Background Despite advances in pediatric health care over recent decades, it is not clear whether survival in children with congenital heart disease (CHD) is still increasing. Methods and Results We identified all patients with CHD using nationwide Swedish health registries for 1980 to 2017. We examined the survival trends in children with CHD; we investigated the mortality risk in patients with CHD compared with matched controls without CHD from the general population using Cox proportional regression models and Kaplan-Meier survival analysis. Among 64 396 patients with CHD and 639 012 matched controls without CHD, 3845 (6.0%) and 2235 (0.3%) died, respectively. The mean study follow-up (SD) was 11.4 (6.3) years in patients with CHD. The mortality risk was 17.7 (95% CI, 16.8-18.6) times higher in children with CHD compared with controls. The highest mortality risk was found during the first 4 years of life in patients with CHD (hazard ratio [HR], 19.6; 95% CI, 18.5-20.7). When stratified by lesion group, patients with non-conotruncal defects had the highest risk (HR, 97.2; 95% CI, 80.4-117.4). Survival increased substantially according to birth decades, but with no improvement after the turn of the century where survivorship reached 97% in children with CHD born in 2010 to 2017. Conclusions Survival in children with CHD has increased substantially since the 1980s; however, no significant improvement has been observed this century. Currently, >97% of children with CHD can be expected to reach adulthood highlighting the need of life-time management.
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http://dx.doi.org/10.1161/JAHA.120.017704 | DOI Listing |
JMIR Pediatr Parent
January 2025
Department of Design Innovation, College of Design, University of Minnesota, Twin Cities, Minneapolis, MN, United States.
Background: Congenital heart disease (CHD) is the most common birth defect, affecting 40,000 births annually in the United States. Despite advances in medical care, CHD is often a chronic condition requiring continuous management and education. Effective care management depends on children's understanding of their condition.
View Article and Find Full Text PDFJAMA Netw Open
January 2025
Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora.
Importance: A recent advisory from the American Heart Association delineated the potential benefits of developmental care for hospitalized children with congenital heart disease (CHD) and a critical gap in research evaluating the association of such inpatient programs with neurodevelopmental outcomes.
Objective: To investigate associations between the Cardiac Inpatient Neurodevelopmental Care Optimization (CINCO) program interventions, delirium, and neurodevelopment in young children (newborn through age 2 years) hospitalized with CHD.
Design, Setting, And Participants: This cohort study used quality improvement data from inpatient cardiac units at a tertiary care children's hospital in the US.
J Diabetes Investig
January 2025
Department of Diabetes, Endocrinology and Metabolism, Center Hospital, National Center for Global Health and Medicine, Tokyo, Japan.
Aim: To determine the epidemiological characteristics and risk factors for heart failure (HF) among Japanese patients with type 2 diabetes.
Methods: A retrospective cohort analysis, using J-DREAMS database, was conducted from December 2015 to January 2020 with type 2 diabetes. The primary objectives were to describe patient characteristics stratified by HF history at baseline and new HF events during follow-up.
J Cardiovasc Electrophysiol
January 2025
Department of Electrophysiology, German Heart Center Munich, TUM University Hospital, Munich, Bavaria, Germany.
Introduction: Data regarding safety and long-term outcome of very high-power-short duration (vHPSD) ablation in adult congenital heart disease (ACHD) patients with paroxysmal or persistent atrial fibrillation (AF) are lacking.
Methods: Retrospective observational single-center study. The data of 66 consecutive ACHD patients (mean age 60 ± 12.
Bioengineering (Basel)
December 2024
Department of Pathology, University of Yamanashi, Yamanashi 409-3898, Japan.
The latest World Health Organization (WHO) classification of central nervous system tumors (WHO2021/5th) has incorporated molecular information into the diagnosis of each brain tumor type including diffuse glioma. Therefore, an artificial intelligence (AI) framework for learning histological patterns and predicting important genetic events would be useful for future studies and applications. Using the concept of multiple-instance learning, we developed an AI framework named GLioma Image-level and Slide-level gene Predictor (GLISP) to predict nine genetic abnormalities in hematoxylin and eosin sections: , , mutations, promoter mutations, homozygous deletion (CHD), amplification (amp), 7 gain/10 loss (7+/10-), 1p/19q co-deletion, and promoter methylation.
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