Classification Of Congenital Heart Disease Research Articles

Neonatal outcomes in preterm infants with severe congenital heart disease: a national cohort analysis.

Prematurity and congenital heart disease (CHD) are the leading causes of neonatal mortality and morbidity. Limited data are available about the outcomes of premature infants with severe CHD. We queried The National Inpatient Database using ICD-10 codes for premature patients (<37 weeks) with severe CHD from 2016 to 2020. Severe CHDs were grouped into three categories: A. left-sided lesions with impaired systemic output, B. Cyanotic CHD, and C. Shunt lesions with pulmonary overcirculation. Patients with isolated atrial or ventricular septal defects and patent ductus arteriosus were excluded. We also excluded patients with chromosomal abnormalities and major congenital anomalies. Patients' demographics, clinical characteristics, and outcomes were evaluated by comparing premature infants with vs. without CHD adjusting for gestational age (GA), birth weight, and gender. A total of 27710 (1.5%) out of 1,798,245 premature infants had severe CHD. This included 27%, 58%, and 15% in groups A, B, and C respectively. The incidence of severe CHD was highest between 25 and 28 weeks of gestation and decreased significantly with increasing GA up to 36 weeks (p < 0.001). Premature infants with severe CHD had a significantly higher incidence of neonatal morbidities including necrotizing enterocolitis (NEC) [OR = 4.88 (4.51-5.27)], interventricular hemorrhage [OR = 6.22 (5.57-6.95)], periventricular leukomalacia [OR = 3.21 (2.84-3.64)] and bronchopulmonary dysplasia [OR = 8.26 (7.50-10.06) compared to preterm infants of similar GA without CHD. Shunt lesions had the highest incidence of NEC (8.5%) compared to 5.3% in cyanotic CHD and 3.7% in left-sided lesions (p < 0.001). Mortality was significantly higher in premature infants with CHD compared to control [11.6% vs. 2.5%, p < 0.001]. Shunt lesions had significantly higher mortality (11.0%) compared to those with left-sided lesions (8.3%) and cyanotic CHD (6.4%), p < 0.001. Premature infants with severe CHD are at high risk of neonatal morbidity and mortality. Morbidity remains increased across all GA groups and in all CHD categories. This significant risk of adverse outcomes is important to acknowledge when managing this patient population and when counseling their families. Future research is needed to examine the impact of specific rather than categorized congenital heart defects on neonatal outcomes.

Outlet Ventricular Septal Defect: A Case Report on the Importance of Multi-Window Sonographic Assessment

Congenital heart disease (CHD) is the existence of structural heart problems since the formation of the heart prebirth. There are different categories of CHD, all having their own unique frequency and severity characteristics. One type of CHD is a ventricular septal defect (VSD), more specifically outlet VSD. A VSD is the most common congenital heart defect; however, their presentation can be quite complex. Color and spectral Doppler transthoracic echocardiography (TTE) are valuable tools used in the diagnosis of VSDs. The often-challenging presentation of these defects requires sonographers to confirm their existence through the parasternal long-axis, parasternal short-axis, apical four chamber, apical three chamber, and apical five chamber scanning windows. Although VSDs are the most common congenital heart defect, their wide-ranging uniqueness and location along the interventricular septum (IVS) to assess all areas along the IVS on all exams, specifically those that demonstrate left-to-right shunting. The objective of this case report was to stress the importance of multi-window assessment during the sonographer’s interrogation of outlet VSDs, due to the potential implications on adjacent anatomy and the potential for surgical intervention.

A Novel Machine Learning-Based Prediction Method for Early Detection and Diagnosis of Congenital Heart Disease Using ECG Signal Processing

