Carbapenem Resistant Bacterial InfectionICUNCT06167083
Machine Learning in the ICU: Predicting Mortality in Bloodstream Infections (ICU:Intensive Care Unit)
An observational study of Carbapenem Resistant Bacterial Infection, run by Kocaeli University at 1 site in Turkey (Türkiye).
Trial summary
Using our own patient data, our study aimed to predict mortality that can develop in Carbapenem-resistant Gram-negative bacilli bloodstream infections with a machine learning-based model. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Carbapenem Resistant Bacterial Infection
- What is being tested?
- Machine Learning to Estimate Mortality
- How many people
- 197 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 12 Apr 2024to 28 Jun 2025
- 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: All patients who were monitored in our tertiary intensive care unit for six years retrospectively and developed bloodstream infections with Carbapenem-resistant Enterobacteriaceae, Acinetobacter baumannii and Pseudomonas aeruginosa have been included in the study with their personal data anonymized
Who may be able to join
- In our study, patients who were monitored in our hospital's tertiary Intensive Care Unit between June 2017 and June 2023 and developed bloodstream infections with Carbapenem-resistant Enterobacteriaceae, Carbapenem-resistant Acinetobacter baumannii and Carbapenem-resistant Pseudomonas aeruginosa will be retrospectively included.
Who cannot join
- Patients under the age of 18 and those with infections other than bloodstream infections will not be included.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Machine Learning in the ICU: Predicting Mortality in Patients With Carbapenem-Resistant Gram-Negative Bacilli Bloodstream Infections
Using our own patient data, our study aimed to predict mortality that can develop in Carbapenem-resistant Gram-negative bacilli bloodstream infections with a machine learning-based model.
In the intensive care unit, patients with bloodstream infections, both with and without mortality, will be examined retrospectively in two subgroups for comparison.
Read the detailed description
Carbapenems are one of the last-resort antibiotics used to treat severe infections caused by multi-drug resistant Gram-negative pathogens. Infections with Carbapenem-resistant Gram-negative bacilli (CR-GNB) have become widespread in the past decade, posing serious threats to public health. Carbapenem-resistant Enterobacteriaceae (CRE), Carbapenem-resistant Acinetobacter baumannii (CRAB), and Carbapenem-resistant Pseudomonas aeruginosa (CRPA) top the priority list of antibiotic-resistant bacteria worldwide. CR-GNB causes a broad spectrum of infections, including bacteremia, urinary tract infections, pneumonia, and intra-abdominal infections. Carbapenem-resistant bloodstream infections are a significant cause of morbidity and mortality, and therapeutic options in treatment are extremely limited. By evaluating risk factors in patients monitored in the intensive care unit, scoring systems that can predict prognosis reduce mortality risk by ensuring the early application of effective antibiotics and timely hemodynamic support that are currently in use.
With the accumulation of big data and advancements in data storage techniques, innovative and pragmatic machine learning methods that have entered our lives demonstrate good prediction performance in the medical field. Machine learning-based models developed to predict mortality in patients monitored in the intensive care unit are available in the literature and provide an opportunity for earlier intervention in patients.
Using our own patient data, In the intensive care unit, patients with bloodstream infections, both with and without mortality, will be examined retrospectively in two subgroups for comparison. The investigators aim to predict mortality that can develop in Carbapenem-resistant Gram-negative bacilli bloodstream infections with a machine learning-based model.
- Conditions
- Carbapenem Resistant Bacterial Infection
- Being tested
- Machine Learning to Estimate Mortality
- Keywords
- Intensive Care Unit
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Machine Learning to Estimate MortalityDiagnostic test
Using deep learning we try to develop an algorithm and anticipate mortality
| Study group | Role | What they receive |
|---|---|---|
| Deceased Patients | Carbapenem-resistant Gram-negative bacilli Blood Stream Infection With mortality Receives: Diagnostic Test: Machine Learning to Estimate Mortality | |
| Surviving Patients | Carbapenem-resistant Gram-negative bacilli Blood Stream Infection Without mortality Receives: Diagnostic Test: Machine Learning to Estimate Mortality |
What is being measured
The primary outcome is the main question the study is designed to answer.
Risk of Mortality
The sensitivity and specificity will be defined with AUC-ROC curve (Area Under the Receiver Operating Characteristic curve) using machine learning algorithm
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 posted12 Dec 2023
- Study start12 Apr 2024
- Primary completion28 Jun 2025
- Study completion28 Jun 2025
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 Bacterial 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
- 197 participants (actual)
Sponsor and investigators
- Lead sponsor
- Kocaeli University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Özlem Güler, Kocaeli University
- Investigators
- özlem güler, Principal investigator, Kocaeli University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06167083
- Sponsor's protocol ID
- GOKAEK-2023/12.31
- Acronym
- ICU
- Status last verified
- Mar 2026
- First submitted
- 4 Dec 2023
- Last updated
- 11 Mar 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Guler O, Alparslan V, Inner B, Balci S, Duzgun A, Baykara N, Kus A. Machine Learning in the ICU: Predicting Mortality in Patients with Carbapenem-Resistant Gram-Negative Bacilli Bloodstream Infections. J Intensive Care Med. 2026 Apr;41(4):309-319. doi: 10.1177/08850666261423499. Epub 2026 Mar 16.
