Specialization
Focus of research
Stijn Haas (1995) obtained a medical degree from the University of Amsterdam in 2019. During his bachelor he became interested in cardiology contributing to a study on prediction of survival after percutaneous treatment of mitral regurgitation. This work resulted in a peer reviewed publication in Structural Heart. As a medical intern at the Amsterdam University Medical Center he was drawn to pediatrics and did a study into cardiac function of children with Fabry disease at the department of pediatric cardiology of the Amsterdam UMC. The results were presented in a poster at the Association for European Pediatric and Congenital Cardiology Annual Meeting of 2021.
Stijn Haas is currently appointed at the Emma Children’s Hospital Amsterdam UMC, where he works as an attending physician and works on a PhD. As part of his PhD he has contributed to the set-up of a newly developed outpatient clinic for children with congenital heart disease requiring open-heart surgery early in life, including children with Tetralogy of Fallot, transposition of the great arteries and (atrio)ventricular septal defects. The new outpatient clinic offers multidisciplinary check-ups at the ages 6, 10 and 14, by a team involving pediatric cardiologists, psychologists, physiotherapists, radiologists and geneticists. The newly developed outpatient clinic is part of the Emma Children’s Hospital Amsterdam UMC Follow Me program, an ambitious program that aims to establish multidisciplinary follow-up programs for all tertiary care pediatric patients with the ambition to enhance clinical follow-up, support routine outcome monitoring, and fuel clinical multidisciplinary research to improve clinical care.
His research is embedded in this follow-up program and focuses on the prediction of long-term physical fitness and neurocognitive development after early open-heart surgery in children with congenital heart disease. The prognostic value of disease characteristics, perioperative data and early echocardiographic and electrocardiographic parameters will be explored. Furthermore, research will be done into the added value of Machine Learning models for the prediction of these long-term outcomes.