Congenital heart disease (CHD) represents a multifaceted medical condition that requires early detection and diagnosis for effective management, given its diverse presentations and subtle symptoms that manifest from birth. This research article introduces a groundbreaking healthcare application, the Machine Learning-based Congenital Heart Disease Prediction Method (ML-CHDPM), tailored to address these challenges and expedite the timely identification and classification of CHD in pregnant women. The ML-CHDPM model leverages state-of-the-art machine learning techniques to categorize CHD cases, taking into account pertinent clinical and demographic factors. Trained on a comprehensive dataset, the model captures intricate patterns and relationships, resulting in precise predictions and classifications. The evaluation of the model’s performance encompasses sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve. Remarkably, the findings underscore the ML-CHDPM’s superiority across six pivotal metrics: accuracy, precision, recall, specificity, false positive rate (FPR), and false negative rate (FNR). The method achieves an average accuracy rate of 94.28%, precision of 87.54%, recall rate of 96.25%, specificity rate of 91.74%, FPR of 8.26%, and FNR of 3.75%. These outcomes distinctly demonstrate the ML-CHDPM’s effectiveness in reliably predicting and classifying CHD cases. This research marks a significant stride toward early detection and diagnosis, harnessing advanced machine learning techniques within the realm of ECG signal processing, specifically tailored to pregnant women.

Machine Learning Informed Diagnosis for Congenital Heart Disease in Large Claims Data Source

BackgroundWith an increasing interest in using large claims databases in medical practice and research, it is a meaningful and essential step to efficiently identify patients with the disease of interest. ObjectivesThis study aims to establish a machine learning (ML) approach to identify patients with congenital heart disease (CHD) in large claims databases. MethodsWe harnessed data from the Quebec claims and hospitalization databases from 1983 to 2000. The study included 19,187 patients. Of them, 3,784 were labeled as true CHD patients using a clinician developed algorithm with manual audits considered as the gold standards. To establish an accurate ML-empowered automated CHD classification system, we evaluated ML methods including Gradient Boosting Decision Tree, Support Vector Machine, Decision tree, and compared them to regularized logistic regression. The Area Under the Precision Recall Curve was used as the evaluation metric. External validation was conducted with an updated data set to 2010 with different subjects. ResultsAmong the ML methods we evaluated, Gradient Boosting Decision Tree led the performance in identifying true CHD patients with 99.3% Area Under the Precision Recall Curve, 98.0% for sensitivity, and 99.7% for specificity. External validation returned similar statistics on model performance. ConclusionsThis study shows that a tedious and time-consuming clinical inspection for CHD patient identification can be replaced by an extremely efficient ML algorithm in large claims database. Our findings demonstrate that ML methods can be used to automate complicated algorithms to identify patients with complex diseases.

A deep learning-based method for pediatric congenital heart disease detection with seven standard views in echocardiography

BackgroundWith the aggregation of clinical data and the evolution of computational resources, artificial intelligence-based methods have become possible to facilitate clinical diagnosis. For congenital heart disease (CHD) detection, recent deep...

Oligogenic Architecture of Rare Noncoding Variants Distinguishes 4 Congenital Heart Disease Phenotypes.

Congenital heart disease (CHD) is highly heritable, but the power to identify inherited risk has been limited to analyses of common variants in small cohorts. We performed reimputation of 4 CHD cohorts (n=55 342) to the TOPMed reference panel (freeze 5), permitting meta-analysis of 14 784 017 variants including 6 035 962 rare variants of high imputation quality as validated by whole genome sequencing. Meta-analysis identified 16 novel loci, including 12 rare variants, which displayed moderate or large effect sizes (median odds ratio, 3.02) for 4 separate CHD categories. Analyses of chromatin structure link 13 of the genome-wide significant loci to key genes in cardiac development; rs373447426 (minor allele frequency, 0.003 [odds ratio, 3.37 for Conotruncal heart disease]; P=1.49×10-8) is predicted to disrupt chromatin structure for 2 nearby genes BDH1 and DLG1 involved in Conotruncal development. A lead variant rs189203952 (minor allele frequency, 0.01 [odds ratio, 2.4 for left ventricular outflow tract obstruction]; P=1.46×10-8) is predicted to disrupt the binding sites of 4 transcription factors known to participate in cardiac development in the promoter of SPAG9. A tissue-specific model of chromatin conformation suggests that common variant rs78256848 (minor allele frequency, 0.11 [odds ratio, 1.4 for Conotruncal heart disease]; P=2.6×10-8) physically interacts with NCAM1 (PFDR=1.86×10-27), a neural adhesion molecule acting in cardiac development. Importantly, while each individual malformation displayed substantial heritability (observed h2 ranging from 0.26 for complex malformations to 0.37 for left ventricular outflow tract obstructive disease) the risk for different CHD malformations appeared to be separate, without genetic correlation measured by linkage disequilibrium score regression or regional colocalization. We describe a set of rare noncoding variants conferring significant risk for individual heart malformations which are linked to genes governing cardiac development. These results illustrate that the oligogenic basis of CHD and significant heritability may be linked to rare variants outside protein-coding regions conferring substantial risk for individual categories of cardiac malformation.

