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OpenTrials
Completed

NCT Number: NCT06167083

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

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.

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Key information

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Kocaeli University

Kocaeli, Turkey (Türkiye)

About this study

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.

Who can participate

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Machine Learning to Estimate Mortality

Diagnostic Test

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

Primary outcomes

  1. 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

Sponsors and collaborators

Lead sponsor

Kocaeli University

Other

Registry information

Official study title

Machine Learning in the ICU: Predicting Mortality in Patients With Carbapenem-Resistant Gram-Negative Bacilli Bloodstream Infections

Acronym: ICU

Important dates

Study start
2024
Primary completion
2025
Study completion
2025
First posted
Dec 12, 2023
Registry last updated
Mar 11, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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