Diagnosis Of Congenital Heart Disease Research Articles

New Diagnosis of Congenital Heart Disease in a 54-Year-Old Male After Presenting With Spontaneous Atrial Flutter With 1:1 AV Conduction: A Case Report

New Diagnosis of Congenital Heart Disease in a 54-Year-Old Male After Presenting With Spontaneous Atrial Flutter With 1:1 AV Conduction: A Case Report

Detection of congenital heart disease by neonatologist performed cardiac ultrasound in preterm infants.

We aimed to assess the frequency of de novo congenital heart disease (CHD) detection via neonatologist-performed cardiac ultrasounds (NPCU) in premature infants born at <30 weeks of gestation. In this cross-sectional study (2004-2023) clinicians completing NPCU flagged de novo suspected CHD. All flagged NPCUs were cross-checked with cardiologists to confirm CHD diagnosis. There were 2088 out of 3739 infants (56%) with at least one NPCU; 294 (14%) with cardiology referral. CHD diagnosis was confirmed in 109 of the 2088 (5.2%) infants. All major and critical CHD on NPCU imaging were suspected during NPCU and had prompt referral to the cardiology department. De novo presentation of significant CHD continues to occur in the preterm population, emphasizing the need for recognizing CHD during NPCU. Optimizing NPCU training may benefit patients with early cardiology referral and review.

High-Quality Hospital Status on Health Care Costs for Pediatric Congenital Heart Disease Care for U.S. Military Beneficiaries.

Congenital heart disease (CHD) is the most common and resource demanding birth defect managed in the United States, with approximately 40,000 children undergoing CHD surgery year. Researchers have compared high-volume to low-volume hospitals and found significant hospital-level variation in major complications, health resource utilization, and health care costs after CHD surgery. Using nationwide representative claim data from the United States Military Health System from 2016 to 2020, TRICARE beneficiaries diagnosed with CHD were tabulated based on ICD-10 codes (International Classification of Diseases, 10th revision). We examined the relationships between total costs and total hospitalizations costs post 1-year CHD diagnosis and presence or absence of High-Quality Hospital (HQH) designation. We applied both the naive generalized linear model (GLM) to control for the observed patient and hospital characteristics and the 2-stage least squares (2SLS) model to account for the unobserved confounding factors. This study was approved by University of Maryland, College Park Institutional Review Board (IRB) (Approval Number: 1576246-2). A relationship between HQH designation and total CHD related costs was not seen across 2SLS model specifications (marginal effect; -$41,579; 95% CI, -$83,429 to $271). For patients diagnosed with a moderate-complex or single ventricle CHD, the association of HQH status was a statistically significant reduction in total costs (marginal effect; -$84,395; 95% CI, -$140,560 to -$28,229) and hospitalization costs (marginal effect; -$73,958; 95% CI, -$121,878 to -$26,039). It is very imperative for clinicians and patient support advocates to urge policymakers to deliberate the establishment of a quality designation authority for CHD management. These efforts will not only help to identify and standardize quality care metrics but to improve long-term health, effectiveness, and equity in the management of CHD. Furthermore, these efforts can be used to navigate patients to proven HQH, thereby improving care and reducing associated treatment costs for CHD patients.

Specification for genetic diagnosis of congenital heart disease

Congenital heart disease (CHD) is one of the most common congenital malformations and a major cause of mortality among neonates and children. Conventional methods for the diagnosis of CHD have relied on clinical features and imaging findings. With the rapid development of genetic techniques, to identify the cause of CHD through genetic diagnosis has gained great significance for the early diagnosis, treatment, and prevention of CHD. However, currently there is still a lack of norms and standards for the genetic diagnosis of CHD. In view of this, experts from the relevant fields have formulated the present norm by integrating the latest research advances on CHD-related genes with the current clinical practice on the diagnosis and treatment of CHD and status quo of genetic diagnosis in China. The norm has been recommended by the Cardiology Section of the Chinese Medical Education Association, the Medical Genetics Branch and the Heart Group of Pediatric Surgery Branch of the Chinese Medical Association, which has formulated the procedures and norms of genetic testing, prenatal diagnosis, and genetic counseling for CHD, with an aim to provide reference for clinicians as the standards for the integrated diagnosis, early treatment, and prevention of CHD.

