Analysis of maternal factors affecting the occurrence of congenital heart disease in newborns in Lanzhou area and potency of a nomogram prediction model

Abstract

Abstract Objective To analyze the maternal influencing factors of neonatal congenital heart defects (CHDs), to achieve the effect of prevention of neonatal CHDs. Methods A questionnaire survey was conducted on 134 newborns with CHDs from March 2022 to January 2023 as the case group, and 268 pregnant women were included in the control group according to the 1: 2 matching principle with age as the matching condition. Baseline data, pregnancy complications, and other clinical data of all subjects were collected. Logistic regression analysis was used to screen the risk factors affecting neonatal congenital heart disease. R software was used to construct a nomogram model for predicting the incidence of congenital heart disease. Results Logistic regression analysis showed that hypertensive disorder complicating pregnancy ( HDCP ) was a risk factor for CHDs ( OR = 3.77,95% CI : 2.18–6.53 ), gestational diabetes mellitus ( GDM ) was a risk factor for CHDs ( OR = 3.69,95% CI : 2.11–6.46 ), and keeping cats during pregnancy was a risk factor for CHDs ( OR = 2.73,95% CI : 1.02–7.34 ). The probability of GDM leading to congenital heart disease in offspring was 49.70%, the probability of HDCP leading to congenital heart disease in offspring was 48.60%, and the probability of raising cats during pregnancy leading to congenital heart disease in offspring was 38.00%.he probability of CHDs in offspring induced by HDCP and GDM was 78.70%, the probability of CHDs in offspring induced by HDCP and cats was 70.90%, the probability of CHDs in offspring induced by GDM and cats was 70.30%, and the probability of CHDs in offspring induced by HDCP, GDM, and cats was 90.50%. The nomogram predicts that the correction curve of the offspring CHDs model approaches the ideal curve. Conclusion We established a nomogram model of maternal influencing factors to predict the risk of congenital heart disease in neonates with good differentiation and consistency. This novel predictive model will help clinicians prevent congenital heart disease in offspring by improving maternal influencing factors.