The Potential Confounders Hiding in a United States Cohort About Severe Acute Respiratory Syndrome Coronavirus 2 Infection During Pregnancy

Pei-Yun Shih, Y. Chou, J. Wei
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引用次数: 1

Abstract

TO THE EDITOR—With great interest, we read the article by Regan et al [1]. In their cohort study, the authors investigated the association between prenatal severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and increased risk of adverse pregnancy outcomes. These findings support previous studies that suggest maternal SARS-CoV-2 infection harms fetal health. However, there are some issues that should be discussed. First, SARS-CoV-2 infection may not be the only risk factor of adverse pregnancy outcomes after adjustment in this cohort. This study was based on de-identified administrative claims and electronic health records data from OptumLabs Data Warehouse [2]. However, some confounders recorded in electronic health records data were not considered in this study, such as parity. A comparative study showed that primary cesarean delivery contributes to the increasing rate of patients’ refusal to undergo vaginal delivery, thus inducing the secondary or repeat cesarean delivery [3]. Moreover, SARS-CoV-2–infected pregnancy complicated with high-risk gestational factors should be considered when evaluating the risk of adverse pregnancy outcomes. For example, previous studies indicated that maternal obesity is linked to a greater risk of preterm birth [4]. As a result, we suggest that importing the known residual confounders into the adjusted model would improve the precision of this study. Second, personal factors were effect modifiers for the association between coronavirus disease 2019 (COVID-19) and clinician-initiated events, including induced abortion, cesarean delivery, and clinician-initiated preterm birth. The administrative codes cannot reflect the exact condition. The preference of patients may influence the decisions of the doctors [3]. On the other hand, doctors may execute clinician-initiated events for COVID-19 patients for other reasons than SARS-CoV-2 infection. The personal factors were residual confounders to the outcomes. Owing to the large infected-to-uninfected ratio in this study, we propose that matching the infected and uninfected cases by time-dependent propensity score matching can minimize the impact of the residual confounders [5]. After sequential matching with timedependent propensity score, the effect of exposure can be identified by the Cox regression model used in this cohort.
在美国一项关于怀孕期间感染严重急性呼吸综合征冠状病毒2的队列中隐藏的潜在混杂因素
致编辑:我们怀着极大的兴趣阅读了Regan等人的文章。在他们的队列研究中,作者调查了产前严重急性呼吸综合征冠状病毒2 (SARS-CoV-2)感染与不良妊娠结局风险增加之间的关系。这些发现支持了之前的研究,即母体感染SARS-CoV-2会损害胎儿健康。然而,有一些问题应该讨论。首先,在该队列中调整后,SARS-CoV-2感染可能不是不良妊娠结局的唯一危险因素。本研究基于OptumLabs数据仓库[2]的去识别行政索赔和电子健康记录数据。然而,本研究并未考虑电子健康记录数据中记录的一些混杂因素,例如胎次。一项比较研究表明,首次剖宫产导致患者拒绝阴道分娩的比例增加,从而诱发二次或多次剖宫产bbb。此外,在评估不良妊娠结局的风险时,应考虑sars - cov -2感染妊娠合并高危妊娠因素。例如,先前的研究表明,母亲肥胖与早产风险增加有关。因此,我们建议在调整后的模型中引入已知的残留混杂因素可以提高本研究的精度。其次,个人因素是2019冠状病毒病(COVID-19)与临床引发的事件(包括人工流产、剖宫产和临床引发的早产)之间关联的影响修饰因子。行政法规不能反映确切的情况。患者的偏好可能会影响医生的决策。另一方面,医生可能会因为SARS-CoV-2感染以外的其他原因对COVID-19患者实施临床发起的活动。个人因素是影响结果的残余混杂因素。由于本研究中感染与未感染的比例较大,我们建议通过时间依赖的倾向评分匹配来匹配感染和未感染的病例,可以最大限度地减少残留混杂因素[5]的影响。在与时间依赖倾向评分进行序列匹配后,可以通过本队列中使用的Cox回归模型来确定暴露的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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