Detection Of Congenital Heart Disease Research Articles

Impact of isolated fetal congenital heart disease on pregnancy and perinatal outcomes.

The aim of the present study was to evaluate the obstetric complications associated with isolated fetal congenital heart disease (CHD) by comparing pregnancies with and without this condition. In this retrospective matched comparative study at Siriraj Hospital, Thailand, we included 233 postnatally confirmed fetal CHD cases and 466 unaffected fetuses. Controls were selected at a 2:1 ratio, ensuring that they matched the cases in terms of maternal age, parity, and history of preterm deliveries. Fetal CHD was significantly associated with an increased risk of spontaneous preterm labor (30% vs 9.7%; adjusted odds ratio [aOR] 2.42; 95% confidence interval [CI]: 1.35-4.36; P = 0.003), delivery before 34 gestational weeks (11.6% vs 0.6%; aOR 12.33; 95% CI: 3.32-45.78; P < 0.001), and pre-eclampsia (11.6% vs 2.8%; aOR 2.19; 95% CI: 1.01-4.76; P = 0.047). Newborns with CHD were significantly more likely to be small for gestational age (10.7% vs 5.2%; aOR 2.09; 95% CI: 1.11-3.94; P = 0.022). Intriguingly, a prenatal diagnosis of CHD was associated with a reduced risk of preterm delivery in affected pregnancies (P = 0.002). Pregnancies affected by isolated fetal CHD demonstrated a higher propensity for several adverse outcomes. These findings underscore the importance of prenatal CHD detection and tailored perinatal care to potentially improve both pregnancy outcomes and neonatal health.

Potential candidate maternal serum miRNAs for the diagnosis of fetal congenital heart disease.

Congenital heart disease (CHD) is one of the most significant birth defects leading to infant mortality worldwide. Circulating microRNAs (miRNAs) are emerging as novel biomarkers for the detection of cardiovascular diseases. In this study, we aimed to investigate the role of maternal serum miRNAs expression as biomarkers in the diagnosis and prediction of children with CHD. High-throughput sequencing of peripheral blood from pregnant women with abnormal and normal fetal hearts identified 1939 differentially expressed miRNAs, the first 11 of which were selected as predictive biomarkers of CHD. The expression of miRNAs in more clinical samples was then quantitatively verified by reverse transcriptase polymerase chain reaction and the correlation between abnormal miRNAs and CHD was analyzed. Two miRNAs (hsa-miR-3195 and hsa-miR-122-5p) were found to be significantly down-regulated in pregnant women with fetal CHD. By further bioinformatics analysis, we predicted that hsa-miR-3195 and hsa-miR-122-5p could induce CHD by influencing biometabolic processes. hsa-miR-3195 and hsa-miR-122-5p may serve as novel non-invasive biomarkers for prenatal detection of fetal CHD.

Data for AI in Congenital Heart Defects: Systematic Review.

Congenital heart disease (CHD) represents a significant challenge in prenatal care due to low prenatal detection rates. Artificial Intelligence (AI) offers promising avenues for precise CHD prediction. In this study we conducted a systematic review according to the PRISMA guidelines, investigating the landscape of AI applications in prenatal CHD detection. Through searches on PubMed, Embase, and Web of Science, 621 articles were screened, yielding 28 relevant studies for analysis. Deep Learning (DL) emerged as the predominant AI approach. Data types were limited to ultrasound and MRI sequences mainly. This comprehensive analysis provides valuable insights for future research and clinical practice in CHD detection using AI applications.

A Coarse-Fine Collaborative Learning Model for Three Vessel Segmentation in Fetal Cardiac Ultrasound Images.

