Author ORCID Identifier

https://orcid.org/0000-0001-8814-5834

Year of Publication

2020

Degree Name

Doctor of Philosophy (PhD)

Document Type

Doctoral Dissertation

College

Arts and Sciences

Department

Statistics

First Advisor

Dr. Arnold Stromberg

Abstract

Sepsis occurs in a patient when an infection enters into the blood stream and spreads throughout the body causing a cascading response from the immune system. Sepsis is one of the leading causes of morbidity and mortality in today’s hospitals. This is despite published and accepted guidelines for timely and appropriate interventions for septic patients. The largest barrier to applying these interventions is the early identification of septic patients. Early identification and treatment leads to better outcomes, shorter lengths of stay, and financial savings for healthcare institutions. In order to increase the lead time in recognizing patients trending towards septicemia a multivariate discrimination model was developed to create an early identification sepsis score to identify patients who are starting to show signs of sepsis. The model utilizes the patient’s heart rate, respiratory rate, systolic blood pressure, temperature, and oxygen saturation and the change from each of their respective baselines. Patient specific baselines are based on each patient’s previous vital sign measures leading up to the current set of measures.

Theoretical assumptions are applied to this sepsis score to investigate distributional properties of the measure for applicable inferences. Finally, a new approximation to the degrees of freedom of a t-distribution, 𝜈𝑠, is proposed. This new approximation is investigated and compared to the Satterthwaite approximation.

Digital Object Identifier (DOI)

https://doi.org/10.13023/etd.2020.393

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