An artificial neural network prediction model of congenital heart disease based on risk factors

Li, Huixia; Luo, Miyang; Zheng, Jianfei; Luo, Jiayou; Zeng, Rong; Feng, Na; Du, Qianqian; Fang, Junqun

doi:10.1097/md.0000000000006090

Cited by 81 publications

(61 citation statements)

References 35 publications

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“…We observed that maternal adverse emotions during pregnancy (OR = 2.650, 95% CI: 1.903~3.692) were the most important factor in the occurrence of CHD. This association was also observed in the studies of LI Huixia et al 18 . With the slowdown of Chinese economic growth, the major pressures of life and the gap between the rich and the poor, the fast pace of life and negative habits make psychological problems increasingly common 19 .…”

Section: Discussionsupporting

confidence: 80%

Parental non-hereditary teratogenic exposure factors on the occurrence of congenital heart disease in the offspring in the northeastern Sichuan, China

Liang

et al. 2020

Sci Rep

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nonhereditary factors play an important role in the occurrence of congenital heart disease (cHD). this study was to explore the possible parental nonhereditary exposure factors relevant to the occurrence of CHD in the northeastern Sichuan area. 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. This study was designed as a case-control study with 1:1 frequency matching, in which the parents of cases and controls were interviewed with the same questionnaire according to the gestational age of the child, maternal age during pregnancy and the same maternal race/ethnicity. Then, 322 matched case-control pairs were analysed by SPSS 22. Thirty-one suspicious factors were entered into the binary logistic regression analysis after univariate regression analysis of 55 factors (alpha = 0.05). The analysis results showed that 7 factors were significantly associated with the occurrence of CHD. Thus, augmenting maternal mental healthcare, improving the quality of drinking water, obtaining adequate nutrition, maintaining a healthy physical condition during pregnancy, enhancing parents' level of knowledge and maintaining a healthy lifestyle may lower the occurrence of cHD. Resultscomposition of the object of study. The 322 matched case-control pairs were analysed by SPSS from the original cases in the study population (Tables 1 and 2). Among the 322 analysed cases, 182 (56.5%) were males and 140 (43.5%) were females. There were 163 (50.6%) males and 159 (49.4%) females in the matched control group. There was no significant difference in the gender distribution between the two groups (χ 2 = 2.254, P = 0.133) ( Table 3). The distribution of children in different age groups in the two groups is shown in Table 3, and the difference was not statistically significant (χ 2 = 4.826, P = 0.566). Scientific RepoRtS |(2020) 10:3905 | https://doi.The exclusion criteria were as follows: (1) Children with congenital malformations of other systems and other family genetic diseases; (2) children with myocarditis, cardiomyopathy and other cardiovascular diseases; (3) parents with conscious disorder, mental disorder, communication disorder, or poor memory of events during pregnancy; and (4) parents of children who did not agree to participate in the survey.Selection of the control group. With respect to the admission criteria, controls without any congenital malformations were randomly selected among children who were undergoing physical disease treatment.The exclusion criteria were as follows: (1) Children with other inherited familial diseases;(2) children with cardiovascular diseases such as myocarditis and cardiomyopathy; (3) parents with conscious disorder, mental disorder, communication disorder, or poor memory of events during pregnancy; and (4) parents who did not agree to participate in the survey. Scientific RepoRtS |(2020) 10:3905 | https...

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Section: Discussionsupporting

confidence: 80%

Parental non-hereditary teratogenic exposure factors on the occurrence of congenital heart disease in the offspring in the northeastern Sichuan, China

Liang

et al. 2020

Sci Rep

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“…We identified 31 studies published between 1990 and 2018 according to the inclusion criteria. Study selection was summarized in Figure .…”

Section: Resultsmentioning

confidence: 99%

“…Apart from the above paternal risk factors, there are also several studies concerned about other paternal risk factors, such as chronic disease, viral infection, etc. Paternal chronic disease was another risk factor for CHDs (OR = 4.87, 95% CI: 1.23‐19.24), according to the findings of Li's investigation . And paternal virus infection (OR = 2.46, 95% CI: 1.13‐5.35), antibiotics usage (OR = l0.04, 95% CI: 1.28‐78.45) may also increase the risk of CHDs .…”

