Maternal Morbidity and Mortality: Risk Factors, Early Detection and Personalized Intervention

项目摘要 在美国，从2011年到2014年，7208例孕产妇死亡，这一趋势逐年恶化(CDC， 2016)。除了700多人死亡外，至少有5万名妇女出现了危及生命的并发症， 每年一次。根据美国疾病控制与预防中心(2019年)的数据，每发生一起死亡事件，就会有70多名女性遭受本可避免的创伤 怀孕引起的并发症。 MedStar健康研究所和Invaryant，Invaryant公司建议评估心脏风险 针对孕妇和产后妇女的评估工具；此工具可直接从患者那里自动更新 医疗记录；可穿戴设备；以及患者调查。成功意味着女性的颠覆性进步 健康。拟议的研究涉及三个关键因素：技术、数据和健康的社会决定因素 (SDOH)使用患者位置的地理空间地图。 技术：研究技术应基于Invaryant Health Platform(IHP)，这是一项 使用专有AI自动从医疗记录、可穿戴设备和其他来源获取数据 基于互操作技术的Mesh-Complex方法交换(Mesh-CMX)。我们建议使用 国际水文计划结合新的原型技术，即所有妊娠的健康结局 经验-心血管风险评估技术(HOPE-CAT)和恒定机器学习 监测病人的技术，基于信号、超出范围的“绊线”和母亲的趋势 值得医疗干预的健康数据。通过将概念证明扩展到早期的商业版本 软件，并将其集成到IHP中，IHP将自动更新患者的医疗更改 通过记录，我们将为母亲和她们的养育者提供“预警”系统。 数据：这项研究将使用MedStar的分析平台(MAP)对患者的医疗记录进行测试，这是一项 登记了500多万名独特的患者。该工具随后将有可能被杠杆化 MedStar和Cerner医院系统的9000万份医疗记录，以满足特定的要求 资格标准包括性别、年龄、种族、怀孕和医疗结果。这方面的第二阶段 项目将从回溯性研究(这项拨款申请)中获得结果，并在 Medstar医院系统，在“真实世界环境”中验证研究结果的有效性。使用我们的 专有人工智能技术，我们将把每一位母亲的进步与一组回顾数据进行比较，以 增强诊断并向护理人员和患者提供实时反馈。 地理空间映射：将患者病历和健康信息映射到他们的社交环境 这对于了解影响不同地区母亲健康的基本社会结构至关重要。

PROJECT ABSTRACT In the U.S., from 2011 to 2014, 7,208 maternal fatalities, with the trend worsening year-on-year (CDC, 2016). In addition to the 700+ fatalities, at least 50,000 woman experienced life-threatening complications, annually. According to CDC (2019) for every fatality, 70 more women suffer avoidable, traumatic complications as a result of pregnancy. Medstar Health Research Institute and Invaryant, Inc, propose the evaluation of a cardiac risk assessment tool for pregnant and postpartum women; this tool updates automatically directly from patient medical records; wearable devices; and patient surveys. Success implies disruptive improvement in women's health. Proposed research involves three key elements: technology, data, and social determinants of health (SDOH) using geospatial mapping of patient locations. Technology: Study technology shall be based on the Invaryant Health Platform (IHP), a technology that automatically ingests data, from medical records, wearable devices, and other sources using proprietary AI based interoperability technology called Mesh-Complex Method Exchange (Mesh-CMX). We propose using the IHP in conjunction with novel prototype-level technology, namely Healthy Outcomes for all Pregnancy Experiences-Cardiovascular-risk Assessment Technology (HOPE-CAT) and the Invaryant machine learning technologies to monitor the patient, based on signals, out-of-range “trip-wires”, and trends in the mother's health data that merit medical intervention. By extending the proof of concept into an early commercial version of the software, and integrating it to the IHP which will automatically update changes in the patient's medical record, we will provide an “early warning” system for mothers and their providers. Data: The study will be tested on patients' medical records using the MedStar's Analytics Platform (MAP), a registry of over 5 Million unique patients. The tool will subsequently have the potential to be leveraged to over 90 million medical records for the MedStar and Cerner hospital systems, distilled down to meet specific eligibility criteria including, gender, age, race, pregnancy and medical outcomes. A second phase of this project would take the findings from the retrospective study (this grant request) and use the technology within the Medstar hospital system, to validate the efficacy of the findings in a “real-world setting”. Using our proprietary AI technology, we will compare each mother's progress against a cohort of retrospective data to enhance diagnostics and provide real-time feedback to caregivers and patients. Geospatial mapping: Mapping the patient medical record and the health information to their social setting is vital for understanding the underlying social constructs that affect the health of mothers in different regions.