Neuropsychological features in children and adults with congenital heart disease: an exploratory data analysis

ABSTRACT We aimed to recognize clinically meaningful patterns among patients with congenital heart disease to support clinical decision-making and better classification in practice. This research was a secondary analysis of data from the Congenital Heart Disease Genetic Network Study conducted from December 2010 to November 2014 in the United States. The analytic dataset included 6002 patients ≥1 year of age with non-syndromic congenital heart disease. For each patient, features included demographic, clinical, maternal and paternal characteristics. We clustered patients to identify subgroups that shared similarities in their clinical features. The performance of the clustering algorithm was evaluated with a random forest. Next, we used the apriori algorithm to generate clinical rules from patients’ characteristics. The clustering algorithm identified two discernible groups of patients. The two classes of patients were different in maternal diabetes and in neuropsychological indicators [Accuracy (95% CI) = 97.1% (96.2, 97.8), area under the ROC curve = 96.8%]. Our rule extraction suggested the presence of clinical pictures with high lift values among patients with maternal diabetes or with seizure, depression, attention-deficit hyperactivity disorder, anxiety, developmental delay, learning disability and speech problem. Beyond the age of 1 year, maternal diabetes and neuropsychological characteristics identify two clusters of patients with congenital heart disease. These characteristics have the potential of being incorporated into the current systems for the classification of congenital heart disease.

Acute Kidney Injury Predictors and Outcomes after Cardiac Surgery in Children with Congenital Heart Disease: An Observational Cohort Study.

Acute Kidney Injury (AKI) commonly complicates cardiac surgery in children with congenital heart disease (CHD). In this study we assessed incidence, risk factors, and outcomes of postoperative AKI, while testing the hypothesis that, depending on the underlying diagnosis, there would be significant differences in AKI incidence among different diagnostic groups. We conducted an observational cohort study of children with CHD undergoing cardiac surgery in a single tertiary center between January 2019 and August 2021 (n = 362). Kidney Disease Improving Global Outcome (KDIGO) criteria were used to determine the incidence of postoperative AKI. Diagnosis was incorporated into multivariate models using an anatomic-based CHD classification system. Overall survival was estimated using Kaplan–Meier curves. Log-rank test and adjusted Cox proportional hazard modelling were used to test for differences in survival distributions and determine AKI effect on survival function, respectively. AKI occurred in 70 (19.3%), with 21.4% in-hospital mortality for AKI group. Younger age, lower weight, longer cardiopulmonary bypass time, preoperative mechanical ventilation and diagnostic category were associated with postoperative AKI. Resolution rate was 92.7% prior to hospital discharge for survivors. AKI was associated with longer duration of mechanical ventilation, ICU and hospital length of stay. AKI patients had significantly higher probability of all-cause mortality postoperatively when compared to the non-AKI group (log-rank test, p < 0.001). Adjusted hazard ratio for AKI versus non-AKI group was 11.08 (95% CI 2.45–50.01; p = 0.002). Diagnostic category was associated with cardiac surgery-related AKI in children with CHD, a finding supporting the development of lesion specific models for risk stratification. Postoperative AKI had detrimental impact on clinical outcomes and was associated with decreased survival to hospital discharge.

