Using machine learning to predict blood culture outcomes in the emergency department: a single-centre, retrospective, observational study.
Anneroos W Boerman, Michiel Schinkel, Lotta Meijerink...
https://pubmed.ncbi.nlm.nih.gov/34983764Actively Recruiting
Phase Not Applicable
Age: 18Years +
All Genders
Healthy Volunteers
ID06163781
Appropriate Use of Blood Cultures in the Emergency Department Through Machine Learning: a Randomized Controlled Trial
Led by Amsterdam UMC, location VUmc · Updated on 2024-05-07
7584
Participants Needed
1
Research Sites
25 weeks
Total Duration
On this page
AI-Summary
What this Trial Is About
This research aims to evaluate whether using a blood culture prediction tool based on machine learning is as effective as current practices for adult patients in the emergency department who need a blood culture analysis. The study focuses on patients who have a clinical indication for blood cultures as determined by their treating physician. The primary goal is to compare 30-day mortality rates, with additional outcomes including hospital admission rates, in-hospital mortality, and length of hospital stay. Participants will be randomly assigned to one of two groups. In the intervention group, doctors will use the blood culture prediction tool to decide whether to perform the blood culture test; if the predicted chance of a positive result is less than 5%, the test will be cancelled. In the control group, all patients will undergo blood culture analysis as usual. This randomized controlled trial aims to see if the prediction tool can reduce unnecessary blood cultures and associated issues without increasing risks. During the study, patients will be monitored for 30 days to assess mortality, with hospital admission, in-hospital mortality, and length of stay tracked up to 90 days. Data such as laboratory results and vital signs are collected to support predictions. Safety and outcomes will be carefully reviewed to determine if the tool offers a non-inferior approach compared to standard blood culture testing. Participation duration and follow-up will be based on these timelines to capture relevant clinical outcomes.
CONDITIONS
Brief Title
Appropriate Use of Blood Cultures in the Emergency Department Through Machine Learning
Who Can Participate
Age: 18Years +
All Genders
Healthy Volunteers
Eligibility Criteria
You may qualify if you...
- Age 18 years or older
- Clinical indication for blood culture analysis according to the treating physician
- Sufficient recorded data available (at least 20% of required laboratory results and vital signs) for prediction
You will not qualify if you...
- Presence of Central Venous Line (CVL) or Peripherally Inserted Central Catheter (PICC)
- Neutrophil count less than 0.5 x 10^9/L
- History of candidemia or Staphylococcus aureus bacteraemia in the past 3 months
- Likely diagnosis of endocarditis, spondylodiscitis, or infected prosthetic material
- Pregnant or breastfeeding
- Unable to provide informed consent
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
1
2
3
Your Study Journey
Screening
Duration - 2 to 4 weeks
Participants are screened for eligibility to participate in the trial.
Implementation
Duration - Up to 30 days
Participants undergo blood culture analysis based on either a machine learning prediction tool or the treating physician's decision.
1 visit (in-person) at the emergency department
Follow-up
Duration - Up to 90 days
Participants are monitored for outcomes including mortality, hospital admission, and length of stay following the blood culture analysis.
Follow-up assessments during hospital stay and up to 90 days after initial visit
Trial Site Locations
Total: 1 location
1
Amsterdam UMC - location AMC
Amsterdam, Netherlands
Actively Recruiting
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Research Team
P
Prabath WB Nanayakkara, MD, PhD
S
Sheena C Bhagirath, MD
How is the study designed?
Study Type
INTERVENTIONAL
Masking
NONE
Allocation
RANDOMIZED
Model
PARALLEL
Primary Purpose
TREATMENT
Number of Arms
2
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Published Research Related To This Trial
Diagnostic stewardship for blood cultures in the emergency department: A multicenter validation and prospective evaluation of a machine learning prediction tool.
Michiel Schinkel, Anneroos W Boerman, Frank C Bennis...
https://pubmed.ncbi.nlm.nih.gov/35853298Appropriate use of blood cultures in the emergency department through machine learning (ABC): study protocol for a randomised controlled non-inferiority trial.
Anuschka Y van der Zaag, Sheena C Bhagirath, Anneroos W Boerman...
https://pubmed.ncbi.nlm.nih.gov/38821574