Clinical Trial Details
— Status: Recruiting
Administrative data
| NCT number |
NCT02330679 |
| Other study ID # |
REB 14-0194 |
| Secondary ID |
|
| Status |
Recruiting |
| Phase |
Phase 4
|
| First received |
December 31, 2014 |
| Last updated |
January 2, 2015 |
| Start date |
December 2014 |
| Est. completion date |
December 2016 |
Study information
| Verified date |
December 2014 |
| Source |
University of Calgary |
| Contact |
Rajamannar Ramasubbu, MD, FRCP(C) |
| Phone |
403-210-6890 |
| Email |
rramasub[@]ucalgary.ca |
| Is FDA regulated |
No |
| Health authority |
Canada: Ethics Review Committee |
| Study type |
Interventional
|
Clinical Trial 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.
Description:
Major depressive disorder (MDD) is a highly prevalent, chronic disabling condition with
substantial morbidity and mortality. Depression currently is the fourth leading cause of
global burden of disease (DALYs) and disability worldwide, and is expected to be second by
20201. Around one in eight people in Canada will develop depression during their lifetime,
with the total cost to the Canadian economy estimated at $51 billion per year2. The costs of
treating MDD are high in part due to limitations in effectiveness of antidepressant
treatment. Approximately 60% of patients fail to remit to the first antidepressant
prescribed3 and the subsequent selection of antidepressants remains a matter of trial and
error. Using this trial and error approach, it may take a year or more to find the
successful treatment for a patient4,5. The protracted ineffective treatment results in
prolonged suffering, substantial morbidity, loss of productivity and an increased burden on
patient's family. Brain-based biomarkers could assist in predicting clinical response to
treatment intervention and in tailoring treatment for individual patients. The results of
previous neuroimaging studies that examined brain markers of treatment response were derived
from group averages 6,-9 and have limited predictive value at individual level. Another
limitation of these studies is that predictors derived from the pretreatment baseline brain
scans could be influenced by many personal (personality, childhood trauma, genotypes) and
clinical (course, duration of illness, episodes, symptom clusters, and severity of symptoms
and past medication exposure) characteristics which may limit the generalizability. On the
other hand, there is growing evidence that the early clinical response within 2 weeks of
antidepressant treatment and EEG changes in the first week of treatment can predict later
outcomes. Furthermore, early treatment changes in brain function may provide crucial
information on the brain's capacity to change with treatment and on the interactive effects
between personal/ clinical characteristics and pharmacological factors, which may help
differential prediction of treatment responses to two antidepressants. Hence, examining the
predictive value of dynamic brain changes during the first two weeks of treatment in
individual patients would improve statistical reliability and predictive accuracy and
minimize the confounding effect inherent to pretreatment scans.
In this study, we propose to investigate the predictive value of resting state and task
related fMRI data collected at the pretreatment baseline and 2 weeks after treatment to
predict remitters and non-remitters to desvenlafaxine antidepressant treatment at week 12
using machine learning classifier. Desvenlafaxine is a serotonin norepinephrine reuptake
inhibitor (SNRI) with proven efficacy, and safety and is easy to administer in single daily
dose. It has limited sedative and cognitive side effects such as drowsiness, lack of
alertness and poor attention, which may confound early brain changes with treatment. This
study will provide brain-based predictive biomarkers that can be tested prospectively in
clinical trials and eventually in clinical practice for accuracy.