High-risk genes involved in common septal defects of congenital heart disease

The septation defect is one of the main categories of congenital heart disease (CHD). They can affect the septation of the atria leading to atrial septal defect (ASD), septation of ventricles leading to ventricular septal defect (VSD), and formation of the central part of the heart leading to atrioventricular septal defect (AVSD). Disruption of critical genetic factors involved in the proper development of the heart structure leads to CHD manifestation. Because of this, to identify the high-risk genes involved in common septal defects, a comprehensive search of the literature with the help of databases and the WebGestalt analysis tool was performed. The high-risk genes identified in the analysis were checked in 16 Indian whole-exome sequenced samples, including 13 VSD and three Tetralogy of Fallot for in silico validation. This data revealed three variations in GATA4, i.e., c.C1223A at exon 6: c.C602A and c.C1220A at exon 7; and one variation in MYH6, i.e., c.G3883C at exon 28 in two VSD cases. This study supports previously published studies that suggested GATA4 and MYH6 as the high-risk genes responsible for septal defects. Thus, this study contributes to a better understanding of the genes involved in heart development by identifying the high-risk genes and interacting proteins in the pathway.

Automatic pediatric congenital heart disease classification based on heart sound signal

Congenital heart diseases (CHD) are the most common birth defects, and the early diagnosis of CHD is crucial for CHD therapy. However, there are relatively few studies on intelligent auscultation for pediatric CHD, due to the fact that effective cooperation of the patient is required for the acquisition of useable heart sounds by electronic stethoscopes, yet the quality of heart sounds in pediatric is poor compared to adults due to the factors such as crying and breath sounds. This paper presents a novel pediatric CHD intelligent auscultation method based on electronic stethoscope. Firstly, a pediatric CHD heart sound database with a total of 941 PCG signal is established. Then a segment-based heart sound segmentation algorithm is proposed, which is based on PCG segment to achieve the segmentation of cardiac cycles, and therefore can reduce the influence of local noise to the global. Finally, the accurate classification of CHD is achieved using a majority voting classifier with Random Forest and Adaboost classifier based on 84 features containing time domain and frequency domain. Experimental results show that the performance of the proposed method is competitive, and the accuracy, sensitivity, specificity and f1-score of classification for CHD are 0.953, 0.946, 0.961 and 0.953 respectively.

Brain Oxygen Perfusion and Oxidative Stress Biomarkers in Fetuses with Congenital Heart Disease-A Retrospective, Case-Control Pilot Study.

Fetuses with congenital heart disease (CHD) have circulatory changes that may lead to predictable blood flow disturbances that may affect normal brain development. Hypoxemia and hypoperfusion may alter the redox balance leading to oxidative stress (OS), that can be assessed measuring stable end-products. OS biomarkers (OSB) were measured in amniotic fluid in fetuses with (n = 41) and without CHD (n = 44) and analyzed according to aortic flow, expected cyanosis after birth, and a CHD classification derived from this. Birth head circumference (HC) was used as a neurodevelopment biomarker. CHD fetuses had higher levels of ortho-Tyrosine (o-Tyr) than controls (p = 0.0003). There were no differences in o-Tyr levels considering aortic flow obstruction (p = 0.617). Fetuses with expected extreme cyanosis presented the highest levels of o-Tyr (p = 0.003). Among groups of CHD, fetuses without aortic obstruction and extreme cyanosis had the highest levels of o-Tyr (p = 0.005). CHD patients had lower HC than controls (p = 0.023), without correlation with OSB. Patients with HC < 10th percentile, presented high levels of o-Tyr (p = 0.024). Fetuses with CHD showed increased OSB and lower HC when compared to controls, especially those with expected extreme cyanosis. Our results suggest that increased levels of OSB are more influenced by the effect of low oxygenation than by aortic flow obstruction. Future studies with larger sample size are needed to further investigate the role of OSB as an early predictor of neurodevelopmental problems in CHD survivors.

Application of permutation entropy and factorization network in the classification of congenital heart disease

AbstractOwing to the nonstationary feature of heart sound signal which is often disturbed by noise, a method based on permutation entropy and factorization network in the classification of heart sound of congenital heart disease is proposed. Firstly, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) of normal and abnormal heart sound signals is performed to obtain several intrinsic mode function (IMF) components. Secondly, the optimal parameters in the permutation entropy algorithm are determined by C_C algorithm, and then according to the algorithm, the entropy values of the IMF components of each order are calculated to form high‐dimensional eigenvectors. Finally, the recommended models of factorization machines (FMs) are used to classify the heart sounds and the generalisation performance of the FM network model are evaluated from accuracy and Area Under ROC Curve (AUC). The results show that the FM accuracy is 0.865, the AUC value is 0.887; the SVM accuracy is 0.812, and the AUC value is 0.805.

