SBIR Phase I: Mobile Health Platform for Malignant Hypertension Prediction

SBIR 第一阶段：用于恶性高血压预测的移动健康平台

• 批准号: 1914340
• 负责人: Nirav Shah
• 金额: $ 22.46万
• 依托单位: Sentinel Healthcare Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1914340&HistoricalAwards=false
• 关键词: SBIR; Phase; Mobile; Health; Platform

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will result from the development of a scalable and predictive software platform with a remote monitoring service that analyzes patient blood pressure data and delivers clinical recommendations, enabling healthcare providers to triage patients before their hypertension devolves into heart attack and stroke, and shortening the interval to achieve blood pressure control from the standard of care (18 to 24 months) to as little as 10 weeks. This model is beneficial on a societal level because the hypertension epidemic affects 1 in 3 adults in the United States, creating financial and quality of life burdens on more than 100 million people and costs the healthcare system $131 billion annually. On a business and financial level, the model's prevention of adverse healthcare events will result in major cost savings for healthcare providers, insurers, and public health authorities. High blood pressure affects 40% of adults over 25 globally, and is a major factor in cardiovascular disease, the number one worldwide cause of death, which occurs primarily in developing countries. Preventing and predicting high blood pressure will transform how chronic diseases are treated around the world. This Small Business Innovation Research (SBIR) Phase I project aims to develop an algorithmic model to predict malignant hypertension physiology. Pharmacology-based blood pressure prediction has been demonstrated in small studies of continuous 24-hour blood pressure monitoring but has not been translated to point-of-care mobile management in the outpatient setting. Developing a model of pharmacology-based blood pressure prediction using blood pressure variability, rate of progression, and deviation from pharmacologic baseline that portends heart attack and stroke is critical to point-of-care mobile management for hypertension. Moreover, predictive analytics based on contextual data related to patient physiology has not yet been developed. This Phase 1 project applies existing algorithmic modeling to hypertension management by collecting unique data from hypertensive patients including daily physiological data collected from wearable blood pressure devices, pharmacology data derived from tracking medication adherence and time of medication consumption, contextual data including diet, exercise and sleep, and patients' clinical records, to develop a predictive model that alerts clinicians when patients' blood pressure is trending towards a negative health event. This predictive model will incur minimal cost and structural changes to existing healthcare infrastructure while reducing hospitalizations, generating massive cost savings for providers, and significantly improving patient outcomes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力将来自于开发一个可扩展的预测性软件平台，该平台具有远程监测服务，可分析患者血压数据并提供临床建议，使医疗保健提供者能够在高血压发展为心脏病发作和中风之前对患者进行分类，并将实现血压控制的间隔从标准护理（18至24个月）缩短至10周。这种模式在社会层面上是有益的，因为高血压流行病影响了美国三分之一的成年人，给1亿多人带来了经济和生活质量负担，每年花费医疗保健系统1310亿美元。在商业和财务层面上，该模型对不良医疗事件的预防将为医疗服务提供者、保险公司和公共卫生当局节省大量成本。高血压影响全球40%的25岁以上成年人，并且是心血管疾病的主要因素，心血管疾病是全球头号死亡原因，主要发生在发展中国家。预防和预测高血压将改变世界各地慢性病的治疗方式。这个小企业创新研究（SBIR）第一阶段项目旨在开发一种算法模型来预测恶性高血压生理学。基于药理学的血压预测已在连续24小时血压监测的小型研究中得到证实，但尚未转化为门诊环境中的床旁移动的管理。开发一个基于药理学的血压预测模型，使用血压变异性、进展速度和预示心脏病发作和卒中的药理学基线偏差，对高血压的床旁移动的管理至关重要。此外，尚未开发基于与患者生理学相关的上下文数据的预测分析。该I期项目通过收集高血压患者的独特数据，将现有算法建模应用于高血压管理，包括从可穿戴血压设备收集的日常生理数据，从跟踪药物依从性和药物消耗时间中获得的药理学数据，包括饮食，运动和睡眠在内的背景数据，以及患者的临床记录，开发一个预测模型，当患者的血压趋向于负面健康事件时，该模型会提醒临床医生。该预测模型将对现有医疗基础设施产生最小的成本和结构变化，同时减少住院率，为供应商节省大量成本，并显着改善患者结局。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。