Delivery room and early postnatal management of neonates with congenital heart disease.

Advancements in prenatal detection have improved postnatal outcomes for patients with congenital heart disease (CHD). Detailed diagnosis during pregnancy allows for preparation for the delivery and immediate postnatal care for the newborns with CHD. Most CHDs do not result in hemodynamic instability at the time of birth and can be stabilized following the guidelines of the neonatal resuscitation program (NRP). Critical CHD that requires intervention immediately after birth is recommended to be delivered in facilities where immediate neonatal and cardiology care can be provided. Postnatal stabilization and resuscitation for these defects warrant deviation from the standardized NRP. For neonatal providers, knowing the diagnosis of fetal CHD allows for preparation for the anticipated instability in the delivery room. Prenatal detection fosters collaboration between fetal cardiology, cardiology specialists, obstetrics, and neonatology, improving outcomes for neonates with critical CHD.

Role of artificial-intelligence-assisted automated cardiac biometrics in prenatal screening for coarctation of aorta.

Although remarkable strides have been made in fetal medicine and the prenatal diagnosis of congenital heart disease, around 60% of newborns with isolated coarctation of the aorta (CoA) are not identified prior to birth. The prenatal detection of CoA has been shown to have a notable impact on survival rates of affected infants. To this end, implementation of artificial intelligence (AI) in fetal ultrasound may represent a groundbreaking advance. We aimed to investigate whether the use of automated cardiac biometric measurements with AI during the 18-22-week anomaly scan would enhance the identification of fetuses that are at risk of developing CoA. We developed an AI model capable of identifying standard cardiac planes and conducting automated cardiac biometric measurements. Our data consisted of pregnancy ultrasound image and outcome data spanning from 2008 to 2018 and collected from four distinct regions in Denmark. Cases with a postnatal diagnosis of CoA were paired with healthy controls in a ratio of 1:100 and matched for gestational age within 2 days. Cardiac biometrics obtained from the four-chamber and three-vessel views were included in a logistic regression-based prediction model. To assess its predictive capabilities, we assessed sensitivity and specificity on receiver-operating-characteristics (ROC) curves. At the 18-22-week scan, the right ventricle (RV) area and length, left ventricle (LV) diameter and the ratios of RV/LV areas and main pulmonary artery/ascending aorta diameters showed significant differences, with Z-scores above 0.7, when comparing subjects with a postnatal diagnosis of CoA (n = 73) and healthy controls (n = 7300). Using logistic regression and backward feature selection, our prediction model had an area under the ROC curve of 0.96 and a specificity of 88.9% at a sensitivity of 90.4%. The integration of AI technology with automated cardiac biometric measurements obtained during the 18-22-week anomaly scan has the potential to enhance substantially the performance of screening for fetal CoA and subsequently the detection rate of CoA. Future research should clarify how AI technology can be used to aid in the screening and detection of congenital heart anomalies to improve neonatal outcomes. © 2024 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Intelligent diagnosis of atrial septal defect in children using echocardiography with deep learning

BackgroundAtrial septal defect (ASD) is one of the most common congenital heart diseases. The diagnosis of ASD via transthoracic echocardiography is subjective and time-consuming. MethodsThe objective of this study was to evaluate the feasibility and accuracy of automatic detection of ASD in children based on color Doppler echocardiographic static images using end-to-end convolutional neural networks. The proposed depthwise separable convolution model identifies ASDs with static color Doppler images in a standard view. Among the standard views, we selected two echocardiographic views, i.e., the subcostal sagittal view of the atrium septum and the low parasternal four-chamber view. The developed ASD detection system was validated using a training set consisting of 396 echocardiographic images corresponding to 198 cases. Additionally, an independent test dataset of 112 images corresponding to 56 cases was used, including 101 cases with ASDs and 153 cases with normal hearts. ResultsThe average area under the receiver operating characteristic curve, recall, precision, specificity, F1-score, and accuracy of the proposed ASD detection model were 91.99, 80.00, 82.22, 87.50, 79.57, and 83.04, respectively. ConclusionsThe proposed model can accurately and automatically identify ASD, providing a strong foundation for the intelligent diagnosis of congenital heart diseases.

Ethical Considerations in Fetal Cardiology.

