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Prediction of Antidepressant Treatment Response Using Machine Learning Classification Analysis

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ClinicalTrials.gov Identifier: NCT02330679
Recruitment Status : Unknown
Verified December 2014 by Rajamannar Ramasubbu, University of Calgary.
Recruitment status was:  Recruiting
First Posted : January 5, 2015
Last Update Posted : January 5, 2015
Sponsor:
Collaborator:
University of Alberta
Information provided by (Responsible Party):
Rajamannar Ramasubbu, University of Calgary

Brief Summary:

Despite significant advances in pharmacological treatment, the global burden of depression is increasing worldwide. The major challenge in antidepressant treatment is the clinicians' inability to predict the variability in individual response to the treatment. The development of biomarkers to predict treatment outcomes would enable clinician to find the right medication for a particular patient at the early stage of the treatment and thus could reduce prolonged suffering and ineffective protracted treatment. Brain imaging studies that examined brain predictors of treatment response based on group comparisons have limited value in classifying individuals as responders or non-responders. Machine learning classification techniques such as the support vector machine (SVM) method have proven useful in the classification of individual brain image observations into distinct groups or classes. However, studies that have applied the SVM method to structural and functional magnetic resonance scans (fMRI) involved small sample sizes and were confounded by placebo responses. Furthermore, a recent meta-analysis of clinical trials and EEG studies have shown that early clinical responses and brain changes at the early phase of antidepressant treatment may predict later clinical outcomes suggesting that neural markers measured in the early phase of antidepressant treatment may improve predictive accuracy. However, there is no fMRI study to date that has examined the predictive accuracy of data obtained in early phase of the treatment. We have preliminary fMRI data relating to early treatment response that form the basis of this proposed study.

The main objective of this study is to use machine learning method to examine the predictive value (sensitivity, specificity, accuracy) of resting state and emotional task-related fMRI data collected at pre-treatment baseline (week 0) and in the early phase of antidepressant treatment (week 2) in the classification of remitters (< 10 MADRS scores after 12 weeks of treatment) and non-remitters in patients with major depressive disorder (MDD). A secondary objective is to determine which data set (week 0 or week 2) gives the best predictive value.


Condition or disease Intervention/treatment Phase
Major Depressive Disorder Drug: Desvenlafaxine Drug: Placebo Phase 4

  Show Detailed Description

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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 61 participants
Intervention Model: Single Group Assignment
Masking: Single (Participant)
Primary Purpose: Treatment
Official Title: Prediction of Individual Treatment Response Based on Brain Changes at the Early Phase of Antidepressant Treatment in Major Depressive Disorder Using Machine Learning Classification Analysis
Study Start Date : December 2014
Estimated Primary Completion Date : December 2016
Estimated Study Completion Date : December 2016

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Antidepressants

Arm Intervention/treatment
Desvenlafaxine
2-week single-blind placebo run-in phase followed by a 12-week open-label trial with desvenlafaxine
Drug: Desvenlafaxine
The intervention will consist of a 2-week single-blind placebo run-in phase followed by a 12-week open-label trial with desvenlafaxine (a SNRI medication)
Other Name: Prestiq

Drug: Placebo



Primary Outcome Measures :
  1. The resting state and emotional task related brain activity pattern at the pretreatment baseline and two weeks post treatment as measured by functional MRI and analyzed by machine learning techniques [ Time Frame: 2 weeks ]
    The predictive value of brain activity pattern at the baseline and two weeks post treatment to classify remitters and non-remitters at 12 weeks of antidepressant treatment using machine learning classifiers


Secondary Outcome Measures :
  1. The clinical response to antidepressant treatment as measured by Montgomery-Asberg Depression Rating (MADRS) scale. [ Time Frame: 12 weeks ]
    MADRS scores at week 12 will be used to determine remitters and non-remitters. Patients who score less than 10 in MADRS at week 12 will be considered as remitters.



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Ages Eligible for Study:   20 Years to 55 Years   (Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Criteria

Inclusion Criteria:

  1. Acute episode of major depressive disorder of unipolar subtype and a score of 22 or higher in the Montgomery-Asberg Depression Rating (MADRS) scale
  2. Free of psychotropic medication for a minimum of 4 weeks at recruitment

Exclusion Criteria:

  1. Axis I disorders such as bipolar disorder, anxiety disorders, psychosis or history of substance abuse within 6 months of study participation
  2. severe borderline personality disorder
  3. severe medical and neurological disorders
  4. severe suicidal patients
  5. failure to respond to three trials of antidepressant medication
  6. subjects who arecontraindicated for MRI. Subjects considered unsuitable for MRI include those with cardiac pacemakers, neural pacemakers, surgical clips, metal implants, cochlear implants, or metal objects or particles in their body. Pregnancy, a history of claustrophobia, weight over 250 lb, or uncorrected vision will also be causes of exclusion for participation.

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT02330679


Contacts
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Contact: Rajamannar Ramasubbu, MD, FRCP(C) 403-210-6890 rramasub@ucalgary.ca
Contact: Darren Clark, PhD 403-210-6353 dlclark@ucalgary.ca

Locations
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Canada, Alberta
University of Calgary, TRW Building, Foothills Hospital Campus Recruiting
Calgary, Alberta, Canada, T2N4Z6
Contact: Rajamannar Ramasubbu, MD, FRCP(C)    403-210-6890    rramasub@ucalgary.ca   
Contact: Darren Clark, PhD    403-210-6353    dlclark@ucalgary.ca   
Principal Investigator: Rajamannar Ramasubbu, MD, FRCP(C)         
University of Calgary: Foothills Hospital Recruiting
Calgary, Alberta, Canada, T2N4Z6
Principal Investigator: Rajamannar Ramasubbu, MD, FRCP(C)         
Sponsors and Collaborators
University of Calgary
University of Alberta
Investigators
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Principal Investigator: Rajamannar Ramasubbu, MD, FRCP(C) University of Calgary

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Responsible Party: Rajamannar Ramasubbu, Associate Prof, University of Calgary
ClinicalTrials.gov Identifier: NCT02330679     History of Changes
Other Study ID Numbers: REB 14-0194
First Posted: January 5, 2015    Key Record Dates
Last Update Posted: January 5, 2015
Last Verified: December 2014

Keywords provided by Rajamannar Ramasubbu, University of Calgary:
Major Depression, Neuroimaging, Desvenlafaxine, Machine learning

Additional relevant MeSH terms:
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Depressive Disorder
Depression
Depressive Disorder, Major
Mood Disorders
Mental Disorders
Behavioral Symptoms
Antidepressive Agents
Desvenlafaxine Succinate
Psychotropic Drugs
Serotonin and Noradrenaline Reuptake Inhibitors
Neurotransmitter Uptake Inhibitors
Membrane Transport Modulators
Molecular Mechanisms of Pharmacological Action
Neurotransmitter Agents
Physiological Effects of Drugs