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

NCT ID: NCT06163781

Last Updated: 2024-05-07

Basic Information

• Recruitment Status: RECRUITING
• Clinical Phase: NA
• Total Enrollment: 7584 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2024-02-19
• Study Completion Date: 2027-07-31

Brief 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.

Detailed 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.

Conditions

Artificial Intelligence; Machine Learning; Microbiology; Emergency Service, Hospital; Randomized Controlled Trial

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: TREATMENT
• Blinding Strategy: NONE

Study Groups

Blood culture taken based on machine learning tool

Group Type: EXPERIMENTAL

Blood culture prediction tool

Intervention Type: DEVICE

Machine learning based predicition tool

Blood culture taken based on the treating physician

Group Type: NO_INTERVENTION

No interventions assigned to this group

Interventions

Blood culture prediction tool

Machine learning based predicition tool

Intervention Type: DEVICE

Eligibility Criteria

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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Amsterdam UMC, location VUmc

OTHER

Sponsor Role: lead

Responsible Party

Prabath W.B. Nanayakkara

MD, PhD

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Amsterdam UMC - location AMC

Amsterdam, , Netherlands

Site Status: RECRUITING

Countries

Netherlands

Central Contacts

Prabath WB Nanayakkara, MD, PhD

Role: CONTACT

p.nanayakkara@amsterdamumc.nl

+31204444444

Sheena C Bhagirath, MD

Role: CONTACT

s.bhagirath@amsterdamumc.nl

+31204444444

Facility Contacts

Prabath Nanayakkara, MD, PhD

Role: primary

References

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 emergency department: a single-centre, retrospective, observational study. BMJ Open. 2022 Jan 4;12(1):e053332. doi: 10.1136/bmjopen-2021-053332.

Reference Type: RESULT

PMID: 34983764 (View on PubMed)

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 evaluation of a machine learning prediction tool. EBioMedicine. 2022 Aug;82:104176. doi: 10.1016/j.ebiom.2022.104176. Epub 2022 Jul 16.

Reference Type: RESULT

PMID: 35853298 (View on PubMed)

van der Zaag AY, Bhagirath SC, Boerman AW, Schinkel M, Paranjape K, Azijli K, Ridderikhof ML, Lie M, Lissenberg-Witte B, Schade R, Wiersinga J, de Jonge R, Nanayakkara PWB. Appropriate use of blood cultures in the emergency department through machine learning (ABC): study protocol for a randomised controlled non-inferiority trial. BMJ Open. 2024 May 31;14(5):e084053. doi: 10.1136/bmjopen-2024-084053.

Reference Type: DERIVED

PMID: 38821574 (View on PubMed)

Other Identifiers

NL81971.000.22

Identifier Type: -

Identifier Source: org_study_id