Improving Anaphylaxis Outcomes Through Personalized Care Strategies - Project Summary/Abstract Anaphylaxis can be life-threatening but exacts an outsized toll on patients and the healthcare system. This system’s burden is due in part to one size fits all national guidelines that necessitate emergency department (ED) care for all patients and ≥ 4-6-hour observational periods that are agnostic to reaction severity and patient characteristics. This generic approach is ill-suited to most reactions, thereby contributing to ED overutilization, long ED wait times, ED overcrowding, avoidable hospitalizations, and inflated healthcare costs. The proposed K23 study seeks to deliver personalized anaphylaxis care and will further develop my skillset as an independent translational investigator. I hypothesize that machine learning can be applied to my actively enrolling KL2 cohort to develop prediction models capable of customizing ED observation periods in real-time based on patient- specific risk. The rationale for this hypothesis comes from preliminary KL2 data (n = 91) in which 51.6% of patients had complete symptom resolution within 2 hours from the first epinephrine dose. The next logical step is to expand my prospective cohort and employ machine learning to develop a model capable of predicting time-to-symptom resolution in real-time to reduce observation periods, prevent unnecessary hospitalizations, and identify patients who may not require ED care and can instead be safely managed at home. Additionally, there is an unmet need for clinically actionable biomarkers to enhance patient risk stratification and enable customizable therapeutic approaches; however, there is an incomplete understanding of immunological mechanisms contributing to human anaphylaxis pathogenesis. Most research has focused on the role of IgE- mediated mast cell and basophil activation, yet emerging data support my second hypothesis that neutrophils are key drivers of anaphylaxis that govern divergent progression into either refractory or biphasic courses. The rationale for this hypothesis comes from preliminary KL2 data in which 100% of patients with severe reactions exhibited elevated markers of neutrophil activation and levels were dynamic over time. The specific aims of this application are 1) To derive a dynamic clinical prediction model to customize anaphylaxis observation periods. Simultaneously, I will acquire training in biostatistical and biomedical informatics, machine learning, dynamic prediction modeling, and mediation analysis; and 2) Demonstrate that serum biomarkers of neutrophil activation predict reaction severity and clinical courses. This aim will help me acquire essential training in translational approaches to neutrophil biology and will provide a mechanistic gateway for future research on the immunology of anaphylaxis courses. Findings from this study will result in a novel prediction model to optimize anaphylaxis observation periods and prevent unnecessary hospitalizations. These outcomes will aid my transition to independent investigator status by conducting validation studies of the predictive model in a multicenter cohort. Study findings will also open avenues of investigation on the integration of neutrophil measures into clinical care and the prognostic machine learning tool.