Congenital heart disease (CHD) is the most frequent birth defect and a leading cause of infant mortality, emphasizing the crucial need for its early diagnosis. Ultrasound is the primary imaging modality for prenatal CHD screening. As a complement to the four-chamber view, the three-vessel view (3VV) plays a vital role in detecting anomalies in the great vessels. However, the interpretation of fetal cardiac ultrasound images is subjective and relies heavily on operator experience, leading to variability in CHD detection rates, particularly in resource-constrained regions. In this study, we propose an automated method for segmenting the pulmonary artery, ascending aorta, and superior vena cava in the 3VV using a novel deep learning network named CoFi-Net. Our network incorporates a coarse-fine collaborative strategy with two parallel branches dedicated to simultaneous global localization and fine segmentation of the vessels. The coarse branch employs a partial decoder to leverage high-level semantic features, enabling global localization of objects and suppression of irrelevant structures. The fine branch utilizes attention-parameterized skip connections to improve feature representations and improve boundary information. The outputs of the two branches are fused to generate accurate vessel segmentations. Extensive experiments conducted on a collected dataset demonstrate the superiority of CoFi-Net compared to state-of-the-art segmentation models for 3VV segmentation, indicating its great potential for enhancing CHD diagnostic efficiency in clinical practice. Furthermore, CoFi-Net outperforms other deep learning models in breast lesion segmentation on a public breast ultrasound dataset, despite not being specifically designed for this task, demonstrating its potential and robustness for various segmentation tasks.

Deep learning-based differentiation of ventricular septal defect from tetralogy of Fallot in fetal echocardiography images.

Congenital heart disease (CHD) seriously affects children's health and quality of life, and early detection of CHD can reduce its impact on children's health. Tetralogy of Fallot (TOF) and ventricular septal defect (VSD) are two types of CHD that have similarities in echocardiography. However, TOF has worse diagnosis and higher morality than VSD. Accurate differentiation between VSD and TOF is highly important for administrative property treatment and improving affected factors' diagnoses. TOF and VSD were differentiated using convolutional neural network (CNN) models that classified fetal echocardiography images. We collected 105 fetal echocardiography images of TOF and 96 images of VSD. Four CNN models, namely, VGG19, ResNet50, NTS-Net, and the weakly supervised data augmentation network (WSDAN), were used to differentiate the two congenital heart diseases. The performance of these four models was compared based on sensitivity, accuracy, specificity, and AUC. VGG19 and ResNet50 performed similarly, with AUCs of 0.799 and 0.802, respectively. A superior performance was observed with NTS-Net and WSDAN specific for fine-grained image categorization tasks, with AUCs of 0.823 and 0.873, respectively. WSDAN had the best performance among all models tested. WSDAN exhibited the best performance in differentiating between TOF and VSD and is worthy of further clinical popularization.

Prenatal detection rates for congenital heart disease using abnormal obstetrical screening ultrasound alone as indication for fetal echocardiography.

To determine the live born prenatal detection rate of significant congenital heart disease (CHD) in a large, integrated, multi-center community-based health system using a strategy of referral only of patients with significant cardiac abnormalities on obstetrical screening ultrasound for fetal echocardiography. Detection rates were assessed for screening in both radiology and maternal fetal medicine (MFM). The impact on fetal echocardiography utilization was also assessed. This was a retrospective cohort study using an electronic health record, outside claims databases and chart review to determine all live births between 2016 and 2020 with postnatally confirmed sCHD that were prenatally detectable and resulted in cardiac surgery, intervention, or death within 1year. There were 214,486 pregnancies resulting in live births. Prenatally detectable significant CHD was confirmed in 294 infants. Of those 183 were detected for an overall live-born detection rate of 62%. Detection rates in MFM were 75% and in radiology were 52%. The number of fetal echocardiograms needed to detect (NND) sCHD was 7. A focus on quality and standardization of obstetrical screening ultrasound with referral to fetal echocardiography for cardiac abnormalities alone achieves benchmark targets for live-born detection of significant CHD requiring fewer fetal echocardiograms.

Development and Validation of a Deep-Learning Network for Detecting Congenital Heart Disease from Multi-View Multi-Modal Transthoracic Echocardiograms.

