Evaluation of machine learning pipeline for blood culture outcome prediction on prospectively collected emergency department data.

Abstract

<b>Introduction.</b> Bloodstream infections (BSIs) are particularly problematic in the emergency department (ED) of hospitals, where patients often present with undiagnosed illness, one cause of which may be undetected BSI. Identifying whether a patient needs a blood culture (BC) performed is one component of this challenge with implications for diagnostic efficiency and avoidance of unnecessary resource expenditure.<b>Gap statement.</b> In Western Australia, there has been no previous study investigating methods to predict BC outcome in an ED patient cohort.<b>Aim.</b> This article assesses the feasibility of previously developed machine learning (ML) models for BC outcome prediction using a prospectively collected ED patient dataset.<b>Methodology.</b> An ML pipeline containing models previously trained using complete blood count (CBC), white blood cell differential (DIFF) and cell population data (CPD) generated by Sysmex XN-2000 haematology analysers was further evaluated using prospectively collected data containing patient sample results from the ED at Sir Charles Gairdner Hospital (SCGH), Perth, Western Australia. Blood samples used to produce CBC, DIFF and CPD were obtained at the same time as BC samples.<b>Results.</b> There were 59 samples from 58 unique patients. Forty-nine of those samples were associated with negative BC results and 10 with positive BC results. We evaluated previously developed XGBoost (XG) and random forest (RF) ML models for positive BC outcome prediction. The RF and XG models obtained mean area under the receiver operating characteristic curve scores of 0.865 (95% CI, 0.763-0.947) and 0.833 (95% CI, 0.683-0.953) with the ED dataset.<b>Conclusion.</b> The results presented in this study provide a foundation for further validation and shadow deployment of BC outcome prediction models in clinical settings and support future planning of clinical trials.