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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:
In the comparison group all patients will undergo a blood culture analysis.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Blood culture taken based on machine learning tool | Experimental |
| |
| Blood culture taken based on the treating physician | No Intervention |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Blood culture prediction tool | Device | Machine learning based predicition tool |
|
| Measure | Description | Time Frame |
|---|---|---|
| 30-day mortality | 30 days |
| Measure | Description | Time Frame |
|---|---|---|
| hospital admission rates | 1 day | |
| in-hospital mortality | 90 days | |
| hospital length-of-stay |
| Measure | Description | Time Frame |
|---|---|---|
| 30-day readmission rates | 30 days | |
| Length of stay in the ED in hours | 2 days | |
| Percentage of blood cultures avoided in the intervention group |
Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Prabath WB Nanayakkara, MD, PhD | Contact | +31204444444 | p.nanayakkara@amsterdamumc.nl | |
| Sheena C Bhagirath, MD | Contact | +31204444444 | s.bhagirath@amsterdamumc.nl |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Amsterdam UMC - location AMC | Recruiting | Amsterdam | Netherlands |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 34983764 | Result | 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. | |
| 35853298 |
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: Participant data underlying the results of this study can be shared. The data can be requested following publication of this work. The data can be shared with researchers who provide a methodologically sound proposal, which is allowed under our local privacy regulations. Proposals should be directed to the corresponding author and requestors will need to sign a data access agreement.
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| ID | Term |
|---|---|
| D004630 | Emergencies |
| ID | Term |
|---|---|
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
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| 90 days |
| 90 days |
| 90 day mortality | 90 days |
| Number of blood cultures on each day of hospital stay (in admitted patients) | 90 days |
| Percentage of positive blood cultures in each group | 90 days |
| Total number of laboratory- and microbiology tests in the ED | 2 days |
| Total number of laboratory- and microbiology test on each day of hospital stay (in admitted patients) | 90 days |
| Percentage of patients receiving antibiotics in the ED | 2 days |
| Duration of antibiotic therapy | 90 days |
| Types of antibiotics given in the ED | 2 days |
| Model performance (AUC) during the trial | 3 years |
| Model performance in subgroup of Immunocompromised patients (triple immunosuppressive therapy) | 3 years |
| Model performance in subgroup of transplanted patients | 3 years |
| Result |
| 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. |
| 38821574 | Derived | 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. |