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: 10430187
• 负责人: Jaime Lynn Speiser
• 金额: $ 11.82万
• 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/10430187
• 关键词: Address; Aging; Award; Big Data; Biometry; Body Composition; Caregivers; Caring; Clinical; Communication; Dangerousness; Data; Data Analyses; Data Set; Dependence; Development; Early identification; Educational workshop; Elderly; Electronic Health Record; Ensure; Environment; Future; Geriatrics; Gerontology; Goals; Grant; Health; Healthcare; Healthcare Systems; Individual; Informatics; Journals; Knowledge; 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; Research Training; Risk; Risk Factors; Sampling; Statistical Methods; Techniques; Testing; Time; Training; Trees; Validation; Visit; Work; Writing; adverse outcome; aged; base; 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; model development; 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 我的目标是让自己成为一名独立的生物统计学研究人员，开发和验证 预测模型对于识别具有不良健康后果高风险的老年人至关重要，使用新的 机器学习方法。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，特别是那些易受伤害的老年人。我开发了一台机器 一种称为二进制混合模型(BIMM)森林的学习方法，特别适合于发展预测 重复测量老年人结局的模型，因为它适应了动态波动 随着时间的推移，这在衰老中是常见的(例如，功能状态)。环境：维克森林医学院 是全国公认的老年学研究领先者，为 完成我的目标。导师：我有一个优秀的跨学科导师团队，由以下方面的专家组成 老龄化、生物统计学和信息学，致力于支持我完成研究和培训 在这项K25拨款中建议的AIMS。培训：我将完成旨在解决培训中存在的差距的活动 在我的指导团队的指导下。我的培训目标是：1)获得老年学知识 和老年病学，2)加深我对丢失数据机制和技术的理解，3)了解 为研究目的适当使用电子健康记录(EHR)数据，以及4)培养领导力， 人际关系、沟通和助学金写作技巧。为了实现这些目标，我将出席会议， 研讨会，期刊俱乐部，案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助学金将允许我延长之前的培训，这将为我提供更好的 知识、经验和技能为我未来成为一名老龄化方面的生物统计学研究员提供了帮助。研究：早期 识别有跌倒风险的老年人至关重要，这样才能实施预防性护理计划 改善患者结局，减轻医疗系统负担。最新的预测模型 无法处理一段时间内的重复测量，并且尚未用于EHR数据。我提议用我的 BIMM森林方法开发随时间推移的跌倒风险预测模型。我的研究具体目标是1) 用一项新的研究归因于(填补)健康、老龄化和身体成分(Health ABC)研究中缺失的预测数据 机器学习方法(BIMM森林)；2)开发预测模型，用于识别老年人 评估使用EHR数据对预测模型进行外部验证的可行性。这一预测 本提案中开发的模型将有助于识别跌倒的高危个人，使提供商能够 及早干预，减轻患者、护理者和医疗系统的跌倒负担。由此产生的结果 格兰特将为未来的R01提交提供基础，以使用EHR数据进一步验证预测模型 并评价该模式在实践中的临床应用价值。