Emma Payne^1,2, Thomas Wilson^2,3, Marjan Haghighi^1,4, Zoe McCallum^2,3, Yves d’Udekem⁵, Julian Ayer^1,4,*

Congenital Heart Disease, Vol.17, No.6, pp. 617-639, 2022, DOI:10.32604/chd.2022.024775

Abstract Background: Patients born with a single ventricle circulation commonly experience growth failure in early life, which is associated with adverse outcomes in infancy. However, associations between bodyweight or weight trajectory and clinical outcome post-Fontan procedure are yet to be determined. Methods: On the 1st of July 2021, a systematic review was performed in MEDLINE, EMBASE, the Cochrane Library, and Scopus of studies of patients with clinical outcome data post-Fontan procedure and association with bodyweight. Quality of studies was evaluated by Newcastle–Ottawa scale for cohort studies and Joanna Briggs Institute tool for cross-sectional studies. Results: Of 527 studies that underwent title… More >

Rui Jiang^1,3,*, Kaisheng Lai², Jianping Xu¹, Xiang Feng¹, Shaoye Wang¹, Xiaojian Wang³, Zhe Liu²

Congenital Heart Disease, Vol.17, No.6, pp. 675-686, 2022, DOI:10.32604/chd.2022.021626

Abstract Background: The etiology of pulmonary arterial hypertension associated with congenital heart disease (PAHCHD) is complicated and the phenotype is heterogeneous. Genetic defects of NOTCH3 were associated with cerebral disease and pulmonary hypertension. However, the relationship between NOTCH3 mutations and the clinical phenotype has not been reported in CHD-PAH. Methods: We eventually enrolled 142 PAH-CHD patients from Fuwai Hospital. Whole exome sequencing (WES) was performed to screen the rare deleterious variants of NOTCH3 gene. Results: This PAH-CHD cohort included 43 (30.3%) men and 99 (69.7%) women with the mean age 29.8 ± 10.9 years old. The pathogenic or likely pathogenic mutations… More >

Jun Sung Park¹, Go Hun Seo², Yunha Choi¹, Soojin Hwang¹, Minji Kang³, Hyo-Sang Do³, Young-Hwue Kim⁴, Jeong Jin Yu⁴, Ellen Ai-Rhan Kim⁵, Euiseok Jung⁵, Byong Sop Lee⁵, Jae Suk Baek⁴, Beom Hee Lee^1,6,*

Congenital Heart Disease, Vol.17, No.6, pp. 653-673, 2022, DOI:10.32604/chd.2022.021580

Abstract Background: Over 400 genes contribute to the development of congenital heart disease (CHD). Additionally, multisystemic manifestations accompanying syndromic CHD pose a higher risk of genetic diseases. This study investigated the diagnostic yield of whole-exome sequencing (WES) in patients with sporadic syndromic CHD and the phenotypic factors affecting the genetic diagnostic rate. Methods: Sixty-four patients with sporadic syndromic CHD aged <18 years underwent WES between May 2018 and December 2020 in a single tertiary center, and the association between genetic testing data and extracardiac phenotypes was analyzed. Results: Extracardiac phenotypes were measured as 3.66 ± 3.05 (standard deviation, interquartile range: 2–5)… More > Graphic Abstract

Shuting Chang^1,2, Guanzhao Zhang^2,3, Nengjun Sun², Xinpeng Yuan⁴, Qingting Wang⁵, Lin Zhu⁶, Feiyue Zhang⁷, Yu Xiu⁴, Yang Dong⁸, Yonghong Chen⁹, Zhanpeng Zhao¹⁰, Xiao Liu⁴, Qiang Shao¹¹, Xiaofeng Xu¹², Anshun Wang¹³, Mengjiao Li¹⁴, Bo Li^2,*

Congenital Heart Disease, Vol.17, No.6, pp. 709-716, 2022, DOI:10.32604/chd.2022.018657

Abstract Background: This study aimed to illustrate the prevalence of CHD by screening children in extremely high-altitude areas (over 4000 m to even 5000 m above sea level) and explore an aid model for early diagnosis and treatment for the Tibetan population. Methods: A total of 2242 students from different schools in Ngamring County, Xigaze city, Tibet from September 2019 to September 2020 were selected for screening. The students were examined through the inquiry of their current medical history and family history, cardiac auscultation and a physical examination, in order to screen out the suspected cases of CHD, and then the… More >

Ghada Abdulsalam¹, Souham Meshoul^2,*, Hadil Shaiba³

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 761-779, 2023, DOI:10.32604/iasc.2023.032262

