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

NCT06167083 | Completed

• 1 other identifier: GOKAEK-2023/12.31
• Study Type: observational
• Target: 197
• Locations: 1 country
• Sites: 1

Brief 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. In the intensive care unit, patients with bloodstream infections, both with and without mortality, will be examined retrospectively in two subgroups for comparison.

Conditions

Carbapenem Resistant Bacterial Infection

Keywords

Intensive Care Unit

Outcome Measures

Primary Outcomes (1)

• 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

  3 months

Study Arms (2)

Deceased Patients

Carbapenem-resistant Gram-negative bacilli Blood Stream Infection With mortality

Diagnostic Test: Machine Learning to Estimate Mortality

Surviving Patients

Carbapenem-resistant Gram-negative bacilli Blood Stream Infection Without mortality

Diagnostic Test: Machine Learning to Estimate Mortality

Interventions

Machine Learning to Estimate Mortality DIAGNOSTIC_TEST

Using deep learning we try to develop an algorithm and anticipate mortality

Deceased Patients; Surviving Patients

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• 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

You may qualify if:

• 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.

You may not qualify if:

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

Sponsors & Collaborators

• Kocaeli University: lead

Study Sites (1)

Kocaeli University

Kocaeli, Turkey (Türkiye)

Location

Related Publications (1)

• 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 Mar 16:8850666261423499. doi: 10.1177/08850666261423499. Online ahead of print.

  PMID: 41837809 DERIVED

Related Links

• Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU

Study Officials

• özlem güler

  Kocaeli University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Phd Medical Doctor

Study Record Dates

• First Submitted: December 4, 2023
• First Posted: December 12, 2023
• Study Start: April 12, 2024
• Primary Completion: June 28, 2025
• Study Completion: June 28, 2025
• Last Updated: March 11, 2026

Record last verified: 2026-03

Locations

TU (1)