Sunday, December 22, 2024
12/22/2024
Abstract Worldwide, nearly 3 million children die each year from sepsis. Red blood cells (RBC) are transfused in ~50% of children with septic shock, with the intent to enhance oxygen delivery, help resolve shock, and prevent organ dysfunction. However, RBC transfusion has repeatedly been associated with adverse outcomes in critical illness, suggesting harm. For most children with septic shock we lack data to identify who should or should not be transfused. Interventional trials and RBC transfusion guidelines have used hemoglobin concentration alone to inform RBC transfusion decisions. However, this is likely not the best way to decide when to transfuse RBCs to a patient with shock. Our preliminary data in septic children suggest that hemodynamic measures better identify children who might benefit from RBC transfusion and that children with severe immune suppression may be at greater risks of transfusion-related harm. Lastly, our data show that RBC transfusion effects likely differ based on RBC unit collection, storage, and processing methods, and resultant quantities of soluble mediators. Together, these data support our hypothesis that physiologic and blood product factors will predict when children with septic shock will benefit from, or be harmed by, RBC transfusion. The overall goal of our research program is to develop a personalized-medicine approach to RBC transfusion in children with septic shock. Our objective for this proposal is to build decision support tools that can be used in future clinical trials to identify when children with septic shock should or should not be transfused with RBCs. Our proposal uses robust machine learning techniques that are designed to use a very large number of dynamic variables to define personalized treatment decisions that maximize clinical outcomes. With these methods, and an agnostic, data-driven approach, the resultant decision rules will be free of clinician bias and can consider a much wider array of clinical parameters than have traditionally been used in transfusion trials. We will conduct a multicenter prospective observational study of 660 children with septic shock to accomplish the following specific aims: Aim 1: Determine which clinical, hemodynamic, and blood product-specific factors should drive RBC transfusion decision-making in children with septic shock. Using data from the electronic medical record, we will use outcome-weighted learning to build dynamic transfusion algorithms to minimize predicted daily organ dysfunction (PELOD2 scores). Aim 2: Identify immune phenotypes that predict differential response to RBC transfusion in children with septic shock. We will collect serial blood samples from subjects to measure a validated panel of inflammatory and immune function biomarkers and test the hypothesis that outcomes related to RBC transfusion will differ based on pre-transfusion levels of biomarkers of inflammation and/or immune suppression. We expect our data to transform the design of interventional trials of data-driven RBC transfusion algorithms to shift the transfusion paradigm away from hemoglobin-based strategies and improve outcomes for critically ill septic children.
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