<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/NCT04448340</url>
  </required_header>
  <id_info>
    <org_study_id>251ATHENS HOSPITAL</org_study_id>
    <nct_id>NCT04448340</nct_id>
  </id_info>
  <brief_title>A Novel Machine Learning Algorithm to Predict the Lewy Body Dementias</brief_title>
  <acronym>MLDLB</acronym>
  <official_title>A Novel Machine Learning Algorithm to Predict the Lewy Body Dementias Using Clinical and Neuropsychological Scores</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>National and Kapodistrian University of Athens</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>National and Kapodistrian University of Athens</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>
      Parkinson's disease dementia (PDD) and Dementia with lewy bodies (DLB) are dementia syndromes&#xD;
      that overlap in many clinical features, making their diagnosis difficult in clinical&#xD;
      practice, particularly in advanced stages. We propose a machine learning algorithm, based&#xD;
      only on non-invasively and easily in-the-clinic collectable predictors, to identify these&#xD;
      disorders with a high prognostic performance.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      The algorithm will be develop using dataset from two specialized memory centers, employing a&#xD;
      sample of PDD and DLB subjects whose diagnostic follow-up is available for at least 3 years&#xD;
      after the baseline assessment. A restricted set of information regarding clinico- demographic&#xD;
      characteristics, 6 neuropsychological tests (mini mental, PD Cognitive Rating Scale, Brief&#xD;
      Visuospatial Memory test, Symbol digit written, Wechsler adult intelligence scale, trail&#xD;
      making A and B) was used as predictors. Two classification algorithms, logistic regression&#xD;
      and K-Nearest Neighbors (K-NNs), will be investigated for their ability to predict&#xD;
      successfully whether patients suffered from PDD or DLB.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Unknown status</overall_status>
  <last_known_status>Recruiting</last_known_status>
  <start_date type="Actual">September 1, 2019</start_date>
  <completion_date type="Anticipated">March 1, 2021</completion_date>
  <primary_completion_date type="Anticipated">October 1, 2020</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>MMSE predictive for dlb or PDD</measure>
    <time_frame>1 year</time_frame>
    <description>Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Parkinson's Disease - Cognitive Rating Scale (PD-CRS) predictive for DLB or PDD</measure>
    <time_frame>1 year</time_frame>
    <description>Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Brief Visuospatial Memory Test (BVMT-TR) predictive for DLB or PDD</measure>
    <time_frame>1 year</time_frame>
    <description>Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Symbol digit written predictive for DLB or PDD</measure>
    <time_frame>1 year</time_frame>
    <description>Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Wechsler adult intelligence scale,predictive for DLB or PDD</measure>
    <time_frame>1 year</time_frame>
    <description>Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>trail making A and B predictive for DLB or PDD</measure>
    <time_frame>1 year</time_frame>
    <description>Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.</description>
  </primary_outcome>
  <number_of_groups>2</number_of_groups>
  <enrollment type="Anticipated">200</enrollment>
  <condition>Dementia</condition>
  <arm_group>
    <arm_group_label>Parkinson Disease Dementia</arm_group_label>
    <description>the PDD group comprised of 58 patients fulfilling the Criteria for probable PDD of the Movement Disorders Society</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Dementia with Lewy Bodies</arm_group_label>
    <description>the DLB group comprised of 40 patients, according to the recent revised criteria for probable DLB</description>
  </arm_group>
  <intervention>
    <intervention_type>Diagnostic Test</intervention_type>
    <intervention_name>machine learning model</intervention_name>
    <description>Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), were investigated for their ability to predict successfully whether patients suffered from PDD or DLB.</description>
    <arm_group_label>Dementia with Lewy Bodies</arm_group_label>
    <arm_group_label>Parkinson Disease Dementia</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        the PDD group comprised of patients fulfilling the Criteria for probable PDD and the DLB&#xD;
        group.Patients will be enrolled from the register-based database of two clinics. The&#xD;
        following data were collected: gender, age, education, hand dominance, Disease duration&#xD;
        (years) and levodopa equivalent daily dose (LEDD). The burden of disease will be assess by&#xD;
        the Movement Disorders Society-United Parkinson's Disease Rating Scale (MDS-UPDRS) part III&#xD;
        in the Off medication state and the following six cognitive/behavioral tests: Mini-Mental&#xD;
        State Examination (MMSE), PD- Cognitive Rating Scale (PD-CRS), Brief Visuospatial Memory&#xD;
        test (BVMT-TR), Symbol digit written (SDMT), Trail making test (TMT A,B), Wechsler&#xD;
        adultintelligence scale (WAIS-V). All patients will undergo brain MRI and blood test to&#xD;
        exclude secondarycauses of dementia.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
        the PDD group comprised of patients fulfilling the Criteria for probable PDD of the&#xD;
        Movement Disorders Society (b) the DLB group comprised of patients, according to the recent&#xD;
