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A Novel Approach to Antimicrobial Resistance: Machine Learning Predictions for Carbapenem-Resistant Klebsiella in ICUs (ICU)

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ClinicalTrials.gov Identifier: NCT05985057
Recruitment Status : Recruiting
First Posted : August 14, 2023
Last Update Posted : February 5, 2024
Sponsor:
Information provided by (Responsible Party):
Volkan Alparslan, Kocaeli University

Study Description
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Brief Summary:

The aim of this study to predict carbapenem resistant Klebsiella spp. earlier in our patients monitored in our Intensive Care Unit in the future, using artificial intelligence.

Patients with bloodstream infection and pneumonia caused by Klebsiella spp. will be comparatively examined in two groups, as sensitive and resistant. Resistance will be attempted to be predicted with deep machine learning.


Condition or disease Intervention/treatment
Carbapenem Resistant Enterobacteriaceae Infection Artificial Intelligence Intensive Care Unit Other: Artificial intelligence

Detailed Description:

Antimicrobial resistance is a globally increasing threat and has serious consequences on both public health and the economy. In an infection that may develop with a resistant microorganism, therapeutic options are limited, hence early and effective treatment that can be initiated by predicting resistance will make a difference in patient prognosis.

Today, artificial intelligence and machine learning are changing our medical practice. When the literature is reviewed, there are studies suggesting that machine learning can predict antimicrobial resistance.Risk factors for carbapenem-resistant Klebsiella spp. have been previously identified. These previously identified risk factors will be evaluated retrospectively in our own patients and an algorithm related to the prediction of resistance will be developed with the help of machine learning.

Our goal is to predict bacterial resistance earlier in our patients monitored in our Intensive Care Unit in the future, using artificial intelligence, and to facilitate our patients' access to early and effective treatment options.

Secondarily, it is also aimed to provide economic benefits by preventing unnecessary antibiotic use.

Access to patients' data will be obtained retrospectively through the hospital automation system.

Publications in the literature will be examined, and the risk factors causing the development of infection with carbapenem-resistant Klebsiella spp. will be evaluated.

Patients with carbapenem resistance and sensitivity will be compared in two separate subgroups.

The obtained features will be classified using various decision trees and neural algorithms separately. The data obtained will be statistically compared in the distinction of resistance and sensitivity. Statistical evaluation was done with IBM SPSS 29.0 (IBM Corp., Armonk, NY, USA). Demographic data, descriptive statistics, Categorical variables will be expressed in terms of frequency (percentage).

Categorical variables will be expressed with the chi-square test. The performance of Machine Learning algorithms will be evaluated by ROC analysis, AUC, classification accuracy, sensitivity, and specificity values will be calculated.

Study Design
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Study Type : Observational
Estimated Enrollment : 300 participants
Observational Model: Cohort
Time Perspective: Retrospective
Official Title: A Novel Approach to Antimicrobial Resistance: Machine Learning Predictions for Carbapenem-Resistant Klebsiella in ICUs
Actual Study Start Date : December 1, 2023
Estimated Primary Completion Date : June 22, 2024
Estimated Study Completion Date : June 30, 2024

Resource links provided by the National Library of Medicine


Groups and Cohorts
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Group/Cohort Intervention/treatment
Patients with carbapenem resistant Klebsiella spp. infection Other: Artificial intelligence
Prediction of carbapenem resistance via deep machine learning model
Patients with carbapenem sensitive Klebsiella spp. infection Other: Artificial intelligence
Prediction of carbapenem resistance via deep machine learning model


Outcome Measures
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Primary Outcome Measures :
  1. Risk of Carbapenem Resistant Klebsiella Infection [ Time Frame: 3 months ]
    The sensitivity and specificity of a diagnostic method based on machine learning will be measured with the AUC-ROC curve (Area Under the Receiver Operating Characteristic curve)


Eligibility Criteria
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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Sampling Method:   Non-Probability Sample
Study Population
Patients monitored in our third-level intensive care unit between June 2017 and June 2023 will be evaluated retrospectively. Patients with pneumonia and bloodstream infection developed with Klebsiella spp. will be included in the study.
Criteria

Inclusion Criteria:

Patients monitored in our third-level intensive care unit between June 2017 and June 2023 will be evaluated retrospectively. Patients with pneumonia and bloodstream infection developed with Klebsiella spp. will be included in the study.

Exclusion Criteria:

  • Patients under the age of 18 have not been included in the study.
  • Infections outside of the respiratory tract and bloodstream have not been included in the study.
  • Patients with respiratory tract colonization and without active inflammation have also not been included.
Contacts and Locations
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Top of Page Study Description Study Design Groups and Cohorts Outcome Measures Eligibility Criteria Contacts and Locations More Information

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT05985057


Contacts
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Contact: volkan Alparslan 905059374578 volknn@hotmail.com

Locations
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Turkey
Kocaeli University Recruiting
Kocaeli, Turkey
Contact: Volkan Alparslan    +90 5059374578    volknn@hotmail.com   
Sponsors and Collaborators
Kocaeli University
More Information
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Additional Information:

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Responsible Party: Volkan Alparslan, Medical Doctor, Kocaeli University
ClinicalTrials.gov Identifier: NCT05985057    
Other Study ID Numbers: GOKAEK-2023/12.32
First Posted: August 14, 2023    Key Record Dates
Last Update Posted: February 5, 2024
Last Verified: February 2024
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Undecided

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Additional relevant MeSH terms:
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Enterobacteriaceae Infections
Gram-Negative Bacterial Infections
Bacterial Infections
Bacterial Infections and Mycoses
Infections