Prenatal diagnosis of critical congenital heart disease reduces risk of death from cardiovascular compromise prior to planned neonatal cardiac surgery: a meta-analysis

2015 ◽  
Vol 45 (6) ◽  
pp. 631-638 ◽  
Author(s):  
B. J. Holland ◽  
J. A. Myers ◽  
C. R. Woods
Keyword(s):  
Congenital Heart Disease ◽  
Cardiac Surgery ◽  
Heart Disease ◽  
Prenatal Diagnosis ◽  
Congenital Heart ◽  
Meta Analysis ◽  
Critical Congenital Heart Disease ◽  
Risk Of Death ◽  
Neonatal Cardiac Surgery

scholarly journals Oximetry and neonatal examination for the detection of critical congenital heart disease: a systematic review and meta-analysis

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 242 ◽  
Author(s):  
Hernán Camilo Aranguren Bello ◽  
Dario Londoño Trujillo ◽  
Gloria Amparo Troncoso Moreno ◽  
Maria Teresa Dominguez Torres ◽  
Alejandra Taborda Restrepo ◽  
...  
Keyword(s):  
Systematic Review ◽  
Congenital Heart Disease ◽  
Heart Disease ◽  
Physical Examination ◽  
Congenital Heart ◽  
Heart Defects ◽  
Meta Analysis ◽  
Review Of The Literature ◽  
Critical Congenital Heart Disease ◽  
Operational Characteristics

Background: Undiagnosed congenital heart disease in the prenatal stage can occur in approximately 5 to 15 out of 1000 live births; more than a quarter of these will have critical congenital heart disease (CCHD). Late postnatal diagnosis is associated with a worse prognosis during childhood, and there is evidence that a standardized measurement of oxygen saturation in the newborn by cutaneous oximetry is an optimal method for the detection of CCHD. We conducted a systematic review of the literature and meta-analysis comparing the operational characteristics of oximetry and physical examination for the detection of CCHD. Methods: A systematic review of the literature was conducted on the following databases including published studies between 2002 and 2017, with no language restrictions: Pubmed, Science Direct, Ovid, Scopus and EBSCO, with the following keywords: oximetry screening, critical congenital heart disease, newborn OR oximetry screening heart defects, congenital, specificity, sensitivity, physical examination. Results: A total of 419 articles were found, from which 69 were selected based on their titles and abstracts. After quality assessment, five articles were chosen for extraction of data according to inclusion criteria; data were analyzed on a sample of 404,735 newborns in the five included studies. The following values were found, corresponding to the operational characteristics of oximetry in combination with the physical examination: sensitivity: 0.92 (CI 95%, 0.87-0.95), specificity: 0.98 (CI 95%, 0.89-1.00), for physical examination alone sensitivity: 0.53 (CI 95%, 0.28-0.78) and specificity: 0.99 (CI 95%, 0.97-1.00). Conclusions: Evidence found in different articles suggests that pulse oximetry in addition to neonatal physical examination presents optimal operative characteristics that make it an adequate screening test for detection of CCHD in newborns, above all this is essential in low and middle-income settings where technology medical support is not entirely available.

Education of children with cyanotic congenital heart disease after neonatal cardiac surgery

2020 ◽  
Author(s):  
Constanze Pfitzer ◽  
Aleksandra Buchdunger ◽  
Paul C. Helm ◽  
Maximilian J. Blickle ◽  
Lisa-Maria Rosenthal ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Cardiac Surgery ◽  
Heart Disease ◽  
Congenital Heart ◽  
Cyanotic Congenital Heart Disease ◽  
Neonatal Cardiac Surgery

SOCIOECONOMIC BARRIERS TO PRENATAL DIAGNOSIS OF CRITICAL CONGENITAL HEART DISEASE

2020 ◽  
Vol 75 (11) ◽  
pp. 559
Author(s):  
Matthew Campbell ◽  
Scott Lorch ◽  
Jack Rychik ◽  
Michael D. Quartermain ◽  
Peter Groeneveld
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Prenatal Diagnosis ◽  
Congenital Heart ◽  
Critical Congenital Heart Disease

scholarly journals Prenatal Detection of Critical Congenital Heart Disease

2016 ◽  
Vol 10 (2) ◽  
pp. 131-135
Author(s):  
Lina W Irshaid ◽  
Najwa Elfky
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Prenatal Diagnosis ◽  
Infant Mortality ◽  
Congenital Heart ◽  
Obstet Gynecol ◽  
Chromosomal Abnormalities ◽  
Critical Congenital Heart Disease ◽  
Prenatal Detection ◽  
Associated Malformations

ABSTRACT Congenital heart disease (CHD) is a leading cause of infant mortality and 30% fetuses born with CHDs have other associated malformations and chromosomal abnormalities. Prenatal diagnosis also allows parents to opt for termination of the pregnancy. How to cite this article Irshaid LW, Elfky N, Ahmed B. Prenatal Detection of Critical Congenital Heart Disease. Donald School J Ultrasound Obstet Gynecol 2016;10(2):131-135.

scholarly journals Association of Prenatal Diagnosis of Critical Congenital Heart Disease With Postnatal Brain Development and the Risk of Brain Injury

2016 ◽  
Vol 170 (4) ◽  
pp. e154450 ◽  
Author(s):  
Shabnam Peyvandi ◽  
Veronica De Santiago ◽  
Elavazhagan Chakkarapani ◽  
Vann Chau ◽  
Andrew Campbell ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Prenatal Diagnosis ◽  
Brain Injury ◽  
Brain Development ◽  
Congenital Heart ◽  
Critical Congenital Heart Disease ◽  
Postnatal Brain

scholarly journals Previsão de mortalidade após cirurgia cardíaca congênita utilizando aprendizagem de máquina

2020 ◽  
Author(s):  
◽  
F. Binuesa
Keyword(s):  
Machine Learning ◽  
Congenital Heart Disease ◽  
Cardiac Surgery ◽  
Heart Disease ◽  
Random Forest ◽  
Health Professionals ◽  
Congenital Heart ◽  
Sensitivity Index ◽  
Surgical Mortality ◽  
Risk Of Death

Congenital heart disease is the most common cause of major anomalies of the same gender, accounting for almost a third of all major congenital anomalies. Congenital heart defects are serious and common conditions with a significant impact on morbidity, martality and health costs for children and adults. In the treatment of patients with congenital heart disease, research related to the risk of pre-surgical mortality is rare. This study aims to propose a model ops individual risk of death prediction for cardiac surgery of patients with congenital heart disease and to assist health professionals in understanding which diagnoses or variables are assoaciated with the risk of death. Teh use of machine learning techniques as a tool to suppoort decision making in the field of medicine has been increasing in recent years. With the information on surgeries performed on patients with congenital heart disease extracted from the ASSIST database of InCor, it was possible to rtain six different machine learning algorithms in predictiong the risk of pre-surgical mortality and to understand which variables impact the risk death of these patients. The algorithms trained inthis study were: Miltilayer Perceptron (MLP), Random Forest (RF), Extra Trees (ET), Stochastic Gradient Boosting(SGB), AdaBoost Classification (ABC) and Bagged Decision Trees (BDT). To predict the risk of patient mortality, the model with the best performance was the Random Forest (RF) with ROC AUC (area under the receiver's operating characteritics) of 90,2%, AP indexes (average precision) 0f 0,73 and sensitivity index (recall) mof 92,2%. The machine learning algorithm (machine learning0 can assist in understanding the mortality risks of patients with congenital heart disease when undergoing cardiac surgery and using clinical drugs that understand the best risks associated with surgical interventions, providing information to support the decision, health professionals, patients and their families

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