Early detection and treatment of congenital heart disease (CHD) can significantly improve the prognosis of children. However, inexperienced sonographers often face difficulties in recognizing CHD through transthoracic echocardiogram (TTE) images. In this study, 2-dimensional (2D) and Doppler TTEs of children collected from 2 clinical groups from Beijing Children's Hospital between 2018 and 2022 were analyzed, including views of apical 4 chamber, subxiphoid long-axis view of 2 atria, parasternal long-axis view of the left ventricle, parasternal short-axis view of aorta, and suprasternal long-axis view. A deep learning (DL) framework was developed to identify cardiac views, integrate information from various views and modalities, visualize the high-risk region, and predict the probability of the subject being normal or having an atrial septal defect (ASD) or a ventricular septaldefect (VSD). A total of 1,932 children (1,255 healthy controls, 292 ASDs, and 385 VSDs) were collected from 2 clinical groups. For view classification, the DL model reached a mean [SD] accuracy of 0.989 [0.001]. For CHD screening, the model using both 2D and Doppler TTEs with 5 views achieved a mean [SD] area under the receiver operating characteristic curve (AUC) of 0.996 [0.000] and an accuracy of 0.994 [0.002] for within-center evaluation while reaching a mean [SD] AUC of 0.990 [0.003] and an accuracy of 0.993 [0.001] for cross-center test set. For the classification of healthy, ASD, and VSD, the model reached the mean [SD] accuracy of 0.991 [0.002] and 0.986 [0.001] for within- and cross-center evaluation, respectively. The DL models aggregating TTEs with more modalities and scanning views attained superior performance to approximate that of experienced sonographers. The incorporation of multiple views and modalities of TTEs in the model enables accurate identification of children with CHD in a noninvasive manner, suggesting the potential to enhance CHD detection performance and simplify the screening process.

The Value of Fetal Echocardiography for Early Detection of Congenital Heart Defects: A Report of our Experience in a Great Referral Heart Center in Iran

Background: Prenatal diagnosis of cardiac defects in fetus is of paramount importance to enable a better monitoring of heart defects and to allow planning a proper management. We reported our experience on fetal heart echocardiography in a great referral heart center in Iran with the approach to assessment of the diagnostic value of this tool for detecting Congenital Heart Diseases (CHDs). Methods: This retrospective cohort study included 234 pregnant women attended at a great referral heart center in Tehran, Iran between 2012 and 2016 for heart abnormalities screening. All women were evaluated by trans-abdominal fetal echocardiography from 12 weeks of gestation to term and after birth. Results: Fetal echocardiography showed different types of CHDs in 5.9%. CHDs detected in 9.9% of neonates by echocardiography after birth. The most common heart abnormalities detected was VSD in 4.3%. Considering echocardiography findings after birth as the reference, fetal echocardiography had a sensitivity of 92.8%, a specificity of 66.6%, a positive predictive value of 59.8%, a negative predictive value of 86.6%, and an accuracy of 84.1% for detection of heart defects. Conclusion: Fetal heart echocardiography is a sensitive tool for detection of CHDs, but its specificity is moderate.

Abstract 13945: Evaluation of a Deep Neural Network for Detection of D-Transposition of the Great Arteries on Fetal Echocardiograms

Introduction: While prenatal detection of congenital heart disease (CHD) has improved over the years, a significant proportion of CHD remains unrecognized on fetal ultrasound. Prenatal identification of d-transposition of the great arteries (d-TGA) in particular would significantly alter delivery planning and postnatal course. Research Questions: We investigated whether a deep neural network (DNN) can accurately detect d-TGA on fetal echocardiograms. Aim: To evaluate a DNN for detection of d-TGA on second (2T) or third (3T) trimester fetal echocardiograms. Methods: Patients with a singleton pregnancy with a 2T or 3T fetal echocardiogram performed between Jan 1, 2022 and May 1, 2023 at one center were retrospectively evaluated. Cases of d-TGA and negative cases referred for family history of CHD or increased nuchal translucency were included. Cases were reviewed by 3 pediatric cardiologists to confirm that the recorded ultrasound clips were sufficient to diagnose the presence or absence of d-TGA, and were excluded otherwise. The DNN was trained on patients not included in the evaluation to detect the absence of the great artery cross-over pattern, a distinct finding in d-TGA. The DNN outputs the presence or absence of this finding using all recorded video clips of an examination. Results: We included 18 positive and 99 negative cases. The DNN detected suspicion for d-TGA with an AUC of 91.4%, sensitivity of 83.3% (60.8-94.2, 95%CI) and specificity of 92.9% (86.1-96.5, 95%CI). Conclusions: D-TGA can be accurately detected on fetal echocardiograms using a DNN. These promising results lay the foundation for reliable, computer-aided detection of CHD on screening fetal ultrasounds.

