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dc.contributor.authorPastoor, Devin DeForest
dc.date.accessioned2019-03-18T18:16:11Z
dc.date.available2019-03-18T18:16:11Z
dc.date.issued2018
dc.identifier.urihttp://hdl.handle.net/10713/8516
dc.description2018en_US
dc.descriptionPharmaceutical Sciencesen_US
dc.descriptionUniversity of Maryland, Baltimoreen_US
dc.descriptionPh.D.en_US
dc.description.abstractPersonalized medicine continues to gain momentum as a topic for discussion, yet directly linking patient-level decision support to more advanced analytical techniques, such as nonlinear mixed effects modeling, is not being practiced in most hospitals. Current practice for Vancomycin therapy uses dosing nomograms to determine the dosing regimen for patients. For simplicity, these nomograms stratify patients into bins based on some combination of weight, serum creatinine, and/or age to adjust starting regimens. Yet, studies across the US and Europe have shown as few as 37% of neonates achieve recommended target concentrations using such nomograms. The purpose of this research was to develop a bayesian decision support toolkit to provide adaptive, individualized dose recommendations for neonates. First, a bayesian nonlinear mixed effect model was developed and qualified for predictive forecasting in individual patients. Second, this model was used to develop a novel algorithm for dose individualization. Finally, a web application was developed to allow clinicians to provide decision support for clinicians involved in vancomycin dosing decisions. The proposed strategy can decrease the number of patients improperly dosed up to 90%, drastically reducing the chance for treatment failure, toxicity-related adverse events, and resistance development.en_US
dc.language.isoen_USen_US
dc.subjectBayesian decision support toolkiten_US
dc.subjectdose individualizationen_US
dc.subjectnonlinear mixed effects
dc.subjectpharmaceutical sciences
dc.subjectpharmacometrics
dc.subject.meshDecision Support Techniques
dc.subject.meshInfant, Newborn
dc.subject.meshPrecision Medicine
dc.titleInnovation of Vancomycin Treatment in Neonates Via A Bayesian Dose Optimization Toolkit For Adaptive Individualized Therapeutic Managementen_US
dc.typedissertationen_US
dc.date.updated2019-03-18T16:00:30Z
dc.language.rfc3066en
dc.contributor.advisorGobburu, Jogarao
refterms.dateFOA2019-03-18T18:16:12Z


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