Sean J. Cooney, a pediatric cardiology fellow at Johns Hopkins, aims to use metabolomics to improve post-operative outcomes in neonates undergoing cardiac surgery for congenital heart disease. This topic was presented during Blalock-Taussig-Thomas Heart Center Research Day at Johns Hopkins.
Hello, my name is Sean Cooney and I'm a second year pediatric cardiology fellow here at Johns Hopkins. My presentation is called Perioperative Metabol loic profiling in neonatal cardiac surgery identifies the high-risk phenotype for adverse outcomes. And my mentor is Doctor Alan Everett. I presented this project last week at our Blaylock Tausig Thomas Heart Center Research Day. Our work aims to improve post-operative outcomes in neonates undergoing cardiac surgery for congenital heart disease through the use of metabolisms. Although mortality rates have improved significantly over the years through advancements in surgical techniques, mechanical circulatory support and post-operative intensive care. Morbidity remains quite high. A big challenge in addressing this issue is the lack of risk models with modifiable variables. But metabol oates may offer a solution. Metabolites are the breakdown products of all the metabolic pathways in the body. In this field. Metabolites are identified and patterns in both individual and groups of metabolites are analyzed in order to identify risk factors for such things as post surgical morbidity, uh insight into pathophysiology processes that may drive these outcomes and to discover unique targets for intervention we collected and analyzed the serum of over 200 neonates undergoing congenital heart surgery from three different centers across the country. We then used machine learning statistical techniques to group patterns based on similarities in these metabolic profiles. What we found was promising we saw that specific patterns of metabolite changes were associated with higher risk of morbidity and other patterns were associated with lower risk. Furthermore, metabolite groups, we identified corresponded with known pathophysiology, significant metabolic pathways like skeletal muscle breakdown for energy inflammation and inotrope anabolism. Furthermore, these metabolic profiles were not related to standard risk factors such as single ventricle heart disease or long cardiopulmonary bypasses. This work is important for many reasons. We showed that metabolic profiles can be used to gain insights into the mechanisms, driving post-operative morbidity in neonatal congenital cardiac surgery and that this may help risk stratify our patients and identify unique therapeutic targets. We also show that we can use machine learning to analyze such complex data. We're also supporting the notion of personalized medicine as we hope to better tailor care to our patient's individual needs by understanding their metabolic profiles. I'd like to thank the Everett lab and our biostatistics team. Thank you so much for listening today.
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