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FALLS AMONG MIDDLE-AGED VETERANS: STEPS TOWARDS PREVENTION

中年退伍军人跌倒：预防措施

基本信息

批准号：
9889082
负责人：
Julie A Womack
金额：
--
依托单位：
VA CONNECTICUT HEALTHCARE SYSTEM
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-04-01 至 2020-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9889082
关键词：
Accident and Emergency department Accidental Injury Address Adult Age Alcohol consumption Algorithms Antihypertensive Agents Anxiety Arthritis Bayesian Modeling Birth Blindness Calibration Caring Cessation of life Chronic Communities Data Development Discrimination Drug usage Elderly Electronic Health Record Elements Event Exhibits Fall prevention Frequencies Future Gender General Population HIV/HCV Health Health Personnel Hospitalization Hospitals Hypertension Incidence Individual Injury Inpatients Intervention Interview Investigation Machine Learning Medical Mental Depression Methods Modeling Nurses Obesity Occupations Orthostasis Outcome Outpatients Participant Patient risk Patients Pharmaceutical Preparations Play Polypharmacy Population Prevalence Prevention Prevention program Preventive Intervention Probability Qualitative Methods Radiology Specialty Reference Standards Reporting Research Research Personnel Risk Risk Factors Role Sampling Seminal Sports Substance abuse problem Validation Veterans Viral Woman Work age group clinical care clinical decision support clinical practice cohort comorbidity design disability disability-adjusted life years experience fall risk falls hypnotic injured innovation machine learning algorithm male member men middle age mortality risk multidisciplinary multiple chronic conditions patient oriented patient safety preference prevent risk prediction model scaffold screening statistics support tools tool

项目摘要

Background. Among middle-aged individuals (45-65 years), falls that occur in the community (community falls) are a leading cause of non-fatal injuries treated in hospital emergency departments and are responsible annually for the loss of 422,000 disability-adjusted life-years (DALYs). Intrinsic risk factors (risk factors inherent to the individual) likely contribute significantly to falls risk in this age group, but a consistently effective approach to outpatient fall prevention has not been realized within the VA. Objectives. The proposed project will explore community falls among middle-aged Veterans by characterizing prevalence incidence, sequelae, and risk factors for medically significant community falls among middle-aged Veterans (SA1). We will then develop a risk prediction tool to calculate the one year probability of a community fall (SA2). Long-term, we will develop a tool that will provide useful information to clinicians (RNs, APRNs, MDs, PAs) regarding falls risk and that will be easy to use. To this end, we will explore barriers and facilitators that clinicians experience when using clinical decision support tools, highlighting input from RNs and APRNs in the context of a multidisciplinary team (SA3). This project challenges the assumption held by most healthcare providers that community falls related to intrinsic risk factors are only a problem in older adults. We suggest that this is an important problem among middle-aged adults as well but that risk factors differ by age group, suggesting that interventions appropriate to older adults may not be effective among middle-aged. This project will provide the information necessary to develop falls prevention interventions for middle-aged Veterans. This project also uses an innovative approach to identify falls in the EHR: the use of machine learning to identify falls in radiology reports. Methods. We will use data obtained from the electronic health record (EHR) of Veterans ages 45-65 in the VA Birth Cohort. We have developed a machine learning algorithm that identifies community falls in radiology reports and will validate this algorithm in the VA Birth Cohort. We will develop a reference standard from a randomly selected subset of the radiology reports in this cohort that have been reviewed by a clinician and identified as addressing a fall or not. These results will be compared with those from the algorithm. We will first calculate rates of occurrence of community falls, rates of related injury, hospitalization and death, and the prevalence of related risk factors among middle-aged Veterans. Descriptive statistics (means, medians, frequencies, and standard deviations) will be used to characterize the distribution of risk factors and outcomes among the study participants. We will then develop a prediction tool for community falls in middle-aged Veterans. We will apply Bayesian Model Averaging which will identify a small group of risk factor models within a given range of the minimal value of the Bayesian Information Criterion. The final model will be an average of this small set of models. We will also assess facilitators and barriers to the successful implementation and use of clinical decision support tools by clinician-members of patient aligned care teams (RNs, APRNs, MDs, PAs).To maximize the utility of our falls prediction tool, we will interview all types of clinicians, with a particular focus on RNs and APRNs, to assess barriers and facilitators to clinical decision support implementation and use by potential end- users. The information from these interviews will inform future studies that address the development and implementation of the falls prediction tool as an important element of clinical care. Expected results. We anticipate that the machine learning algorithm will detect falls with a sensitivity >90%. We anticipate that falls risk factors identified in middle-aged Veterans will be different from those identified in older age groups, suggesting that falls prevention interventions will also differ. Screening efforts will need to take clinician preferences into account.

