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

Study Overview
=================
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.

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.

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

Conditions
-----------------
Carbapenem Resistant Bacterial Infection

Intervention / Treatment
-----------------
* Diagnostic Test: Machine Learning to Estimate Mortality


Participation Criteria
=================
Eligibility Criteria
-----------------
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.

Ages Eligible for Study
-----------------
Minimum Age: 18 Years

Sexes Eligible for Study
-----------------
All

Study Plan
=================
How is the study designed?
-----------------

Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Deceased Patients<br>Carbapenem-resistant Gram-negative bacilli Blood Stream Infection With mortality | Diagnostic Test: Machine Learning to Estimate Mortality<br>* Using deep learning we try to develop an algorithm and anticipate mortality<br>|
| Surviving Patients<br>Carbapenem-resistant Gram-negative bacilli Blood Stream Infection Without mortality | Diagnostic Test: Machine Learning to Estimate Mortality<br>* Using deep learning we try to develop an algorithm and anticipate mortality<br>|

What is the study measuring?
-----------------
Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 

 Terms related to the study
=================
Keywords Provided by Centre Hospitalier Valida
-----------------
Intensive Care Unit