Clinical Pilot of Augmented Human Intelligence in Major Depressive Disorder (AHI/Depression Pilot)
Phase: NA
Conditions: Depression, Major Depressive Disorder
Sponsor: Mayo Clinic

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TITLE 
Clinical Pilot of Augmented Human Intelligence in Major Depressive Disorder 
(AHI/Depression Pilot) 
 
INVESTIGATORS/KEY STUDY PERSONNEL 
William V. Bobo, M.D., M.P.H.,1 Mohit Chauhan, M.D.,1 William Leasure, M.D.,2 John 
Presutti, M.D., Ph.D.,1 Joshua Keith, M.D.,1 Mark Williams, M.D.,2 Arjun Athreya, 
Ph.D.,2 Paul E. Croarkin, M.D.,2 Jennifer Vande Voort, M.D.,2 Balwinder Singh, M.D.,2 
Abd Moain Abu Dabrh, M.B., B. Ch., M.S.,1 Michelle Skime,2 Cynthia Stoppel,2 Liewei 
Wang, M.D.,2 Leila Jones, Ph.D.,2 Ravishankar Iyer, Ph.D.,3 and Richard Weinshilboum, 
M.D.,2 Mark A. Frye, M.D.,2 Katherine M. Moore, M.D.,2 Hannah K. Betcher, M.D.2 
 
1 Mayo Clinic, Jacksonville, FL (USA) 
2 Mayo Clinic, Rochester, MN (USA) 
3 University of Illinois, Urbana-Champaign, IL (USA) 
 
ABSTRACT  
This study will pilot the clinical implementation of a validated Augmented Human 
Intelligence (AHI)-driven algorithm and clinical decision support tool to predict the 8-
week response to selective serotonin reuptake inhibitor (SSRI) and serotonin-
norepinephrine reuptake inhibitor (SNRI) treatment of adults with a clinical diagnosis of 
Major Depressive Disorder.  This study will enroll 120 patients over two years.   
 
Major depressive disorder (MDD) is a chronic and severe psychiatric illness that affects 
over 15 million people in the U.S. and is the leading cause of disability worldwide. 
Treatment choices are often made on a “trial and error” basis and an individual patient 
can receive several months of ineffective treatments before a clear “non-response” profile 
can be established. Investigators at Mayo Clinic Florida, Mayo Clinic Rochester, and the 
University of Illinois at Urbana-Champaign (UIUC) have developed an augmented 
human intelligence (AHI) algorithm for response prediction during short-term treatment 
with SSRIs and SNRIs, validated in three large, 8-week SSRI clinical trial datasets, and 
an additional pooled dataset of 4 SNRI clinical trials of 8-12 weeks duration. The 
algorithm takes as inputs clinical and demographic characteristics at baseline, depression 
severity at baseline and at interim treatment time points (including a smaller sub-set of 
highly-prognostic individual depression symptoms) measured using validated rating 
scales, and genomic information (when available). The output is a probability of eventual 
non-response and remission of depressive symptoms at an interim treatment time point 
when that information can be used to inform clinical judgment as to whether the current 
course of treatment should continue or be changed. A web-based tool was developed for 
clinical decision support in naturalistic treatment settings, based on the AHI algorithm. 
Mayo Clinic is now poised to implement and test the effectiveness of the clinical decision 
support tool with an 8-week prospective, naturalistic pilot in the clinical practice. The 
main hypothesis to be tested is that the eventual antidepressant treatment outcome (non-

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response, remission) predicted by the decision support tool at 2- and 4 weeks will have 
high concordance with the observed outcome at 8 weeks.  
 
