scholarly journals Clinical Application of Chromosomal Microarray Analysis in Fetuses with Congenital Heart Disease

2021 ◽  
Author(s):  
Yuxia Jin ◽  
Suping Li ◽  
Ping Tang ◽  
Jie Chen ◽  
Jing Yang ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Pregnant Women ◽  
Clinical Application ◽  
Detection Rate ◽  
Congenital Heart ◽  
Chromosomal Abnormalities ◽  
Karyotype Analysis ◽  
Snp Array ◽  
Genetic Causes

Abstract Background: Congenital heart disease (CHD) is an important birth defect, but its mechanism is still unclear. In recent years, genetic causes including chromosomal abnormalities are associated with the occurrence of congenital heart disease. In this study, CMA technology is applied to explore the genetic causes of congenital heart disease, so as to further clarify the correlation between genotype and phenotype and prepare for late pregnancy intervention and postnatal diagnosis and treatment.Objective: To explore the chromosomal abnormalities and copy number variation (CNVs) of fetuses with CHD by CMA technology, and to clarify the clinical application value of CMA technology as a detection method of first-tier antenatal CHD.Methods: Amniotic fluid sample from 155 pregnant women diagnosed with fetus CHD by prenatal ultrasound from 2018 to 2021 are collected for SNP-array detection and karyotype analysis. According to the detected CNVs results, FISH, CMA or karyotype analysis are further selected for parental verification.Results: Among the 155 fetuses with CHD, a total of 32 (20.6%) cases of chromosomal abnormalities are detected, of which 31.3% are chromosome number abnormalities. CNVs of likely pathogenicity and unknown significance are 2.5% and 5.2% respectively. The detection rate of chromosomal abnormalities in CHD of different subtypes is different, among which the high detection rate is complex CHD (31.2%), right ventricular outflow tract obstruction (30.7%) and conotruncal defects (25%). The detection rate of chromosomal abnormalities in CHD with extracardiac structural abnormalities is significantly higher than that in isolated CHD (52.4% vs 11.3%, p<0.05). In addition, the detection rate of CHD with abnormal extracardiac structure is significantly higher than that of CHD with soft markers (52.4% vs 17.8%, p<0.05), which is statistically significant. There is no significant difference in detection rate between CHD with soft markers and isolated CHD (17.8% vs 11.3%). Of the 155 pregnant women with fetus CHD, 59 chose to terminate their pregnancies, some of which were terminated according to the results of SNP-array, and some of which were terminated according to the severity of CHD.Conclusion: SNP-array technology can be used to detect chromosomal abnormalities of first-tier antenatal CHD fetuses, with high resolution, short reporting period and high efficiency. Meanwhile, pregnancy intervention can be taken according to the results.

scholarly journals Accuracy of risk prediction scores in pregnant women with congenital heart disease

2019 ◽  
Vol 14 (3) ◽  
pp. 470-478 ◽  
Author(s):  
Yuli Y. Kim ◽  
Leah A. Goldberg ◽  
Katherine Awh ◽  
Tanmay Bhamare ◽  
David Drajpuch ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Pregnant Women ◽  
Risk Prediction ◽  
Congenital Heart

scholarly journals MORTALITY IN PREGNANT WOMEN WITH CONGENITAL HEART DISEASE: A SUBANALYSIS OF THE CALIFORNIA PREGNANCY-ASSOCIATED MORTALITY REVIEW

2017 ◽  
Vol 69 (11) ◽  
pp. 606
Author(s):  
Aarthi Sabanayagam ◽  
Anushree Agarwal ◽  
Christy MacCain ◽  
Elizabeth Lawton ◽  
Elliot Main ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Pregnant Women ◽  
Congenital Heart ◽  
Mortality Review

1346: Health behaviour of pregnant women with congenital heart disease

2007 ◽  
Vol 6 (1_suppl) ◽  
pp. 27-28
Author(s):  
Philip Moons ◽  
Els Costermans ◽  
Els Huyghe ◽  
Wim Drenthen ◽  
Petronella Pieper ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Pregnant Women ◽  
Health Behaviour ◽  
Congenital Heart

scholarly journals Comparison of 3 Risk Estimation Methods for Predicting Cardiac Outcomes in Pregnant Women With Congenital Heart Disease

2015 ◽  
Vol 79 (7) ◽  
pp. 1609-1617 ◽  
Author(s):  
Chun-Wei Lu ◽  
Jin-Chung Shih ◽  
Ssu-Yuan Chen ◽  
Hsin-Hui Chiu ◽  
Jou-Kou Wang ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Pregnant Women ◽  
Congenital Heart ◽  
Risk Estimation ◽  
Estimation Methods ◽  
Cardiac Outcomes

