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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.

项目总结/摘要心力衰竭（HF）是一种使人衰弱的疾病，在美国影响超过500万人。发生与HF相关的发病率和因HF住院治疗具有严重的经济影响。2012年，HF有一个每年的直接费用超过307亿美元，其中大部分是直接医疗费用。到2030年，HF 直接成本总额预计达六百九十七亿元，增幅达百分之一百二十七。成本的增加将受到以下因素的推动：老龄化人口的增加，使得预防HF和护理效率势在必行。的百分之五十由于HF导致的再入院是可以预防的，缺乏对规定的自我护理的依从性是驱动因素。支持坚持自我护理和改善HF结局的远程医疗干预研究的结果如下不确定过去对HF的远程医疗干预利用了一系列方法，包括：传感器、电话服务、网站和护士的家访。结构化电话支持显示，在某些情况下，可减少HF患者的住院治疗，改善临床结局，并降低全因死亡率患者然而，患者参与远程医疗干预的情况差别很大。这种变化部分是由于在这种家庭监测干预中给患者带来的高治疗负担，这需要他们参与新的行为，包括使用新的不熟悉的硬件和花时间与家人会面保健护士本R 01的目标是：1）证明患者在以下情况下遵守家庭监测方案：使用微创监测技术，包括腕戴式消费者活动跟踪器; 2）联合收割机微创家庭监测方案，采用预测算法预测再入院; 3）使用电子健康记录（EHR）数据和基线调查开发模型，以预测依从性水平家庭监测方案;以及4）探索使用移动的应用程序进行在前瞻性试点研究中与患者沟通。为了实现这些目标，我们将招募500名HF患者，参与微创家庭监测方案。我们将衡量遵守方案，并使用收集的传感器数据和已知的再入院事件来创建一个新的隐半马尔可夫持续预测再入院风险的模型。将预测患者的依从性水平电子健康记录数据和基线调查。最后，我们将开发一个移动的应用程序，使患者能够在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。本提案中概述的工作将产生一套用于执行HF家庭监测的基础工具患者我们将发现EHR表型和移动的传感器生物标志物，它们可以预测再入院和坚持，这将使未来的随机试验，精确地针对计算病人的档案，基于行为经济学的量身定制的激励措施，以减少再次入院。