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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

197 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-04-12

Study Completion Date

2025-06-28

Brief Summary

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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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Detailed Description

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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

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Carbapenem Resistant Bacterial Infection

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

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Deceased Patients

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

Machine Learning to Estimate Mortality

Intervention Type DIAGNOSTIC_TEST

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

Surviving Patients

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

Machine Learning to Estimate Mortality

Intervention Type DIAGNOSTIC_TEST

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

Interventions

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Machine Learning to Estimate Mortality

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

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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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.