Abstract Nowadays, quantum machine learning is attracting great interest in a wide range of fields due to its potential superior performance and capabilities. The massive increase in computational capacity and speed of quantum computers can lead to a quantum leap in the healthcare field. Heart disease seriously threatens human health since it is the leading cause of death worldwide. Quantum machine learning methods can propose effective solutions to predict heart disease and aid in early diagnosis. In this study, an ensemble machine learning model based on quantum machine learning classifiers is proposed to predict the risk of heart disease. The proposed… More >

Jodi L. Feinberg¹, Peter Sheng², Stephanie Pena², Adam J. Small¹, Susanna Wendelboe¹, Katlyn Nemani³, Vikram Agrawal⁴, Dan G. Halpern^1,*

Congenital Heart Disease, Vol.17, No.5, pp. 519-531, 2022, DOI:10.32604/chd.2022.024174

Abstract Background: Adults with congenital heart disease (ACHD) have increased prevalence of mood and anxiety disorders. There are limited data regarding the influence of the COVID-19 pandemic on the mental health and health behaviors of these patients. Objective: The purpose is to evaluate the perceptions, emotions, and health behaviors of ACHD patients during the COVID-19 pandemic. Methods: In this cross-sectional study of ACHD patients, we administered surveys evaluating self-reported emotions, perceptions and health behaviors. Logistic regressions were performed to determine the adjusted odds of displaying each perception, emotion and health behavior based on predictor variables. Results: Ninety-seven patients (mean age 38.3… More > Graphic Abstract

Jae Hee Seol^1,4,#, Se Yong Jung^1,#, Jae Young Choi¹, Han Ki Park², Young Hwan Park², Nam Kyun Kim^1,3,*

Congenital Heart Disease, Vol.17, No.5, pp. 569-578, 2022, DOI:10.32604/chd.2022.018436

Abstract Objective: Junctional ectopic tachycardia is common after cardiac surgery for congenital heart disease. However, its incidence and related risk factors in infants after cardiac surgery are not well known. The objective of this study was to determine the overall incidence and related risk factors for junctional ectopic tachycardia in neonates and infants. Methods: We enrolled a total of 271 patients aged <1 year who underwent open cardiac surgery at Severance Cardiovascular Hospital from January 2018 to December 2020. Exclusion criteria were immediate postoperative mortality, other arrhythmias detected in the perioperative period, and prematurity. Result: The overall incidence of junctional ectopic… More >

Nisha Mary¹, Bilal Khan¹, Abdullah A. Asiri², Fazal Muhammad^3,*, Salman Khan³, Samar Alqhtani⁴, Khlood M. Mehdar⁵, Hanan Talal Halwani⁴, Muhammad Irfan⁶, Khalaf A. Alshamrani²

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6595-6616, 2022, DOI:10.32604/cmc.2022.032384

Abstract Heart disease prognosis (HDP) is a difficult undertaking that requires knowledge and expertise to predict early on. Heart failure is on the rise as a result of today’s lifestyle. The healthcare business generates a vast volume of patient records, which are challenging to manage manually. When it comes to data mining and machine learning, having a huge volume of data is crucial for getting meaningful information. Several methods for predicting HD have been used by researchers over the last few decades, but the fundamental concern remains the uncertainty factor in the output data, as well as the need to decrease… More >

Abeer Abdulaziz AlArfaj, Hanan Ahmed Hosni Mahmoud^*

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2527-2540, 2023, DOI:10.32604/iasc.2023.030168

Abstract The detection of heart disease is a problematic task in medical research. This diagnosis utilizes a thorough analysis of the clinical tests from the patient’s medical history. The massive advances in deep learning models pursue the development of intelligent computerized systems that aid medical professionals to detect the disease type with the internet of things support. Therefore, in this paper, we propose a deep learning model for elderly patients to aid and enhance the diagnosis of heart disease. The proposed model utilizes a deeper neural architecture with multiple perceptron layers with regularization learning techniques. The model performance is verified with… More >

S. Raghavendra¹, Vasudev Parvati², R. Manjula³, Ashok Kumar Nanda⁴, Ruby Singh⁵, D. Lakshmi⁶, S. Velmurugan^7,*

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1353-1368, 2023, DOI:10.32604/iasc.2023.027977

Abstract In contemporary medicine, cardiovascular disease is a major public health concern. Cardiovascular diseases are one of the leading causes of death worldwide. They are classified as vascular, ischemic, or hypertensive. Clinical information contained in patients’ Electronic Health Records (EHR) enables clinicians to identify and monitor heart illness. Heart failure rates have risen dramatically in recent years as a result of changes in modern lifestyles. Heart diseases are becoming more prevalent in today’s medical setting. Each year, a substantial number of people die as a result of cardiac pain. The primary cause of these deaths is the improper use of pharmaceuticals… More >