Almanac 2014: congenital heart disease

Kahr, Peter C.; Diller, Gerhard‐Paul

doi:10.1136/heartjnl-2014-306025

Cited by 5 publications

(2 citation statements)

References 93 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Congenital heart disease (CHD) is the most common congenital malformations. The prevalence of CHD at birth is about 75–90/10,000 for live births and total pregnancies, with CHD occurring in approximately 1% of live births and 10% of aborted fetuses [ 1 , 2 ]. In addition, CHD is the leading cause of mortality in children with birth defects [ 3 ] and affects 0.7% children born in China [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Model for Predicting Risk of In-Hospital Mortality after Surgery in Congenital Heart Disease Patients

Du¹,

Wang²,

Wang³

et al. 2022

Rev. Cardiovasc. Med.

View full text Add to dashboard Cite

Background: A machine learning model was developed to estimate the in-hospital mortality risk after congenital heart disease (CHD) surgery in pediatric patient. Methods: Patients with CHD who underwent surgery were included in the study. A Extreme Gradient Boosting (XGBoost) model was constructed based onsurgical risk stratification and preoperative variables to predict the risk of in-hospital mortality. We compared the predictive value of the XGBoost model with Risk Adjustment in Congenital Heart Surgery-1 (RACHS-1) and Society of Thoracic Surgery-European Association for Cardiothoracic Surgery (STS-EACTS) categories. Results: A total of 24,685 patients underwent CHD surgery and 595 (2.4%) died in hospital. The area under curve (AUC) of the STS-EACTS and RACHS-1 risk stratification scores were 0.748 [95% Confidence Interval (CI): 0.707-0.789, p < 0.001] and 0.677 (95% CI: 0.627-0.728, p < 0.001), respectively. Our XGBoost model yielded the best AUC (0.887, 95% CI: 0.866-0.907, p < 0.001), and sensitivity and specificity were 0.785 and 0.824, respectively. The top 10 variables that contribute most to the predictive performance of the machine learning model were saturation of pulse oxygen categories, risk categories, age, preoperative mechanical ventilation, atrial shunt, pulmonary insufficiency, ventricular shunt, left atrial dimension, a history of cardiac surgery, numbers of defects. Conclusions: The XGBoost model was more accurate than RACHS-1 and STS-EACTS in predicting in-hospital mortality after CHD surgery in China.

show abstract

Section: Introductionmentioning

confidence: 99%

Machine Learning Model for Predicting Risk of In-Hospital Mortality after Surgery in Congenital Heart Disease Patients

Du¹,

Wang²,

Wang³

et al. 2022

Rev. Cardiovasc. Med.

View full text Add to dashboard Cite

show abstract

“…CHD is the most common birth malformation, occurring in 28% of all congenital birth defects. The prevalence of CHD at birth is estimated to be between 75 and 90/10,000 live births 1 . CHD is the single largest cause of infant morbidity and mortality worldwide and represents a significant global economic burden 2 .…”

mentioning

confidence: 99%

Advances in molecular genetics for pulmonary atresia

Gao

Zheng

2016

Cardiol Young

View full text Add to dashboard Cite

Genetic and environmental factors may be similar in certain CHD. It has been widely accepted that it is the cumulative effect of these risk factors that results in disease. Pulmonary atresia is a rare type of complex cyanotic CHD with a poor prognosis. Understanding the molecular mechanism of pulmonary atresia is essential for future diagnosis, prevention, and therapeutic approaches. In this article, we reviewed several related copy number variants and related genetic mutations, which were identified in patients with pulmonary atresia, including pulmonary atresia with ventricular septal defect and pulmonary atresia with intact ventricular septum.

show abstract