Pattern of Congenital Heart Diseases in Children in a Tertiary Care Hospital of Bangladesh

The most common congenital anomaly and a major contributor to infant and toddler mortality is congenital heart disease (CHD). Although it has high mortality rate but majority of the cases are asymptomatic during early life and usually discovered during routine checkup. To determine the incidence and pattern of congenital heart disease in children in a tertiary care hospital of Bangladesh. This cross-sectional study was conducted among children aged 1 day to 10 years old with cardiac problems who were hospitalized to the Paediatric department of Rajshahi Medical College Hospital, Rajshahi, Bangladesh from August 2018 to August 2021. Early detection of CHD is possible by thoroughly analyzing heart murmurs, clinical manifestations, and outcomes. The data were analyzed utilizing IBM SPSS (version-23). A total of 3512 children under the age of 10 years were evaluated. Here 1.54% of the patients had a CHD murmur. In this study, the most common type of congenital heart illness was a ventricular septal defect (VSD) (37.05%). Atrial septal defect (ASD) (24.07%) was found second most common CHD. Rapid breathing (63%) and poor growth (44.4%) were major clinical presentation. According to types of murmur among patients, 27(50%) had pansystolic murmur, 15(27.77%) had ejection systolic murmur and 12(22.23%) had continuous murmur. Congenital heart disease is a major health concern for Paediatricians due to its significant health hazards and high mortality rate. Early identification of congenital cardiac disease is crucial for preventing complications, lowering mortality, and for proper therapy. J. Bio-Sci. 30(2): 39-45, 2022

Fetal electrocardiography and artificial intelligence for prenatal detection of congenital heart disease.

This study aims to investigate non-invasive electrocardiography as a method for the detection of congenital heart disease (CHD) with the help of artificial intelligence. An artificial neural network was trained for the identification of CHD using non-invasively obtained fetal electrocardiograms. With the help of a Bayesian updating rule, multiple electrocardiographs were used to increase the algorithm's performance. Using 122 measurements containing 65 healthy and 57 CHD cases, the accuracy, sensitivity, and specificity were found to be 71%, 63%, and 77%, respectively. The sensitivity was however 75% and 69% for CHD cases requiring an intervention in the neonatal period and first year of life, respectively. Furthermore, a positive effect of measurement length on the detection performance was observed, reaching optimal performance when using 14 electrocardiography segments (37.5 min) or more. A small negative trend between gestational age and accuracy was found. The proposed method combining recent advances in obtaining non-invasive fetal electrocardiography with artificial intelligence for the automatic detection of CHD achieved a detection rate of 63% for all CHD and 75% for critical CHD. This feasibility study shows that detection rates of CHD might improve by using electrocardiography-based screening complementary to the standard ultrasound-based screening. More research is required to improve performance and determine the benefits to clinical practice.

Role of Pulse Oximetry as a Screening Tool for the Detection of Congenital Heart Disease in Newborn Babies

