POWER: Predicting Obesity with Enhanced EHR Resources

POWER：通过增强的 EHR 资源预测肥胖

• 批准号: 10523257
• 负责人: Charles T Wood
• 金额: $ 16.11万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10523257
• 关键词: 2 year old; 3 year old; Active Learning; Address; Adolescent; Award; Behavioral; Bioinformatics; Biometry; Body mass index; Cardiometabolic Disease; Cessation of life; Characteristics; Child; Child Health; Childhood; Clinical; Clinical Research; Clinical Trials; Consultations; Consumption; Data; Data Linkages; Data Science; Data Set; Development Plans; Electronic Health Record; Enrollment; Ensure; Environment; Epidemiologist; Epidemiology; Food; Goals; Growth; Health; Health Sciences; Health Services Research; Healthcare; Individual; Infant; Intervention; Intervention Trial; Learning; Length; Life; Life Cycle Stages; Measurement; Measures; Medical center; Mentors; Mentorship; Modeling; Morbidity - disease rate; Obesity; Outcome; Outcomes Research; Overweight; Parents; Patients; Pattern; Pediatric Hospitals; Pediatrics; Prevalence; Prevention strategy; Prevention trial; Primary Health Care; Primary Prevention; Provider; Public Health; Research; Research Institute; Research Personnel; Research Proposals; Resources; Risk; Risk Factors; Series; Source; Testing; Time; Training; Translations; Universities; Validation; Visit; Weight; Wood material; Work; base; cardiometabolic risk; career; career development; clinical decision support; clinical implementation; clinically relevant; comparative; cost; data harmonization; design; diabetes risk; early childhood; experience; feeding; heart disease risk; high risk; implementation evaluation; implementation science; improved; infancy; insight; longitudinal analysis; modifiable risk; novel; obese person; obesity in children; obesity prevention; obesity risk; obesity treatment; population health; predictive modeling; premature; prevent; preventive intervention; primary care setting; professor; prognostic model; programs; prospective; rapid infant weight gain; research and development; risk prediction; risk prediction model; skills; statistics; success; support tools; tool

ABSTRACT Obesity is a pervasive public health problem with origins in childhood, and despite advances in treatment for obesity, primary prevention is essential to prevent morbidity and early death. There is an urgent, unmet need to predict which infants and young children are at highest risk of obesity. The first years of life provide a promising sensitive period to address excess, rapid infant weight gain and subsequent obesity and cardiometabolic risk. Previous work demonstrates associations between risk factors and rapid infant weight gain, but novel growth characteristics, such as magnitude and timing of infancy BMI peak, may provide better insight into obesity risk when combined with known risk factors. Primary care visits in the first years of life, with brief and frequent contact with parents eager for guidance, provide an ideal setting for both risk prediction and interventions. The overarching hypothesis is that childhood obesity prevention strategies in the clinical settings can be improved through predictive models and clinical decision support designed to incorporate modifiable risk factors and infancy growth patterns. The overall objectives of this proposal are to improve clinically-relevant prediction of childhood obesity and design a clinical decision support tool that incorporates real-time risk prediction. Aim 1 identifies associations between known risk factors during infancy and novel infancy growth patterns. Aim 2 develops, compares, and validates predictive models using novel infant growth characteristics, including individual and group-based patterns. Aim 3 tests implementation of a risk prediction and clinical decision support tool within the electronic health record. The outlined research aims and career development plan provides Charles Wood, MD, MPH the skills to achieve his overall career goal of becoming an independent investigator focused on obesity prevention in the primary care setting focused on the first years of life. Dr. Wood’s training plan includes experiential learning and didactic coursework to achieve the following short-term training goals: 1) master approaches to repeated measures analysis and predictive modelling construction and validation; 2) execute data linkage and harmonization using electronic health record (EHR) sources; 3) learn and practice optimal use of EHR data for research; 4) incorporate state-of-the-art approaches to designing clinical decision support tools; and 5) continue to develop career and professional skills. Dr. Wood will receive focused mentorship and consultation from a team of experts of pediatric outcomes research (Dr. Smith), primary care obesity interventions (Dr. Perrin), obesity and population health (Dr. Skinner), epidemiology of childhood growth (Dr. Woo), repeated measures analysis (Dr. Kuchibhatla), and EHR use for research (Dr. Goldstein). The rich research environment at Duke University will allow Dr. Wood to fulfill his research and career development plans and begin to address his long-term goals of comparing risk prediction strategies for childhood obesity and conducting prospective observational and interventional trials in the primary care setting.

抽象的 肥胖是一个普遍存在的公共卫生问题，起源于儿童时期，尽管肥胖的治疗取得了进步 对于肥胖，一级预防对于预防发病和过早死亡至关重要。有一个紧迫的、未满足的需求 预测哪些婴儿和幼儿肥胖的风险最高。生命的最初几年提供了充满希望的 解决婴儿体重过度、快速增加以及随后的肥胖和心脏代谢风险的敏感期。 先前的研究表明危险因素与婴儿体重快速增加之间存在关联，但新的增长 诸如婴儿期 BMI 峰值的大小和时间等特征，可以更好地了解肥胖风险 与已知的风险因素相结合。生命最初几年的初级保健就诊，时间短暂且频繁 与渴望指导的父母联系，为风险预测和干预提供了理想的环境。这 总体假设是临床环境中的儿童肥胖预防策略可以得到改进 通过预测模型和临床决策支持，旨在纳入可修改的风险因素和 婴儿期的生长模式。该提案的总体目标是改善临床相关预测 儿童肥胖症并设计一个包含实时风险预测的临床决策支持工具。目标1 确定婴儿期已知风险因素与新婴儿期生长模式之间的关联。目标2 使用新颖的婴儿生长特征开发、比较和验证预测模型，包括 基于个人和群体的模式。目标 3 测试风险预测和临床决策的实施情况 电子健康记录中的支持工具。概述的研究目标和职业发展计划 为 Charles Wood（医学博士、公共卫生硕士）提供了实现成为独立人士的总体职业目标的技能 研究人员专注于初级保健机构中针对生命最初几年的肥胖预防。博士。 伍德的培训计划包括体验式学习和教学课程，以实现以下短期目标 培训目标：1）掌握重复测量分析和预测模型构建的方法 验证; 2) 使用电子健康记录（EHR）来源执行数据链接和协调； 3）学习 并实践最佳地利用 EHR 数据进行研究； 4）采用最先进的设计方法 临床决策支持工具； 5) 继续发展职业和专业技能。伍德博士将收到 儿科结果研究专家团队（史密斯博士）的重点指导和咨询， 初级保健肥胖干预措施（Perrin 博士）、肥胖与人口健康（Skinner 博士）、流行病学 儿童成长（Woo 博士）、重复测量分析（Kuchibhatla 博士）以及 EHR 用于研究（Dr. Kuchibhatla）。 戈德斯坦）。杜克大学丰富的研究环境将使伍德博士能够顺利完成他的研究和工作 职业发展计划，并开始解决他的长期目标，即比较风险预测策略 儿童肥胖症并在初级保健环境中进行前瞻性观察和干预试验。