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

摘要 妊娠相关高血压（PRH）疾病、先兆子痫（PE）和妊娠期高血压（GH）， 高达10%的怀孕会导致并发症，并且是孕产妇和围产期发病率的主要原因， 这些疾病也与晚年高血压（HTN）的高风险有关 和女性心血管疾病（CVD）。2013年，美国妇产科学院（American College of Obstetrics and Gynecology） 确定有必要及早识别有可能患上公共租住房屋疾病的妇女，因为现有的预测 模型不能证明在低风险妇女中的临床实用性。2017年，美国预防服务 特别工作组的报告列举了研究差距，包括需要“进一步开发和验证风险管理工具， 使用严格的方法进行预测，包括适当的校准统计数据和验证模型， 使用常规护理中可用的参数（例如，临床病史和临床试验）。拟定研究 解决这些差距利用统计方法，旨在确定潜在类的个人与类似 血压（BP）的模式随时间而变化。这种先进的统计技术将妇女分为 BP轨迹组，可以确定那些在高风险的PE和GH中的“低风险”的妇女。我们选择 这种方法，因为它已经被证明是非常有效的预测未来的心血管疾病在非怀孕 成年人了这项研究将利用BP轨迹组和临床风险因素来评估和验证模型， 在指数和随后的怀孕期间的PE和GH的预测，以及以后的HTN和CVD的风险。 我们对2009-2018年分娩的妊娠（约330，000例）进行了一项回顾性队列研究， 前瞻性随访女性晚年HTN和CVD结局。这个大型的，以社区为基础的，高度 来自Kaiser Permanente北方加州（KPNC）综合医疗保健提供系统的不同样本 利用自2008年以来建立的电子健康记录（EHR），链接所有临床数据源。研究 将开发PE和GH的预测模型，这些模型在大多数情况下显示出较高的临床实用性， 对低危妇女进行危险分层，并预测晚年新发HTN和CVD。具体目标 目的1：确定与PE和GH风险相关的妊娠前20周BP轨迹组， 评估和验证BP轨迹模型识别PE和GH风险女性的预测能力; 目的2：评价和验证妊娠前20周血压轨迹组的预测能力， 整个妊娠BP轨迹组，以确定有或无PE和GH的女性， 分娩后1 - 2年内新发HTN和CVD;目标3：在无PE或GH的女性中（目标1-2）， 评估和验证整个妊娠BP轨迹组预测随后的PE和GH的能力。 怀孕临床翻译是为低风险女性开发一种自动化算法， 改善PE和GH的早期风险分层以及晚年HTN和CVD的数据。该方法可以 直接的临床监测，使用现有的治疗方法，以及测试新的早期预防治疗方法。

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.