        revised criteria for probable DLB .&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  major psychiatrics disorders, depression&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>50 Years</minimum_age>
    <maximum_age>90 Years</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>ANASTASIA BOUGEA</last_name>
    <role>Principal Investigator</role>
    <affiliation>National and Kapodistrian University of Athens</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>ANASTASIA BOUGEA, DR</last_name>
    <phone>+306930481046</phone>
    <email>annita139@yahoo.gr</email>
  </overall_contact>
  <overall_contact_backup>
    <last_name>ANASTASIA BOUGEA</last_name>
    <phone>+306930481046</phone>
    <email>annita139@yahoo.gr</email>
  </overall_contact_backup>
  <location>
    <facility>
      <name>Anastasia Bougea</name>
      <address>
        <city>Athens</city>
        <state>Attiki</state>
        <zip>16674</zip>
        <country>Greece</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>EFTHYMIA EFTHYMIIOPOULOU, dr</last_name>
      <phone>00306943061632</phone>
      <email>faih.efthymiopoulou@gmail.com</email>
    </contact>
    <investigator>
      <last_name>Christos Goumas, dr</last_name>
      <role>Sub-Investigator</role>
    </investigator>
  </location>
  <location_countries>
    <country>Greece</country>
  </location_countries>
  <verification_date>September 2020</verification_date>
  <study_first_submitted>June 21, 2020</study_first_submitted>
  <study_first_submitted_qc>June 24, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">June 25, 2020</study_first_posted>
  <last_update_submitted>September 8, 2020</last_update_submitted>
  <last_update_submitted_qc>September 8, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">September 10, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>National and Kapodistrian University of Athens</investigator_affiliation>
    <investigator_full_name>Anastasia Bougea</investigator_full_name>
    <investigator_title>DR</investigator_title>
  </responsible_party>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Dementia</mesh_term>
    <mesh_term>Lewy Body Disease</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/NCT04448340
org study id: 251ATHENS HOSPITAL
nct id: NCT04448340
lead sponsor: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
Parkinson's disease dementia (PDD) and Dementia with lewy bodies (DLB) are dementia syndromes
      that overlap in many clinical features, making their diagnosis difficult in clinical
      practice, particularly in advanced stages. We propose a machine learning algorithm, based
      only on non-invasively and easily in-the-clinic collectable predictors, to identify these
      disorders with a high prognostic performance.
The algorithm will be develop using dataset from two specialized memory centers, employing a
      sample of PDD and DLB subjects whose diagnostic follow-up is available for at least 3 years
      after the baseline assessment. A restricted set of information regarding clinico- demographic
      characteristics, 6 neuropsychological tests (mini mental, PD Cognitive Rating Scale, Brief
      Visuospatial Memory test, Symbol digit written, Wechsler adult intelligence scale, trail
      making A and B) was used as predictors. Two classification algorithms, logistic regression
      and K-Nearest Neighbors (K-NNs), will be investigated for their ability to predict
      successfully whether patients suffered from PDD or DLB.
observational model: Cohort
time perspective: Prospective
measure: MMSE predictive for dlb or PDD
time frame: 1 year
description: Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.
measure: Parkinson's Disease - Cognitive Rating Scale (PD-CRS) predictive for DLB or PDD
time frame: 1 year
description: Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.
measure: Brief Visuospatial Memory Test (BVMT-TR) predictive for DLB or PDD
time frame: 1 year
description: Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.
measure: Symbol digit written predictive for DLB or PDD
time frame: 1 year
description: Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.
measure: Wechsler adult intelligence scale,predictive for DLB or PDD
time frame: 1 year
description: Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.
measure: trail making A and B predictive for DLB or PDD
time frame: 1 year
description: Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.
arm group label: Parkinson Disease Dementia
description: the PDD group comprised of 58 patients fulfilling the Criteria for probable PDD of the Movement Disorders Society
arm group label: Dementia with Lewy Bodies
description: the DLB group comprised of 40 patients, according to the recent revised criteria for probable DLB
intervention type: Diagnostic Test
intervention name: machine learning model
description: Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), were investigated for their ability to predict successfully whether patients suffered from PDD or DLB.
arm group label: Dementia with Lewy Bodies
arm group label: Parkinson Disease Dementia
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 50 Years
maximum age: 90 Years
healthy volunteers: No
last name: ANASTASIA BOUGEA
role: Principal Investigator
affiliation: National and Kapodistrian University of Athens
last name: ANASTASIA BOUGEA, DR
phone: +306930481046
email: annita139@yahoo.gr
last name: ANASTASIA BOUGEA
phone: +306930481046
email: annita139@yahoo.gr
facility: 
status: Recruiting
contact: 
investigator: 
country: Greece
responsible party type: Principal Investigator
investigator affiliation: National and Kapodistrian University of Athens
investigator full name: Anastasia Bougea
investigator title: DR
mesh term: Dementia
mesh term: Lewy Body Disease
sharing ipd: No