Fetal cardiology has evolved over the last 40 years and changed the timing of diagnosis and counseling of congenital heart disease, decision-making, planning for treatment at birth, and predicting future surgery from the postnatal to the prenatal period. Ethical issues in fetal cardiology transect multiple aspects of biomedical ethics including improvement in prenatal detection and diagnostic capabilities, access to equitable comprehensive care that preserves a pregnant person's right to make decisions, access to all reproductive options, informed consent, complexity in shared decision-making, and appropriate use of fetal cardiac interventions. This paper first reviews the literature and then provides an ethical analysis of accurate and timely diagnosis, equitable delivery of care, prenatal counseling and shared decision-making, and innovation through in utero intervention.

Heart and great vessels segmentation in congenital heart disease via CNN and conditioned energy function postprocessing.

The segmentation of the heart and great vessels in CT images of congenital heart disease (CHD) is critical for the clinical assessment of cardiac anomalies and the diagnosis of CHD. However, the diverse types and abnormalities inherent in CHD present significant challenges to comprehensive heart segmentation. We proposed a novel two-stage segmentation approach, integrating a Convolutional Neural Network (CNN) with a postprocessing method with conditioned energy function for pulmonary and aorta. The initial stage employs a CNN enhanced by a gated self-attention mechanism for the segmentation of five primary heart structures and two major vessels. Subsequently, the second stage utilizes a conditioned energy function specifically tailored to refine the segmentation of the pulmonary artery and aorta, ensuring vascular continuity. Our method was evaluated on a public dataset including 110 3D CT volumes, encompassing 16 CHD variants. Compared to prevailing segmentation techniques (U-Net, V-Net, Unetr, dynUnet), our approach demonstrated improvements of 1.02, 1.04, and 1.41% in Dice Coefficient (DSC), Intersection over Union (IOU), and the 95th percentile Hausdorff Distance (HD95), respectively, for heart structure segmentation. For the two great vessels, the enhancements were 1.05, 1.07, and 1.42% in these metrics. The outcomes on the public dataset affirm the efficacy of our proposed segmentation method. Precise segmentation of the entire heart and great vessels can significantly aid in the diagnosis and treatment of CHD, underscoring the clinical relevance of our findings.

The Evolving Role of Genetic Evaluation in the Prenatal Diagnosis and Management of Congenital Heart Disease.

Congenital heart disease (CHD) is increasingly diagnosed prenatally and the ability to screen and diagnose the genetic factors involved in CHD have greatly improved. The presence of a genetic abnormality in the setting of prenatally diagnosed CHD impacts prenatal counseling and ensures that families and providers have as much information as possible surrounding perinatal management and what to expect in the future. This review will discuss the genetic evaluation that can occur prior to birth, what different genetic testing methods are available, and what to think about in the setting of various CHD diagnoses.

Neonatal heart transplantation in the United States: Trends and outcomes.

Heart transplantation in the neonatal period is associated with excellent survival. However, outcomes data are scant and have been obtained primarily from two single-center reports within the United States. We sought to analyze the outcomes of all neonatal heart transplants performed in the United States using the United Network for Organ Sharing (UNOS) dataset. The UNOS dataset was queried for patients who underwent infant heart transplantation from 1987 to 2021. Patients were divided into two groups based on age - neonates (<=31 days), and older infants (32 days-365 days). Demographic and clinical characteristics were analyzed and compared, along with follow up survival data. Overall, 474 newborns have undergone heart transplantation in the United States since 1987. Freedom from death or re-transplantation for neonates was 63.5%, 58.8% and 51.6% at 5, 10, and 20 years, respectively. Patients in the newborn group had lower unadjusted survival compared to older infants (p < .001), but conditional 1-year survival was higher in neonates (p = .03). On multivariable analysis, there was no significant difference in survival between the two age groups (p = .43). Black race, congenital heart disease diagnosis, earlier surgical era, and preoperative mechanical circulatory support use were associated with lower survival among infant transplants (p < .05). Neonatal heart transplantation is associated with favorable long-term clinical outcomes. Neonates do not have a significant survival advantage over older infants. Widespread applicability is limited by the small number of available donors. Efforts to expand the donor pool to include non-standard donor populations ought to be considered.

Is there an increased risk of genetic abnormalities in fetuses with congenital heart disease in the setting of growth restriction?

