Carbapenem Resistant Enterobacteriaceae InfectionICUNCT05985057
A Novel Approach to Antimicrobial Resistance: Machine Learning Predictions for Carbapenem-Resistant Klebsiella in ICUs
An observational study of Carbapenem Resistant Enterobacteriaceae Infection and Artificial Intelligence, run by Kocaeli University at 1 site in Turkey (Türkiye).
Trial summary
The aim of this study to predict carbapenem resistant Klebsiella spp. earlier in our patients monitored in our Intensive Care Unit in the future, using artificial intelligence. Patients with bloodstream infection and pneumonia caused by Klebsiella spp. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Carbapenem Resistant Enterobacteriaceae Infection, Artificial Intelligence, Intensive Care Unit
- What is being tested?
- Artificial intelligence
- How many people
- 289 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 1 Dec 2023to 30 Jun 2024
- Phase
- Observational
- 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
- Adult, Older adult
Study population: Patients monitored in our third-level intensive care unit between June 2017 and June 2023 will be evaluated retrospectively. Patients with pneumonia and bloodstream infection developed with Klebsiella spp. will be included in the study.
Who may be able to join
- Patients monitored in our third-level intensive care unit between June 2017 and June 2023 will be evaluated retrospectively. Patients with pneumonia and bloodstream infection developed with Klebsiella spp. will be included in the study.
Who cannot join
- Patients under the age of 18 have not been included in the study.
- Infections outside of the respiratory tract and bloodstream have not been included in the study.
- Patients with respiratory tract colonization and without active inflammation have also not been included.
Trial locations and contacts
1 site in 1 country.
About this study
The aim of this study to predict carbapenem resistant Klebsiella spp. earlier in our patients monitored in our Intensive Care Unit in the future, using artificial intelligence.
Patients with bloodstream infection and pneumonia caused by Klebsiella spp. will be comparatively examined in two groups, as sensitive and resistant. Resistance will be attempted to be predicted with deep machine learning.
Read the detailed description
Antimicrobial resistance is a globally increasing threat and has serious consequences on both public health and the economy. In an infection that may develop with a resistant microorganism, therapeutic options are limited, hence early and effective treatment that can be initiated by predicting resistance will make a difference in patient prognosis.
Today, artificial intelligence and machine learning are changing our medical practice. When the literature is reviewed, there are studies suggesting that machine learning can predict antimicrobial resistance.Risk factors for carbapenem-resistant Klebsiella spp. have been previously identified. These previously identified risk factors will be evaluated retrospectively in our own patients and an algorithm related to the prediction of resistance will be developed with the help of machine learning.
Our goal is to predict bacterial resistance earlier in our patients monitored in our Intensive Care Unit in the future, using artificial intelligence, and to facilitate our patients' access to early and effective treatment options.
Secondarily, it is also aimed to provide economic benefits by preventing unnecessary antibiotic use.
Access to patients' data will be obtained retrospectively through the hospital automation system.
Publications in the literature will be examined, and the risk factors causing the development of infection with carbapenem-resistant Klebsiella spp. will be evaluated.
Patients with carbapenem resistance and sensitivity will be compared in two separate subgroups.
The obtained features will be classified using various decision trees and neural algorithms separately. The data obtained will be statistically compared in the distinction of resistance and sensitivity. Statistical evaluation was done with IBM SPSS 29.0 (IBM Corp., Armonk, NY, USA). Demographic data, descriptive statistics, Categorical variables will be expressed in terms of frequency (percentage).
Categorical variables will be expressed with the chi-square test. The performance of Machine Learning algorithms will be evaluated by ROC analysis, AUC, classification accuracy, sensitivity, and specificity values will be calculated.
- Conditions
- Carbapenem Resistant Enterobacteriaceae Infection, Artificial Intelligence, Intensive Care Unit
- Being tested
- Artificial intelligence
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial intelligenceOther
Prediction of carbapenem resistance via deep machine learning model
| Study group | Role | What they receive |
|---|---|---|
| Patients with carbapenem resistant Klebsiella spp. infection | Not described Receives: Other: Artificial intelligence | |
| Patients with carbapenem sensitive Klebsiella spp. infection | Not described Receives: Other: Artificial intelligence |
What is being measured
The primary outcome is the main question the study is designed to answer.
Risk of Carbapenem Resistant Klebsiella Infection
The sensitivity and specificity of a diagnostic method based on machine learning will be measured with the AUC-ROC curve (Area Under the Receiver Operating Characteristic curve)
Measured: 3 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 Aug 2023
- Study start1 Dec 2023
- Primary completion22 Jun 2024
- Study completion30 Jun 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Carbapenem Resistant Enterobacteriaceae 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
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 289 participants (actual)
Sponsor and investigators
- Lead sponsor
- Kocaeli University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Volkan Alparslan, Kocaeli University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05985057
- Sponsor's protocol ID
- GOKAEK-2023/12.32
- Acronym
- ICU
- Status last verified
- Apr 2025
- First submitted
- 2 Aug 2023
- Last updated
- 9 Apr 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Alparslan V, Guler O, Inner B, Duzgun A, Baykara N, Kus A. A novel approach to antimicrobial resistance: Machine learning predictions for carbapenem-resistant Klebsiella in intensive care units. Int J Med Inform. 2025 Mar;195:105751. doi: 10.1016/j.ijmedinf.2024.105751. Epub 2024 Dec 7.
