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.

项目概要/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。 States.具体的干预措施，如营养咨询和预防性使用阿司匹林，是众所周知的，以防止 HDP的发作和加重。然而，目前在怀孕早期识别患者的方法是 由于从电子健康记录（EHR）中整理患者数据的挑战和低精度， 传统的基于规则的医疗计算器的召回。机器学习（ML）方法，可以灵活地捕获 HDP风险因素之间的复杂关系提供了一个潜在的解决方案，但往往只是呈现静态的 在一个时间点进行预测，并且在妊娠期间收集额外信息时不会更新。的 该项目的目标是开发一个动态更新的临床可操作机器学习模型 因为患者在怀孕期间会跟踪血压。 具体而言，在目标1中，我们将评估利用血压增加的预测能力 远程血压监测（RBPM）产生的测量结果与诊室内测量结果相比 测量.我们将对患者的血压轨迹进行表型分析，并研究 表型和HDP诊断。在目标2中，我们将使用贝叶斯机器学习方法， 目标1中开发的RBPM表型，用于增强基于EHR数据构建的现有静态HDP模型。的 开发的模型将能够在整个妊娠期间的多个时间点评估患者， 他们的家庭血压测量最后，在目标3中，我们将与产科医生进行混合方法研究， 认证的护士助产士建立一个以用户为中心的显示，有效地沟通的结果， 动态模型 本提案中概述的项目将为产科医生提供一个临床可解释的工具- BotoML -以 帮助他们确定在怀孕早期就能从干预中受益的患者。完成 这些目标将使未来的第二阶段能够在地理上多样化的环境中部署和前瞻性地验证BotoML。 提供者和患者群体。