  
INTRODUCTION  
MDD is a chronic and severe psychiatric illness that affects over 15 million people in the 
U.S. (Kessler et al., 2005) and is the leading cause of disability worldwide (WHO 2017). 
MDD can generally be managed with antidepressants (Belmaker & Agam 2008). 
However, the most common antidepressants for treating MDD are ineffective for many 
patients (Frochtmann et al., 2005; Gartlehner et al., 2012; Rush et al., 2004, 2006), and 
multiple therapeutic trials of antidepressants (each lasting several weeks) are typically 
needed to achieve meaningful benefit (Trivedi et al., 2006). This means that an individual 
patient can receive several months of ineffective treatments before a clear “non-response” 
profile can be established (Rush et al., 2003). Hence, at an early point during treatment, 
there is a compelling clinical need in primary care and specialty care settings, where 
systematic recording of depressive symptoms may be accomplished through interactive 
devices (e.g., computers, tablets, smart-phones), to know, “given a patient’s depression 
severity change after X weeks, are there specific thresholds of change in Y depression 
symptoms, such that there is a  Z% chance of patient not responding favorably to 
additional weeks of treatment with the current antidepressant medication.” The 
translational impact of generating quantitative prognoses that forecast poor response 
based on early response to antidepressant medication rests in the availability of objective 
measures to prompt an early change in treatment. Avoiding extended weeks of 
medication that is unlikely to benefit the patient could potentially reduce disease burden 
and suffering. 
 
In a preliminary study, we developed a novel machine learning workflow that identified 
core symptoms from the full rating scales (QIDS-C and HDRS) using data from 3 large 
8-week clinical trials of citalopram/escitalopram treatment in over 1,400 MDD patients. 
Supervised learning methods predicted sex-specific non-response and remission at 8 
weeks with AUC 0.62–0.95 using the baseline severity of core symptoms and their 
associated changes at 4 weeks. The prognostic capabilities of the core symptoms 
identified using our approach replicated across all 3 clinical trials (manuscript in 
preparation). The algorithm was then applied to an additional pooled dataset of 4 
randomized, placebo-controlled SNRI clinical trials, which resulted in a replication of 
core symptom identification and prognostic outcomes in the active treatment groups 
(manuscript in preparation). In both studies, specific thresholds of early improvement in 
core symptoms were identified that were highly-prognostic and predictive of non-
response and remission after 8-12 weeks of antidepressant treatment. 
 
Using the data from these two studies, we have since developed a web-based electronic 
decision support tool to derive predictions and prognostic evidence of likely non-
response and remission based on early change in core depressive symptom severity. With 
the use of this AHI-based decision support tool, we are now poised to implement and test 
its effectiveness with an 8-week prospective, naturalistic pilot in the clinical practice. 
 

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Figure 1. Study overview. 
 
STUDY OVERVIEW (Fig 1) 
Adults (aged 18-64 years) who meet 
DSM-5 diagnostic criteria for non-
psychotic unipolar major depressive 
disorder (MDD) and meet study 
eligibility criteria will receive 8 
weeks of open-label treatment with 
an SSRI or SNRI antidepressant. 
Depressive 
symptoms 
will 
be 
assessed using the subject- and 
clinician-rated versions of the 16-
items QIDS scale (QIDS-SR and 
QIDS-CR) and the 17-item HAMD 
at baseline, week 2 (via telephone), 
week 4, and week 8; with an 
additional phone contact at week 24. 
The QIDS scale scores at 2- and 4-weeks will be entered into the AHI-based clinical 
decision support tool, and the outcome predicted by the tool (e.g., the predicted eventual 
treatment outcome at 8 weeks) will be recorded. Clinicians and patients will be blinded to 
the clinical decision support tool prediction of outcome provided at the 2- and 4 weeks. 
The overarching hypothesis to be tested in this study is that the antidepressant treatment 
outcome (non-response, remission) predicted by the decision support tool at 2- and 4 
weeks will have high concordance with the observed outcome at 8 weeks. 
 
Note:  As part of this protocol, blood will also be collected for genotyping at the baseline 
study visit. When available, a SNP panel will be performed to examine whether 
information on SNP profiles in four specific genes improve the accuracy of the 
predictions achieved using the AHI-based clinical decision support tool. 
 
SPECIFIC AIMS 
1. 
Evaluate the degree of statistical concordance between observed clinical outcomes 
(non-response/remission) after 8 weeks of treatment and the outcomes predicted by 
an AHI-based clinical decision support tool after 2 weeks of follow up (i.e., 
concordance between 2-week predicted outcome and 8-week observed outcome), 
as assessed using the QIDS-SR and QIDS-CR,  in adults with DSM-5-defined 
MDD who receive prospective treatment with an SSRI or SNRI antidepressant.   
2. 
Evaluate the degree of statistical concordance between observed clinical outcomes 
(non-response/remission) after 8 weeks of treatment and the outcomes predicted by 
an AHI-based clinical decision support tool after 4 weeks of follow up (i.e., 
concordance between 4-week predicted outcome and 8-week observed outcome), 
as assessed using the QIDS-SR and QIDS-CR, in adults with DSM-5-defined 
MDD who receive prospective treatment with an SSRI or SNRI antidepressant.     
3. 
   

