Clinical trial NCT06163781: Appropriate Use of Blood Cultures in the Emergency Department Through Machine Learning
Official title: Appropriate Use of Blood Cultures in the Emergency Department Through Machine Learning: a Randomized Controlled Trial
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Artificial Intelligence
Machine Learning
Microbiology
Emergency Service, Hospital
Randomized Controlled Trial
Lead sponsor: Amsterdam UMC, location VUmc
Sponsor class: OTHER
First posted: 2023-12-11
Enrollment: 7584
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: TREATMENT

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.

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

Primary outcomes:
measure: 30-day mortality
timeFrame: 30 days
Secondary outcomes:
measure: hospital admission rates
timeFrame: 1 day
measure: in-hospital mortality
timeFrame: 90 days
measure: hospital length-of-stay
timeFrame: 90 days
Other outcomes:
measure: 30-day readmission rates
timeFrame: 30 days
measure: Length of stay in the ED in hours
timeFrame: 2 days
measure: Percentage of blood cultures avoided in the intervention group
timeFrame: 90 days
measure: 90 day mortality
timeFrame: 90 days
measure: Number of blood cultures on each day of hospital stay (in admitted patients)
timeFrame: 90 days
measure: Percentage of positive blood cultures in each group
timeFrame: 90 days
measure: Total number of laboratory- and microbiology tests in the ED
timeFrame: 2 days
measure: Total number of laboratory- and microbiology test on each day of hospital stay (in admitted patients)
timeFrame: 90 days
measure: Percentage of patients receiving antibiotics in the ED
timeFrame: 2 days
measure: Duration of antibiotic therapy
timeFrame: 90 days
measure: Types of antibiotics given in the ED
timeFrame: 2 days
measure: Model performance (AUC) during the trial
timeFrame: 3 years
measure: Model performance in subgroup of Immunocompromised patients (triple immunosuppressive therapy)
timeFrame: 3 years
measure: Model performance in subgroup of transplanted patients
timeFrame: 3 years
Locations:
city: Amsterdam
country: Netherlands
facility: Amsterdam UMC - location AMC
Study officials:
References:
citation: 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.
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 evaluation of a machine learning prediction tool. EBioMedicine. 2022 Aug;82:104176. doi: 10.1016/j.ebiom.2022.104176. Epub 2022 Jul 16.