<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/NCT03286491</url>
  </required_header>
  <id_info>
    <org_study_id>TurkishMOHITH</org_study_id>
    <nct_id>NCT03286491</nct_id>
  </id_info>
  <brief_title>Clinical Decision Support System to Define 30 Day Adverse Clinical Events for Emergency Department Acute Coronary Syndromes</brief_title>
  <official_title>Development of Clinical Decision Support System to Define 30 Day Adverse Clinical Events (Non Fatal Myocardial Infarction, Cardiac Related Death or Coronary Intervention) for Patients Presenting to Emergency Department With Acute Coronary Syndrome</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Turkish Ministry of Health Izmir Teaching Hospital</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Turkish Ministry of Health Izmir Teaching Hospital</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>
      Acute coronary syndromes are among main complains for patients presenting to emergency&#xD;
      department. Risk classification systems are used to classify patients to appropriate risks&#xD;
      and help physicians manage diagnosis strategies and treatments. Purpose of this study is to&#xD;
      develop a clinical decision support system for patients presenting to emergency department&#xD;
      with the help of statistical machine learning.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Acute coronary syndromes are among main complains for patients presenting to emergency&#xD;
      department and create a burden to emergency departments and hospitals. Risk classification&#xD;
      systems were developed and used to classify patients to appropriate risk groups. According to&#xD;
      risk classification, different diagnosis and treatment modalities can be used and help&#xD;
      physicians manage diagnosis strategies and treatments. Purpose of this study is to develop a&#xD;
      clinical decision support system for patients presenting to emergency department with the&#xD;
      help of statistical machine learning.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Unknown status</overall_status>
  <last_known_status>Recruiting</last_known_status>
  <start_date type="Actual">August 12, 2017</start_date>
  <completion_date type="Anticipated">February 1, 2018</completion_date>
  <primary_completion_date type="Anticipated">December 12, 2017</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>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Non fatal myocardial infarction, cardiac related death or coronary intervention in 30 days</measure>
    <time_frame>30 days</time_frame>
    <description>Patients will be screened for 30 days for Non fatal myocardial infarction, cardiac related death or coronary intervention (coronary angiogram, coronary artery bypass surgery)</description>
  </primary_outcome>
  <number_of_groups>1</number_of_groups>
  <enrollment type="Anticipated">400</enrollment>
  <condition>Acute Coronary Syndrome</condition>
  <condition>Clinical Decision Support System</condition>
  <arm_group>
    <arm_group_label>Patients presenting with acute coronary syndrome</arm_group_label>
    <description>Patients presenting to emergency department with acute coronary syndrome</description>
  </arm_group>
  <intervention>
    <intervention_type>Diagnostic Test</intervention_type>
    <intervention_name>Further cardiac imaging</intervention_name>
    <description>Study patients will be reviewed whether they will have coronary interventions in 30 days</description>
    <arm_group_label>Patients presenting with acute coronary syndrome</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        Patients presenting to emergency deparmtent with acute chest pain consistent with acute&#xD;
        coronary syndrome will be included for analysis.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Presenting to emergency department with acute chest pain consistent with acute&#xD;
             coronary syndrome&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Patients with less than 18 years old&#xD;
&#xD;
          -  Patients presenting with ST-segment elevation myocardial infarction&#xD;
&#xD;
          -  Patients presenting with other causes of acute chest pain (pneumothorax, pulmonary&#xD;
             -embolism, aortic dissection&#xD;
&#xD;
          -  Decline to participate in study&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
  </eligibility>
  <overall_contact>
    <last_name>Nesli Belen, M.D.</last_name>
    <phone>90-551-4363001</phone>
    <email>neslibelen@yahoo.com</email>
  </overall_contact>
  <overall_contact_backup>
    <last_name>Serhat Akay, M.D.</last_name>
    <phone>90-505-7079350</phone>
    <email>howls_Castle@mynet.com</email>
  </overall_contact_backup>
  <location>
    <facility>
      <name>Izmir Bozyaka Training and Research Hospital</name>
      <address>
        <city>Izmir</city>
        <zip>35000</zip>
        <country>Turkey</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Nesli Belen, M.D.</last_name>
      <phone>90-551-4363001</phone>
      <email>neslibelen@yahoo.com</email>
    </contact>
    <contact_backup>
      <last_name>Serhat Akay, M.D.</last_name>
      <phone>90-505-7079350</phone>
      <email>howls_Castle@mynet.com</email>
    </contact_backup>
  </location>
  <location_countries>
    <country>Turkey</country>
  </location_countries>
  <verification_date>September 2017</verification_date>
  <study_first_submitted>September 15, 2017</study_first_submitted>
  <study_first_submitted_qc>September 15, 2017</study_first_submitted_qc>
  <study_first_posted type="Actual">September 18, 2017</study_first_posted>
  <last_update_submitted>September 15, 2017</last_update_submitted>
  <last_update_submitted_qc>September 15, 2017</last_update_submitted_qc>
  <last_update_posted type="Actual">September 18, 2017</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Turkish Ministry of Health Izmir Teaching Hospital</investigator_affiliation>
    <investigator_full_name>Serhat Akay</investigator_full_name>
    <investigator_title>M.D.</investigator_title>
  </responsible_party>
  <keyword>acute coronary syndrome, risk classification, 30 day adverse event, clinical decision support system</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Acute Coronary Syndrome</mesh_term>
    <mesh_term>Syndrome</mesh_term>
    <mesh_term>Emergencies</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


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/NCT03286491
org study id: TurkishMOHITH
nct id: NCT03286491
lead sponsor: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
Acute coronary syndromes are among main complains for patients presenting to emergency
      department. Risk classification systems are used to classify patients to appropriate risks
      and help physicians manage diagnosis strategies and treatments. Purpose of this study is to
      develop a clinical decision support system for patients presenting to emergency department
      with the help of statistical machine learning.
Acute coronary syndromes are among main complains for patients presenting to emergency
      department and create a burden to emergency departments and hospitals. Risk classification
      systems were developed and used to classify patients to appropriate risk groups. According to
      risk classification, different diagnosis and treatment modalities can be used and help
      physicians manage diagnosis strategies and treatments. Purpose of this study is to develop a
      clinical decision support system for patients presenting to emergency department with the
      help of statistical machine learning.
observational model: Cohort
time perspective: Prospective
measure: Non fatal myocardial infarction, cardiac related death or coronary intervention in 30 days
time frame: 30 days
description: Patients will be screened for 30 days for Non fatal myocardial infarction, cardiac related death or coronary intervention (coronary angiogram, coronary artery bypass surgery)
arm group label: Patients presenting with acute coronary syndrome
description: Patients presenting to emergency department with acute coronary syndrome
intervention type: Diagnostic Test
intervention name: Further cardiac imaging
description: Study patients will be reviewed whether they will have coronary interventions in 30 days
arm group label: Patients presenting with acute coronary syndrome
study pop: 
sampling method: Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
last name: Nesli Belen, M.D.
phone: 90-551-4363001
email: neslibelen@yahoo.com
last name: Serhat Akay, M.D.
phone: 90-505-7079350
email: howls_Castle@mynet.com
facility: 
status: Recruiting
contact: 
contact backup: 
country: Turkey
responsible party type: Principal Investigator
investigator affiliation: Turkish Ministry of Health Izmir Teaching Hospital
investigator full name: Serhat Akay
investigator title: M.D.
mesh term: Acute Coronary Syndrome
mesh term: Syndrome
mesh term: Emergencies
sharing ipd: No