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Figure 2. Variation of depression 
severity during 8 weeks of SSRI 
treatment in 1,300 patients 
 
BACKGROUND AND SIGNIFICANCE 
For the reasons presented in the INTRODUCTION to this protocol, there is a crucial 
need for web-based tools that can be used to generate quantitative prognoses that can 
forecast a poor response to a given antidepressant medication in depressed patients, based 
on early responses to antidepressant medication. These objective measures can be used to 
augment clinician judgment in order to prompt an early change in treatment if the 
prognosis for non-response is high. Today, there is no evidence-based guidance on how 
much change in the overall depression score (or the scores for specific symptoms) is 
required to make those decisions.  
  
The main challenge for developing clinically 
useful 
prognostic 
tools 
is 
the 
large 
heterogeneity (inter-individual variability) in 
response to antidepressant medication (Arnow 
et al., 2015; Fried 2017; Kessler et al., 2017; 
Musliner et al., 2016; Senn 2016). In 
measurement-based care of patients with 
MDD, standardized rating scales such as the 
Hamilton Depression Rating Scale (HDRS) 
(Hamilton 1960) and the Quick Inventory of 
Depressive Symptoms – clinician rated 
version (QIDS-CR) (Rush et al., 2003) are 
used to assess the total depression severity by 
adding up scores for scale items relating to 
sleep, appetite, mood, fatigability, etc. The 
ability to know whether an early change (e.g., 
at 2 or 4 weeks) in total depressive severity is 
prognostic of eventual outcomes (e.g., at 8 
weeks) is challenged by the fact that patients 
with 
identical 
MDD 
severity 
respond 
differently to the same drug. In mathematical 
terms, if there are n levels of depression 
severity scores across k time-points of treatment, then there are nk transitions – resulting 
in multiple hundreds of trajectories, as shown in Fig. 2. It is not tractable for even an 
experienced clinician to be able to memorize all possible trajectories of change in MDD 
severity to derive treatment prognoses at an individual patient level. 
 
Others have attempted to utilize machine learning (ML) and artificial intelligence (AI) 
approaches to analyzing large datasets from clinical trials of antidepressants for 
depressed adults, with the goal of deriving more homogeneous patient subgroups or 
trajectories of clinical response (Chekroud et al., 2016, 2017; Iniesta et al., 2016). These 
approaches have relied on the use of baseline depressive symptoms and 
sociodemographic predictor variables. The clinical utility of these approaches is severely 
limited by the weak predictive effects of sociodemographic variables, either individually 
or in aggregate, for predicting outcomes of antidepressant treatment (Chekroud et al., 
2016, 2017; Initesta et al., 2016). Furthermore, these ML/AI-based methods are unable to 

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consider changes in depressive symptoms or other clinical assessments at intermediate 
time-points, before a therapeutic antidepressant trial is fully completed—a central 
concept in depression management (Crismon et al., 1999; Friedman et al., 2000; Kennedy 
et al., 2016). 
 
