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.

项目概要/摘要心力衰竭 (HF) 是一种使人衰弱的疾病，影响着美国超过 500 万人。心力衰竭相关的发病率和住院治疗具有严重的财务影响 2012 年，心力衰竭造成了严重的财务影响。每年直接费用超过 307 亿美元，其中大部分是直接医疗费用，到 2030 年，心力衰竭。总直接成本预计将达到 697 亿美元，增长 127% 将受到成本增长的推动。人口老龄化的增加，使得预防心力衰竭和提高护理效率势在必行。心力衰竭导致的再入院是可以预防的，但驱动因素是缺乏遵守规定的自我护理。支持坚持自我护理和改善心力衰竭结果的远程医疗干预研究的结果是过去对心力衰竭的远程医疗干预采用了一系列方法，包括：无线。传感器、电话服务、网站和护士家访已显示出结构化的电话支持。在某些情况下可以减少心力衰竭的住院治疗、改善临床结果并降低全因死亡率然而，这种差异部分是由于患者参与远程医疗干预的程度不同所致。这种家庭监测干预措施给患者带来了沉重的治疗负担，这需要他们参与新奇的行为，包括使用新的不熟悉的硬件以及花时间与家人见面保健护士。 R01 的目标是：1) 证明患者在以下情况下遵守家庭监测方案：使用微创监控技术，包括腕戴式消费者活动跟踪器 2) 结合；采用预测算法预测再入院的微创家庭监测方案；3) 使用电子健康记录 (EHR) 数据和基线调查开发模型来预测依从性水平家庭监控方案；4) 探索使用移动应用程序进行监控的实用可行性为了实现这些目标，我们将招募 500 名心力衰竭患者进行前瞻性研究。参与微创家庭监测方案。我们将衡量对该方案的遵守程度。方案，并使用收集的传感器数据和已知的再入院事件来创建新颖的隐藏半马尔可夫将执行持续预测再入院风险的模型。最后，我们将开发一款移动应用程序，让患者能够在一项针对 50 名患者的试点研究中，监测他们的进展并接收依从性通知和简短调查。该提案中概述的工作将产生一套用于执行家庭高频监测的基础工具我们将发现可预测再入院的 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能够精确地针对计算的患者资料基于行为经济学的定制激励措施以减少再入院率。