Section: Resultsmentioning

confidence: 99%

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The non‐genetic paternal factors for congenital heart defects: A systematic review and meta‐analysis

Peng

Meng

Zhou

et al. 2019

Clinical Cardiology

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Background Advances have been made in identifying genetic etiologies and maternal risk factors of congenital heart defects (CHDs), while few literatures are available regarding paternal risk factors for CHDs. Thus, we aim to conduct a meta‐analysis and systematic review about the non‐genetic paternal risk factors for CHDs. Methods We searched the PubMed, MEDLINE, and Cochrane Library online databases and identified 31 studies published between 1990 and 2018 according to the inclusion criteria. Paternal risk factors were divided into subgroups, and summarized odd ratios (OR) were calculated. Results Paternal age between 24 and 29 years decreased the risk of CHDs in the offspring (OR = 0.90 [0.82, 0.98]), while paternal age ≥ 35 years old increased the risk of CHDs (35‐39 years old: OR = 1.14 [1.09, 1.19], and ≥ 40 years: OR = 1.27 [1.14, 1.42]). Paternal cigarette smoking increased the risk of CHDs in a dose‐dependent way. Paternal wine drinking (OR = 1.47 [1.05, 2.07]) and exposure to chemical agents or drugs (OR = 2.15 [1.53, 3.02]) also increased the risk of CHDs. Some specific paternal occupations were also associated with increased risk for CHDs or CHD subtypes including factory workers, janitors, painters, and plywood mill workers. Conclusions This meta‐analysis and systematic review suggested that advanced paternal age, cigarette smoking, wine drinking, exposure to chemical agents or drugs and some specific occupations were associated with an increased risk of CHDs. More measures should be taken to reduce occupational and environment exposures. At the same time, fertility at certain age and establishment of healthy life habits are strongly recommended.

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“…where several studies such as the one carried out by Li et al assessed the risk of heart attack utilizing an artificial neuron network. 22 Other studies employed machine learning in order to identify the risk of developing other diseases, such as colorectal cancer, 23 respiratory virus affinity, 24 melanoma, 25 mortality in smokers, 26 depression 1 or HIV transmission, 27 among others.…”

Section: Preventive Medicinementioning

confidence: 99%

<p>On the Role of Artificial Intelligence in Genomics to Enhance Precision Medicine</p>

Álvarez-Machancoses¹,

deAndrés-Galiana²,

Cernea³

et al. 2020

PGPM

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The complexity of orphan diseases, which are those that do not have an effective treatment, together with the high dimensionality of the genetic data used for their analysis and the high degree of uncertainty in the understanding of the mechanisms and genetic pathways which are involved in their development, motivate the use of advanced techniques of artificial intelligence and in-depth knowledge of molecular biology, which is crucial in order to find plausible solutions in drug design, including drug repositioning. Particularly, we show that the use of robust deep sampling methodologies of the altered genetics serves to obtain meaningful results and dramatically decreases the cost of research and development in drug design, influencing very positively the use of precision medicine and the outcomes in patients. The target-centric approach and the use of strong prior hypotheses that are not matched against reality (disease genetic data) are undoubtedly the cause of the high number of drug design failures and attrition rates. Sampling and prediction under uncertain conditions cannot be avoided in the development of precision medicine.

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An artificial neural network prediction model of congenital heart disease based on risk factors

Cited by 81 publications

References 35 publications

Parental non-hereditary teratogenic exposure factors on the occurrence of congenital heart disease in the offspring in the northeastern Sichuan, China

Parental non-hereditary teratogenic exposure factors on the occurrence of congenital heart disease in the offspring in the northeastern Sichuan, China

The non‐genetic paternal factors for congenital heart defects: A systematic review and meta‐analysis

<p>On the Role of Artificial Intelligence in Genomics to Enhance Precision Medicine</p>

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