A Systematic Machine Learning-Based Investigation of Bloodstream Infection Biomarkers to Predict Clinical Outcome

Gilliland, Rory Lewis

A Systematic Machine Learning-Based Investigation of Bloodstream Infection Biomarkers to Predict Clinical Outcome

dc.contributor.advisor	MacDonald, M. Ethan
dc.contributor.advisor	Lewis, Ian A.
dc.contributor.author	Gilliland, Rory Lewis
dc.contributor.committeemember	Dingle, Tanis C.
dc.contributor.committeemember	Lee, Joon
dc.contributor.committeemember	Messier, Geoffrey
dc.date	2024-05
dc.date.accessioned	2024-04-17T17:04:27Z
dc.date.available	2024-04-17T17:04:27Z
dc.date.issued	2024-04-16
dc.description.abstract	Bloodstream infections (BSI) represent a major burden on modern medicine, annually causing millions of cases worldwide with high mortality rates. Concerted efforts have been made in recent decades to improve BSI diagnostics to treat these dangerous infections more rapidly and precisely. However, these efforts have been hindered by an incomplete understanding of what factors make certain BSIs more severe than others. To address this shortcoming, this thesis applied statistical, machine learning, and epidemiological analyses to systematically investigate patient- and microbe-related traits as biomarkers of BSI clinical outcome. The analyses were facilitated by the Calgary BSI Cohort, a collection of over 35,000 BSI episodes detailing microbial genomic, proteomic, and metabolomic profiles linked to extensive patient medical records. Unsurprisingly, the results demonstrated that patient-related traits (e.g., age and comorbidity) are tightly linked to BSI clinical outcome. Patient mortality, hospital stay duration, and healthcare cost could all be predicted using patient features with areas under the receiver operating characteristic curve exceeding 0.80. Several microbe-related traits such as species classification and virulence factors were also found to be associated — albeit less strongly — with BSI patient mortality risk. Interestingly though, when patient- and microbe-related traits were combined, their predictive performance for BSI patient mortality did not surpass that of patient traits alone. Follow-up analyses revealed a compelling possible explanation: many “predictive” microbial traits may simply report the underlying characteristics of patients that tend to be infected by the pathogens carrying those traits. Taken together, the results suggest that patient-related traits are critically important as markers of BSI clinical outcome. Prompt development of formalized, patient-factor-based BSI risk stratification tools seems warranted to assist physicians in precisely identifying high-risk infections early in the clinical trajectory. In contrast, while microbial characteristics are invaluable for directing clinical therapy of BSIs, they provide little unique predictive information for BSI clinical outcome, making them unsuitable as biomarkers in the context of BSI risk stratification. Future research investigating the diagnostic relevance of the microbe should take great care to adequately correct for confounding patient dynamics.
dc.identifier.citation	Gilliland, R. L. (2024). A systematic machine learning-based investigation of bloodstream infection biomarkers to predict clinical outcome (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.uri	https://hdl.handle.net/1880/118424
dc.language.iso	en
dc.publisher.faculty	Graduate Studies
dc.publisher.institution	University of Calgary
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subject	Machine Learning
dc.subject	Genomics
dc.subject	Proteomics
dc.subject	Artificial Intelligence
dc.subject	Bloodstream Infection
dc.subject	Bacteremia
dc.subject	Biomarkers
dc.subject.classification	Microbiology
dc.subject.classification	Engineering--Biomedical
dc.subject.classification	Artificial Intelligence
dc.subject.classification	Biochemistry
dc.title	A Systematic Machine Learning-Based Investigation of Bloodstream Infection Biomarkers to Predict Clinical Outcome
dc.type	master thesis
thesis.degree.discipline	Engineering – Biomedical
thesis.degree.grantor	University of Calgary
thesis.degree.name	Master of Science (MSc)
ucalgary.thesis.accesssetbystudent	I do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.