xml: <clinical_study>
    <!-- This xml conforms to an XML Schema at:
      https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
    <required_header>
      <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
      <link_text>Link to the current ClinicalTrials.gov record.</link_text>
      <url>https://clinicaltrials.gov/ct2/show/NCT05985057</url>
    </required_header>
    <id_info>
      <org_study_id>GOKAEK-2023/12.32</org_study_id>
      <nct_id>NCT05985057</nct_id>
    </id_info>
    <brief_title>A Novel Approach to Antimicrobial Resistance: Machine Learning Predictions for Carbapenem-Resistant Klebsiella in ICUs</brief_title>
    <acronym>ICU</acronym>
    <official_title>A Novel Approach to Antimicrobial Resistance: Machine Learning Predictions for Carbapenem-Resistant Klebsiella in ICUs</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Kocaeli University</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Kocaeli University</source>
    <oversight_info>
      <has_dmc>No</has_dmc>
      <is_fda_regulated_drug>No</is_fda_regulated_drug>
      <is_fda_regulated_device>No</is_fda_regulated_device>
    </oversight_info>
    <brief_summary>
      <textblock>
        The aim of this study to predict carbapenem resistant Klebsiella spp. earlier in our patients&#xD;
        monitored in our Intensive Care Unit in the future, using artificial intelligence.&#xD;
  &#xD;
        Patients with bloodstream infection and pneumonia caused by Klebsiella spp. will be&#xD;
        comparatively examined in two groups, as sensitive and resistant. Resistance will be&#xD;
        attempted to be predicted with deep machine learning.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        Antimicrobial resistance is a globally increasing threat and has serious consequences on both&#xD;
        public health and the economy. In an infection that may develop with a resistant&#xD;
        microorganism, therapeutic options are limited, hence early and effective treatment that can&#xD;
        be initiated by predicting resistance will make a difference in patient prognosis.&#xD;
  &#xD;
        Today, artificial intelligence and machine learning are changing our medical practice. When&#xD;
        the literature is reviewed, there are studies suggesting that machine learning can predict&#xD;
        antimicrobial resistance.Risk factors for carbapenem-resistant Klebsiella spp. have been&#xD;
        previously identified. These previously identified risk factors will be evaluated&#xD;
        retrospectively in our own patients and an algorithm related to the prediction of resistance&#xD;
        will be developed with the help of machine learning.&#xD;
  &#xD;
        Our goal is to predict bacterial resistance earlier in our patients monitored in our&#xD;
        Intensive Care Unit in the future, using artificial intelligence, and to facilitate our&#xD;
        patients' access to early and effective treatment options.&#xD;
  &#xD;
        Secondarily, it is also aimed to provide economic benefits by preventing unnecessary&#xD;
        antibiotic use.&#xD;
  &#xD;
        Access to patients' data will be obtained retrospectively through the hospital automation&#xD;
        system.&#xD;
  &#xD;
        Publications in the literature will be examined, and the risk factors causing the development&#xD;
        of infection with carbapenem-resistant Klebsiella spp. will be evaluated.&#xD;
  &#xD;
        Patients with carbapenem resistance and sensitivity will be compared in two separate&#xD;
        subgroups.&#xD;
  &#xD;
        The obtained features will be classified using various decision trees and neural algorithms&#xD;
        separately. The data obtained will be statistically compared in the distinction of resistance&#xD;
        and sensitivity. Statistical evaluation was done with IBM SPSS 29.0 (IBM Corp., Armonk, NY,&#xD;
        USA). Demographic data, descriptive statistics, Categorical variables will be expressed in&#xD;
        terms of frequency (percentage).&#xD;
  &#xD;
        Categorical variables will be expressed with the chi-square test. The performance of Machine&#xD;
        Learning algorithms will be evaluated by ROC analysis, AUC, classification accuracy,&#xD;
        sensitivity, and specificity values will be calculated.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">August 16, 2023</start_date>
    <completion_date type="Anticipated">December 30, 2023</completion_date>
    <primary_completion_date type="Anticipated">November 2, 2023</primary_completion_date>
    <study_type>Observational</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Cohort</observational_model>
      <time_perspective>Retrospective</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>Risk of Carbapenem Resistant Klebsiella Infection</measure>
      <time_frame>3 months</time_frame>
      <description>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)</description>
    </primary_outcome>
    <number_of_groups>2</number_of_groups>
    <enrollment type="Anticipated">300</enrollment>
    <condition>Carbapenem Resistant Enterobacteriaceae Infection</condition>
    <condition>Artificial Intelligence</condition>