C94. The characteristics and outcome of pediatric patients with congenital heart disease (CHD) infected by coronavirus disease 2019 (COVID-19) between march 2020 to february 2021 at sanglah hospital: a case series of 12 patients

BackgroundCoronavirus disease 2019 (COVID-19) has a complex connection to cardiovascular system. COVID-19 infection has been proven to cause significant dysfunction in lung, heart, and endothelial cells. Therefore, patients with complex congenital heart lesions have a high risk of developing severe complications and worse outcomes. This paper aims to determine the characteristics and outcome of pediatric congenital heart disease (CHD) patients infected by COVID-19 at Sanglah hospital.Case SummaryWithin the period of March 2020 to February 2021, there were 12 cases of pediatric CHD accompanied by COVID-19 infection, which consists of 8 females and 4 males within the age of 6 days to 18 years. Based upon symptomatic CHD classification, there were 4 cyanotic CHD cases and 1 acyanotic case, respiratory problems were reported in all 5 cases. These are pneumonia in 4 cases and respiratory distress caused by hyaline membrane disease (HMD) in 1 case. 2 cases require mechanical ventilation. The outcome of all inpatient cases were favorable.DiscussionTo date, there are only few scientific evidence detailing how COVID-19 affects CHD. We were unable to calculate the correlation analysis since the population sample was too small. The most severe cases that requires mechanical ventilation had comorbidities in the form of malnutrition and down syndrome. From the perspective of pathophysiology, one of the most important factor determining outcome was the severity of CHD itself. Other contributing factors were COVID-19’s destructive effect on heart tissue and co-infection by other microorganism.

C94. The characteristics and outcome of pediatric patients with congenital heart disease (CHD) infected by coronavirus disease 2019 (COVID-19) between march 2020 to february 2021 at sanglah hospital: a case series of 12 patients

BackgroundCoronavirus disease 2019 (COVID-19) has a complex connection to cardiovascular system. COVID-19 infection has been proven to cause significant dysfunction in lung, heart, and endothelial cells. Therefore, patients with complex congenital heart lesions have a high risk of developing severe complications and worse outcomes. This paper aims to determine the characteristics and outcome of pediatric congenital heart disease (CHD) patients infected by COVID-19 at Sanglah hospital.Case SummaryWithin the period of March 2020 to February 2021, there were 12 cases of pediatric CHD accompanied by COVID-19 infection, which consists of 8 females and 4 males within the age of 6 days to 18 years. Based upon symptomatic CHD classification, there were 4 cyanotic CHD cases and 1 acyanotic case, respiratory problems were reported in all 5 cases. These are pneumonia in 4 cases and respiratory distress caused by hyaline membrane disease (HMD) in 1 case. 2 cases require mechanical ventilation. The outcome of all inpatient cases were favorable.DiscussionTo date, there are only few scientific evidence detailing how COVID-19 affects CHD. We were unable to calculate the correlation analysis since the population sample was too small. The most severe cases that requires mechanical ventilation had comorbidities in the form of malnutrition and down syndrome. From the perspective of pathophysiology, one of the most important factor determining outcome was the severity of CHD itself. Other contributing factors were COVID-19’s destructive effect on heart tissue and co-infection by other microorganism.

