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首页> 外文期刊>International Journal of Medical Sciences >A predictive model of offspring congenital heart disease based on maternal risk factors during pregnancy: a hospital based case-control study in Nanchong City
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A predictive model of offspring congenital heart disease based on maternal risk factors during pregnancy: a hospital based case-control study in Nanchong City

机译:基于孕产妇危险因素的后代先天性心脏病预测模型:南充市基于医院的案例控制研究

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Objective: Based on epidemiological field data, this study was to develop a prediction model which can be used as a preliminary screening tool to identify pregnant women who were at high risk of offspring congenital heart disease (CHD) in Nanchong City, and be beneficial in guiding prenatal management and prevention. Methods: A total of 367 children with CHD and 367 children without congenital malformations aged 0 to 14 years old were recruited from the Affiliated Hospital of North Sichuan Medical College and Nanchong Central Hospital between March 2016 and November 2018. Using the SPSS 22.0 case-control matching module, the controls were matched to the cases at a rate of 1:1, according to the same gestational age of child (premature delivery or full-term), the maternal age of pregnancy (less than 1 year). 327 matched case-control pairs were analyzed by SPSS 22. Univariate and multivariate analysis were performed to find the important maternal influencing factors of offspring CHD. A logistic regression disease prediction model was constructed as the final predictors, and Hosmer-Lemeshow goodness of fit test and receiver operating characteristic (ROC) curve were used to evaluate the model. Results: 654 subjects (327 cases and 327 controls) were matched. The 25 variables were analysed. The logistic regression model established in this study was as follows: Logit(P)= -2.871 (0.686×respiratory infections) (1.176×water pollution) (1.019×adverse emotions during pregnancy) - (0.617×nutrition supplementation). The Hosmer-Lemeshow chi-square value was 7.208 (df = 6), with a nonsignificant p value of 0.302, which indicates that the model was well-fitted. The calibration plot showed good agreement between the bias-corrected prediction and the ideal reference line. Area under the ROC curve was 0.72 (95% CI: 0.681~0.759), which means that the predictive power of the model set fitted the data. Conclusion: In Nanchong city, more attention should be paid to mother who had a history of respiratory infections, exposure to polluted water, adverse emotions during pregnancy and nutritional deficiency. The risk model might be an effective tool for predicting of the risk of CHD in offspring by maternal experience during pregnancy, which can be used for clinical practise in Nanchong area.? The author(s).
机译:目的:基于流行病学现场数据,该研究是开发一种预测模型,可用作初步筛选工具,以识别南充市后代先天性心脏病(CHD)高风险的孕妇,并有益指导产前管理和预防。方法:从2016年3月至2016年3月之间的南充中央医院附属医院招募了367名没有先天性畸形的367名没有先天性畸形的儿童。使用SPSS 22.0案例控制匹配模块,根据儿童(早产或全职),孕妇年龄(不到1年)的母体时代,对照组匹配率为1:1的速率。 SPSS 22分析了327个匹配的病例对照对。进行单变量和多变量分析,以寻找后代CHD的重要孕产量影响因素。逻辑回归疾病预测模型被构造为最终的预测因子,并且使用HOSMER-LEMESHOW的拟合测试和接收机操作特征(ROC)曲线来评估模型。结果:654名受试者(327例和327个对照)匹配。分析了25个变量。本研究建立的逻辑回归模型如下:Logit(P)= -2.871(0.686×呼吸道感染)(1.176×水污染)(怀孕期间的1.019×不良情绪) - (0.617×营养补充)。 Hosmer-Lemeshow Chi-Square值为7.208(DF = 6),不显着的P值为0.302,这表明该模型很好。校准图显示了偏置预测和理想的参考线之间的良好一致性。 ROC曲线下的面积为0.72(95%CI:0.681〜0.759),这意味着模型集的预测力拟合了数据。结论:在南充市,应更多地关注患有呼吸道感染史,接触污染水,妊娠期不良情绪的母亲和营养缺乏的母亲。风险模型可能是预测怀孕期间母体经验中后代患者CHD风险的有效工具,可用于南充地区的临床实践。作者。

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