Modeling disease management decisions for patients with pneumonia-related sepsis.

摘要

Sepsis, the tenth-leading cause of death in the United States, accounts for more than {dollar}16.7 billion in annual health care spending. A significant factor in these costs are unnecessarily long hospital lengths of stay, which stem from the lack of optimal hospital discharge policies and the inability to assess a patient's true underlying health state effectively. Researchers have explored ways of standardizing hospital discharge policies by comparing various strategies, but have not been able to determine optimal policies due to the large number of treatment options.; Furthering the state of research into decisions made in the management of patients with sepsis, this dissertation presents clinically based optimization models of pneumonia-related sepsis that use patient data to model disease progression over time. Formulated using Markov Decision Process (MDP) and Partially Observable Markov Decision Process (POMDP) techniques, these models consider the clinician's decisions of when to test for additional information about the patient's underlying health state and when to discharge the patient from the hospital.; This work utilizes data from the Genetic and Inflammatory Markers for Sepsis (GenIMS) study, a large multi-center clinical trial led by the University of Pittsburgh School of Medicine. A key aim of the GenIMS trial is to demonstrate that the levels of certain cytokines are predictors of patient survival. Utilizing these results, the models presented in this dissertation consider the question of when to test for cytokine levels using testing procedures that may be costly and inaccurate. A significant result of this dissertation demonstrates that testing should be performed when a clinician is considering the decision to discharge the patient from the hospital.; This study characterizes optimal testing and hospital discharge policies for multiple problem instances. In particular, multi-region control-limit policies are demonstrated for specific patient cohorts defined by age and race. It is shown that these control-limit policies depend on the patient's length of stay in the hospital. The effects of testing cost and accuracy on the optimal testing and discharge policies are also explored. Finally, clinical interpretations of the optimal policies are provided to demonstrate how these models can be used to inform clinical practice.; Keywords. Markov decision processes, partially observable Markov decision processes, medical decision making, sepsis