Prenatal Blood Pressure Patterns to Predict Pregnancy-Related Hypertension and Later Life Cardiovascular Risk

产前血压模式可预测妊娠相关高血压和晚年心血管​​风险

• 批准号: 10263402
• 负责人: Erica Pauline Gunderson
• 金额: $ 24.32万
• 依托单位: KAISER FOUNDATION RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-12-15 至 2022-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10263402
• 关键词: Address; Adult; Advisory Committees; Affect; American; Area; Aspirin; Biological Markers; Birth; Blood Pressure; Calibration; California; Cardiovascular Diseases; Child; Chronic; Clinical; Clinical Data; Clinical Trials Design; Communities; Data Sources; Diagnosis; Discipline of obstetrics; Disease; Disease Outcome; Early Intervention; Early identification; Early treatment; Elderly; Electronic Health Record; Etiology; Event; First Pregnancy Trimester; Follow-Up Studies; Future; Goals; Gynecology; Health Benefit; Healthcare; High Risk Woman; Hospitals; Hypertension; Individual; Integrated Delivery of Health Care; Intervention Trial; Laboratories; Life; Link; Measurement; Measures; Methodology; Methods; Minority; Modeling; Monitor; Morbidity - disease rate; Pattern; Perinatal mortality demographics; Pharmaceutical Preparations; Population; Pre-Eclampsia; Pregnancy; Pregnant Women; Prevention; Preventive service; Recording of previous events; Reporting; Research; Resources; Retrospective cohort study; Risk; Risk Factors; Risk stratification; Sampling; Scanning; Second Pregnancy Trimester; Severities; Statistical Methods; Subgroup; System; Techniques; Testing; Time; Woman; automated algorithm; base; cardiovascular disorder risk; cardiovascular risk factor; clinical risk; clinical translation; cohort; college; congenital heart disorder; design; fetal; follow-up; health care delivery; health care settings; high risk; improved; improved outcome; indexing; innovation; maternal morbidity; mortality; novel; novel therapeutics; perinatal morbidity; population based; predictive modeling; pregnancy hypertension; prenatal; prepregnancy obesity; prevent; prospective; research clinical testing; routine care; skills; sociodemographics; statistics; therapy design; tool

ABSTRACT Pregnancy-related hypertensive (PRH) disorders, preeclampsia (PE) and gestational hypertension (GH), complicate up to 10% of all pregnancies and are a leading cause of maternal and perinatal morbidity and mortality in the U.S. These disorders also have been linked with higher risk of later life hypertension (HTN) and cardiovascular disease (CVD) in women. In 2013, the American College of Obstetrics and Gynecology identified the need for early identification of women at risk for PRH disorders because available predictive models could not demonstrate clinical utility among low risk women. In 2017, the U.S. Preventive Services Task Force report cited research gaps including the need “to further develop and validate tools for risk prediction using rigorous methodology, including appropriate calibration statistics and validated models that use parameters available in routine care (e.g., clinical history and clinical testing).” The proposed study addresses these gaps utilizing statistical methods designed to identify latent classes of individuals with similar patterns of blood pressure (BP) change over time. This advanced statistical technique classifies women into BP trajectory groups that may identify those at higher risk for PE and GH among “low risk” women. We chose this method because it has already proven to be highly effective for prediction of future CVD in non-pregnant adults. This study will utilize BP trajectory groups and clinical risk factors to evaluate and validate models for prediction of PE and GH during the index and subsequent pregnancies, as well as later risk of HTN and CVD. We propose a retrospective cohort study of pregnancies delivered in 2009-2018 (~330,000) along with prospective follow up for later life HTN and CVD outcomes in women. This large, community-based, highly diverse sample from the Kaiser Permanente Northern California (KPNC) integrated healthcare delivery system leverages the established electronic health record (EHR) since 2008 linking all clinical data sources. The study will develop prediction models for PE and GH that show high clinical utility across most settings for the early risk stratification of low risk women, and prediction of new onset HTN and CVD in later life. The specific aims are: Aim 1: To identify the first 20 wks' gestation BP trajectory groups associated with risk of PE and GH, and evaluate and validate the BP trajectory model's predictive ability to identify women at risk for PE and GH; Aim 2: To evaluate and validate the predictive ability of first 20 wks' gestation BP trajectory groups, and the entire pregnancy BP trajectory groups to each identify women with or without PE and GH who are at risk for new onset HTN and CVD up to 12 years post-delivery; Aim 3: Among women without PE or GH (Aims 1-2), to evaluate and validate the entire pregnancy BP trajectory groups ability to predict PE and GH in a subsequent pregnancy. The clinical translation is to develop an automated algorithm for low risk women with EHR clinical data to improve early risk stratification for PE and GH, and later life HTN and CVD. The method may direct clinical monitoring, use of available therapies, and testing of new therapies for early prevention.

