mHealth for Heart Failure: Predictive Models of Readmission Risk and Self-care Using Consumer Activity Trackers

心力衰竭的移动医疗：使用消费者活动跟踪器预测再入院风险和自我护理模型

基本信息

批准号：
10358621
负责人：
Corey Wells Arnold
金额：
$ 72.13万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-04-05 至 2025-02-28
项目状态：
未结题

项目摘要

PROJECT SUMMARY/ABSTRACT Heart failure (HF) is a debilitating disease that affects over five million people in the United States. Occurrence of, morbidity related to, and hospitalization due to HF have serious financial implications. In 2012, HF had a direct cost of over $30.7 billion annually, the majority of which was due to direct medical costs. By 2030, HF total direct costs are predicted to reach $69.7 billion, an increase of 127%. Increases in costs will be driven by an increase in the aging population, making prevention of HF and care efficiency imperative. Fifty percent of readmissions due to HF are preventable, with lack of adherence to prescribed self-care as the driving factor. Results of telemedicine intervention studies to support adherence to self-care and improve HF outcomes are inconclusive. Past telemedicine interventions for HF have utilized an array of methods including: wireless sensors, telephone services, websites, and home visits from nurses. Structured telephone support has shown in some cases to reduce hospitalization, improve clinical outcomes, and reduce all-cause mortality in HF patients. However, patient participation in telemedicine interventions varies widely. This variation is due in part to the high treatment burden placed upon patients in such home monitoring interventions, which require them to engage in novel behaviors, including using new unfamiliar hardware and spending time meeting with home health nurses. The goals of this R01 are to: 1) demonstrate that patients are adherent to a home monitoring regimen when using minimally-invasive monitoring technologies, including wrist-worn consumer activity trackers; 2) combine the minimally-invasive home monitoring regimen with predictive algorithms to forecast hospital readmission; 3) develop models using electronic health record (EHR) data and a baseline survey to predict levels of adherence to the home monitoring regimen; and 4) explore the pragmatic feasibility of using a mobile app for communicating with patients in prospective pilot study. Towards these goals, we will recruit 500 HF patients to participate in a minimally-invasive home monitoring regimen. We will measure levels of adherence to the regimen, and use collected sensor data and known readmission events to create a novel hidden semi-Markov model that continuously predicts readmission risk. Predicting a patient’s level of adherence will be performed with EHR data and a baseline survey. Finally, we will develop a mobile application that will allow patients to monitor their progress and receive adherence notifications and short surveys in a pilot study of 50 patients. The work outlined in this proposal will produce a set of foundational tools for performing home monitoring of HF patients. We will discover EHR phenotypes and mobile sensor biomarkers that are predictive of readmission and adherence, which will enable a future randomized trial that precisely targets computational patient profiles with tailored incentives based on behavioral economics to reduce hospital readmission.

项目摘要/摘要心力衰竭（HF）是一种使人衰弱的疾病，影响了美国超过500万人。发生与HF相关的发病率和住院有严重的财务影响。 2012年，HF有一个每年的直接成本超过3007亿美元，其中大多数是由于直接医疗费用造成的。到2030年，HF 预计总直接成本将达到697亿美元，增长127％。成本增加将由人口老龄化的增加，可以预防HF和护理效率。百分之五十由于HF引起的再入院是可以预防的，由于缺乏规定的自我保健作为驱动因素。远程医疗干预研究的结果以支持遵守自我保健和改善HF结果的结果是尚无定论。过去针对HF的远程医疗干预措施已经使用了一系列方法，包括：无线传感器，电话服务，网站以及护士的家庭访问。结构化电话支持已显示在某些情况下，减少住院，改善临床结果并降低HF的全因死亡率患者。但是，患者参与远程医疗干预措施差异很大。这种变化应部分原因在此类家庭监测干预措施中，对患者的高度治疗燃烧，这需要他们从事新颖的行为，包括使用新的陌生硬件和花费时间与家会面卫生护士。该R01的目标是：1）证明患者在何时遵守家庭监测方案使用最小侵入性的监测技术，包括腕上的消费者活动跟踪器； 2）结合具有预测算法预测医院再入院的最小侵入性房屋监测方案； 3）使用电子健康记录（EHR）数据和基线调查来预测依从性水平，开发模型到家庭监测方案； 4）探索使用移动应用程序的务实可行性在潜在的试点研究中与患者进行交流。达到这些目标，我们将招募500名HF患者参加最小侵入性的家庭监测方案。我们将衡量遵守水平方案，并使用收集的传感器数据和已知的再入再入院事件来创建一个新颖的隐藏半毛电夫不断预测再入院风险的模型。预测患者的依从性水平将进行使用EHR数据和基线调查。最后，我们将开发一个移动应用程序，该应用程序将允许患者在一项针对50名患者的试点研究中，监视他们的进度并接收依从性通知和简短的调查。该提案中概述的工作将产生一组基础工具，用于执行HF的家庭监控患者。我们将发现可预测入院的EHR表型和移动传感器生物标志物和依从性，这将使未来的随机试验精确针对计算患者概况根据行为经济学的量身定制的激励措施，以减少医院再入院。