Little research attention has been paid to the occurrence of heart failure (HF) in children with complex congenital heart diseases (CHDs). Herein, we describe the prevalence, risk factors, and costs associated with HF in complex CHD. Patients aged ≤17 years and diagnosed with a complex CHD on multiple service visits over a 15-year period in the SC Medicaid dataset (1996-2010) were tracked and analyzed. The cohort included 2999 unduplicated patients; 51.0% were male; 34.4% were African American. HF was diagnosed in 7.6%. Single ventricle lesions, genetic syndromes, and ventricular arrhythmia were significantly associated with an increased likelihood of being diagnosed with HF, controlling for development of comorbid pulmonary hypertension. Patients with HF received significantly more subspecialty care, more surgeries, more hospitalizations, more total days of inpatient care, and more emergency department care than those without HF. Patients with significantly higher total care costs paid by Medicaid had HF, more cardiac surgeries, and more specialized mechanical or other support procedures, controlling for diagnosed single ventricle CHD, a genetic syndrome, and number of non-cardiac surgeries. Complex CHD patients with HF incur significantly higher care costs but require multifaceted, intensive supports for management of incident complications and comorbid conditions.
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http://dx.doi.org/10.1007/s00246-017-1712-8 | DOI Listing |
Eur J Hum Genet
January 2025
Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium.
The etiology of congenital heart disease (CHD) is complex, comprising both genetic and environmental factors. Despite documented familial occurrences, the genetic etiology remains largely elusive. Trio exome sequencing identified a heterozygous FLT4 splice site variant in two families with respectively tetralogy of Fallot (TOF), and variable CHD comprising both the TOF spectrum and aortic coarctation.
View Article and Find Full Text PDFCardiol Young
January 2025
Paediatric Cardiology Department, Royal Belfast Hospital for Sick Children, Belfast, Northern Ireland.
Objective: The COVID-19 pandemic presented unique challenges to global healthcare. Face-to-face outpatient care was dramatically reduced. This study implemented a remote consultation service via a mobile app (Pexip) to monitor patients with major CHD.
View Article and Find Full Text PDFBr J Hosp Med (Lond)
January 2025
Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
The relationship between retinal fundus hemorrhage and the severity of coronary artery lesions remains unclear. This study aimed to explore the incidence of fundus hemorrhage in patients at high risk of coronary heart disease (CHD) and to examine its correlation with the SYNTAX score, a tool used to assess the complexity of coronary artery disease. This retrospective study consecutively enrolled patients undergoing coronary angiography (CAG) at Beijing Anzhen Hospital Hospital from June 2019 to January 2020.
View Article and Find Full Text PDFMedicina (Kaunas)
January 2025
Psychiatry Service, Complejo Hospitalario Universitario Insular-Materno Infantil, Avenida Marítima del Sur, s/n, 35016 Las Palmas de Gran Canaria, Spain.
: Anxiety and depression are significant mental health concerns for individuals with congenital heart disease (CHD). As group therapy has proven to be a valuable and effective treatment option for managing anxiety and depression, the aim of this study was to determine its effects on patients with CHD and anxious-depressive symptoms. : We used non-pharmacological psychological group intervention, of six weekly sessions of 90 min each, administered by trained personnel, in adult patients with CHD.
View Article and Find Full Text PDFChildren (Basel)
December 2024
Division of Pediatric Cardiology, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL 60611, USA.
Echocardiography is the main modality in diagnosing acquired and congenital heart disease (CHD) in fetal and pediatric patients. However, operator variability, complex image interpretation, and lack of experienced sonographers and cardiologists in certain regions are the main limitations existing in fetal and pediatric echocardiography. Advances in artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offer significant potential to overcome these challenges by automating image acquisition, image segmentation, CHD detection, and measurements.
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