In utero antidepressant exposures and risk for autism

描述(由申请人提供)：在一个大型医疗保健索赔数据库的分析中，子宫内暴露于抗抑郁药物最近与自闭症风险增加有关。这一结果与双胞胎研究一致，双胞胎研究表明，与非双胞胎兄弟姐妹相比，异卵双胞胎患自闭症的一致性更高，这可能表明子宫内风险相同。有必要 澄清抗抑郁药构成的风险是严重的，因为它具有深刻的公共健康影响。证实这一发现将支持自闭症的一种罕见的可改变的危险因素，并有助于阐明这种疾病的潜在病理生理学。相反，如果发现是假阳性，可能会导致孕妇中的重度抑郁症治疗不足，从而对母亲和胎儿都造成风险。这项拟议的研究将利用新英格兰一个大型医疗系统中400多万人的电子病历(EMR)数据。研究人员和他们的同事此前曾应用他们帮助开发的EMR查询工具包来表征治疗结果，并在精神病学和其他领域进行药物警戒和遗传学研究。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，是儿童神经精神障碍的转诊中心。在这个系统中出生的4-12岁被诊断为自闭症的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的匹配(2：1)儿童队列，以及接受常规儿科护理的匹配(5：1)对照队列。然后，EMR将被用来将每个孩子与他们的母亲配对，从而能够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情况 护理、暴露和并发症，以及父亲的年龄和社会人口状况。回归模型将应用于最终的数据集，涵盖700多名自闭症儿童、1400名ADHD儿童和3500名健康对照组儿童。除了检查抗抑郁药物暴露与自闭症易感性之间的联系，并对混淆进行适当的控制外，该数据集还将允许调查其他假定的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，它将解决先前抗抑郁药药物警戒研究中存在的指征混淆的问题，使用研究人员开发的工具来表征抑郁的严重程度/共病和病程，以更好地匹配病例和对照。其次，它将建立一种基于EMR的方法来研究怀孕暴露和结局的效用。

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.