Comparison of secondhand smoke exposure measures during pregnancy in the development of a clinical prediction model for small-for-gestational-age among non-smoking Chinese pregnant women
Comparison of secondhand smoke exposure measures during pregnancy in the development of a clinical prediction model for small-for-gestational-age among non-smoking Chinese pregnant women
复制标题
中国不吸烟孕妇妊娠期二手烟暴露指标在建立小于胎龄儿临床预测模型中的比较
DOI：
10.1136/tobaccocontrol-2014-051569
复制
发表时间：
2015-10-01
期刊：
影响因子：
5.2
通讯作者：
Chen, Weiqing
中科院分区：
文献类型：
--
作者：
Xie, Chuanbo;Wen, Xiaozhong;Chen, Weiqing
目的比较不同的孕期二手烟暴露量对小于胎龄儿（SGA）的预测价值，建立并验证孕期二手烟暴露量沿着社会人口学和妊娠因素对SGA的预测模型。然后使用最佳SHS测量沿着其他临床可用的因素来开发和验证SGA的预测模型。我们拟合logistic回归模型，通过SHS暴露的单一测量来预测SGA（自我报告，血清可替宁和CYP 2A 6 *4）和不同组合（自我报告+可替宁，可替宁+CYP 2A 6 *4，自我报告+CYP 2A 6 *4和自我报告+可替宁+CYP 2A 6 *4）。结果我们发现，自我报告的SHS暴露单独预测SGA（受试者工作特征曲线下面积或受试者工作曲线下面积（AUROC），0.578）优于其他两个单一指标（可替宁，0.547; CYP 2A 6 *4，0.529）或与联合SHS测量值（0.545-0.584）一样准确。最终预测模型包括自我报告的SHS暴露、孕前体重指数、妊娠中晚期体重增加速度、妊娠期糖尿病、妊娠期高血压和妊娠晚期双顶径Z评分，可以相当准确地预测SGA结论自我报告的围产期SHS暴露在预测SGA方面优于同一时间血清可替宁的单一测量，尽管在整个怀孕期间重复的生化可替宁评估可能是最佳的。我们简单的预测模型是相当准确的，可以潜在地用于常规产前护理。
Objective To compare predictive values of small-for-gestational-age (SGA) by different measures for secondhand smoke (SHS) exposure during pregnancy and to develop and validate a prediction model for SGA using SHS exposure along with sociodemographic and pregnancy factors.Methods We compared the predictability of different measures of SHS exposure during pregnancy for SGA among 545 Chinese pregnant women, and then used the optimal SHS measure along with other clinically available factors to develop and validate a prediction model for SGA. We fit logistic regression models to predict SGA by single measures of SHS exposure (self-report, serum cotinine and CYP2A6*4) and different combinations (self-report +cotinine, cotinine+CYP2A6*4, self-report+CYP2A6*4 and self-report+cotinine+CYP2A6*4).Results We found that self-reported SHS exposure alone predicted SGA (area under the receiver operating characteristic curve or area under the receiver operating curve (AUROC), 0.578) better than the other two single measures (cotinine, 0.547; CYP2A6*4, 0.529) or as accurately as combined SHS measures (0.545-0.584). The final prediction model that contained self-reported SHS exposure, prepregnancy body mass index, gestational weight gain velocity during the second and third trimesters, gestational diabetes, gestational hypertension and the third-trimester biparietal diameter Z-score could predict SGA fairly accurately (AUROC, 0.698).Conclusions Self-reported SHS exposure at peribirth performs better in predicting SGA than a single measure of serum cotinine at the same time, although repeated biochemical cotinine assessments throughout pregnancy may be optimal. Our simple prediction model is fairly accurate and can be potentially used in routine prenatal care.