Supplement to Informing the Emergency Care of Septic Shock Patients A Novel Application of Data-Driven Analytics

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PROJECT SUMMARY Background: Septic shock is a commonly, costly, and deadly condition. There is increasing recognition that septic shock patients vary significantly in terms of (1) clinical presentation, (2) response to treatments, and (3) clinical outcomes. This patient-level heterogeneity may explain why optimal early septic shock management remains poorly understood. Patients with septic shock are four times more likely to die than septic patients without shock. Our preliminary data shows that Black patients have higher odds of mortality from septic shock compared to White patients. Current studies do not characterize patient heterogeneity among septic shock patients and do not explicate pharmacogenetic factors that may influence disparities in outcomes. Objective: Insights from synergistic data types are necessary to provide a more complete understanding of septic shock heterogeneity and hereditable factors that may influence vasopressor response and disparities in outcomes. The overall objective of the proposed research is to characterize both phenotypic and genetic aspects of heterogeneity in septic shock. This work is organized into two aims: (1) Identify Septic Shock Phenotypes Using Advanced Analytic Methods and (2) Quantify Vasopressor Pharmacogenetic Polymorphisms by Race and Vasopressor Response. Our overall hypothesis is that advanced analytic methods applied to clinical and genetic data can identify defining features of septic shock heterogeneity that are relevant to early septic shock management in the Emergency Department and disparities in outcomes. Methods: (Aim 1) We will use a national dataset of septic patients with hypotension refractory to initial Emergency Department fluid resuscitation and apply unsupervised machine learning clustering methods to define clinically relevant phenotypes of early septic shock patients. We will analyze phenotypic variation in clinical characteristics and outcomes. Then, we will develop a supervised model for phenotype classification. (Aim 2) We will perform targeted pharmacogenomics of 100 samples balanced for race, 73 of which are part of an existing research biobank of septic shock patients from our urban, safety-net hospital. We will enroll an additional 27 patients to complete the sample. We will examine the presence of risk alleles of single nucleotide polymorphisms for vasopressor-relevant genes by race. We will also examine the association between the targeted genetic polymorphisms and shock reversal. Career Development: During the proposed Career Development Award, I will work with my mentorship team to build the skills necessary to achieve independence as a clinical researcher. Specifically, I will 1) receive hands-on experience in the design and conduct of translational clinical research studies, 2) take didactic coursework in data science, biomedical informatics, and implementation science, 3) receive training and education in translational data science, clinical decision support, pharmacogenomics, and precision public health, and 4) become a leader and an effective mentor in academic emergency medicine.