Clinical Trial Details
— Status: Not yet recruiting
Administrative data
| NCT number |
NCT05312034 |
| Other study ID # |
15054519.3.0000.5249C |
| Secondary ID |
|
| Status |
Not yet recruiting |
| Phase |
N/A
|
| First received |
|
| Last updated |
|
| Start date |
April 1, 2022 |
| Est. completion date |
December 29, 2023 |
Study information
| Verified date |
March 2022 |
| Source |
D'Or Institute for Research and Education |
| Contact |
Fernando Bozza, PhD |
| Phone |
55 21 993031551 |
| Email |
bozza.fernando[@]gmail.com |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Interventional
|
Clinical 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. 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.
Description:
This proposal is a five-step quality improvement project.
1. 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.
2. 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.
3. 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.
