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Analysis of VRE Transmission In A Major Hospital Setting Using Hierarchical Clustering and Bayesian Phylodynamic Methods

Clemons II, Arvon A (2022) Analysis of VRE Transmission In A Major Hospital Setting Using Hierarchical Clustering and Bayesian Phylodynamic Methods. Master's Thesis, University of Pittsburgh. (Unpublished)

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Abstract

Healthcare-associated infections (HAIs) can prolong and add substantial costs to hospital stays. One study estimated that 1 out of 25 hospitalized patients were expected to be infected by a HAI on a daily basis. Minimizing HAIs would increase the quality of healthcare within hospitals; thus infection prevention methods must utilize various strategies to identify the cause of HAIs and develop interventions to reduce cases.

Currently many healthcare institutions utilize whole-genome sequencing (WGS) in identifying outbreaks and combine them with epidemiological methods in developing protocols to minimize the size of an outbreak. A recent example would be researchers in the University of Pittsburgh – Medical Center Presbyterian Hospital (UPMC), who have developed a machine-learning based method to incorporate WGS data with electronic health records (EHRs) to determine the most likely routes of transmission during an outbreak of vancomycin-resistant enterococci (VRE).

Using a ground truth (GT) dataset based on the VRE outbreak, we performed an assessment to compare two methods for categorizing bacterial isolates into transmission routes. We compared hierarchical clustering methods with a Bayesian phylodynamic model to determine which classification had the most similarity to the GT dataset.

Our analysis proved inconclusive in identifying a method with superior performance due to computational limitations for the Bayesian phylodynamic model, however the urgency and time constraints of an active outbreak have shown to be better suited for the hierarchical clustering method and we recommend the Bayesian phylodynamic model as part of a retrospective analysis of an outbreak. This analysis which identifies routes of infectious disease transmission within a hospital setting could be utilized in optimizing infection prevention strategies within the hospital setting and lower the rate of HAIs – making a positive public health impact through reducing cost of care and increasing quality of care.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Clemons II, Arvon Aaac103@pitt.eduaac1030000-0002-6257-9519
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorCarlson, Jenna Cjnc35@pitt.edujnc350000-0001-5483-0833
Committee MemberAda, Youkayouk@pitt.edu0000-0001-6912-9759
Committee MemberDaria, Van Tynevantyne@pitt.edu0000-0001-7284-0103
Date: 10 May 2022
Date Type: Publication
Defense Date: 22 April 2022
Approval Date: 10 May 2022
Submission Date: 29 April 2022
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 81
Institution: University of Pittsburgh
Schools and Programs: Graduate School of Public Health > Biostatistics
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: Bayesian; Phylogenetic; Hierarchical Clustering; VRE; Infection Prevention; Machine Learning; Outbreak; HAI; Infection Control; EHR; Electronic Health Record; Healthcare-Associated Infections
Related URLs:
Date Deposited: 10 May 2022 20:56
Last Modified: 10 May 2022 20:56
URI: http://d-scholarship.pitt.edu/id/eprint/42864

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