Artificial IntelligenceABCNCT06163781
Appropriate Use of Blood Cultures in the Emergency Department Through Machine Learning
A study testing Blood culture prediction tool in people with Artificial Intelligence and Machine Learning, run by Amsterdam UMC, location VUmc at 1 site in Netherlands.
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… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Artificial Intelligence, Machine Learning, Microbiology
- What is being tested?
- Blood culture prediction tool
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 7,584 participants
- Where
- 1 siteNetherlands
- Study dates
- 19 Feb 2024to Jul 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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)
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Netherlands1 site, 1 recruiting
- Recruiting
Amsterdam UMC - location AMC
Amsterdam
Prabath Nanayakkara, MD, PhD
About this study
Official title: Appropriate Use of Blood Cultures in the Emergency Department Through Machine Learning: a Randomized Controlled Trial
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.
Read the 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
- Being tested
- Blood culture prediction tool
- Medical subject headings
- Emergencies
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Blood culture prediction toolDevice
Machine learning based predicition tool
| Study group | Role | What they receive |
|---|---|---|
| Blood culture taken based on machine learning tool | Experimental | Not described Receives: Device: Blood culture prediction tool |
| Blood culture taken based on the treating physician | No intervention | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
30-day mortality
Measured: 30 days
Secondary outcomes (3)
hospital admission rates
Measured: 1 day
in-hospital mortality
Measured: 90 days
hospital length-of-stay
Measured: 90 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Dec 2023
- Study start19 Feb 2024
- Primary completionJan 2027 (estimated)
- Study completionJul 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for Artificial IntelligenceStudies of the same condition, open and under way.
- More trials run by Amsterdam UMC, location VUmcThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 7,584 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Amsterdam UMC, location VUmc
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Prabath W.B. Nanayakkara, Amsterdam UMC, location VUmc
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06163781
- Sponsor's protocol ID
- NL81971.000.22
- Acronym
- ABC
- Status last verified
- May 2024
- First submitted
- 12 Oct 2023
- Last updated
- 7 May 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: : 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.
Publications
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.
PubMed 34983764Schinkel 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.
PubMed 35853298van 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.
PubMed 38821574
Study at a glance
From the registry record, last updated by the sponsor on 7 May 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Appropriate Use of Blood Cultures in the Emergency Department Through Machine Learning: a Randomized Controlled Trial
- ClinicalTrials.gov ID
- NCT06163781
- Acronym / sponsor ID
- ABC / NL81971.000.22
- Conditions
- Artificial Intelligence, Machine Learning, Microbiology, Emergency Service, Hospital, Randomized Controlled Trial
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 7,584 participants (estimated)
- Lead sponsor
- Amsterdam UMC, location VUmcUniversity, hospital or other
- Study start
- 19 Feb 2024 (actual)
- Primary completion
- Jan 2027 (estimated)
- Study completion
- Jul 2027 (estimated)
- First posted
- 11 Dec 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Blood culture taken based on machine learning tool | Experimental | Device: Blood culture prediction tool |
| Blood culture taken based on the treating physician | No intervention | – |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- 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)
Key exclusion criteria (6 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Blood culture prediction toolMachine learning based predicition tool | Device | Blood culture taken based on machine learning tool |
Primary outcome
30-day mortality
Time frame: 30 days
3 secondary outcomes
hospital admission rates
Time frame: 1 day
in-hospital mortality
Time frame: 90 days
hospital length-of-stay
Time frame: 90 days
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 May 2024.
- Results publication: PubMed 34983764Boerman 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.
- Results publication: PubMed 35853298Schinkel 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.
- Derived from this study: PubMed 38821574van 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.
- Individual participant dataSharing planned: Yes. : 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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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Blood culture prediction tool in people with Artificial Intelligence and Machine Learning, run by Amsterdam UMC, location VUmc at 1 site in Netherlands.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 7 May 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Netherlands. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Prabath WB Nanayakkara, MD, PhD (+31204444444, p.nanayakkara@amsterdamumc.nl), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
30-day mortality (time frame: 30 days). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06163781, which also lists 3 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 7 May 2024.