背景在中年个体（45-65岁）中，发生在社区中的福尔斯（社区福尔斯）是在医院急诊室治疗的非致命伤害的主要原因，每年损失422，000残疾调整生命年（DIFFERS）。内在风险因素（内在风险因素）对个体而言）可能显著增加该年龄组的福尔斯风险，但持续有效预防门诊跌倒的方法尚未在VA内实现。目标.拟议的项目将探讨社区福尔斯之间的中年退伍军人的特点，中年人群中具有医学意义的社区福尔斯跌倒的患病率、发生率、后遗症和危险因素退伍军人（SA 1）。然后，我们将开发一个风险预测工具来计算一个社区一年的概率下降（SA 2）。从长远来看，我们将开发一种工具，为临床医生提供有用的信息（RN，APRN， MD，PA）关于福尔斯风险，这将是很容易使用。为此，我们将探讨障碍和促进因素临床医生在使用临床决策支持工具时会遇到的问题，突出显示RN和APRN的输入，多学科团队（SA 3）。该项目挑战了大多数医疗保健提供者所持的假设，即社区福尔斯与内在风险因素只是老年人的问题。我们认为，这是一个重要的问题，中年人也是如此，但风险因素因年龄组而异，这表明，老年人可能对中年人无效。该项目将提供必要的信息，为中年退伍军人制定福尔斯预防干预措施。该项目还采用了创新方法识别EHR中的福尔斯：使用机器学习识别放射学报告中的福尔斯。方法.我们将使用从VA中45-65岁退伍军人的电子健康记录（EHR）中获得的数据出生队列。我们开发了一种机器学习算法，可以识别放射学中的社区福尔斯报告并将在VA出生队列中验证此算法。我们将从在该队列中随机选择的已由临床医生审查的放射学报告子集，确定是否解决跌倒问题。将这些结果与算法的结果进行比较。我们将首先计算社区福尔斯的发生率、相关伤害率、住院率和死亡率，以及中年退伍军人中相关危险因素的患病率。描述性统计（平均值，中位数、频率和标准差）将用于表征风险因素的分布，研究参与者的结果。然后，我们将开发一个预测工具，社区福尔斯在中年退伍军人。我们将应用贝叶斯模型平均，将在给定的最小风险系数范围内确定一小组风险系数模型。贝叶斯信息准则的值。最终的模型将是这一小组模型的平均值。我们还将评估成功实施和使用临床决策的促进者和障碍支持工具由临床医生患者对齐护理团队（RN，APRN，MD，PA）的成员。为了最大限度地提高我们的福尔斯预测工具的实用性，我们将采访所有类型的临床医生，特别关注RN和 APRN，以评估潜在终端的临床决策支持实施和使用的障碍和促进因素，用户.从这些采访中获得的信息将为未来的研究提供信息，实施福尔斯预测工具作为临床护理的重要元素。预期成果。我们预计机器学习算法将以> 90%的灵敏度检测福尔斯。我们预计中年退伍军人中确定的福尔斯风险因素将不同于老年人中确定的风险因素。年龄组，这表明福尔斯预防干预措施也会有所不同。筛查工作需要临床医生的偏好。