Appropriate use of blood cultures in the emergency department through machine learning (ABC): study protocol for a randomised controlled non-inferiority trial.

Introduction: The liberal use of blood cultures in emergency departments (EDs) leads to low yields and high numbers of false-positive results. False-positive, contaminated cultures are associated with prolonged hospital stays, increased antibiotic usage and even higher hospital mortality rates. This trial aims to investigate whether a recently developed and validated machine learning model for predicting blood culture outcomes can safely and effectively guide clinicians in withholding unnecessary blood culture analysis.

Detecting changes in the performance of a clinical machine learning tool over time.

Background: Excessive use of blood cultures (BCs) in Emergency Departments (EDs) results in low yields and high contamination rates, associated with increased antibiotic use and unnecessary diagnostics. Our team previously developed and validated a machine learning model to predict BC outcomes and enhance diagnostic stewardship. While the model showed promising initial results, concerns over performance drift due to evolving patient demographics, clinical practices, and outcome rates warrant continual monitoring and evaluation of such models.

Embracing cohort heterogeneity in clinical machine learning development: a step toward generalizable models.

This study is a simple illustration of the benefit of averaging over cohorts, rather than developing a prediction model from a single cohort. We show that models trained on data from multiple cohorts can perform significantly better in new settings than models based on the same amount of training data but from just a single cohort. Although this concept seems simple and obvious, no current prediction model development guidelines recommend such an approach.