Incidence and risk factors of congenital heart disease in Qingdao: a prospective cohort study

Systematic review and meta-analysis of prenatal risk factors for congenital heart disease: maternal chronic diseases and parental exposures

BACKGROUND

There is considerable heterogeneity in studies on prenatal risk factors for congenital heart diseases (CHDs). We performed a meta-analyse of all non-genetic factors of CHDs. This report presents results of factors related to maternal chronic diseases and parental exposures.

METHODS

A systematic search encompassing concepts of CHD and risk factors was used, using the following inclusion criteria: (1) original peer-reviewed articles, (2) quantifying the effects of risk factors for CHDs, (3) between 1989 and 2022. Pooled odds ratios (OR) and 95% confidence interval (CI) were calculated using a random effect model.

RESULTS

Inclusion criteria were met for 170 studies. There was an association between being overweight/obese and CHDs (OR 1.26; 95% CI 1.15-1.37), with a dose-effect relationship. Pregestational diabetes (PGDM) was associated with CHDs (OR 3.51; 95% CI 2.86-4.3), without difference between type I and type II PGDM. The effect size of gestational diabetes was less than that of PGDM (OR 1.38;95% CI: 1.18-1.61). There was an association between CHDs and preeclampsia (OR 2.01; 95% CI 1.32-3.05), and paternal smoking (OR 1.32; 95% CI 1.03-1.70) and alcohol use (OR 1.50; 95%CI 1.08-2.08). A smaller association was found with maternal smoking and advanced maternal age.

CONCLUSIONS

There exists robust evidence for increased risk of CHD in the presence of obesity, maternal diabetes, maternal smoking and increased maternal age. The effect sizes were relatively modest, except for PGDM. The robustness of the evidence decreased when CHDs were divided into subgroups, or when the analyses were restricted to severe CHDs.

Conotruncal Heart Defects: A Narrative Review of Molecular Genetics, Genomics Research and Innovation

Congenital heart defects (CHDs) are most prevalent cardiac defects that occur at birth, leading to significant neonatal mortality and morbidity, especially in the developing nations. Among the CHDs, conotruncal heart defects (CTDs) are particularly noteworthy, comprising a significant portion of congenital cardiac anomalies. While advances in imaging and surgical techniques have improved the diagnosis, prognosis, and management of CTDs, their molecular genetics and genomic substrates remain incompletely understood. This expert review covers the recent advances from January 2016 onward and examines the complexities surrounding the genetic etiologies, prevalence, embryology, diagnosis, and clinical management of CTDs. We also emphasize the known copy number variants and single nucleotide variants associated with CTDs, along with the current planetary health research efforts aimed at CTDs in large cohort studies. In all, this comprehensive narrative review of molecular genetics and genomics research and innovation on CTDs draws from and highlights selected works from around the world and offers new ideas for advances in CTD diagnosis, precision medicine interventions, and accurate assessment of prognosis and recurrence risks.

The effect of China's birth policy changes on birth defects-A large hospital-based cross-sectional study

A retrospective analysis of birth data hospital-based obtained from 14 monitoring areas in the Huaihe River Basin from 2009 to 2019 was conducted. Trend in the total prevalence of birth defects (BDs) and subgroups were analyzed using the Joinpoint Regression model. The incidence of BDs increased gradually from 118.87 per 10,000 in 2009 to 241.18 per 10,000 in 2019 (AAPC = 5.91, P < 0.001). Congenital heart diseases were the most common subtype of BDs. The proportion of maternal age younger than 25 decreased but the age 25-40 years increased significantly (AAPC<20=-5.58; AAPC20-24=-6.38; AAPC25-29 = 5.15; AAPC30-35 = 7.07; AAPC35-40 = 8.27; All P < 0.05). Compared with the one-child policy period, the risk of BDs was greater for groups among maternal age younger than 40 years during the partial and universal two-child policy period (P < 0.001). The incidence of BDs and the proportion of women with advanced maternal age in Huaihe River Basin is increasing. There was an interaction between changes in birth policy and the mother's age on the risk of BDs.

Identifying the Factors Affecting the Incidence of Congenital Heart Disease Using Support Vector Machine and Particle Swarm Optimization

Background

Congenital malformations are defined as "any defect in the structure of a person that exists from birth". Among them, congenital heart malformations have the highest prevalence in the world. This study focuses on the development of a predictive model for congenital heart disease in Isfahan using support vector machine (SVM) and particle swarm intelligence.

Materials and Methods

It consists of four parts: data collection, preprocessing, identify target features, and technique. The proposed technique is a combination of the SVM method and particle swarm optimization (PSO).

Results

The data set includes 1389 patients and 399 features. The best performance in terms of accuracy, with 81.57%, is related to the PSO-SVM technique and the worst performance, with 78.62%, is related to the random forest technique. Congenital extra cardiac anomalies are considered as the most important factor with averages of 0.655.

Conclusion

Congenital extra cardiac anomalies are considered as the most important factor. Detecting more important feature affecting congenital heart disease allows physicians to treat the variable risk factors associated with congenital heart disease progression. The use of a machine learning approach provides the ability to predict the presence of congenital heart disease with high accuracy and sensitivity.