Open Access

ARTICLE

Noninherited Factors in Fetal Congenital Heart Diseases Based on Bayesian Network: A Large Multicenter Study

Yanping Ruan^1,#, Xiangyu Liu^2,#, Haogang Zhu^3,*, Yijie Lu³, Xiaowei Liu¹, Jiancheng Han¹, Lin Sun¹, Ye Zhang¹, Xiaoyan Gu¹, Ying Zhao¹, Lei Li², Suzhen Ran⁴, Jingli Chen⁵, Qiong Yu⁶, Yan Xu⁷, Hongmei Xia⁸, Yihua He^1,*

1 Department of Echocardiography, Maternal-Fetal Medicine Research Consultation Center, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, China
2 School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
3 State Key Laboratory of Software Development Environment, School of Computer Science and Engineering, Beihang University, Beijing, 100191, China
4 Department of Ultrasound, Chongqing Health Center for Women and Children, Chongqing, 400013, China
5 Department of Ultrasound, Urumqi Maternal and Child Health Hospital, Urumqi, 830001, China
6 Department of Ultrasound, Pingxiang Maternal and Child Care, Pingxiang, 337055, China
7 Department of Ultrasound, People’s Hospital of Rizhao, Rizhao, 276800, China
8 Department of Ultrasound, Xinqiao Hospital Army Medical University, Chongqing, 400038, China

* Corresponding Authors: Haogang Zhu. Email: ; Yihua He. Email:
# These authors contributed equally to the manuscript

Congenital Heart Disease 2021, 16(6), 529-549. https://doi.org/10.32604/CHD.2021.015862

Received 07 February 2021; Accepted 12 April 2021; Issue published 08 July 2021

Abstract

Background: Current studies have confirmed that fetal congenital heart diseases (CHDs) are caused by various factors. However, the quantitative risk of CHD is not clear given the combined effects of multiple factors. Objective: This cross-sectional study aimed to detect associated factors of fetal CHD using a Bayesian network in a large sample and quantitatively analyze relative risk ratios (RRs). Methods: Pregnant women who underwent fetal echocardiography (N = 16,086 including 3,312 with CHD fetuses) were analyzed. Twenty-six maternal and fetal factors were obtained. A Bayesian network is constructed based on all variables through structural learning and parameter learning methods to find the environmental factors that directly and indirectly associated with outcome, and the probability of fetal CHD in the two groups is predicted through a junction tree reasoning algorithm, so as to obtain RR for fetal CHD under different exposure factor combinations. Taking into account the effect of gestational week on the accuracy of model prediction, we conducted sensitivity analysis on gestational week groups. Results: The single-factor analysis showed that the RRs for the numbers of births, spontaneous abortions, and parental smoking were 1.50, 1.38, and 1.11 (P < 0.001), respectively. The risk gradually increased with the synergistic effect of ranging from one to more environmental factors above. The risk was higher among subjects with five synergistic factors, including the number of births, upper respiratory tract infection during early pregnancy, anemia, and mental stress as well as a history of spontaneous abortions or parental smoking, than in those with less than 5 factors (RR = 2.62 or 2.28, P < 0.001). This result was consistent across the participants grouped by GWs. Conclusion: We identified six factors that were directly associated with fetal CHD. A higher number of these factors led to a higher risk of CHD. These findings suggest that it is important to strengthen healthcare and prenatal counseling for women with these factors.

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