Prediction of Antidepressant Treatment Response Using Machine Learning Classification Analysis
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
1 other identifier
interventional
61
1 country
2
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P25-P50 for phase_4 major-depressive-disorder
Started Dec 2014
2 active sites
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
December 1, 2014
CompletedFirst Submitted
Initial submission to the registry
December 31, 2014
CompletedFirst Posted
Study publicly available on registry
January 5, 2015
CompletedPrimary Completion
Last participant's last visit for primary outcome
December 1, 2016
CompletedStudy Completion
Last participant's last visit for all outcomes
December 1, 2016
CompletedJanuary 5, 2015
December 1, 2014
2 years
December 31, 2014
January 2, 2015
Conditions
Keywords
Outcome Measures
Primary Outcomes (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
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
2 weeks
Secondary Outcomes (1)
The clinical response to antidepressant treatment as measured by Montgomery-Asberg Depression Rating (MADRS) scale.
12 weeks
Study Arms (1)
Desvenlafaxine
OTHER2-week single-blind placebo run-in phase followed by a 12-week open-label trial with desvenlafaxine
Interventions
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)
Eligibility Criteria
You may qualify if:
- Acute episode of major depressive disorder of unipolar subtype and a score of 22 or higher in the Montgomery-Asberg Depression Rating (MADRS) scale
- Free of psychotropic medication for a minimum of 4 weeks at recruitment
You may not qualify if:
- Axis I disorders such as bipolar disorder, anxiety disorders, psychosis or history of substance abuse within 6 months of study participation
- severe borderline personality disorder
- severe medical and neurological disorders
- severe suicidal patients
- failure to respond to three trials of antidepressant medication
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- University of Calgarylead
- University of Albertacollaborator
Study Sites (2)
University of Calgary, TRW Building, Foothills Hospital Campus
Calgary, Alberta, T2N4Z6, Canada
University of Calgary: Foothills Hospital
Calgary, Alberta, T2N4Z6, Canada
MeSH Terms
Conditions
Interventions
Condition Hierarchy (Ancestors)
Intervention Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Rajamannar Ramasubbu, MD, FRCP(C)
University of Calgary
Central Study Contacts
Study Design
- Study Type
- interventional
- Phase
- phase 4
- Allocation
- NA
- Masking
- SINGLE
- Who Masked
- PARTICIPANT
- Purpose
- TREATMENT
- Intervention Model
- SINGLE GROUP
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Associate Prof
Study Record Dates
First Submitted
December 31, 2014
First Posted
January 5, 2015
Study Start
December 1, 2014
Primary Completion
December 1, 2016
Study Completion
December 1, 2016
Last Updated
January 5, 2015
Record last verified: 2014-12