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

基本信息

批准号：
10065013
负责人：
Erica Pauline Gunderson
金额：
$ 75.39万
依托单位：
KAISER FOUNDATION RESEARCH INSTITUTE
依托单位国家：
美国
项目类别：
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/10065013
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 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 risk prediction risk stratification routine care skills sociodemographics statistics therapy design tool

项目摘要

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.

抽象的与妊娠相关的高血压（PRH）疾病，先兆子痫（PE）和妊娠高血压（GH），使多达10％的所有妊娠复杂复杂，是孕产妇和围产期发病率的主要原因美国的死亡率这些疾病也与更高的以后生命高血压风险有关（HTN）女性的心血管疾病（CVD）。 2013年，美国妇产科学院确定需要尽早确定有PRH疾病风险的妇女，因为可用的预测性模型无法证明低风险女性的临床效用。 2017年，美国预防服务工作队报告引用了研究差距，包括“进一步开发并验证工具有风险的需求使用严格的方法论的预测，包括适当的校准统计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）。”拟议的研究利用统计方法来解决这些差距血压（BP）的模式随时间变化。这种高级统计技术将女性分类为 BP轨迹群体可能会识别“低风险”妇女中PE和GH风险较高的轨迹。我们选择这种方法是因为它已经被证明对预测非怀孕的未来CVD非常有效成年人。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，以及后来的HTN和CVD风险。我们提出了一项关于2009 - 2018年（约330,000）的怀孕的回顾性队列研究妇女中的前期终生和CVD成果的前瞻性跟进。这个大型，基于社区的高度来自Kaiser Permanente北加州（KPNC）综合医疗保健提供系统的不同样本自2008年以来，利用已建立的电子健康记录（EHR），将所有临床数据源连接起来。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显示出很高的临床实用性低风险妇女的风险分层，以及后来的新发作HTN和CVD的预测。具体目标为：目标1：确定与PE和GH风险相关的前20周的妊娠BP轨迹组，以及评估和验证BP轨迹模型的预测能力，以鉴定有PE和GH风险的女性；目标2：评估和验证前20周的妊娠BP轨迹组的预测能力，以及整个怀孕的BP轨迹组为每个识别有或没有PE和GH的女性，他们有风险新发作的HTN和CVD最多12年后交付；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BP轨迹组在随后的序列中预测PE和GH的能力怀孕。临床翻译是为具有EHR临床的低风险女性开发自动化算法数据以改善PE和GH的早期风险分层，以及后来的HTN和CVD。方法可以直接临床监测，可用疗法的使用以及对早期预防的新疗法的测试。