Abstract 11567: Congenital Heart Disease Classification Improves SRTR Risk Estimation of Waitlist and Early Post-Transplant Mortality for Pediatric Heart Transplantation

Introduction: Risk estimation for waitlist and 1-year post-transplant mortality utilized by SRTR for pediatric heart transplantation dichotomizes patients based upon presence or absence of congenital heart disease (CHD). Hypothesis: Current SRTR risk estimations can be improved by adding further detail in categorization of CHD. Methods: A retrospective analysis of the Pediatric Heart Transplant Society (PHTS) database of pediatric (&lt;18yo) heart transplant listings from 2010 to 2020 was performed, excluding patients listed for retransplantation. Cox hazard models were developed for waitlist mortality during the first year following listing (WaitDeath) and one year post-transplant mortality (TxDeath). The initial model was constructed using covariates included in the current SRTR model and a second model was developed by adding CHD classifications (SRTR+) including: single ventricle vs. biventricular heart disease, ventricular morphology, and surgical interventions. Results: The WaitDeath model included 5,790 patients in which 722 of which died. In the SRTR WaitDeath model, CHD had a HR of 1.8 (95%CI of 1.4-2.3) compared to cardiomyopathy patients. Where as in SRTR+ model, patients with different types of CHD had HR’s that ranged from 1.5 to 2.1 (Table 1). The TxDeath model included 4,185 patients in which 304 which died. (Table 1) In the SRTR model of TxDeath, patients with CHD had a HR of 3.9 (2.9-5.1) compared to cardiomyopathy patients, whereas in the SRTR+ model patients with different types of CHD had HRs that ranged from 2.4-5.3 (Table 1). Conclusions: Inclusion of granular CHD data may improve waitlist and post-transplant mortality risk estimation in pediatric heart transplant patients, allowing for more refinement in allocation policies and understanding of difference in post-transplant outcomes across sites. This data supports further refinement of current SRTR risk adjustment models.

Heart sound classification based on sub-band envelope and convolution neural network

Automatic classification of heart sounds plays an important role in the early diagnosis of congenital heart disease. A kind of heart sound classification algorithms based on sub-band envelope feature and convolution neural network was proposed in this paper, which did not need to segment the heart sounds according to cardiac cycle accurately. Firstly, the heart sound signal was divided into some frames. Then, the frame level heart sound signal was filtered with Gammatone filter bank to obtain the sub-band signals. Next, the sub-band envelope was extracted by Hilbert transform. After that, the sub-band envelope was stacked into a feature map. Finally, type Ⅰ and type Ⅱ convolution neural network were selected as classifier. The result shown that the sub-band envelope feature was better in type Ⅰ than type Ⅱ. The algorithm is tested with 1 000 heart sound samples. The test results show that the overall performance of the algorithm proposed in this paper is significantly improved compared with other similar algorithms, which provides a new method for automatic classification of congenital heart disease, and speeds up the process of automatic classification of heart sounds applied to the actual screening.

First-Trimester Maternal Folic Acid Supplementation Modifies the Effects of Risk Factors Exposures on Congenital Heart Disease in Offspring.

This study aimed to examine effect modification of maternal risk factor exposures and congenital heart disease (CHD) by maternal folic acid supplementation (FAS)/non-FAS. We included 8379 CHD cases and 6918 CHD-free controls from 40 clinical centers in Guangdong Province, Southern China, 2004–2016. Controls were randomly chosen from malformation-free fetuses and infants and frequency matched to the echocardiogram-confirmed cases by enrollment hospital and year of birth. We used multiple regression models to evaluate interactions between FAS/non-FAS and risk factors on CHDs and major CHD categories, adjusted for confounding variables. We detected statistically significant additive and multiplicative interactions between maternal FAS/non-FAS and first-trimester fever, viral infection, and threatened abortion on CHDs. An additive interaction on CHDs was also identified between non-FAS and living in a newly renovated home. We observed a statistically significant dose-response relationship between non-FAS and a greater number of maternal risk factors on CHDs. Non-FAS and maternal risk factors interacted additively on multiple critical CHDs, conotruncal defects, and right ventricular outflow tract obstruction. Maternal risk factor exposures may have differential associations with CHD risk in offspring, according to FAS. These findings may inform the design of targeted interventions to prevent CHDs in highly susceptible population groups.