Machine Learning Classification (Support Vector Machine): The support vector machine (SVM)
is a computer based analytical technique designed for high dimensional biological data such
as fMRI data and provides the best classification of individual observations into distinct
groups 38. Diagnostic classification (depression diagnosis and healthy control) and
classification of treatment responsiveness (responders and non-responders) have been
examined in a clinical population with fMRI data using SVM 39-41. This technique consists of
two phases: training phase and testing phase. During the training phase an SVM is trained to
develop a decision function or hyperplane that separates the data into two groups according
to a class label. In the testing phase, this decision function can be used to predict the
class label of a new subject as being a responder or non-responder. The accuracy of
prediction by SVM depends on its specificity (identification of true negatives) and
sensitivity (identification of true positives). In recent years a few neuroimaging studies
have employed SVM to structural and functional MRI data in order to predict the MDD patients
who improved with treatment and who did not. Fu et al (2008) showed that applying SVM on
emotional task-related fMRI data, 62% of patients who achieved remission (sensitivity) and
75% of patients who did not achieve remission (specificity) following 8 weeks of fluoxetine
treatment could be predicted. But these results were not statistically significant due to
small sample sizes (remitters =8, non-remitters=10). Similarly, Costafreda et al (2009)
applied SVM to pretreatment structural scans and showed prediction with a sensitivity of
88.9 % and a specificity of 88.9% and accuracy of 88.9% in a small sample comprised of 18
patients 40. In a recent study involving 61 MDD patients, SVM analysis of pretreatment white
matter data predicted clinical outcome of refractory and non- refractory depression with an
accuracy of 65.22%, sensitivity of 56.2% and specificity of 73.91% 41. Although the results
of the later study were statistically significant, the low sensitivity and accuracy may
limit its clinical use. Moreover, the structural imaging may not be useful to examine
predictive value of early treatment changes in the brain function. In summary, there are no
studies, to date that have applied SVM to functional data generated from a large sample for
use in evaluating predictive accuracy at the individual level.
Main objective : Using machine learning method to examine the predictive value (sensitivity,
specificity, accuracy) of resting state and emotional task-related fMRI data collected at
the pretreatment time (week 0) and at the early phase of antidepressant treatment (week 2)
in the classification of remitters and non-remitters in patients with MDD after 12 weeks of
treatment. Secondary objective: To compare the predictive value of pretreatment baseline
brain activity (week 0) with early treatment brain activity (week 2).
Primary hypothesis: By employing a machine learning method to pretreatment and 2 week
post-treatment fMRI data, we hypothesize that it is possible to predict with significant
accuracy whether an individual patient with MDD could be classified as remitter or
non-remitter at the end of 12 weeks of antidepressant treatment.
Secondary hypothesis (Exploratory): Based on previous EEG studies and our preliminary data
of standard group comparisons showing early treatment response and associated brain changes,
we hypothesize that prediction of antidepressant treatment outcome at an individual level,
will be better using fMRI data obtained early in treatment (2 weeks) as compared with
pretreatment fMRI data.
Rationale: The current study is designed to evaluate the predictive value of early brain
changes related to antidepressant treatment to classify remitters and non-remitters based on
their clinical response at 12 weeks of treatment. Traditionally, the neuroimaging studies
have used group comparisons of pretreatment scans for treatment outcome prediction, which
has limited clinical value to make predictions at the individual level. Machine learning
methods can provide prediction at the individual level, which can be prospectively used in
clinical practice. As the meta-analysis of clinical trials indicate that early clinical
response is a reliable predictor of later treatment outcome11,12, our study will examine
brain scans at both pretreatment and early post-treatment (2 weeks) times.
Experimental Design and Procedure:
The eligible patients with MDD will enter into a single blind placebo treatment for two
weeks. At the end of two weeks of placebo treatment, participants will be considered as
placebo responders based on improvement in depression symptoms as measured by the MADRS
scale ( >50% decrease in MADRS scores from the baseline). The placebo responders will be
excluded from the study. The end of two weeks of placebo treatment will be considered as
week 0 for active treatment. The placebo non-responders who remain eligible with a score of
22 or higher in MADRS will receive desvenlafaxine 50mg/day for 14 days and the dosage will
be increased to 100 mg /day at day 15 if the patient does not improve by 20% reduction in
MADRS scores and the dosage determined at day 15 will be maintained until the end of 12
weeks. The first fMRI session will be performed at week 0 (pretreatment baseline) and the
second session will be performed at the end of week 2 (14 th day). The participants will be
assessed clinically at weeks 1,2,4,6,8,10 and 12 using MADRS, 17-item Hamilton Depression
(HAM-D) rating Scale46, Hamilton anxiety (HAM-A) rating scale47 and clinical global
impression severity of illness scale (CGI-S) and clinical global impression-improvement
scale (CGI-I) 48. HAM-A (Hamilton 1959) will be used to rate anxiety symptoms. To evaluate
the overall clinical improvement, CGI-S and CGI-I will be given. Quality of life measure
(Q-LES-Q) 49 will be given at the baseline (week 0) and at week 12. Adverse effects will be
recorded at each visit. 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 50.
