Cardiovascular Disease in Pregnancy and the Postpartum Period in Maryland

心血管疾病(CVD)是妊娠相关死亡的主要原因，也是 是美国严重孕产妇发病率(SMM)的推动者。两者都与妊娠相关的死亡率 在过去的二十年里，SMM一直在增长。这项研究将检查医院遭遇的情况 对所有育龄妇女(15-49岁)进行心血管疾病诊断，随后重点关注 孕妇和产后妇女。更具体地说，我们将探索前两者之间的关系 妊娠CVD、SMM和产后CVD。我们将使用来自一个家族的数据 马里兰州数据库，包括医院出院和紧急情况 2013-2019年期间对该州的部门(ED)访问。我们的主要分析样本将包括 所有在马里兰州至少一次住院(住院或急诊)的育龄妇女 在此期间，仅有一次或多次分娩住院的妇女的数据； 同一时期将包括一份二级分析样品。我们将得出两个结果衡量标准，一个 主要和相关的CVD遭遇度量，以及SMM和CVD-SMM度量，使用 疾病控制和预防中心的基于ICD的算法。经过探索性分析，我们 将使用专门为使用纵向数据而开发的统计方法来解决我们的特定问题 目标。值得注意的是，生存分析技术将被用于检查到医院相遇的时间。 在发生心血管疾病风险的不同时间，对不同亚群的心血管疾病进行诊断 调查期；以及针对关键(时变)患者和医院调整的随机效应模型 特征将被用来检查以前的心血管疾病住院经历或显著的 分娩与产后心血管疾病医院相遇模式预测SMM/CVD-SMM 住院情况，以及SMM/CVD-SMM是否预测随后的CVD住院情况。致我们的 知识，这将是第一个基于人群的研究，以检查医院遇到的心血管疾病 在所有育龄妇女中进行诊断，也是第一次探索前两者之间的关系 纵向分析妊娠CVD、SMM和产后CVD。我们的 结果将在同行评议的文献中传播，并通过在科学会议上的陈述来传播 开会。这项初步研究将为更大规模的赠款申请提供初步数据，旨在 预防女性终生心血管疾病的干预措施。

Cardiovascular disease (CVD) is the leading cause of pregnancy-related mortality and an important contributor to severe maternal morbidity (SMM) in the United States. Both pregnancy-related mortality and SMM have been increasing over the past two decades. This study will examine hospital encounters with a CVD diagnosis in all women of reproductive age (15-49 years), with a subsequent focus on pregnant and postpartum women. More specifically, we will explore relationships between pre- pregnancy CVD, SMM and CVD beyond the postpartum period. We will use data from a family of Maryland state databases that comprise the universe of hospital discharges and emergency Department (ED) visits in the state during 2013-2019. Our primary analytic sample will be comprised of all women of reproductive age with at least one hospital encounter (inpatient or ED visit) in Maryland hospitals during this period; data from only women with one or more delivery hospitalizations during the same period will comprise a secondary analytic sample. We will derive two outcome measures, a primary and an associated CVD encounter measure, as well as SMM and CVD-SMM measures using Centers for Disease Control and Prevention’s ICD-based algorithm. Following exploratory analyses, we will use statistical methods developed specifically for use with longitudinal data to address our specific aims. Of note, survival analysis techniques will be employed to examine time to hospital encounters with CVD diagnosis for the various subpopulations of interest at different times of CVD risk during the period of investigation; and, random effects models adjusted for key (time-varying) patient and hospital characteristics will be fitted to examine whether a previous CVD hospital encounter or a significant pattern of CVD hospital encounter predicts SMM/CVD-SMM during delivery vs. postpartum hospitalizations, and whether SMM/CVD-SMM predicts a subsequent CVD hospital encounter. To our knowledge, this will be the first population-based study to examine hospital encounters with a CVD diagnosis in all women of reproductive age and also the first to explore relationships between pre- pregnancy CVD, SMM and CVD beyond the postpartum period using a longitudinal approach. Our results will be disseminated in the peer-reviewed literature and through presentations at scientific meetings. This pilot study will provide preliminary data for larger grant applications aiming to design interventions to prevent CVD in women over their life course.