Spatiotemporal models for periodontal disease monitoring and recall frequencies

牙周病监测和召回频率的时空模型

基本信息

批准号：
8983525
负责人：
Dipankar Bandyopadhyay
金额：
$ 40.07万
依托单位：
VIRGINIA COMMONWEALTH UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-25 至 2018-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8983525
关键词：
Accounting Address Age Algorithms American Basic Science Clinical Collaborations Computer software Computers Consensus Data Data Analyses Data Set Databases Dental Dental Care Dental Hygiene Dental Insurance Dependence Deterioration Development Diabetes Mellitus Disease Progression Education Evaluation Exhibits Frequencies Future Gender Goals Individual Institutes Institution Linear Models Location Longitudinal Studies Measures Medical Methodology Methods Minnesota Modeling Monitor National Institute of Dental and Craniofacial Research Normalcy Oral Oral cavity Oral health Patients Performance Periodontal Diseases Policies Prognostic Marker Public Health Race Recommendation Records Research Research Design Research Personnel Resources Risk Assessment Risk Factors Schedule Services Side Site Smoking Status Statistical Methods Statistical Models Tooth Diseases Tooth Loss Tooth structure Translations Uncertainty Visit base checkup examination clinical practice cost cost effective cost effectiveness disorder prevention disorder risk effective therapy evidence base expectation follow-up high risk insight interest longitudinal database novel novel strategies oral care outcome forecast personalized medicine public health relevance sound spatiotemporal time interval tool treatment effect treatment planning user friendly software

项目摘要

DESCRIPTION (provided by applicant): Spatiotemporal models for periodontal disease monitoring and recall frequencies Tooth loss from periodontal disease (PD) remains a major public health burden in the US. With the rising cost of dental insurance premiums, future professional dental treatment plans will seek to prioritize patients based on their risk of disease and spend more resources monitoring and treating high-risk patients. Hence, there is a need to develop appropriate statistical models and tools for efficient risk assessment of PD, short-term prognosis, and periodontal recall intervals leading to cost-effectiveness of dental treatment plans. Dental datasets present many interesting statistical challenges (non-stationarity, non-normality, spatial dependence, non-random missingness, confounding by indication, huge cluster size, etc), which severely limit the potential of currently-available software (such as Patterson's EagleSoft(r), etc) loaded into the chair- side computer of a periodontist. Currently available statistical software might handle some, but not all of these challenges within a unified paradigm. Goals: The proposed study will develop statistical tools to (a) characterize risk factors for PD progression, (b) rapidly and efficiently indentify changes in a patient's PD status, (c) use short-term predictions to guide periodontal recall decisions, and (d) develop user-friendly software to implement these methods. Subjects: The statistical methods will be developed using a rich 8-year longitudinal database from the HealthPartners HMO, consisting of about 15,000 patients with follow-ups. Available data and study design: A clustered- longitudinal (CL) study design comprises the databases that recorded data for age, gender, race, complete restorative and periodontal records with follow-up, smoking status, diabetes status, oral hygiene, and other essential parameters. Significance: The potential translation to dental clinical practice for this project is strong because it will provide dental practitioners with evidence-based criteria to guid 'personalized' periodontal recalls and treatment decisions. The impact generated is expected to be far- reaching, and the long-term goal would incorporate these new methods into existing chair-side dental software leading to development of cost-effective treatment dental plans with prudent expectations.

描述（由适用提供）：用于牙周疾病监测和回忆频率牙周疾病（PD）牙齿丧失频率的时空模型仍然是美国的主要公共卫生伯恩。随着牙科保险费的上涨，未来的专业牙科治疗计划将根据患者的疾病风险来优先考虑患者并花费更多资源监测和治疗高危患者。因此，有必要开发适当的统计模型和工具，以有效地评估PD，短期预后以及牙周召回间隔，从而导致牙科治疗计划的成本效益。牙科数据集提出了许多有趣的统计挑战（非平稳性，非正常性，空间依赖性，非随机失踪性，因指示而混淆，巨大的群集大小等），这些挑战严重限制了当前可用的软件（例如Patterson's Eaglesoft（R）等）的潜力，这些软件被加载到椅子旁的计算机上的年度计算机中。当前可用的统计软件可能会处理一些统一范式中的某些挑战，但并非所有这些挑战。目标：拟议的研究将开发统计工具以（a）表征风险因素对于PD的进展，（b）快速有效地缩小患者PD状态的变化，（c）使用短期预测指导牙周召回决策，以及（d）开发用户友好的软件来实施这些方法。受试者：统计方法将使用来自HealthPartners HMO的8年纵向数据库开发，由大约15,000名随访患者组成。可用的数据和研究设计：聚类 - 纵向（CL）研究设计包括记录年龄，性别，种族，完整修复和牙周记录的数据库，具有随访，吸烟状态，糖尿病状态，口腔卫生和其他必要参数。意义：该项目的牙科临床实践的潜在转化很强，因为它将为牙科实践者提供基于证据的标准，以指导“个性化”牙周召回和治疗决策。预计所产生的影响将是遥不可及的，并且长期目标将这些新方法纳入现有的椅子牙科软件中，从而导致具有谨慎期望的具有成本效益的治疗牙科计划。