Background: Congenital heart disease (CHD) is one of the commonly seen malformations, with incidence varying from 7 to 8/1000 live birth. Causes are multifactorial. Routine examination of neonates may miss more than 50% of cases. Pulse oximetry is a simple, noninvasive, bedside test which estimates the percentage of oxygen bound to hemoglobin (oxygen saturation [SpO2]). Detection of critical CHD (CCHD) has been possible with SpO2 screening. Many countries included SpO2 as part of newborn screening due to this. As a primary approach, both pre- and post-ductal extremity SpO2 is measured after 24 h of life. Echocardiography (ECHO) will be done on neonates with SpO2 readings &lt;95%. Neonates born in high-altitude regions, studies have suggest to use adjusted threshold values. In India, there are limited studies. Aims: The aim of this study was to determine the usefulness of pulse oximetry as a screening tool for early detection of CHD in otherwise asymptomatic newborns. To determine, the accuracy of SpO2 for detecting clinically unrecognized CCHD in newborns. Materials and Methods: This is a prospective observational study done in the department of pediatrics in a tertiary hospital. The study was conducted over 12 months. During the study period, all neonates born who fulfilled the inclusion criteria were included in the study. After 24 h of life, neonates were examined clinically and the pre- and post-ductal SpO2 was measured. Neonates with SpO2 &lt;90% in room air were excluded from the study. If the SpO2 was between 90% and 94%, clinical examination was repeated, if suspicious of CHD, they were referred for ECHO. If there was no suspicion of CHD, SpO2 was repeated after 6 h and ECHO was done if SpO2 ≤95. The difference of SpO2 &gt;3% between the right upper limb and right lower limb was considered positive. Positive neonates were evaluated with two-dimensional echocardiograph. Inclusion criteria - All hemodynamically stable neonates were born during the study period. Exclusion criteria (1) Antenatally diagnosed cardiac anomalies, (2) Outborn neonates, (3) Parents/guardians are not willing to participate in the study and/or further investigation, (4) Sick neonates and those with SpO2 &lt;90% at birth. Results: 1117 (83.7%) were eligible for the study out of 1333 neonates born. 669 (59.9%) were born by cesarean section and 448 (40.1%) by vaginal delivery. 996 (89.2%) were born at term (≥37 weeks of gestation) and 121 (10.8%) were preterm (&lt;37 weeks of gestation). The male-to-female ratio was 1.03:1. The mean birth weight of the neonates was 2.91 ± 0.46 kg (mean ± standard deviation). The mean SPO2 in the right upper limb was 96.62 ± 1.73, in the right lower limb was 96.87 ± 1.76, in the left upper limb was 96.59 ± 1.90, and in the left lower limb was 97.06 ± 1.74. The average SPO2 difference between the right upper limb and right lower limb was 1.04 ± 1.07. Based on SpO2, 858 (76.8%) cases were not suspected of having CHD and 259 (23.3%) were suspected of having CHD and were evaluated with ECHO. Six (0.5%) neonates had CHD in whom echo was done. In our study, for detecting CCHD, SPO2 cutoff value of ≤90% showed 90% sensitivity, 99.94% specificity, 75% positive predictive value, and 99.98% negative predictive value. Conclusions: This study emphasizes noninvasive SPO2 as reliable and feasible, with good negative predictive value screening for CHD in neonates.

Cardiac problems in the fetus: a review for pediatric providers.

The aim of this study was to provide pediatric providers with a review of the diagnosis and management of fetal cardiac disease in the current era. Prenatal detection of congenital heart disease (CHD) has improved but is still imperfect. In experienced hands, fetal echocardiography can detect severe CHD as early as the first trimester and a majority of more subtle conditions in the second and third trimesters. Beyond detection, a prenatal diagnosis allows for lesion-specific counseling for families as well as for development of a multidisciplinary perinatal management plan, which may involve in-utero treatment. Given the diversity of cardiac diagnoses and the rarity of some, collaborative multicenter fetal cardiac research has gained momentum in recent years. Accurate diagnosis of fetal cardiac disease allows for appropriate counseling, pregnancy and delivery planning, and optimization of immediate neonatal care. There is potential for improving fetal CHD detection rates. Fetal interventions are available for certain conditions, and fetal and pediatric cardiac centers have developed management plans specific to the expected postnatal physiology.

ISUOG Practice Guidelines (updated): fetal cardiac screening.

ISUOG Practice Guidelines (updated): fetal cardiac screening.

