Appropriate Use of Blood Cultures in the Emergency Department Through Machine Learning

Artificial Intelligence Clinical Trial

— ABC  

Official title:

Appropriate Use of Blood Cultures in the Emergency Department Through Machine Learning: a Randomized Controlled Trial

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06163781
• Other study ID #: NL81971.000.22
• Status: Recruiting
• Phase: N/A
• Start date: February 19, 2024
• Est. completion date: July 2027

Study information

• Verified date: May 2024
• Source: Amsterdam UMC, location VUmc
• Contact: Prabath WB Nanayakkara, MD, PhD
• Phone: +31204444444
• Email: p.nanayakkara[@]amsterdamumc.nl
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

The goal of this clinical trial is to study whether the use of our blood culture prediction tool is non-inferior to current practice and if it can improve certain outcomes in all adult patients presenting to the emergency department with a clinical indication for a blood culture analysis (according to the treating physician). The primary endpoint is 30-day mortality. Key secondary outcomes are: - hospital admission rates - in-hospital mortality - hospital length-of-stay. In the intervention group, the physician will follow the advice of our blood culture prediction tool. In the comparison group all patients will undergo a blood culture analysis.

Description:

Rationale: The overuse of blood cultures in emergency departments leads to low yields and high numbers of contaminated cultures, which is associated with increased diagnostics, antibiotic usage, prolonged hospitalisation, and mortality. Ideally, blood cultures would only be performed in patients with a high risk for a positive culture. The investigators have developed a machine learning model to predict the outcome of blood cultures in the ED. Retrospective and prospective validation of the tool in various settings show that it can be used to reduce the number of blood culture analyses by at least 30% and help avoid the hidden costs of contaminated cultures. Objective: This study aims to investigate whether the use of our blood culture prediction tool is non-inferior to current practice and if it can improve certain outcomes. Study design: A randomized controlled non-inferiority trial. Study population: All adult patients presenting to the emergency department with a clinical indication for a blood culture analysis (according to the treating physician). Intervention: In the control group, all patients will undergo a blood culture analysis. In the intervention group, the physician will follow the advice of our blood culture prediction tool. If the chance of a positive blood culture is < 5%, the blood culture analysis will be cancelled and the sample destroyed. If the change of a positive blood culture is > 5%, the blood culture analysis will be performed as usual. Main study parameters/endpoints: The primary endpoint is 30-day mortality, for which the investigators aim to show non-inferiority. Key secondary outcomes, for which the investigators also aim to show non-inferiority, are hospital admission rates, in-hospital mortality, and hospital length-of-stay.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 7584
• Est. completion date: July 2027
• Est. primary completion date: January 2027
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Age >= 18 years
• Have a clinical indication for a blood culture analysis (according to the treating physician)
• Have sufficient data recorded (laboratory results and vital sign measurements) for a prediction to be made (at least 20% of the needed parameters)

Exclusion Criteria:

• Central Venous Line (CVL) or Peripherally Inserted Central Catheter (PICC) in situ
• Neutrophil count < 0.5 * 109/L
• Candidemia or S. aureus bacteraemia in the past 3 months.
• Most likely diagnosis of endocarditis/spondylodiscitis/infected prosthetic material
• Pregnant or breastfeeding patients
• Not capable of giving informed consent

Related Conditions & MeSH terms

• Artificial Intelligence
• Emergencies
• Emergency Service, Hospital
• Machine Learning
• Randomized Controlled Trial

Intervention

Device:
• Blood culture prediction tool
Machine learning based predicition tool

Locations

Country	Name	City	State
Netherlands	Amsterdam UMC - location AMC	Amsterdam

Sponsors (1)

Lead Sponsor	Collaborator
Amsterdam UMC, location VUmc

Country where clinical trial is conducted

Netherlands, 

References & Publications (2)

Boerman AW, Schinkel M, Meijerink L, van den Ende ES, Pladet LC, Scholtemeijer MG, Zeeuw J, van der Zaag AY, Minderhoud TC, Elbers PWG, Wiersinga WJ, de Jonge R, Kramer MH, Nanayakkara PWB. Using machine learning to predict blood culture outcomes in the e — View Citation

Schinkel M, Boerman AW, Bennis FC, Minderhoud TC, Lie M, Peters-Sengers H, Holleman F, Schade RP, de Jonge R, Wiersinga WJ, Nanayakkara PWB. Diagnostic stewardship for blood cultures in the emergency department: A multicenter validation and prospective ev — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Other	30-day readmission rates		30 days
Other	Length of stay in the ED in hours		2 days
Other	Percentage of blood cultures avoided in the intervention group		90 days
Other	90 day mortality		90 days
Other	Number of blood cultures on each day of hospital stay (in admitted patients)		90 days
Other	Percentage of positive blood cultures in each group		90 days
Other	Total number of laboratory- and microbiology tests in the ED		2 days
Other	Total number of laboratory- and microbiology test on each day of hospital stay (in admitted patients)		90 days
Other	Percentage of patients receiving antibiotics in the ED		2 days
Other	Duration of antibiotic therapy		90 days
Other	Types of antibiotics given in the ED		2 days
Other	Model performance (AUC) during the trial		3 years
Other	Model performance in subgroup of Immunocompromised patients (triple immunosuppressive therapy)		3 years
Other	Model performance in subgroup of transplanted patients		3 years
Primary	30-day mortality		30 days
Secondary	hospital admission rates		1 day
Secondary	in-hospital mortality		90 days
Secondary	hospital length-of-stay		90 days