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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

Tracking Information
First Submitted Date August 2, 2023
First Posted Date August 14, 2023
Last Update Posted Date February 5, 2024
Actual Study Start Date December 1, 2023
Estimated Primary Completion Date June 22, 2024   (Final data collection date for primary outcome measure)
Current Primary Outcome Measures
 (submitted: August 16, 2023)		 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)
Original Primary Outcome Measures
 (submitted: August 2, 2023)		 To predict bacterial resistance via artifical intelligence [ Time Frame: 3 months ]
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.
Change History
Current Secondary Outcome Measures Not Provided
Original Secondary Outcome Measures
 (submitted: August 2, 2023)		 To provide economic benefit [ Time Frame: 3 months ]
Secondarily, it is also aimed to provide economic benefits by preventing unnecessary antibiotic use.
Current Other Pre-specified Outcome Measures Not Provided
Original Other Pre-specified Outcome Measures Not Provided
 
Descriptive Information
Brief Title A Novel Approach to Antimicrobial Resistance: Machine Learning Predictions for Carbapenem-Resistant Klebsiella in ICUs
Official Title A Novel Approach to Antimicrobial Resistance: Machine Learning Predictions for Carbapenem-Resistant Klebsiella in ICUs
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.
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
Study Design Observational Model: Cohort
Time Perspective: Retrospective
Target Follow-Up Duration Not Provided
Biospecimen Not Provided
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.
Condition
  • Carbapenem Resistant Enterobacteriaceae Infection
  • Artificial Intelligence
  • Intensive Care Unit
Intervention Other: Artificial intelligence
Prediction of carbapenem resistance via deep machine learning model
Study Groups/Cohorts
  • Patients with carbapenem resistant Klebsiella spp. infection
    Intervention: Other: Artificial intelligence
  • Patients with carbapenem sensitive Klebsiella spp. infection
    Intervention: Other: Artificial intelligence
Publications * Not Provided

*   Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline.
 
Recruitment Information
Recruitment Status Recruiting
Estimated Enrollment
 (submitted: August 2, 2023)		 300
Original Estimated Enrollment Same as current
Estimated Study Completion Date June 30, 2024
Estimated Primary Completion Date June 22, 2024   (Final data collection date for primary outcome measure)
Eligibility 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.
Sex/Gender
Sexes Eligible for Study: All
Ages 18 Years and older   (Adult, Older Adult)
Accepts Healthy Volunteers Not Provided
Contacts
Contact: volkan Alparslan 905059374578 volknn@hotmail.com
Listed Location Countries Turkey
Removed Location Countries  
 
Administrative Information
NCT Number NCT05985057
Other Study ID Numbers GOKAEK-2023/12.32
Has Data Monitoring Committee No
U.S. FDA-regulated Product
Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
IPD Sharing Statement
Plan to Share IPD: Undecided
Current Responsible Party Volkan Alparslan, Kocaeli University
Original Responsible Party Same as current
Current Study Sponsor Kocaeli University
Original Study Sponsor Same as current
Collaborators Not Provided
Investigators Not Provided
PRS Account Kocaeli University
Verification Date February 2024