Testing a CVD screening tool in pregnant and postpartum women

心血管疾病(CVD)已成为美国孕产妇死亡的主要原因。 如果早些时候诊断出心血管疾病，大约三分之一的孕产妇死亡是可以避免的 医疗保健提供者。女性的心血管疾病症状经常被误诊或忽视，导致延误 认识和治疗具有严重短期和长期发病率和死亡率的高风险的心血管疾病。 尤其是，非洲裔美国妇女的死亡率高出三到四倍， 心血管疾病、妊娠高血压疾病和围产期心肌病与其他种族/民族的比较 组。标准化的筛查工具可以指导临床医生识别有心血管疾病风险的孕妇 需要额外的测试和跟进。加州产妇质量护理合作组织开发了一种 指导有症状或高危孕妇的分层和初步评估的筛查算法 产后妇女。该算法在两个地区性的大容量三级分娩中心实现 医疗补助患者和不同种族混杂：加州大学欧文分校(UCI)和阿尔伯特·爱因斯坦 大学/蒙特菲奥里医学中心(MMC)，纽约。在这个新的R21中，我们假设该算法具有 预测孕妇心血管疾病的潜力。我们将通过三个目标来检验这一想法。AIM 1将验证 两个地区3000名孕妇和产后样本的心血管疾病筛查算法 医院网络。我们将进行回顾病历以描述心血管疾病的筛查。 算法在UCI和MMC的23个诊所站点的实施，并获得对经验的反馈 临床医生和患者。目标2将确定妊娠和产后妇女心血管疾病的患病率 按种族/民族分类。估计UCI的真阳性率为1.5%，MMC的真阳性率为3.0%，我们将 在3000名接受筛查的妇女中确定约75名患有心血管疾病的妇女。我们会比较一下流行率 在非裔美国人和白人、西班牙裔、亚洲人和其他/混合种族女性中。目标 3将计算心血管疾病筛查工具的阳性预测值及其每个参数，以及 参数组合。我们将计算CVD算法的总体预测值和FALSE 积极的和真正的积极的价值观。我们将计算18个参数中每个参数的阳性预测值 该算法或该算法的任何信息性组合。尽管心血管疾病是导致 在孕产妇死亡率方面，目前缺乏可靠的临床筛查方法。该提案将确定预测性的 这一创新的心血管疾病筛查工具的价值，它有可能成为所有人的护理标准 对孕妇和产后妇女，并最终降低与VD相关的发病率和死亡率。

Cardiovascular disease (CVD) has emerged as the leading cause of maternal mortality in the United States. Approximately one third of the maternal deaths could have been prevented if CVD was diagnosed earlier by the health care providers. Women's CVD symptoms are often misdiagnosed or dismissed, causing delays in the recognition and treatment of CVD with a high risk for serious short- and long-term morbidity and mortality. In particular, African-American women exhibit three-to-four-fold higher mortality rate, presence of pre-existing CVD, hypertensive disorders of pregnancy, and peripartum cardiomyopathy compared to other racial/ethnic groups. A standardized screening tool could guide clinicians in identifying pregnant women at risk of CVD who require additional testing and follow up. The California Maternal Quality Care Collaborative developed a screening algorithm that guides stratification and initial evaluation of symptomatic or high-risk pregnant or postpartum women. This algorithm was implemented at two regional Level 3 birthing centers with high volume Medicaid patients and diverse racial ethnic mix: University of California, Irvine (UCI), and Albert Einstein College/Montefiore Medical Center (MMC), New York. In this new R21 we hypothesize that this algorithm has a potential in predicting CVD in pregnant women. We will test this idea by three aims. Aim 1 will validate a CVD screening algorithm in a sample of 3,000 pregnant and postpartum women at two regional hospital networks. We will conduct a retrospective medical chart review to describe the CVD screening algorithm's implementation at 23 clinic sites at UCI and MMC and obtain feedback on the experiences of the clinicians and patients. Aim 2 will determine the prevalence of CVD in pregnancy and postpartum women by race / ethnicity. Estimating a 1.5% true positive rate at UCI and a 3.0% true positive rate at MMC, we will identify approximately 75 women with CVD among 3,000 women screened. We will compare the prevalence among the African-American population with that of White, Hispanic, Asian, and Other/mixed race women. Aim 3 will calculate the positive predictive value of the CVD screening tool, each of its parameters, and combination of parameters. We will calculate the overall predictive value of the CVD algorithm and false positive and true positive values. We will calculate the positive predictive value of each of the 18 parameters of the algorithm or any informative combinations of the algorithm. Even though CVD is the leading cause of maternal mortality, a reliable clinical screening approach is lacking. This proposal will determine the predictive value of this innovative CVD screening tool, which has the potential to become the standard of care for all pregnant and postpartum women and, ultimately, decreasing VD related morbidity and mortality.