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

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.

Study Overview

• Status: Completed
• Conditions: Artificial Intelligence; Intensive Care Unit; Carbapenem Resistant Enterobacteriaceae Infection
• Intervention / Treatment: 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 Type: Observational
• Enrollment (Actual): 289

Contacts and Locations

Study Locations

• Turkey
  • Kocaeli, Turkey
    • Kocaeli University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: N/A

• 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.

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

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

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Risk of Carbapenem Resistant Klebsiella Infection	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)	3 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
To provide economic benefit	Secondarily, it is also aimed to provide economic benefits by preventing unnecessary antibiotic use.	3 months

Collaborators and Investigators

• Sponsor: Kocaeli University

Publications and helpful links

Helpful Links

• Using Machine Learning to Predict Antimicrobial Resistance of Acinetobacter Baumannii, Klebsiella Pneumoniae and Pseudomonas Aeruginosa Strains

Study record dates

Study Major Dates

• Study Start (Actual): December 1, 2023
• Primary Completion (Actual): June 22, 2024
• Study Completion (Actual): June 30, 2024

Study Registration Dates

• First Submitted: August 2, 2023
• First Submitted That Met QC Criteria: August 2, 2023
• First Posted (Actual): August 14, 2023

Study Record Updates

• Last Update Posted (Actual): April 9, 2025
• Last Update Submitted That Met QC Criteria: April 7, 2025
• Last Verified: April 1, 2025

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Infections; Bacterial Infections; Bacterial Infections and Mycoses; Gram-Negative Bacterial Infections; Enterobacteriaceae Infections
• Other Study ID Numbers: GOKAEK-2023/12.32

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No