PubMed 39674007
Study at a glance
From the registry record, last updated by the sponsor on 9 Apr 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Novel Approach to Antimicrobial Resistance: Machine Learning Predictions for Carbapenem-Resistant Klebsiella in ICUs
- ClinicalTrials.gov ID
- NCT05985057
- Acronym / sponsor ID
- ICU / GOKAEK-2023/12.32
- Conditions
- Carbapenem Resistant Enterobacteriaceae Infection, Artificial Intelligence, Intensive Care Unit
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 289 participants (actual)
- Lead sponsor
- Kocaeli UniversityUniversity, hospital or other
- Study start
- 1 Dec 2023 (actual)
- Primary completion
- 22 Jun 2024 (actual)
- Study completion
- 30 Jun 2024 (actual)
- First posted
- 14 Aug 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 aim of this study to predict carbapenem resistant Klebsiella spp. earlier in our patients monitored in our Intensive Care Unit in the future, using artificial intelligence.
Patients with bloodstream infection and pneumonia caused by Klebsiella spp. will be comparatively examined in two groups, as sensitive and resistant. Resistance will be attempted to be predicted with deep machine learning.
Detailed description
Antimicrobial resistance is a globally increasing threat and has serious consequences on both public health and the economy. In an infection that may develop with a resistant microorganism, therapeutic options are limited, hence early and effective treatment that can be initiated by predicting resistance will make a difference in patient prognosis.
Today, artificial intelligence and machine learning are changing our medical practice. When the literature is reviewed, there are studies suggesting that machine learning can predict antimicrobial resistance.Risk factors for carbapenem-resistant Klebsiella spp. have been previously identified. These previously identified risk factors will be evaluated retrospectively in our own patients and an algorithm related to the prediction of resistance will be developed with the help of machine learning.
Our goal is to predict bacterial resistance earlier in our patients monitored in our Intensive Care Unit in the future, using artificial intelligence, and to facilitate our patients' access to early and effective treatment options.
Secondarily, it is also aimed to provide economic benefits by preventing unnecessary antibiotic use.
Access to patients' data will be obtained retrospectively through the hospital automation system.
Publications in the literature will be examined, and the risk factors causing the development of infection with carbapenem-resistant Klebsiella spp. will be evaluated.
Patients with carbapenem resistance and sensitivity will be compared in two separate subgroups.
The obtained features will be classified using various decision trees and neural algorithms separately. The data obtained will be statistically compared in the distinction of resistance and sensitivity. Statistical evaluation was done with IBM SPSS 29.0 (IBM Corp., Armonk, NY, USA). Demographic data, descriptive statistics, Categorical variables will be expressed in terms of frequency (percentage).
Categorical variables will be expressed with the chi-square test. The performance of Machine Learning algorithms will be evaluated by ROC analysis, AUC, classification accuracy, sensitivity, and specificity values will be calculated.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Patients with carbapenem resistant Klebsiella spp. infection | – | Other: Artificial intelligence |
| Patients with carbapenem sensitive Klebsiella spp. infection | – | Other: Artificial intelligence |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- Patients monitored in our third-level intensive care unit between June 2017 and June 2023 will be evaluated retrospectively. Patients with pneumonia and bloodstream infection developed with Klebsiella spp. will be included in the study.
Key inclusion criteria (1 listed)
- Patients monitored in our third-level intensive care unit between June 2017 and June 2023 will be evaluated retrospectively. Patients with pneumonia and bloodstream infection developed with Klebsiella spp. will be included in the study.
Key exclusion criteria (3 listed)
- Patients under the age of 18 have not been included in the study.
- Infections outside of the respiratory tract and bloodstream have not been included in the study.
- Patients with respiratory tract colonization and without active inflammation have also not been included.
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 |
|---|---|---|
| Artificial intelligencePrediction of carbapenem resistance via deep machine learning model | Other | Patients with carbapenem resistant Klebsiella spp. infection, Patients with carbapenem sensitive Klebsiella spp. infection |
Primary outcome
Risk of Carbapenem Resistant Klebsiella Infection
Time frame: 3 months
The sensitivity and specificity of a diagnostic method based on machine learning will be measured with the AUC-ROC curve (Area Under the Receiver Operating Characteristic curve)
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Apr 2025.
- Derived from this study: PubMed 39674007Alparslan V, Guler O, Inner B, Duzgun A, Baykara N, Kus A. A novel approach to antimicrobial resistance: Machine learning predictions for carbapenem-resistant Klebsiella in intensive care units. Int J Med Inform. 2025 Mar;195:105751. doi: 10.1016/j.ijmedinf.2024.105751. Epub 2024 Dec 7.
- Using Machine Learning to Predict Antimicrobial Resistance of Acinetobacter Baumannii, Klebsiella Pneumoniae and Pseudomonas Aeruginosa StrainsLink provided by the sponsor.
- Individual participant dataSharing planned: Undecided
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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.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Carbapenem Resistant Enterobacteriaceae Infection and Artificial Intelligence, run by Kocaeli University at 1 site in Turkey (Türkiye).
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 3 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: finished, as of the sponsor's last update on 9 Apr 2025. 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 Turkey (Türkiye). 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Risk of Carbapenem Resistant Klebsiella Infection (time frame: 3 months). 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 NCT05985057, which also lists 1 linked publication.
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 9 Apr 2025.