Effect of congenital heart disease on the recurrence of cough variant asthma in children

BackgroundThe research into the recurrence of cough variant asthma (CVA) in congenital heart disease (CHD) are few in number. The purpose of this study is to investigate the effect of CHD on the risk of the recurrence of CVA.MethodsThis study was a retrospective cohort study of 489 children with CVA aged between one and 14 years, of whom 67 had CHD complicated with CVA and 134 had CVA without CHD at a ratio of 1:2 according to age, sex and index year. The adjusted hazard ratio (aHR) of CVA recurrence in both the CHD cohort and the non-CHD cohort was determined by multivariate analysis using the Cox proportional hazard regression model.ResultsAdjusting for CHD classification, Mycoplasma pneumonia (MP) infection and immunoglobulin E (IgE) sensitization, the recurrence hazard of CVA in the complex congenital heart disease (CCHD) group (aHR = 3.281; 95% CI 1.648–6.530; P < 0.01) was significantly higher than that in the simple congenital heart disease group (aHR = 2.555; 95% CI 1.739–3.752; P < 0.01). Further, children with IgE sensitization (aHR = 2.172; 95% CI 1.482–3.184; P < 0.01) had a higher recurrence hazard of CVA than those without IgE sensitization, and children with MP infection (aHR = 1.777; 95% CI 1.188–2.657; P < 0.01) had a higher recurrence hazard of CVA than those without the MP infection.ConclusionThe hazard of recurrent CVA is higher in children with CHD, especially in the CCHD children. In addition, those children with IgE sensitization or a MP infection had an increased hazard of recurrent CVA.

The Association between Cyanotic and Acyanotic Congenital Heart Disease with Nutritional Status

BACKGROUND: Congenital heart disease (CHD) is one of the most common birth anomalies in the 1st year of life. The incidence of CHD in developed and developing countries is varied, between 6 and 10 cases per 1000 live birth. Some factors contribute to the nutritional status of CHD patients, such as nutrient inputs, energy requirements, and dietary components. Irrespective of the nature of the cardiac defect and the presence or absence of cyanosis, malnutrition is a common finding in children with congenital heart anomalies. Recent studies have tried to investigate malnutrition development based on the type or category of CHD.&#x0D; AIM: This study aims to investigate the association between cyanotic and acyanotic CHD with nutritional status.&#x0D; METHODS: A cross-sectional study was conducted from January to March 2018 in the pediatric cardiology outpatient clinic of the Haji Adam Malik General Hospital, Medan, Indonesia.&#x0D; RESULTS: During the study period, 58 children were admitted, consisting of 31 (53.4%) males and 27 (46.6%) females, with a mean age of 57 months. There was no significant sex predilection found in the study (p = 0.207). The proportion of patients who developed malnutrition was 70.7% (mild-moderate = 48.3% and severe = 22.4%). There was an association between cyanotic and acyanotic CHD with nutritional status (p = 0.015). Wasting was found in 33 children (56.8%) that had a significant association with the type of heart defects (p = 0.001). Patients with cyanotic CHDs were found to have a lower risk for malnutrition compared to the acyanotic group (prevalence odds ratio = 0.218, and prevalence risk = 0.661; p = 0.015).&#x0D; CONCLUSIONS: There is an association between cyanotic and acyanotic CHD with nutritional status.

An ontology-based classification of Ebstein's anomaly and its implications in clinical adverse outcomes

BackgroundEbstein's anomaly (EA) is a rare congenital heart disease with significantly phenotypic heterogeneity, accompanied with multiple associated phenotypes. The classification of cases with EA based on a standardized vocabulary of phenotypic abnormalities from Human Phenotype Ontology (HPO) and its association with adverse clinical outcomes has yet to be investigated. MethodsWe developed a deep phenotyping algorithm for Chinese electronic medical records (EMRs) from the Fuwai Hospital to ascertain EA cases. EA-associated phenotypes were standardized according to HPO annotation, and an unsupervised hierarchical cluster analysis was used to classify EA cases according to their phenotypic similarities. A survival analysis was conducted to study the association of the HPO-based cluster with survival or adverse clinical outcomes. ResultsThe ascertained EA cases were annotated to have a single or multiple HPO terms. Three distinct clusters with different combinations of HPO term in these cases were identified. The HPO-based classification of EA cases was not significantly associated with survival or adverse clinical outcomes at a mid-term follow-up. ConclusionsOur study provided an important implication for studying the classification of congenital heart disease using HPO-based annotation. A long time follow-up will enable to confirm its association with adverse clinical outcomes.