To determine if the presence of fetal growth restriction (FGR) is associated with an increased risk of genetic abnormalities in the setting of congenital heart disease (CHD). This was a retrospective cohort study involving pregnancies that met the following criteria: (i) prenatal diagnosis of CHD, (ii) singleton live-birth, and (iii) genetic testing was performed either pre- or postnatally. Genetic results were reviewed by a clinical geneticist for updated variant classification. Fetal growth was stratified as appropriate for gestational age (AGA) or FGR. Of the total of 445 fetuses that met the study criteria, 325 (73.0%) were AGA and 120 (27.0%) were FGR. Genetic abnormalities were detected in 131 (29.4%) pregnancies. There was a higher rate of genetic abnormalities (36.7% vs. 26.8%, p=0.04), which was driven by aneuploidies (20.8% vs. 8.9%, p=0.0006) in the FGR population. Early onset growth restriction was associated with a higher rate of genetic abnormalities (44.5% vs. 25.9%, p=0.03). The rate of genetic abnormalities was significantly higher in the shunt category as compared to remainder of the cardiac anomalies (62.5% in shunt lesions vs. 24.7%, p<0.00001). The rates of FGR (40.9% vs. 21.4%, p<0.0001) and genetic abnormalities (52% vs. 20.4%, p<0.0001) were significantly higher in the presence of extra-cardiac anomalies (ECA). The presence of FGR in fetal CHD population was associated with underlying genetic abnormalities, specifically aneuploidies. Patients should be appropriately counseled regarding the higher likelihood of a genetic condition in the presence of FGR, early onset FGR, shunt lesions and ECA.

Enhancing parental understanding of congenital heart disease through personalized prenatal counseling with 3D printed hearts.

In addition to a correct prenatal diagnosis of congenital heart disease (CHD), comprehensive parental counseling is crucial to ensure that parents are well-informed about the condition of the fetus. This study aims to investigate whether there is a significant difference in the information acquired by parents through traditional counseling, utilizing 2-dimensional (2D) illustrations and images, compared to an advanced approach utilizing personalized three-dimensional (3D) printed models of the fetal heart developed from 3D ultrasound imaging. This study, designed as a pilot randomized control trial, enrolled pregnant women with gestational ages greater than 18weeks, whose fetuses were diagnosed with CHD and referred to our center between November, 2020 and June, 2021. Two groups of patients were included in the study. The first group received standard medical counseling with 2D images and illustrations, while the second group underwent advanced counseling with 3D-printed patient-specific heart models. Both groups were then required to complete the same survey in which the knowledge of the CHD was investigated. The 3D models were created from 3D ultrasound imaging and printed using resin materials in both 1:1 and 5:1 scale. A comparison of the scores obtained from the two groups revealed that 3D visualization of the fetus's heart has the potential to increase parental knowledge about CHD and the required surgical procedures. Furthermore, all couples expressed interest in receiving a 1:1 scale model of their baby's heart. Personalized prenatal counseling with 3D-ultrasound-based heart models positively impacts parents' understanding of CHD. The use of 3D models provides a more comprehensive and accessible representation of the condition, contributing to an increased knowledge gain, and potentially helping to support informed decisions regarding their child's care.

PP-017 Impact of prenatal diagnosis of congenital heart disease on neonatal morbidity and mortality

PP-017 Impact of prenatal diagnosis of congenital heart disease on neonatal morbidity and mortality

A “twelve-section ultrasonic screening and diagnosis method” and management system for screening and treating neonatal congenital heart disease at the grassroots level in Tang County, Hebei Province, China