WEBINAR TRAINING OF EARLY DETECTION OF CONGENITAL HEART DISEASE FOLLOWED BY ECHOCARDIOGRAPHY SCREENING IN BOJONEGORO, EAST JAVA, INDONESIA

Introduction: Congenital heart disease (CHD) affects approximately 0.8% to 1.2% with incidence rate of mortality was 81 cases per 100,000 live births. Basically, CHD is a structural abnormality of the heart and (or) great vessels that is present at birth. Limited knowledge among health workers for the etiologist, risk factors, and the high heterogeneity in CHD constitutes the major obstacles for prevention and early detection. The educational activities aim to increase the medical personnel's knowledge to conduct early detection and management of CHD in Bojonegoro. Materials and Methods: These activities consisted of training followed by echocardiography screening. The training was implemented through webinar with health workers participants in Bojonegoro. The topics contained how to detect, diagnosis and therapy, and cases simulation of CHD. Pre and Post-tests were used to evaluate knowledge level. The visitation of echocardiographic screening by Pediatric cardiologists was held a week later in Bojonegoro. Results: There were 542 participants during the webinar training. The mean score of pre-tests was 8.02/15 with only 42 participants who got the perfect score. At the end of the webinar, the mean score of post-tests was 11.38/15 with 82 participants who nally got the perfect score. Total 29 children were screened by echocardiography examination. There were 19 (65.51%) children diagnosed with acyanotic CHD, 4 (13.79%) children with cyanotic CHD, and 6 (20.7%) children were normal. Conclusion: Webinar training can improve the knowledge among health workers about early detection of CHD and there were 79.31% children diagnosed with CHD among screening participants in Bojonegoro.

Deep learning model for prenatal congenital heart disease (CHD) screening generalizes to the community setting and outperforms clinical detection.

Congenital heart defects (CHD) are still missed despite nearly universal prenatal ultrasound screening programs, which may result in severe morbidity or even death. Deep machine learning (DL) can automate image recognition from ultrasound. The aim of this study was to apply a previously developed DL model trained on images from a tertiary center, to fetal ultrasound images obtained during the second-trimester standard anomaly scan in a low-risk population. All pregnancies with isolated severe CHD in the Northwestern region of the Netherlands between 2015 and 2016 with available stored images were evaluated, as well as a sample of normal fetuses' examinations from the same region. We compared initial clinical diagnostic accuracy (made in real time), model accuracy, and performance of blinded human experts with access only to the stored images (like the model). We analyzed performance by study characteristics such as duration, quality (independently scored by study investigators), number of stored images, and availability of screening views. A total of 42 normal fetuses and 66 cases of isolated CHD at birth were analyzed. Of the abnormal cases, 31 were missed and 35 were detected at the time of the clinical anatomy scan (sensitivity 53 percent). Model sensitivity and specificity was 91 and 93 percent, respectively. Blinded human experts (n=3) achieved sensitivity and specificity of 55±10 percent (range 47-67 percent) and 71±13 percent (range 57-83 percent), respectively. There was a statistically significant difference in model correctness by expert-grader quality score (p=0.04). Abnormal cases included 19 lesions the model had not encountered in its training; the model's performance (15/19 correct) was not statistically significantly different on previously encountered vs. never before seen lesions (p=0.07). A previously trained DL algorithm out-performed human experts in detecting CHD in a cohort in which over 50 percent of CHD cases were initially missed clinically. Notably, the DL algorithm performed well on community-acquired images in a low-risk population, including lesions it had not been previously exposed to. Furthermore, when both the model and blinded human experts had access to stored images alone, the model outperformed expert humans. Together, these findings support the proposition that use of DL models can improve prenatal detection of CHD.

