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基于医院的病例对照研究:从产前因素预测小耳畸形的风险

Predicting the Risk of Microtia From Prenatal Factors: A Hospital-Based Case-Control Study.

作者信息

Chen Wei, Sun Manqing, Zhang Yue, Zhang Qun, Xu Xiaolin

机构信息

Department of Plastic and Reconstructive Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Department of Pediatrics, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.

出版信息

Front Pediatr. 2022 Apr 21;10:851872. doi: 10.3389/fped.2022.851872. eCollection 2022.

Abstract

BACKGROUND

Although a wide range of risk factors for microtia were identified, the limitation of these studies, however, is that risk factors were not estimated in comparison with one another or from different domains. Our study aimed to uncover which factors should be prioritized for the prevention and intervention of non-syndromic microtia via tranditonal and meachine-learning statistical methods.

METHODS

293 pairs of 1:1 matched non-syndromic microtia cases and controls who visited Shanghai Ninth People's Hospital were enrolled in the current study during 2017-2019. Thirty-nine risk factors across four domains were measured (i.e., parental sociodemographic characteristics, maternal pregnancy history, parental health conditions and lifestyles, and parental environmental and occupational exposures). Lasso regression model and multivariate conditional logistic regression model were performed to identify the leading predictors of microtia across the four domains. The area under the curve (AUC) was used to calculate the predictive probabilities.

RESULTS

Eight predictors were identified by the lasso regression, including abnormal pregnancy history, genital system infection, teratogenic drugs usage, folic acid supplementation, paternal chronic conditions history, parental exposure to indoor decoration, paternal occupational exposure to noise and maternal acute respiratory infection. The additional predictors identified by the multivariate conditional logistic regression model were maternal age and maternal occupational exposure to heavy metal. Predictors selected from the conditional logistic regression and lasso regression both yielded AUCs (95% CIs) of 0.83 (0.79-0.86).

CONCLUSION

The findings from this study suggest some factors across multiple domains are key drivers of non-syndromic microtia regardless of the applied statistical methods. These factors could be used to generate hypotheses for further observational and clinical studies on microtia and guide the prevention and intervention strategies for microtia.

摘要

背景

尽管已确定了多种小耳畸形的危险因素,但这些研究的局限性在于未对危险因素进行相互比较或跨不同领域的评估。我们的研究旨在通过传统和机器学习统计方法,找出在非综合征性小耳畸形的预防和干预中应优先考虑的因素。

方法

2017年至2019年期间,本研究纳入了293对1:1匹配的非综合征性小耳畸形病例及对照,这些病例及对照均就诊于上海第九人民医院。测量了四个领域的39个危险因素(即父母的社会人口学特征、母亲的妊娠史、父母的健康状况和生活方式,以及父母的环境和职业暴露)。采用套索回归模型和多变量条件逻辑回归模型来确定四个领域中小耳畸形的主要预测因素。用曲线下面积(AUC)计算预测概率。

结果

套索回归确定了8个预测因素,包括异常妊娠史、生殖系统感染、致畸药物使用、叶酸补充、父亲慢性病病史、父母接触室内装修、父亲职业性接触噪声以及母亲急性呼吸道感染。多变量条件逻辑回归模型确定的其他预测因素为母亲年龄和母亲职业性接触重金属。从条件逻辑回归和套索回归中选择的预测因素的AUC(95%CI)均为0.83(0.79 - 0.86)。

结论

本研究结果表明,无论采用何种统计方法,多个领域的一些因素都是非综合征性小耳畸形的关键驱动因素。这些因素可用于为进一步的小耳畸形观察性和临床研究提出假设,并指导小耳畸形的预防和干预策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ecf/9070100/a7de46b2aad5/fped-10-851872-g0001.jpg

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