fMRI Scanning Methods
The first fMRI session will be performed at week 0 (pretreatment baseline) and the second
session will be performed at the end of week 2. Images will be collected using a Discovery
MR750 3T MRI system (GE Healthcare, Waukesha, WI, USA) at the Seaman Family MRI Research
Centre at Foothills Hospital, Calgary. Anatomical images will include a 3D T1-weighted
MPRAGE image (TR=9.2ms; TE= minimum; flip angle=20; FOV=25.6 cm; voxel size=1mm3. Resting
state will consist of a 5-min resting-state scan during which the participants will be asked
to keep their eyes closed and hold still ( TR=2000ms;TE=30ms;flip angle=75 degrees; FOV=24
cm; matrix size =64x64, number of slices=36; slice thickness=4mm) Two additional functional
MRI scans will also be collected while participants perform an emotional stroop task (block
design) using the same acquisition parameters as described for the fMRI resting scan.
Machine learning Analysis
We will use support vector machines (SVMs), as these have been successfully applied to
predicting treatment outcomes in MDD from fMRI data. After preprocessing, SVM as implemented
in PROBID software package (http://www.brain.map.co.uk/probid.htm) will be used to
investigate the accuracy of whole brain resting and task-related Blood Oxygen Level
Dependent (BOLD) data in predicting response to antidepressant treatment. Individual brain
scans will be treated as points located in a high dimensional space defined by BOLD response
values in the preprocessed images. During the training phase, a linear decision boundary in
this high dimensional space will be defined by a "hyperplane" that separates the individual
scans according to a class label. SVM classifier will be trained by providing examples of
the form where X represents the fMRI data and C represents the class label (C= 1 for
remitters, and C = -1 for non-remitters). Once the hyperplane is determined from the
training data, it will be used to predict the class label of a test sample. A linear kernel
SVM will be used to extract the weight sector as an image (SVM discriminating map). A
"leave-one-out" cross validation method will be used to validate the classifier. This
procedure involves excluding a single subject from each group and the classifier will be
trained using the remaining subjects. The subject pair excluded will be used to test the
ability of the classifier to distinguish between remitters and non-remitters. The procedure
will be repeated for each subject pair in the sample in order to assess the overall accuracy
of SVM. To establish whether the classification accuracy is statistically significant, we
will perform permutation testing. This will involve repeating the classification procedure
1000 times with a random permutation of the training group labels and counting the number of
permutations that achieve higher sensitivity and specificity than the one observe with the
true labels. The p value will be calculated by dividing this number by 1000. Bonferroni
correction or false discovery rate will be used to correct for multiple testing. This SVM
analysis and permutation testing will be performed on the pretreatment scan and 2-week
post-treatment scan separately. The task-related data and resting state data will be
analyzed separately.
Sample size calculation
The sample size calculation for a classification study is based on precision we want to
achieve for sensitivity and specificity. The precision refers to the width of the 95%
confidence intervals associated with the estimates. To achieve the 95% confidence interval
of plus and minus of 0.16 for 85 % sensitivity and 85% specificity, we will need a total
sample of 40 subjects. Having a sample size of 40 should achieve statistically significant
classification accuracy and clinically meaningful sensitivity and specificity. Accounting a
placebo response of 30% and drop out of 10%, we need to recruit a total of 61 subjects for
the sample of 40.
