Nosocomial InfectionNCT05312034
Application of an Antimicrobial Stewardship Program in Brazilian ICUs Using Machine Learning Techniques and an Educational Model
A study testing Implementation of the predictive model for an antimicrobial management program in people with Nosocomial Infection and Sepsis, run by D'Or Institute for Research and Education.
Trial summary
Antimicrobial agents are frequently used empirically and include therapy for both Gram-positive and Gram-negative bacteria. In Brazil, multidrug-resistant Gram-negative pathogens are the cause of most nosocomial infections in ICUs. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Nosocomial Infection, Sepsis
- What is being tested?
- Implementation of the predictive model for an antimicrobial management program
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 100 participants
- Where
- No sites listed yet
- Study dates
- 1 Apr 2022to 29 Dec 2023
- Phase
- Interventional
- Are there results?
- None posted in the registry
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
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- prescribers from the hospital units participating in the study.
Who cannot join
- prescribers who do not work in intensive care units.
- refusal to participate
Trial locations and contacts
No sites are listed yet.
About this study
Antimicrobial agents are frequently used empirically and include therapy for both Gram-positive and Gram-negative bacteria. In Brazil, multidrug-resistant Gram-negative pathogens are the cause of most nosocomial infections in ICUs. Therefore, the excessive use of antimicrobials to treat Gram-positive bacteria represents an opportunity to reduce unnecessary antibiotic use in critically ill patients. Besides, the success of a program aimed at reducing the use of antibiotics to treat gram-positive bacteria could also evolve to include other microorganisms, such as gram-negative bacteria and fungi. Analyzing data from the ICUs of the associated hospital network, high use of broad-spectrum antibiotics and vancomycin were observed, although MRSA infections rarely occur.
Thus, if physicians could identify patients at high risk of infection by gram-positive bacteriaa reduction in antibiotic consumption could occur.. The more accurate treatments could result in better patient outcomes, reduce the antibiotics' adverse effects, and decrease the prevalence of multidrug-resistant bacteria. Therefore, our main goal is to reduce antibiotic use by applying an intervention with three main objectives: (i) to educate the medical team, (ii) to provide a tool that can help physicians prescribing antibiotics, and (iii) to find and reduce differences in antibiotic prescription between hospitals with low- and high-resources.
Read more
To achieve these objectives, he same intervention will be applied in ICUs of two hospitals with different access to resources. Both are part of a network of hospitals associated with our group.
First, baseline data corresponding to patient characteristics, antibiotic use, microbiological outcomes and current administration programs in practice at selected hospitals will be analyzed. TThen, a predictive model to detect patients at high risk of Gram-positive infection will be developed. After that, t will be applied for three months as an educational tool to improve medical decisions regarding antibiotic prescription. After obtaining feedback and suggestions from physicians and other hospital and infection control members, the model will be adjusted and applied in the two selected hospitals for use in real time. For one year, we will monitor the intervention and analyze the data monthly.
Read the detailed description
This proposal is a five-step quality improvement project.
- Analysis of baseline data [3 months]: Retrospective data will be collected from ten hospitals of Rede D'Or São Luiz. Patient characteristics, microbiological results and the use of antimicrobial agents will be analyzed. Stewardship programs currently in place will also be recorded.
- Development of the predictive model [3 months]: Collected data and machine learning techniques will be used to develop a predictive model to identify patients at risk of Gram-positive infection. This model will be evaluated using standard methods (e.g., accuracy and confusion matrix) and through clinical decision curves. This model will be embedded in an app and a web page to provide real-time guidance on the predicted probability of infection due to Gram-positive agents.
- Educational and calibration phase [3 months]: Firstly it will be used use the predictive model as a simulation tool to educate physicians. For three months, physicians will use the model to understand the main factors associated with Gram-positive infection. They will test the model using real-case data previously collected at the hospitals. The model will provide them information such as the probability of that patient having a Gram-positive infection and the proportion of infected patients in that ICU and hospital.
After that, a meeting with all ICU and infection control members from participating hospitals will be held. A specific probability cutoff will be defined for starting gram-positive coverage. For example, the members can define that they feel comfortable not treating empirically gram-positive bacteria if the predicted probability is below a given threshold (say 5%). Quality improvement protocol will also involve other traditional methods to decrease antibiotic use, including audit feedback and daily remembrances to withdraw gram-positive antibiotic coverage. Educational material will be developed and provided for all sites, as well as in-site training.
This phase will motivate the involvement of the hospital members, especially physicians, which can improve engagement to the intervention to be implemented afterward. Hopefully, it will also generate insights and feedback from the medical team to improve the tool to be implemented.
- Conditions
- Nosocomial Infection, Sepsis
- Being tested
- Implementation of the predictive model for an antimicrobial management program
- Keywords
- antimicrobial, antibiotics
- Medical subject headings
- Cross Infection, Sepsis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Implementation of the predictive model for an antimicrobial management programBehavioural
Firstly it will be used the predictive model as a simulation tool to educate physicians. For three months, physicians will use the model to understand the main factors associated with Gram-positive infection. They will test the model using real-case data previously collected at the hospitals. The model will provide them information such as the probability of that patient having a Gram-positive infection and the proportion of infected patients in that ICU and hospital.