PubMed 41837809
Study at a glance
From the registry record, last updated by the sponsor on 11 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Machine Learning in the ICU: Predicting Mortality in Patients With Carbapenem-Resistant Gram-Negative Bacilli Bloodstream Infections
- ClinicalTrials.gov ID
- NCT06167083
- Acronym / sponsor ID
- ICU / GOKAEK-2023/12.31
- Conditions
- Carbapenem Resistant Bacterial Infection
- Keywords
- Intensive Care Unit
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 197 participants (actual)
- Lead sponsor
- Kocaeli UniversityUniversity, hospital or other
- Study start
- 12 Apr 2024 (actual)
- Primary completion
- 28 Jun 2025 (actual)
- Study completion
- 28 Jun 2025 (actual)
- First posted
- 12 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.
Using our own patient data, our study aimed to predict mortality that can develop in Carbapenem-resistant Gram-negative bacilli bloodstream infections with a machine learning-based model.
In the intensive care unit, patients with bloodstream infections, both with and without mortality, will be examined retrospectively in two subgroups for comparison.
Detailed description
Carbapenems are one of the last-resort antibiotics used to treat severe infections caused by multi-drug resistant Gram-negative pathogens. Infections with Carbapenem-resistant Gram-negative bacilli (CR-GNB) have become widespread in the past decade, posing serious threats to public health. Carbapenem-resistant Enterobacteriaceae (CRE), Carbapenem-resistant Acinetobacter baumannii (CRAB), and Carbapenem-resistant Pseudomonas aeruginosa (CRPA) top the priority list of antibiotic-resistant bacteria worldwide. CR-GNB causes a broad spectrum of infections, including bacteremia, urinary tract infections, pneumonia, and intra-abdominal infections. Carbapenem-resistant bloodstream infections are a significant cause of morbidity and mortality, and therapeutic options in treatment are extremely limited. By evaluating risk factors in patients monitored in the intensive care unit, scoring systems that can predict prognosis reduce mortality risk by ensuring the early application of effective antibiotics and timely hemodynamic support that are currently in use.
With the accumulation of big data and advancements in data storage techniques, innovative and pragmatic machine learning methods that have entered our lives demonstrate good prediction performance in the medical field. Machine learning-based models developed to predict mortality in patients monitored in the intensive care unit are available in the literature and provide an opportunity for earlier intervention in patients.
Using our own patient data, In the intensive care unit, patients with bloodstream infections, both with and without mortality, will be examined retrospectively in two subgroups for comparison. The investigators aim to predict mortality that can develop in Carbapenem-resistant Gram-negative bacilli bloodstream infections with a machine learning-based model.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Deceased Patients | – | Diagnostic Test: Machine Learning to Estimate MortalityCarbapenem-resistant Gram-negative bacilli Blood Stream Infection With mortality |
| Surviving Patients | – | Diagnostic Test: Machine Learning to Estimate MortalityCarbapenem-resistant Gram-negative bacilli Blood Stream Infection Without mortality |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- All patients who were monitored in our tertiary intensive care unit for six years retrospectively and developed bloodstream infections with Carbapenem-resistant Enterobacteriaceae, Acinetobacter baumannii and Pseudomonas aeruginosa have been included in the study with their personal data anonymized
Key inclusion criteria (1 listed)
- In our study, patients who were monitored in our hospital's tertiary Intensive Care Unit between June 2017 and June 2023 and developed bloodstream infections with Carbapenem-resistant Enterobacteriaceae, Carbapenem-resistant Acinetobacter baumannii and Carbapenem-resistant Pseudomonas aeruginosa will be retrospectively included.
Key exclusion criteria (1 listed)
- Patients under the age of 18 and those with infections other than bloodstream infections will not be 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 |
|---|---|---|
| Machine Learning to Estimate MortalityUsing deep learning we try to develop an algorithm and anticipate mortality | Diagnostic test | Deceased Patients, Surviving Patients |
Primary outcome
Risk of Mortality
Time frame: 3 months
The sensitivity and specificity will be defined with AUC-ROC curve (Area Under the Receiver Operating Characteristic curve) using machine learning algorithm
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 11 Mar 2026.
- Derived from this study: PubMed 41837809Guler O, Alparslan V, Inner B, Balci S, Duzgun A, Baykara N, Kus A. Machine Learning in the ICU: Predicting Mortality in Patients with Carbapenem-Resistant Gram-Negative Bacilli Bloodstream Infections. J Intensive Care Med. 2026 Apr;41(4):309-319. doi: 10.1177/08850666261423499. Epub 2026 Mar 16.
- Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICULink 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.
özlem güler principal investigator
Kocaeli University
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 Bacterial Infection, 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 1 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 11 Mar 2026. 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 Mortality (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 NCT06167083, 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 11 Mar 2026.