“The Mental Health Piece is Huge”: perspectives on developing a prenatal maternal psychological intervention for congenital heart disease

2021 ◽  
pp. 1-8
Author(s):  
Kristina M. Espinosa ◽  
Melissa Julian ◽  
Yao Wu ◽  
Catherine Lopez ◽  
Mary T. Donofrio ◽  
...  
Keyword(s):  
Mental Health ◽  
Congenital Heart Disease ◽  
Heart Disease ◽  
Pregnant Women ◽  
Congenital Heart ◽  
Psychological Intervention ◽  
Maternal Distress ◽  
Treatment Preferences ◽  
Targeted Interventions ◽  
Pregnancy Anxiety

Abstract Objectives: Women carrying a fetus diagnosed with congenital heart disease often experience significant distress because of their medical diagnosis. Given the well-documented impact associated with elevated prenatal stress and critical importance of developing targeted interventions, this study aims to examine stressors, coping and resilience resources, and mental health treatment preferences in pregnant women receiving a congenital heart disease diagnosis to inform the development of a psychological intervention to reduce maternal distress prenatally. Methods: Three groups of participants were included consisting of two pregnant women carrying a fetus with congenital heart disease, five women of children (4−16 months) with congenital heart disease, and five paediatric cardiology medical providers. Responses were gathered via semi-structured interviews and analysed using qualitative thematic analysis. Results: Information regarding four broad areas were analysed of emotional distress during pregnancy; experience of initial diagnosis; coping and resilience; and perspectives on a mental health intervention in pregnancy. Anxiety regarding baby’s future, guilt following diagnosis, and various coping strategies emerged as primary themes among the participant sample. Medical staff corroborated mothers’ heightened anxiety and viewed a psychotherapeutic intervention during the prenatal period as essential and complimentary to standard of care. Conclusion: We identified salient themes and preferred components for a future psychological intervention delivered prenatally. Practice Implications: Patients’ and providers’ perspectives regarding the nature of maternal distress, resilience and treatment preferences can inform the development of interventions to support the emotional well-being of pregnant women carrying a fetus with congenital heart disease to optimise care and potentially improve outcomes for fetal brain development.

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.

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