Urinary Continence Index for Prediction of Urinary Incontinence in Older Women

描述（由申请人提供）：许多研究收集了大量的信息，这些信息虽然不是实现其主要目的所必需的，但在回答后续研究问题时被证明是非常有价值的。尽管提取和分析现有数据可能是高效且具有成本效益的，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。开发可靠和有效的统计模型来帮助挖掘数据，可以提高利用数据来回答具有重大潜在影响尿失禁（UI）等令人不安的条件的新研究问题的能力。Beaumont，皇家橡树的研究员和泌尿科医生Ananias Diokno博士在过去的二十年中，从深入的基于人群的尿失禁研究中积累了丰富的数据。Diokno博士在UI研究方面的显著生产力有助于获得令人垂涎的NIH MERIT奖。这些数据现在包含在两个数据库中。第一个包含在原始的医学、流行病学和老龄化社会方面（MESA）项目中收集的信息。这项由美国国家老龄研究所（NIA）资助的多阶段老年男性和女性纵向研究始于1983年，重点研究与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。这项多期前瞻性、随机对照“MESA 2”试验的目的是预防老年妇女的尿失禁。55岁及以上的大陆女性从邮寄和诊所就诊中招募，随机分为行为矫正计划（BMP）治疗组和对照组。在这次预防试验中收集的许多访谈问题和其他数据与最初的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皇家橡树博蒙特的团队将与国际公认的数据挖掘专家Mohammad Siadat博士及其在密歇根州罗切斯特附近的奥克兰大学的同事和密歇根大学的Sampselle博士建立合作关系。Diokno博士的领域知识和MESA数据的拥有提供了一个独特的机会来创建一个强大的“尿失禁指数”，以识别妇女谁可能成为失禁随着时间的推移。由于UI在女性中比在男性中更普遍，而且潜在的原因也不同，因此本项目将侧重于开发一个用于女性的UI指数。然而，所开发的统计建模方法可能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或其他慢性和昂贵疾病的预测指标。我们提出了一种新的应用数据挖掘策略来识别女性UI的显著预测因素，并创建和测试预测指数。我们处于一个特殊的位置，可以进一步利用MESA数据，并朝着开发广泛使用的预测指数迈出重要一步。由于尿失速症是一种代价高昂的疾病，会导致社会孤立和生活质量差，因此建立临床有用的指数将为重点预防和早期干预战略创造重大机会。

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.