Urinary Continence Index for Prediction of Urinary Incontinence in Older Women

描述(由申请人提供)：许多研究收集了丰富的信息，尽管这些信息不是实现其主要目的所不可或缺的，但事实证明，这些信息对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是高效和具有成本效益的，但在通常包含在现有数据集中的数据量中确定有意义的关系可能是具有挑战性的。开发可靠和有效的统计模型来帮助挖掘数据提高了利用数据回答新的研究问题的能力，这些问题具有显著的潜在影响，例如尿失禁(UI)。皇家橡树研究所研究员兼泌尿科医生阿纳尼亚斯·迪奥克诺博士在过去20年里从对尿失禁的深入人群研究中积累了大量数据。迪奥克诺博士在UI研究方面的显著生产力帮助他获得了令人垂涎的NIH优秀奖。这些数据现在包含在两个数据库中。第一份包含在最初的老龄化的医学、流行病学和社会方面(MESA)项目期间收集的信息。这项由美国国家老龄研究所(NIA)资助的针对老年男性和女性的多阶段纵向研究始于1983年，重点关注与UI(MESA 1数据库)流行病学相关的因素。第二个数据库(MESA 2数据库)包含在随后与密歇根大学的Carolyn Sampselle博士合作研究期间收集的数据。这项多阶段前瞻性、随机对照的“MESA-2”试验的目的是预防老年妇女的尿崩症。55岁及以上的可控性妇女从邮寄和诊所就诊中招募，并被随机分配到行为矫正计划(BMP)治疗组或对照组。在这项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市博蒙特的团队将建立在与国际公认的数据挖掘专家Mohammad Siadat博士和密歇根罗切斯特奥克兰大学的同事以及密歇根大学的Sampselle博士建立的合作基础上。迪奥克诺博士的领域知识和对MESA数据的掌握提供了一个独特的机会，可以创建一个强大的“尿液大陆指数”，以识别随着时间的推移可能出现尿失禁的女性。由于尿失禁在女性中比男性更普遍，而且潜在的原因也不同，本项目将专注于开发一种供女性使用的尿失禁指数。然而，开发的统计建模方法可能会应用于未来的项目，使用男性MESA受试者的数据或其他数据来确定尿失禁或其他慢性和昂贵疾病的预测指数。我们提出了一种数据挖掘策略的新应用，以识别女性尿失禁的显著预测因素，并创建和测试预测指数。我们处于非常有利的地位，可以进一步利用MESA数据，并朝着开发可广泛使用的预测性指数迈出重要一步。由于尿失禁是一种代价高昂的疾病，导致社会孤立和生活质量低下，建立一个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的机会。

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.