A Systematic Machine Learning-Based Investigation of Bloodstream Infection Biomarkers to Predict Clinical Outcome
Date
2024-04-16
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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.
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Keywords
Machine Learning, Genomics, Proteomics, Artificial Intelligence, Bloodstream Infection, Bacteremia, Biomarkers
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.