In utero antidepressant exposures and risk for autism

描述（由申请人提供）：最近在一个大型医疗保健索赔数据库的分析中，宫内暴露于抗抑郁药与自闭症风险增加相关。这一结果与双胞胎研究一致，表明与非双胞胎兄弟姐妹相比，双卵双胞胎中自闭症的一致性更高，可能指向子宫内风险的共享。需要 澄清抗抑郁药带来的风险是严重的，因为它具有深刻的公共卫生影响。这一发现的证实将支持自闭症的一个罕见的可改变的风险因素，并有助于阐明这种疾病的潜在病理生理学。相反，如果这一发现是假阳性，可能会导致孕妇对重度抑郁症治疗不足，从而对母亲和胎儿造成风险。这项拟议中的研究将利用新英格兰一个大型卫生系统中400多万人的电子病历（EMR）数据。研究人员和他们的同事以前曾应用他们帮助开发的EMR查询工具包来描述治疗结果，并在精神病学及其他领域进行药物警戒和遗传研究。值得注意的是，这个医疗保健系统包括大型产科、精神病学和儿科实践，并且是儿童神经精神疾病的转诊中心。在该系统中分娩的4-12岁自闭症儿童将使用经过验证的自然语言处理工具进行识别。将确定诊断为ADHD的儿童的匹配（2：1）队列，沿着接受常规儿科护理的儿童的匹配（5：1）对照队列。然后，EMR将用于将每个孩子与他们的母亲相匹配，从而可以描述母亲的社会人口状况，医疗和精神疾病以及怀孕期间的药物治疗。为了进一步提高精度，将查询马萨诸塞州的出生证明数据。这些数据包括围产期的详细信息， 护理、暴露和并发症，以及父亲年龄和社会人口状况。回归模型将被应用于结果数据集，包括700多名自闭症儿童，1400名ADHD儿童和3500名健康对照儿童。除了检查抗抑郁药暴露与自闭症易感性之间的关联，并适当控制混杂因素外，该数据集还将允许调查其他推定的子宫内和围产期风险因素，为未来的研究创造关键资源。该项目将在两个关键方面进行创新。首先，它将解决既往抗抑郁药药物警戒研究中存在的适应症混淆问题，使用研究者开发的用于表征抑郁严重程度/合并症和病程的工具，以更好地匹配病例和对照。其次，它将建立一个基于EMR的方法研究妊娠暴露和结果的效用。

DESCRIPTION (provided by applicant): In utero exposure to antidepressants was recently associated with increased risk for autism in an analysis of a large health care claims database. This result is consistent with twin studies indicating greater concordance for autism among dizygotic twins compared to non-twin siblings, likely pointing to shared in utero risk. The need to clarify the risk posed by antidepressants is acute, as it has profound public health implications. Confirmation of the finding would support a rare modifiable risk factor for autism, and help to elucidate the underlying pathophysiology of the disorder. Conversely, if the finding is a false positive, it might lead to undertreatment of major depression among pregnant women, with consequent risks to both mother and fetus. The proposed study will utilize electronic medical record (EMR) data from more than 4 million individuals in a large New England health system. The investigators and their colleagues have previously applied an EMR query toolkit, which they helped to develop, to characterize treatment outcomes and conduct pharmacovigilance and genetic studies in psychiatry and beyond. Notably, this health care system includes large obstetrics, psychiatry, and pediatric practices, and is a referral center for child neuropsychiatri disorders. Children ages 4-12 with a diagnosis of autism who were delivered in this system will be identified using validated natural language processing tools. A matched (2:1) cohort of children with a diagnosis of ADHD will be identified, along with a matched (5:1) control cohort of children receiving routine pediatric care. The EMR will then be used to match each child with their mother, allowing characterization of maternal socio-demographic status, medical and psychiatric illness, and medication treatment during pregnancy. To further improve precision, Massachusetts state birth certificate data will be queried. This data includes details of perinatal care, exposures, and complications, as well as paternal age and socio-demographic status. Regression models will be applied to the resulting data set, encompassing more than 700 children with autism, 1400 children with ADHD, and 3500 healthy control children. In addition to examining the association between antidepressant exposure and autism liability, with appropriate control for confounding, this data set will allow investigation of other putative in utero and perinatal risk factors, creating a key resource for future studies. This project will innovate in two key respects. First, it will address the problem of confounding by indication present in prior antidepressant pharmacovigilance studies, using tools developed by the investigators for characterizing depression severity/comorbidity and course to better match cases and controls. Second, it will establish the utility of an EMR-based approach for studying pregnancy exposures and outcomes.