Revisiting the role of electrocardiography for screening of congenital heart disease in young adults

Introduction: Congenital heart disease (CHD) is the most prevalent congenital disability found in newborns. Many cases remain unknown until complications occur, usually in young adults. Transthoracic and Doppler echocardiography are modalities of choice for CHD detection, but these are limited in Indonesia. Electrocardiography (ECG) is a widely available and low-cost test. This study investigated the role of ECG as a screening modality for CHD. Methods: A cross-sectional study was held at Atma Jaya School of Medicine and Health Science from August to November 2019. Participants were students aged 18 years old or more. Exclusion criteria was previously detected for CHD. Data were collected through history taking, anthropometrics, blood pressure measurement, and 12 leads ECG. ECG results were interpreted independently by two cardiologists to minimize observer bias. Results: A total of 193 students, 78 male and 115 female, participated. The mean age was 19.22±1.85 years. ECG abnormalities were discovered in 57 (29.5%) participants:15 with crochetage sign, 13 right axis deviation, three left axis deviation, nine right ventricle hypertrophy, and nine left ventricle hypertrophy, six left bundle branch block, and two defective T wave. Further evaluation was done with echocardiography in 20 participants, which resulted in one participant having mitral valve prolapsed (MVP). Conclusion: ECG could detect the characteristic patterns suggestive of CHD, but ECG alone is insufficient to confirm cardiac structural abnormalities.

Antenatal Diagnosis of Congenital Heart Disease in the State of Alabama: Challenges and Opportunities

Background: Antenatal diagnosis of congenital heart disease (CHD) has positive effects on clinical outcomes. However, the prevalence of antenatal diagnosis remains low. The objective of this study is to measure the prevalence and distribution of antenatal CHD diagnosis in Alabama. Methods: Data were obtained from the Society of Thoracic Surgeons national database on surgeries for children with CHD and stratified by antenatal diagnosis. Demographic, census, and hospital data were compared between pre- and post-natally diagnosed cases. Cases were mapped by ZIP code to describe the distribution for the prevalence of CHD antenatal diagnosis. Results: From 2013-2019, 1733 children required cardiac repair for CHD, 20% were diagnosed prenatally and 80% postnatally. Only 43% of those with Hypoplastic Left Heart Syndrome, 22% with Tetralogy of Fallot and 26% with Transposition of the Great Arteries had a prenatal diagnosis. No factors were associated with receiving a prenatal diagnosis. Lastly, 82% of ZIP codes were below the reported national average for antenatal CHD diagnosis. Conclusion: Prenatal detection of CHD in Alabama is lower than the reported national averages. More studies are needed to explore reasons for missed antenatal CHD diagnoses. Mitigation of factors related to low antenatal diagnosis can support patients and improve neonatal outcomes.

Racial and Ethnic Disparities in U.S Children Undergoing Surgery for Congenital Heart Disease: A Narrative Literature Review.

Congenital Heart Disease (CHD) is a significant source of pediatric morbidity and mortality. As in other fields of medicine, studies have demonstrated racial and ethnic disparities in congenital heart disease outcomes. The cause of these outcome disparities is multifactorial, involving biological, behavioral, environmental, sociocultural, and systemic medical factors. Potential contributors include differences in preoperative illness severity secondary to coexisting medical conditions, differences in the rate of prenatal and early postnatal detection of CHD, and delayed access to care, as well as discrepancies in socioeconomic and insurance status, and systemic disparities in hospital care. Understanding the factors that contribute to these disparities is an essential step towards developing strategies to address them. As stewards of the perioperative surgical home, anesthesiologists have an important role in developing institutional policies that mitigate racial disparities. Here, we provide a thorough narrative review of recent research concerning perioperative factors contributing to surgical outcomes disparities for children of all ages with CHD, examine potentially modifiable contributing factors, discuss avenues for future research, and suggest strategies to address disparities both locally and nationally.

The use of cardiac ultrasound imaging in first-trimester prenatal diagnosis of congenital heart diseases.

This study aims to explore ultrasound (US) cardiac and echocardiographic features that may enable the early diagnosis of various major congenital heart diseases (CHDs). Focusing on providing useful US tools for this assessment, high resolution of US cardiac images of various CHDs, such as hypoplastic left heart syndrome, conotruncal anomalies, and univentricular heart, were evaluated. Results show that early US detection of most major CHDs is feasible during first-trimester ultrasonography cardiac evaluation. Concerns about safety issues, findings on early fetal cardiovascular hemodynamics, and cardiac lesions that can progress during the course of pregnancy were also discussed.