This model will be embedded in an app and a web page to provide real-time guidance on the predicted probability of infection due to Gram-positive agents.
The intervention will be implemented in two selected hospitals, aiming at monthly decreasing the use of broad-spectrum antibiotics while maintaining or reducing the ICU standardized mortality ratio and the standardized resource use.
| Study group | Role | What they receive |
|---|---|---|
| Application of an antimicrobial stewardship program in ICUs | Experimental | Application of an antimicrobial stewardship program in Brazilian ICUs using machine learning techniques and an educational model Receives: Behavioral: Implementation of the predictive model for an antimicrobial management program |
What is being measured
The primary outcome is the main question the study is designed to answer.
Antimicrobial consumption
It was evaluated through the Defined Daily Dose (DDD): The assumed average maintenance dose per day for a drug used for its main indication in adults; and Duration of Treatment (DOT):Duration of Treatment with antibiotics.
Measured: Baseline
Antimicrobial consumption
It was evaluated through the Defined Daily Dose (DDD): The assumed average maintenance dose per day for a drug used for its main indication in adults; and Duration of Treatment (DOT): Duration of Treatment with antibiotics
Measured: During the intervention
Secondary outcomes (2)
Mortality
ICU Mortality
Measured: number of deaths in 60 days
Gram-positive infection
Number of patients with missed Gram-positive infection
Measured: immediately after the microbiologics analysis
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Apr 2022
- Study start1 Apr 2022 (estimated)
- Primary completion29 Dec 2023 (estimated)
- Study completion29 Dec 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Nosocomial InfectionStudies of the same condition, open and under way.
- 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
- Prevention
- Allocation
- Not applicable
- Intervention model
- Single group
- Model description
- A predictive model to identify patients at risk of Gram-positive infection. This model will be embedded in an app and a web page to provide real-time guidance on the predicted probability of infection due to Gram-positive agents. The intervention will be implemented in two selected hospitals, aiming at monthly decreasing the use of broad-spectrum antibiotics while maintaining or reducing the ICU standardized mortality ratio and the standardized resource use.
- Masking
- None (open label)
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- D'Or Institute for Research and Education
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Fernando Bozza, PhD, Principal investigator, D'Or Institute for Research and Education (IDOR)
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05312034
- Sponsor's protocol ID
- 15054519.3.0000.5249C
- Status last verified
- Mar 2022
- First submitted
- 16 Mar 2022
- Last updated
- 5 Apr 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 5 Apr 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Application of an Antimicrobial Stewardship Program in Brazilian ICUs Using Machine Learning Techniques and an Educational Model
- ClinicalTrials.gov ID
- NCT05312034
- Acronym / sponsor ID
- 15054519.3.0000.5249C
- Conditions
- Nosocomial Infection, Sepsis
- Keywords
- antimicrobial, antibiotics
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- D'Or Institute for Research and EducationUniversity, hospital or other
- Study start
- 1 Apr 2022 (estimated)
- Primary completion
- 29 Dec 2023 (estimated)
- Study completion
- 29 Dec 2023 (estimated)
- First posted
- 5 Apr 2022 (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.
Antimicrobial agents are frequently used empirically and include therapy for both Gram-positive and Gram-negative bacteria. In Brazil, multidrug-resistant Gram-negative pathogens are the cause of most nosocomial infections in ICUs. Therefore, the excessive use of antimicrobials to treat Gram-positive bacteria represents an opportunity to reduce unnecessary antibiotic use in critically ill patients. Besides, the success of a program aimed at reducing the use of antibiotics to treat gram-positive bacteria could also evolve to include other microorganisms, such as gram-negative bacteria and fungi. Analyzing data from the ICUs of the associated hospital network, high use of broad-spectrum antibiotics and vancomycin were observed, although MRSA infections rarely occur.
Thus, if physicians could identify patients at high risk of infection by gram-positive bacteriaa reduction in antibiotic consumption could occur.. The more accurate treatments could result in better patient outcomes, reduce the antibiotics' adverse effects, and decrease the prevalence of multidrug-resistant bacteria. Therefore, our main goal is to reduce antibiotic use by applying an intervention with three main objectives: (i) to educate the medical team, (ii) to provide a tool that can help physicians prescribing antibiotics, and (iii) to find and reduce differences in antibiotic prescription between hospitals with low- and high-resources.
To achieve these objectives, he same intervention will be applied in ICUs of two hospitals with different access to resources. Both are part of a network of hospitals associated with our group.
