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.

METHODS AND ANALYSIS: A randomised controlled, non-inferiority trial comparing current practice with a machine learning-guided approach. The primary objective is to determine whether the machine learning based approach is non-inferior to standard practice based on 30-day mortality. Secondary outcomes include hospital length-of stay and hospital admission rates. Other outcomes include model performance and antibiotic usage. Participants will be recruited in the EDs of multiple hospitals in the Netherlands. A total of 7584 participants will be included.

ETHICS AND DISSEMINATION: Possible participants will receive verbal information and a paper information brochure regarding the trial. They will be given at least 1 hour consideration time before providing informed consent. Research results will be published in peer-reviewed journals. This study has been approved by the Amsterdam University Medical Centers' local medical ethics review committee (No 22.0567). The study will be conducted in concordance with the principles of the Declaration of Helsinki and in accordance with the Medical Research Involving Human Subjects Act, General Data Privacy Regulation and Medical Device Regulation.


Original languageEnglish
Article numbere084053
Pages (from-to)e084053
JournalBMJ Open
Issue number5
Publication statusPublished - 31 May 2024


  • Anti-Bacterial Agents/therapeutic use
  • Blood Culture/methods
  • Emergency Service, Hospital
  • Equivalence Trials as Topic
  • Hospital Mortality
  • Humans
  • Length of Stay/statistics & numerical data
  • Machine Learning
  • Netherlands
  • Randomized Controlled Trials as Topic
  • Unnecessary Procedures/statistics & numerical data

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