    <condition>Intensive Care Unit</condition>
    <arm_group>
      <arm_group_label>Patients with carbapenem resistant Klebsiella spp. infection</arm_group_label>
    </arm_group>
    <arm_group>
      <arm_group_label>Patients with carbapenem sensitive Klebsiella spp. infection</arm_group_label>
    </arm_group>
    <intervention>
      <intervention_type>Other</intervention_type>
      <intervention_name>Artificial intelligence</intervention_name>
      <description>Prediction of carbapenem resistance via deep machine learning model</description>
      <arm_group_label>Patients with carbapenem resistant Klebsiella spp. infection</arm_group_label>
      <arm_group_label>Patients with carbapenem sensitive Klebsiella spp. infection</arm_group_label>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          Patients monitored in our third-level intensive care unit between June 2017 and June 2023&#xD;
          will be evaluated retrospectively. Patients with pneumonia and bloodstream infection&#xD;
          developed with Klebsiella spp. will be included in the study.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
          Patients monitored in our third-level intensive care unit between June 2017 and June 2023&#xD;
          will be evaluated retrospectively. Patients with pneumonia and bloodstream infection&#xD;
          developed with Klebsiella spp. will be included in the study.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Patients under the age of 18 have not been included in the study.&#xD;
  &#xD;
            -  Infections outside of the respiratory tract and bloodstream have not been included in&#xD;
               the study.&#xD;
  &#xD;
            -  Patients with respiratory tract colonization and without active inflammation have also&#xD;
               not been included.&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>N/A</maximum_age>
    </eligibility>
    <location>
      <facility>
        <name>Kocaeli University</name>
        <address>
          <city>Kocaeli</city>
          <country>Turkey</country>
        </address>
      </facility>
      <contact>
        <last_name>Volkan Alparslan</last_name>
        <phone>+90 5059374578</phone>
        <email>volknn@hotmail.com</email>
      </contact>
    </location>
    <location_countries>
      <country>Turkey</country>
    </location_countries>
    <link>
      <url>https://pubmed.ncbi.nlm.nih.gov/34042702/</url>
      <description>Using Machine Learning to Predict Antimicrobial Resistance of Acinetobacter Baumannii, Klebsiella Pneumoniae and Pseudomonas Aeruginosa Strains</description>
    </link>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>August 2, 2023</study_first_submitted>
    <study_first_submitted_qc>August 2, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 14, 2023</study_first_posted>
    <last_update_submitted>August 16, 2023</last_update_submitted>
    <last_update_submitted_qc>August 16, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 21, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>Kocaeli University</investigator_affiliation>
      <investigator_full_name>Volkan Alparslan</investigator_full_name>
      <investigator_title>Medical Doctor</investigator_title>
    </responsible_party>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Enterobacteriaceae Infections</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>Undecided</sharing_ipd>
    </patient_data>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: download date: ClinicalTrials.gov processed this data on September 20, 2023
  link text: Link to the current ClinicalTrials.gov record.
  url: https://clinicaltrials.gov/ct2/show/NCT05985057
  org study id: GOKAEK-2023/12.32
  nct id: NCT05985057
  lead sponsor: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  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.
  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.
  observational model: Cohort
  time perspective: Retrospective
  measure: Risk of Carbapenem Resistant Klebsiella Infection
  time frame: 3 months
  description: 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)
  arm group label: Patients with carbapenem resistant Klebsiella spp. infection
  arm group label: Patients with carbapenem sensitive Klebsiella spp. infection
  intervention type: Other
  intervention name: Artificial intelligence
  description: Prediction of carbapenem resistance via deep machine learning model
  arm group label: Patients with carbapenem resistant Klebsiella spp. infection
  arm group label: Patients with carbapenem sensitive Klebsiella spp. infection
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: N/A
  facility: 
  contact: 
  country: Turkey
  url: https://pubmed.ncbi.nlm.nih.gov/34042702/
  description: Using Machine Learning to Predict Antimicrobial Resistance of Acinetobacter Baumannii, Klebsiella Pneumoniae and Pseudomonas Aeruginosa Strains
  responsible party type: Principal Investigator
  investigator affiliation: Kocaeli University
  investigator full name: Volkan Alparslan
  investigator title: Medical Doctor
  mesh term: Enterobacteriaceae Infections
  sharing ipd: Undecided