First, baseline data corresponding to patient characteristics, antibiotic use, microbiological outcomes and current administration programs in practice at selected hospitals will be analyzed. TThen, a predictive model to detect patients at high risk of Gram-positive infection will be developed. After that, t will be applied for three months as an educational tool to improve medical decisions regarding antibiotic prescription. After obtaining feedback and suggestions from physicians and other hospital and infection control members, the model will be adjusted and applied in the two selected hospitals for use in real time. For one year, we will monitor the intervention and analyze the data monthly.
Detailed description
This proposal is a five-step quality improvement project.
- Analysis of baseline data [3 months]: Retrospective data will be collected from ten hospitals of Rede D'Or São Luiz. Patient characteristics, microbiological results and the use of antimicrobial agents will be analyzed. Stewardship programs currently in place will also be recorded.
- Development of the predictive model [3 months]: Collected data and machine learning techniques will be used to develop a predictive model to identify patients at risk of Gram-positive infection. This model will be evaluated using standard methods (e.g., accuracy and confusion matrix) and through clinical decision curves. This model will be embedded in an app and a web page to provide real-time guidance on the predicted probability of infection due to Gram-positive agents.
- Educational and calibration phase [3 months]: Firstly it will be used use the predictive model as a simulation tool to educate physicians. For three months, physicians will use the model to understand the main factors associated with Gram-positive infection. They will test the model using real-case data previously collected at the hospitals. The model will provide them information such as the probability of that patient having a Gram-positive infection and the proportion of infected patients in that ICU and hospital.
After that, a meeting with all ICU and infection control members from participating hospitals will be held. A specific probability cutoff will be defined for starting gram-positive coverage. For example, the members can define that they feel comfortable not treating empirically gram-positive bacteria if the predicted probability is below a given threshold (say 5%). Quality improvement protocol will also involve other traditional methods to decrease antibiotic use, including audit feedback and daily remembrances to withdraw gram-positive antibiotic coverage. Educational material will be developed and provided for all sites, as well as in-site training.
This phase will motivate the involvement of the hospital members, especially physicians, which can improve engagement to the intervention to be implemented afterward. Hopefully, it will also generate insights and feedback from the medical team to improve the tool to be implemented.
Study design
- Primary purpose
- Prevention
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
- Model description
- A predictive model to identify patients at risk of Gram-positive infection. This model will be embedded in an app and a web page to provide real-time guidance on the predicted probability of infection due to Gram-positive agents. The intervention will be implemented in two selected hospitals, aiming at monthly decreasing the use of broad-spectrum antibiotics while maintaining or reducing the ICU standardized mortality ratio and the standardized resource use.
| Arm | Type | Receives |
|---|---|---|
| Application of an antimicrobial stewardship program in ICUs | Experimental | Behavioral: Implementation of the predictive model for an antimicrobial management programApplication of an antimicrobial stewardship program in Brazilian ICUs using machine learning techniques and an educational model |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- prescribers from the hospital units participating in the study.
Key exclusion criteria (2 listed)
- prescribers who do not work in intensive care units.
- refusal to participate
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 |
|---|---|---|
| Implementation of the predictive model for an antimicrobial management programFirstly it will be used the predictive model as a simulation tool to educate physicians. For three months, physicians will use the model to understand the main factors associated with Gram-positive infection. They will test the model using real-case data previously collected at the hospitals. The model will provide them information such as the probability of that patient having a Gram-positive infection and the proportion of infected patients in that ICU and hospital. This model will be embedded in an app and a web page to provide real-time guidance on the predicted probability of infection due to Gram-positive agents. The intervention will be implemented in two selected hospitals, aiming at monthly decreasing the use of broad-spectrum antibiotics while maintaining or reducing the ICU standardized mortality ratio and the standardized resource use. | Behavioural | Application of an antimicrobial stewardship program in ICUs |
Primary outcome
Antimicrobial consumption
Time frame: Baseline
It was evaluated through the Defined Daily Dose (DDD): The assumed average maintenance dose per day for a drug used for its main indication in adults; and Duration of Treatment (DOT):Duration of Treatment with antibiotics.
Antimicrobial consumption
Time frame: During the intervention
It was evaluated through the Defined Daily Dose (DDD): The assumed average maintenance dose per day for a drug used for its main indication in adults; and Duration of Treatment (DOT): Duration of Treatment with antibiotics
2 secondary outcomes
Mortality
Time frame: number of deaths in 60 days
Gram-positive infection
Time frame: immediately after the microbiologics analysis
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 Apr 2022.
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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. This study is not recruiting at the moment.
Fernando Bozza, PhD principal investigator
D'Or Institute for Research and Education (IDOR)
Questions about this trial
What is this clinical trial studying?
A study testing Implementation of the predictive model for an antimicrobial management program in people with Nosocomial Infection and Sepsis, run by D'Or Institute for Research and Education.
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 1 inclusion and 2 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: status not confirmed, as of the sponsor's last update on 5 Apr 2022. Status can change between updates, so confirm with the study contact before making plans.
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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Fernando Bozza, PhD (55 21 993031551, bozza.fernando@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Antimicrobial consumption (time frame: Baseline). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 5 Apr 2022.