BackgroundTo explore a method for screening and diagnosing neonatal congenital heart disease (CHD) applicable to grassroots level, evaluate the prevalence of CHD, and establish a hierarchical management system for CHD screening and treatment at the grassroots level.MethodsA total of 24,253 newborns born in Tang County between January 2016 and December 2020 were consecutively enrolled and screened by trained primary physicians via the “twelve-section ultrasonic screening and diagnosis method” (referred to as the “twelve-section method”). Specialized staff from the CHD Screening and Diagnosis Center of Hebei Children’s Hospital regularly visited the local area for definite diagnosis of CHD in newborns who screened positive. Newborns with CHD were managed according to the hierarchical management system.ResultsThe centre confirmed that, except for 2 newborns with patent ductus arteriosus missed in the diagnosis of ventricular septal defect combined with severe pulmonary hypertension, newborns with other isolated or concomitant simple CHDs were identified at the grassroots level. The sensitivity, specificity and diagnostic coincidence rate of the twelve-section method for screening complex CHD were 92%, 99.6% and 84%, respectively. A total of 301 children with CHD were identified. The overall CHD prevalence was 12.4‰. According to the hierarchical management system, 113 patients with simple CHD recovered spontaneously during local follow-up, 48 patients continued local follow-up, 106 patients were referred to the centre for surgery (including 17 patients with severe CHD and 89 patients with progressive CHD), 1 patient died without surgery, and 8 patients were lost to follow-up. Eighteen patients with complex CHD were directly referred to the centre for surgery, 3 patients died without surgery, and 4 patients were lost to follow-up. Most patients who received early intervention achieved satisfactory results. The mortality rate of CHD was approximately 28.86 per 100,000 children.ConclusionsThe “twelve-section method” is suitable for screening neonatal CHD at the grassroots level. The establishment of a hierarchical management system for CHD screening and treatment is conducive to the scientific management of CHD, which has important clinical and social significance for early detection, early intervention, reduction in mortality and improvement of the prognosis of complex and severe CHDs.

B-type natriuretic peptide levels predict long-term mortality in a large cohort of adults with congenital heart disease.

Many adult patients with congenital heart disease (ACHD) are still afflicted by premature death. Previous reports suggested natriuretic peptides may identify ACHD patients with adverse outcome. The study investigated prognostic power of B-type natriuretic peptide (BNP) across the spectrum of ACHD in a large contemporary cohort. The cohort included 3392 consecutive ACHD patients under long-term follow-up at a tertiary ACHD centre between 2006 and 2019. The primary study endpoint was all-cause mortality. A total of 11 974 BNP measurements were analysed. The median BNP at baseline was 47 (24-107) ng/L. During a median follow-up of 8.6 years (29 115 patient-years), 615 (18.1%) patients died. On univariable and multivariable analysis, baseline BNP [hazard ratio (HR) 1.16, 95% confidence interval (CI) 1.15-1.18 and HR 1.13, 95% CI 1.08-1.18, respectively] and temporal changes in BNP levels (HR 1.22, 95% CI 1.19-1.26 and HR 1.19, 95% CI 1.12-1.26, respectively) were predictive of mortality (P < .001 for both) independently of congenital heart disease diagnosis, complexity, anatomic/haemodynamic features, and/or systolic systemic ventricular function. Patients within the highest quartile of baseline BNP (>107 ng/L) and those within the highest quartile of temporal BNP change (>35 ng/L) had significantly increased risk of death (HR 5.8, 95% CI 4.91-6.79, P < .001, and HR 3.6, 95% CI 2.93-4.40, P < .001, respectively). Baseline BNP and temporal BNP changes are both significantly associated with all-cause mortality in ACHD independent of congenital heart disease diagnosis, complexity, anatomic/haemodynamic features, and/or systolic systemic ventricular function. B-type natriuretic peptide levels represent an easy to obtain and inexpensive marker conveying prognostic information and should be used for the routine surveillance of patients with ACHD.

Machine Learning to Predict Outcomes of Fetal Cardiac Disease: A Pilot Study.

Prediction of outcomes following a prenatal diagnosis of congenital heart disease (CHD) is challenging. Machine learning (ML) algorithms may be used to reduce clinical uncertainty and improve prognostic accuracy. We performed a pilot study to train ML algorithms to predict postnatal outcomes based on clinical data. Specific objectives were to predict (1) in utero or neonatal death, (2) high-acuity neonatal care and (3) favorable outcomes. We included all fetuses with cardiac disease at Sunnybrook Health Sciences Centre, Toronto, Canada, from 2012 to 2021. Prediction models were created using the XgBoost algorithm (tree-based) with fivefold cross-validation. Among 211 cases of fetal cardiac disease, 61 were excluded (39 terminations, 21 lost to follow-up, 1 isolated arrhythmia), leaving a cohort of 150 fetuses. Fifteen (10%) demised (10 neonates) and 65 (48%) of live births required high acuity neonatal care. Of those with clinical follow-up, 60/87 (69%) had a favorable outcome. Prediction models for fetal or neonatal death, high acuity neonatal care and favorable outcome had AUCs of 0.76, 0.84 and 0.73, respectively. The most important predictors for death were the presence of non-cardiac abnormalities combined with more severe CHD. High acuity of postnatal care was predicted by anti Ro antibody and more severe CHD. Favorable outcome was most predicted by no right heart disease combined with genetic abnormalities, and maternal medications. Prediction models using ML provide good discrimination of key prenatal and postnatal outcomes among fetuses with congenital heart disease.

