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

Study Overview
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Brief Summary
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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
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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.

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

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

Intervention / Treatment
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* Device: Blood culture prediction tool


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Treatment
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Blood culture taken based on machine learning tool<br> | Device: Blood culture prediction tool<br>* Machine learning based predicition tool<br>|
| No Intervention: Blood culture taken based on the treating physician<br> | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 30-day mortality |  | 30 days | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| hospital admission rates |  | 1 day | 
| in-hospital mortality |  | 90 days | 
| hospital length-of-stay |  | 90 days |