Int J Med Sci 2020; 17(18):3091-3097. doi:10.7150/ijms.48046 This issue
1. Department of Pediatric Surgery, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, P.R. China; The first affiliated Hospital of Jinan University, Guangzhou, 510632, P.R. China.
2. Department of Nursing, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, P.R. China.
3. Department of Cardiology, Nanchong Central Hospital, Nanchong, 637000, P.R. China; Department of Oncology, the second Xiangya Hospital of Central South University Changsha, Yuelu District, 410011, P.R. China (Current Address).
4. Department of Cardiothoracic Surgery, Nanchong Central Hospital, Nanchong, 637000, P.R. China.
#These authors contributed equally to this work.
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.
Keywords: congenital heart disease, risk factors, case-control matching, risk model