Relationship between G protein level with left ventricular systolic function in children with acyanotic heart disease

Heart failure is a pediatric emergency caused by the heart's inability to adequately meet the body metabolic needs and the most common cause is congenital heart disease (CHD). The G protein is the most prominent family of membrane-bound protein known to act in major regulatory events of the cardiovascular system, one of which is heart failure. The aim of this study was to determine the level of G protein and its relationship with left ventricular systolic function in children with acyanotic CHD. A cross-sectional study was conducted in Dr. Zaionel Abidin Hospital, Banda Aceh, Indonesia. The patients aged 0 to 18 years and had acyanotic CHD diagnosis by echocardiography were included. Anthropometry measurement was performed according to standard WHO procedures and G protein level was measured using the ELISA method. The Chi-squared test was used to measure the relationship between G protein level and left ventricular systolic function. Out of a total of 38 children with acyanotic CHD, the mean level of G protein was 36.25 ng/mL and the mean of left ventricular systolic function was 73.1%. There was no relationship between G protein and left ventricular systolic function in children with acyanotic CHD. However, further study with a larger sample size and considering other variables are needed to confirm this finding.

Congenital Heart Disease in a Tertiary Care NICU; Pattern, Risk Factors and Outcome

Background: Identification of congenital heart disease (CHD) is increasing, may be due to advancement of diagnostic technology and availability of trained man power. Several studies have been conducted in Bangladesh to identify the prevalence and pattern of CHD but paucity of data regarding etiology and outcome. Objective: The aim of this study is to identify the frequency and risk factors of so that preventive measures can be taken. Methods: This retrospective case control study was conducted in the neonatal intensive care unit (NICU), BSMMU, Dhaka from January 2021 to June 2022. All admitted neonates in NICU with the diagnosis of CHD were considered as cases. Twice number of newborn having gestational age and birth weight matched neonates diagnosed other than CHD in NICU were considered as control. Data were extracted from the Medical records of patient files and computer electronic data records of “Newborn Birth Defect (NBBD) surveillance in Bangladesh.” The level of significance was set at 5% (P &lt;0.05). Result: Total 914 patient were admitted during the study period among them 119 patient were CHD (frequency rate 13%). Mean birth weight and gestational age of CHD group were 2376±783.85g &amp; 35.80±3.26 weeks respectively. About 85.7% had acyanotic CHD, among them most prevalent were atrial septal defect (42%) then patent ductus arteriosus (11.8%). Cyanotic CHD were 17 (14.3%). Maternal DM significantly increase the risk of developing CHD, Odd’s ratio 1.971 (CI: 1.182 - 3.285). Death were more in CHD group (34.5%) than control group (20.4%), p values = 0.005. Conclusion: Frequency (13%) of congenital heart disease was high. Maternal diabetes mellitus increases the risk of developing CHD of their baby. Death were more among babies with CHD. BANGLADESH J CHILD HEALTH 2022; VOL 46 (3) : 122-129

Applications of artificial intelligence-powered prenatal diagnosis for congenital heart disease.

Artificial intelligence (AI) has made significant progress in the medical field in the last decade. The AI-powered analysis methods of medical images and clinical records can now match the abilities of clinical physicians. Due to the challenges posed by the unique group of fetuses and the dynamic organ of the heart, research into the application of AI in the prenatal diagnosis of congenital heart disease (CHD) is particularly active. In this review, we discuss the clinical questions and research methods involved in using AI to address prenatal diagnosis of CHD, including imaging, genetic diagnosis, and risk prediction. Representative examples are provided for each method discussed. Finally, we discuss the current limitations of AI in prenatal diagnosis of CHD, namely Volatility, Insufficiency and Independence (VII), and propose possible solutions.