Leveraging remote blood pressure monitoring and interpretable machine learning to improve clinical workflows for hypertensive disorders of pregnancy

利用远程血压监测和可解释的机器学习来改善妊娠期高血压疾病的临床工作流程

• 批准号: 10822625
• 负责人: Isabel Fulcher
• 金额: $ 27.57万
• 依托单位: DELFINA CARE INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-18 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10822625
• 关键词: Adopted; American College of Obstetricians and Gynecologists; Aspirin; Bayesian Method; Bayesian learning; Blood Pressure; Blood Pressure Monitors; Caring; Clinical; Communication; Communities; Complex; Computer software; Counseling; Data; Data Sources; Development; Devices; Diagnosis; Discipline of obstetrics; Documentation; Early Intervention; Early identification; Eclampsia; Electronic Health Record; Electronics; Eligibility Determination; Family; Feedback; First Pregnancy Trimester; Focus Groups; Frequencies; Future; Geography; Grant; Growth; Health; Home; Hypertension; Incidence; Infant; Intervention; Machine Learning; Measurement; Measures; Medical; Methods; Modeling; Monitor; Nurse Midwives; Outcome; Patient Education; Patient Monitoring; Patient risk; Patients; Performance; Persons; Phase; Phenotype; Pre-Eclampsia; Pregnancy; Prenatal care; Prophylactic treatment; Provider; Qualitative Research; Research; Risk; Risk Factors; Risk Reduction; Small Business Innovation Research Grant; System; Time; Training; Ultrasonography; United States; Update; Visit; clinically actionable; dashboard; design; electronic health data; flexibility; improved; lifestyle intervention; machine learning method; machine learning model; mobile application; model building; nutrition; patient population; phase 1 study; predictive modeling; pregnancy disorder; pregnancy hypertension; pregnancy related death; pregnant; prepregnancy; prevent; prophylactic; prospective; standard of care; tool

Project Summary/Abstract: Hypertensive disorders of pregnancy (HDP) are a leading cause of pregnancy-related deaths in the United States. Specific interventions, such as nutrition counseling and prophylactic aspirin use, are known to prevent the onset and exacerbation of HDP. However, current approaches to identify patients early in pregnancy are limited due to challenges collating patient data from the electronic health record (EHR) and low precision and recall of traditional rules-based medical calculators. Machine learning (ML) methods that can flexibly capture complex relationships between HDP risk factors offer a potential solution, but often only render a static prediction at one time point and do not update as additional information is collected during pregnancy. The objective of this project is to develop a clinically actionable machine learning model that updates dynamically as patients track blood pressure throughout their pregnancies. Specifically, in Aim 1, we will assess the increased predictive power of utilizing blood pressure measurements arising from remote blood pressure monitoring (RBPM) as compared to in-office measurements. We will phenotype patient blood pressure trajectories and investigate associations between phenotypes and HDP diagnosis. In Aim 2, we will use a Bayesian machine learning approach to incorporate the RBPM phenotypes developed in Aim 1 to enhance an existing static HDP model built on EHR data. The developed model will be able to assess patients at multiple time points throughout their pregnancy based on their at-home BP measures. Finally, in Aim 3, we will conduct a mixed-methods study with obstetricians and certified nurse midwives to build a user-centered display that effectively communicates the results from the dynamic model. The project outlined in this proposal will give obstetricians a clinically interpretable tool – BotoML – to help them identify patients that would benefit from intervention early in their pregnancy. The completion of these aims will enable a future Phase II to deploy and prospectively validate BotoML in geographically diverse provider and patient populations.

项目摘要/摘要： 妊娠期高血压疾病 (HDP) 是美国妊娠相关死亡的主要原因 众所周知，具体的干预措施，例如营养咨询和预防性使用阿司匹林，可以预防疾病。 然而，目前识别怀孕早期患者的方法是 由于整理电子健康记录 (EHR) 中患者数据的挑战以及低精度和 回顾传统的基于规则的医疗计算器，可以灵活地捕获数据。 HDP 风险因素之间的复杂关系提供了潜在的解决方案，但通常只能呈现静态 预测仅在某一时间点进行，并且不会随着怀孕期间收集的其他信息而更新。 该项目的目标是开发一种可动态更新的临床可行的机器学习模型 因为患者在整个怀孕期间都会跟踪血压。 具体来说，在目标 1 中，我们将评估利用血压的增强预测能力 与办公室内血压监测相比，远程血压监测 (RBPM) 产生的测量结果 我们将对患者血压轨迹进行表型分析并研究之间的关联。 在目标 2 中，我们将使用贝叶斯机器学习方法来整合表型和 HDP 诊断。 目标 1 中开发的 RBPM 表型旨在增强基于 EHR 数据构建的现有静态 HDP 模型。 开发的模型将能够根据患者在整个怀孕期间的多个时间点对患者进行评估 最后，在目标 3 中，我们将与产科医生和医生进行混合方法研究。 经过认证的护士助产士建立一个以用户为中心的显示器，有效地传达助产士的结果 动态模型。 该提案中概述的项目将为产科医生提供一种临床可解释的工具——BotoML—— 帮助他们识别可从妊娠早期干预中受益的患者。 这些目标将使未来的第二阶段能够在不同地理位置部署和前瞻性验证 BotoML 提供者和患者群体。