Development and Validation of a Prediction Model for Longitudinal Fall Risk in Older Adults

老年人纵向跌倒风险预测模型的开发和验证

• 批准号: 10592365
• 负责人: Jaime Lynn Speiser
• 金额: $ 11.54万
• 依托单位: WAKE FOREST UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-15 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10592365
• 关键词: Address; Aging; Award; Big Data; Biometry; Body Composition; Caregivers; Caring; Clinical; Communication; Dangerousness; Data; Data Analyses; Data Set; Dedications; Dependence; Development; Early identification; Educational workshop; Elderly; Electronic Health Record; Environment; Future; Geriatrics; Gerontology; Goals; Grant; Health; Healthcare; Healthcare Systems; Individual; Informatics; Journals; Knowledge; Knowledge acquisition; Leadership; Learning; Longitudinal cohort study; Machine Learning; Measurement; Measures; Medicine; Mentors; Methodology; Methods; Modeling; Morbidity - disease rate; Outcome; Outcome Measure; Participant; Patient-Focused Outcomes; Patients; Performance; Persons; Predictive Value; Preventive care; Process; Provider; Research; Research Personnel; Research Proposals; Risk; Risk Factors; Sampling; Statistical Methods; Techniques; Testing; Time; Training; Trees; Validation; Visit; Work; Writing; adverse outcome; aged; career; career development; experience; fall risk; falls; follow-up; forest; functional decline; functional status; health data; high risk; improved; innovation; machine learning method; machine learning model; medical schools; mortality; novel; predictive modeling; preventive intervention; research and development; research study; severe injury; skills; stem; symposium

ABSTRACT My goal is to establish myself as an independent biostatistical researcher who develops and validates prediction models that are critical to identifying older adults at high risk of adverse health outcomes using novel machine learning methodology. My passion for developing prediction models for health outcomes in older adults stems from my desire to help people, especially older adults who are vulnerable. I developed a machine learning method called Binary Mixed Model (BiMM) forest, which is particularly suited for developing prediction models for repeated measures of outcomes in older adults because it accommodates dynamic fluctuations over time which are common in aging (e.g., functional status). Environment: Wake Forest School of Medicine is a nationally-recognized leader in geriatric research and provides an outstanding environment for accomplishing my goals. Mentors: I have an excellent interdisciplinary mentoring team consisting of experts in aging, biostatistics and informatics who are dedicated to supporting me in completing the research and training aims proposed in this K25 grant. Training: I will complete activities aimed to address gaps in my training with the guidance of my mentoring team. My Training Objectives are to 1) acquire knowledge about gerontology and geriatrics, 2) deepen my understanding of missing data mechanisms and techniques, 3) learn about the appropriate use of electronic health record (EHR) data for research purposes, and 4) develop leadership, networking, communication and grant writing skills. To accomplish these objectives, I will attend conferences, seminars, journal club, case conferences and writing workshops, observe a geriatric clinician, and complete coursework. The K25 grant will allow me to extend previous training, which will provide me superior knowledge, experience and skills for my future as a biostatistical researcher in aging. Research: Early identification of older adults at-risk of falling is critical so that preventative care plans may be implemented to improve patient outcomes and reduce burden on the health care system. Most current prediction models cannot handle repeated measurements over time and have not been used with EHR data. I propose to use my BiMM forest method to develop a prediction model for fall risk over time. My Research Specific Aims are to 1) impute (fill in) missing predictor data in Health, Aging, and Body Composition (Health ABC) Study with a novel machine learning method (BiMM forest); 2) develop a prediction model for identifying older adults at-risk for falls; and 3) assess the feasibility of using EHR data to externally validate the prediction model. The prediction model developed in this proposal will aid in identification of at-risk individuals for falls, allowing providers to intervene early to reduce the burden of falls for patients, caregivers and healthcare systems. Results from this grant will provide a basis for future R01 submissions to further validate the prediction model using EHR data from multiple health care centers and evaluate the clinical utility of the model in practice.

摘要 我的目标是建立自己作为一个独立的生物统计研究人员谁开发和验证 预测模型对于使用新方法识别处于不良健康结果高风险的老年人至关重要 机器学习方法论。我对开发老年人健康结果预测模型的热情 成年人源于我帮助人们的愿望，特别是那些脆弱的老年人。我发明了一种机器 一种称为二元混合模型（BiMM）森林的学习方法，特别适合于开发预测 老年人结果的重复测量模型，因为它适应动态波动 随着时间的推移这在老化中是常见的（例如，功能状态）。环境：维克森林医学院 是全国公认的老年医学研究的领导者，并提供了一个优秀的环境， 完成我的目标导师：我有一个优秀的跨学科指导团队， 他们致力于支持我完成研究和培训， 这是K25计划的目标。培训：我将完成旨在弥补培训差距的活动， 我的导师团队的指导我的培训目标是1）获得有关老年学的知识 2）加深我对缺失数据机制和技术的理解，3）了解 适当使用电子健康记录（EHR）数据用于研究目的，以及4）培养领导力， 网络，沟通和赠款写作技巧。为了实现这些目标，我将参加会议， 研讨会，期刊俱乐部，病例会议和写作研讨会，观察老年临床医生，并完成 课程作业K25补助金将允许我延长以前的培训，这将为我提供上级 知识，经验和技能，我的未来作为一个生物统计研究人员在老化。研究：早期 识别有跌倒风险的老年人至关重要，这样可以实施预防性护理计划， 改善患者的治疗效果，减轻医疗保健系统的负担。最新预测模型 无法处理随时间推移的重复测量，并且尚未与EHR数据一起使用。我建议用我的 BiMM森林方法开发了一个预测模型，随着时间的推移跌倒风险。我的研究目标是（1） 在健康、衰老和身体成分（健康ABC）研究中，用一种新的 机器学习方法（BiMM森林）; 2）开发一个预测模型，用于识别老年人的风险， 福尔斯;以及3）评估使用EHR数据来外部验证预测模型的可行性。预测 本提案中开发的模型将有助于识别福尔斯的风险个体，使供应商能够 及早干预，以减轻福尔斯对患者、护理人员和医疗保健系统的负担。结果从这个 一笔赠款将为未来提交R 01提供基础，以进一步验证使用EHR数据的预测模型 从多个卫生保健中心，并评估该模型在实践中的临床效用。