Ventricular efficiency in pregnant women with congenital heart disease

2018 ◽  
Vol 261 ◽  
pp. 58-61
Author(s):  
Jun Muneuchi ◽  
Keiko Yamasaki ◽  
Mamie Watanabe ◽  
Azusa Fukumitsu ◽  
Takeshi Kawakami ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Pregnant Women ◽  
Congenital Heart ◽  
Ventricular Efficiency

scholarly journals Predicting the Risk of Adverse Events in Pregnant Women With Congenital Heart Disease

2020 ◽  
Vol 9 (14) ◽  
Author(s):  
Ran Chu ◽  
Wei Chen ◽  
Guangmin Song ◽  
Shu Yao ◽  
Lin Xie ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Receiver Operating Characteristic Curve ◽  
Pregnant Women ◽  
Receiver Operating Characteristic ◽  
Congenital Heart ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Background Women with congenital heart disease are considered at high risk for adverse events. Therefore, we aim to establish 2 prediction models for mothers and their offspring, which can predict the risk of adverse events occurred in pregnant women with congenital heart disease. Methods and Results A total of 318 pregnant women with congenital heart disease were included; 213 women were divided into the development cohort, and 105 women were divided into the validation cohort. Least absolute shrinkage and selection operator was used for predictor selection. After validation, multivariate logistic regression analysis was used to develop the model. Machine learning algorithms (support vector machine, random forest, AdaBoost, decision tree, k‐nearest neighbor, naïve Bayes, and multilayer perceptron) were used to further verify the predictive ability of the model. Forty‐one (12.9%) women experienced adverse maternal events, and 93 (29.2%) neonates experienced adverse neonatal events. Seven high‐risk factors were discovered in the maternal model, including New York Heart Association class, Eisenmenger syndrome, pulmonary hypertension, left ventricular ejection fraction, sinus tachycardia, arterial blood oxygen saturation, and pregnancy duration. The machine learning–based algorithms showed that the maternal model had an accuracy of 0.76 to 0.86 (area under the receiver operating characteristic curve=0.74–0.87) in the development cohort, and 0.72 to 0.86 (area under the receiver operating characteristic curve=0.68–0.80) in the validation cohort. Three high‐risk factors were discovered in the neonatal model, including Eisenmenger syndrome, preeclampsia, and arterial blood oxygen saturation. The machine learning–based algorithms showed that the neonatal model had an accuracy of 0.75 to 0.80 (area under the receiver operating characteristic curve=0.71–0.77) in the development cohort, and 0.72 to 0.79 (area under the receiver operating characteristic curve=0.69–0.76) in the validation cohort. Conclusions Two prenatal risk assessment models for both adverse maternal and neonatal events were established, which might assist clinicians in tailoring precise management and therapy in pregnant women with congenital heart disease.

scholarly journals Parental reactions, distress, and sense of coherence after prenatal versus postnatal diagnosis of complex congenital heart disease

2019 ◽  
Vol 29 (11) ◽  
pp. 1328-1334 ◽  
Author(s):  
Ewa-Lena Bratt ◽  
Stina Järvholm ◽  
Britt-Marie Ekman-Joelsson ◽  
Antje Johannsmeyer ◽  
Sven-Åke Carlsson ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Life Satisfaction ◽  
Heart Disease ◽  
Prenatal Diagnosis ◽  
Pregnant Women ◽  
Sense Of Coherence ◽  
Congenital Heart ◽  
Depression Scale ◽  
Complex Congenital Heart Disease ◽  
Postnatal Diagnosis

AbstractIntroduction:A diagnosis of congenital heart disease (CHD) in offspring triggers psychological distress in parents. Results of previous studies have been inconsistent regarding the psychological impact of a prenatal versus a postnatal diagnosis. The aim of this study was to evaluate the influence of the time of diagnosis on levels of parental distress.Methods:Pregnant women and their partners with a fetus diagnosed with complex CHD, parents of children with postnatally diagnosed CHD, and pregnant women and their partners with uncomplicated pregnancies were invited to participate. Data were collected during pregnancy and 2–6 months after delivery using the Hospital Anxiety and Depression Scale, sense of coherence, life satisfaction, and Dyadic Adjustment Scale.Results:During pregnancy, the prenatal group scored lower sense of coherence compared to controls (p=0.044). Postnatally the prenatal group scored lower on sense of coherence compared to the postnatal group and controls (p=0.001; p=0.001). Postnatally, the prenatal and postnatal groups had higher levels of anxiety compared to controls (p=0.025; p=0.0003). Life satisfaction was lower in the prenatal group compared to that in the postnatal group and in controls (p=0.000; p=0.0004).Conclusion:Parents with a prenatal diagnosis of CHD in offspring report a low sense of coherence already during pregnancy which decreased further at follow-up. The same group reported a lower satisfaction with life compared to parents of a child with postnatal diagnosis of CHD and parents of a healthy child. This motivates further efforts to improve counselling and support during pregnancy and for parents after a prenatal diagnosis.

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