抽象的 妊娠相关高血压 (PRH) 疾病、先兆子痫 (PE) 和妊娠期高血压 (GH)、 使高达 10% 的妊娠变得复杂，并且是孕产妇和围产期发病率的主要原因 在美国，这些疾病还与晚年高血压 (HTN) 的较高风险有关 和女性心血管疾病（CVD）。 2013年，美国妇产科学院 确定了早期识别有 PRH 疾病风险的女性的必要性，因为现有的预测 模型无法证明低风险女性的临床实用性。 2017 年，美国预防服务部门 特别工作组报告指出了研究差距，包括需要“进一步开发和验证风险工具” 使用严格的方法进行预测，包括适当的校准统计数据和经过验证的模型 使用常规护理中可用的参数（例如临床病史和临床测试）。”拟议的研究 利用旨在识别具有相似特征的潜在个体类别的统计方法来解决这些差距 血压 (BP) 模式随时间而变化。这种先进的统计技术将女性分为 血压轨迹分组可以识别“低风险”女性中 PE 和 GH 风险较高的人群。我们选择了 这种方法已被证明对于预测非怀孕者未来的 CVD 非常有效 成年人。本研究将利用血压轨迹组和临床风险因素来评估和验证模型 预测指数和随后怀孕期间的 PE 和 GH，以及以后的 HTN 和 CVD 风险。 我们建议对 2009 年至 2018 年分娩的妊娠（约 330,000 例）进行回顾性队列研究 对女性晚年高血压和心血管疾病结局的前瞻性随访。这个大型的、以社区为基础的、高度 来自北加州凯撒医疗机构 (KPNC) 综合医疗服务系统的不同样本 利用自 2008 年以来建立的电子健康记录 (EHR)，连接所有临床数据源。研究 将开发 PE 和 GH 的预测模型，这些模型在早期的大多数情况下都显示出很高的临床实用性 低风险女性的风险分层，以及晚年新发高血压和心血管疾病的预测。具体目标 目标 1：确定与 PE 和 GH 风险相关的前 20 周妊娠血压轨迹组，以及 评估和验证血压轨迹模型的预测能力，以识别有 PE 和 GH 风险的女性； 目标 2：评估和验证前 20 周妊娠血压轨迹组的预测能力，以及 整个妊娠期血压轨迹分组，分别识别患有或不患有 PE 和 GH 的女性，这些女性有患 PE 和 GH 的风险 产后 12 年内新发的 HTN 和 CVD；目标 3：在没有 PE 或 GH 的女性中（目标 1-2）， 评估和验证整个妊娠血压轨迹组在后续预测 PE 和 GH 的能力 怀孕。临床转化是为具有 EHR 临床的低风险女性开发一种自动化算法 数据以改善 PE 和 GH 以及晚年 HTN 和 CVD 的早期风险分层。该方法可以 直接临床监测、使用现有疗法以及测试新疗法以进行早期预防。