Publications by authors named "A D Christensen"

Healthcare use among adolescents after vaccination against SARS-CoV-2 is unknown. In a real-life register-based cohort study (trial NCT04786353), healthcare use was compared among Pfizer-BioNTech BNT162b2 COVID-19 vaccinated and unvaccinated 12-18-year-olds. First-dose-vaccinated (between 1 May and 30 September 2021) adolescents were sex and age matched 1:1 with unvaccinated adolescents.

View Article and Find Full Text PDF

Background: Treatment with implantable cardioverter-defibrillators (ICDs) effectively prevents sudden cardiac death (SCD) in patients with hypertrophic cardiomyopathy (HCM). Identifying patients most likely to benefit from a primary prevention ICD remains challenging. We aimed to investigate the long-term incidence of ICD therapy in patients with HCM according to SCD-risk at baseline.

View Article and Find Full Text PDF

We aimed to develop and evaluate Explainable Artificial Intelligence (XAI) for fetal ultrasound using actionable concepts as feedback to end-users, using a prospective cross-center, multi-level approach. We developed, implemented, and tested a deep-learning model for fetal growth scans using both retrospective and prospective data. We used a modified Progressive Concept Bottleneck Model with pre-established clinical concepts as explanations (feedback on image optimization and presence of anatomical landmarks) as well as segmentations (outlining anatomical landmarks).

View Article and Find Full Text PDF

Introduction: Maternal autoimmune systemic connective tissue diseases (CTDs) and their related antibodies have been associated with adverse fetal outcomes, including complete heart block. In this study, we assessed the association between maternal CTD or vasculitis and neonatal electrocardiographic (ECG) parameters.

Methods: Our study population was drawn from the Copenhagen Baby Heart Study (CBHS), a prospective, population-based cohort study open to all neonates born in the Copenhagen area.

View Article and Find Full Text PDF

The initial evaluation of stenosis during coronary angiography is typically performed by visual assessment. Visual assessment has limited accuracy compared to fractional flow reserve and quantitative coronary angiography, which are more time-consuming and costly. Applying deep learning might yield a faster and more accurate stenosis assessment.

View Article and Find Full Text PDF