且昂贵的疾病的预测指数。我们提出了一种数据挖掘策略的新颖应用，以识别女性 UI 的显着预测因素，并创建和测试预测指数。我们处于非常有利的位置，可以进一步利用 MESA 数据，并在开发广泛使用的预测指数方面迈出重要一步。由于 UI 是一种代价高昂的疾病，会导致社会孤立和生活质量差，因此建立临床上有用的指数将为重点预防和早期干预策略创造重要机会。 公共健康相关性：尿失禁 (UI) 是一种代价高昂的疾病，会导致社会孤立和生活质量下降。从多年基于人口的高效 UI 研究中收集的数据现在包含在两个数据库中（MESA 1 和 2）。我们与数据挖掘专家合作，建议开发尿失禁指数，帮助预测老年女性尿失禁，以促进早期预防和治疗策略。