Machine Learning in the ICU: Predicting Mortality in Bloodstream Infections (ICU:Intensive Care Unit) (ICU)

April 11, 2024 updated by: Özlem Güler, Kocaeli University

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.

Study Overview

Status

Active, not recruiting

Conditions

Intervention / Treatment

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. We aim to predict mortality that can develop in Carbapenem-resistant Gram-negative bacilli bloodstream infections with a machine learning-based model.

Study Type

Observational

Enrollment (Estimated)

180

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • Turkey
      • Kocaeli, Turkey
        • Kocaeli University

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

N/A

Sampling Method

Non-Probability Sample

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

Description

Inclusion Criteria:

  • 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.

Exclusion Criteria:

  • Patients under the age of 18 and those with infections other than bloodstream infections will not be included.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Deceased Patients
Carbapenem-resistant Gram-negative bacilli Blood Stream Infection With mortality
Diagnostic test: Machine Learning to Estimate Mortality
Using deep learning we try to develop an algorithm and anticipate mortality
Surviving Patients
Carbapenem-resistant Gram-negative bacilli Blood Stream Infection Without mortality
Diagnostic test: Machine Learning to Estimate Mortality
Using deep learning we try to develop an algorithm and anticipate mortality

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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
3 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Kocaeli University

Investigators

  • Principal Investigator: özlem güler, Kocaeli University

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

Helpful Links

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

April 12, 2024

Primary Completion (Estimated)

October 15, 2024

Study Completion (Estimated)

October 15, 2024

Study Registration Dates

First Submitted

December 4, 2023

First Submitted That Met QC Criteria

December 4, 2023

First Posted (Actual)

December 12, 2023

Study Record Updates

Last Update Posted (Actual)

April 12, 2024

Last Update Submitted That Met QC Criteria

April 11, 2024

Last Verified

April 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • GOKAEK-2023/12.31

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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