To address the problem of antidepressant response heterogeneity while simultaneously 
considering changes in depressive symptoms at intermediate time points, we set out to 
derive more compact and data-driven representations of antidepressant response patterns 
in MDD patients and use this information to achieve interpretable (clinically useful) 
prognoses of antidepressant treatment outcome at an early stage of treatment. Briefly, we 
used probabilistic graphs to algorithmically explore the most-likely longitudinal 
variations (referred to as symptom dynamic paths) of total depression severity to achieve 
an eventual categorical treatment outcome using data from three large SSRI trials 
(n=1,846) and two rating scales—the HDRS and QIDS-CR (see PRELIMINARY 
RESULTS below). This approach provided the mathematical foundation needed to model 
conditional dependencies that follow a clinician’s treatment logic, i.e., accounting for 
improvement in total depression severity, conditioned upon baseline depression severity 
and changes in depressive symptoms at intermediate time-points, in a purely data-driven 
manner, without a priori specification of trajectories (Athreya et al., 2017; Koller 2009). 
Second, using unsupervised machine learning, we identified a subset of individual 
depressive symptoms (referred to as core depressive symptoms) with homogeneous 
responses to symptom severity assessments, and found that their early (4-week) changes 
were prognostic and predictive of categorical treatment outcomes at 8 weeks (remission, 
response, non-response). Crucially, the symptom dynamic paths, core symptoms, and 
their predictive capabilities replicated across all three datasets and both rating scales. 
Specific thresholds of change in core symptoms were identified at 4 weeks that were 
highly prognostic of eventual outcome at 8 weeks. Subsequent application of this ML 
workflow to a large pooled dataset of duloxetine-treated adult patients with MDD 
(totaling 2,510 subjects) resulted in a replication of the same findings with SSRI 
antidepressants (manuscript in preparation). Additional methodological details of this 
preliminary work are provided below under the section entitled, PRELIMINARY 
STUDIES. 
 
To increase the translational impact of our preliminary work, we built an interactive 
system that allows clinicians to input select demographic measures and depression 
severity scores, and obtain from the probabilistic graphical model output that conveys the 
predicted outcome and evidence supporting that prediction (see PRELIMINARY 
STUDIES below). While the methods developed in our preliminary work rely on 
retrospective analyses of clinical trial datasets, the aforementioned clinical impact is 
possible only when prognostic models reflect variability observed in naturalistic clinical 
settings and are shown to be valid prospectively in routine use settings. And this provides 
the motivation for the current study protocol. 
 

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PRELIMINARY STUDIES 
 
Background 
Preliminary studies have been conducted by Mayo Clinic and UIUC investigators in two 
phases. In the initial phase, we used data from 3 open trials of citalopram or escitalopram, 
two closely related SSRI antidepressants, to: (a) Cluster patients based on depressive 
symptoms severity, separately by sex, as a basis for modeling the change in depressive 
symptoms over time (symptom dynamic paths); (b) Identify a subset of core depressive 
symptoms with homogeneous longitudinal responses to study drugs; (c) Use core 
depressive symptom changes at 4 weeks to predict non-response, response (without 
remission), and remission at 8 weeks; and (d) Create a web-based decision support tool 
using the collected data. In the second phase, we replicated the findings from the first 
phase (with citalopram/escitalopram) using data from 9 randomized trials of duloxetine, 
and SNRI antidepressant, for treating adults with MDD, each with a follow-up duration 
of 8–12 weeks (NCT00406848, NCT00536471, NCT0073411, NCT00062673, 
NCT00036335, NCT02229825, NCT01000805, NCT010170329, and NCT02790970). 
Presented here are methods and results from the first phase (methods from the first phase 
were duplicated and the results were replicated in the second phase) and the third phase 
of our work. This workflow is summarized in Fig 3, where steps (a) and (b) are 
summarized under Fig 3 Aim 1, step (c) is summarized under Fig 3 Aim 2, and step (d) 
is summarized under Fig 3 Aim 3. We will conclude with a description of the successful 
integration of biological measures with our ML algorithm. 
 
Figure 3. Overview of preliminary data workflow 
 
 
Data Sources  
The primary source of data for our preliminary work in its first phase was the 
Pharmacogenomics Research Network Antidepressant Medical Pharmacogenomics Study 
(PGRN-AMPS, NCT 00613470). PGRN-AMPS was a single-arm, open trial designed to 
assess antidepressant effects of citalopram/escitalopram over 8 weeks in adults (aged 
18−84 years) with MDD, and to examine metabolomic and genomic predictors of those 
outcome (Ji et al., 2013). Data from complete cases (baseline, 4-, and 8-week data) of 
step 1 of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial 
(NCT 00021528) and International SSRI Pharmacogenomics Consortium (ISPC) were 
used to test the reproducibility of patterns of depressive symptom response inferred in the 
PGRN-AMPS study (Biernacka et al., 2015; Trivedi et al., 2006). The complete pooled 
dataset consisted of 1,846 adults, aged 18-75 years, with MDD. 
 

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Fig 3 Aim 1 – Patient clustering and sex stratification 
Unsupervised learning (Gaussian mixture models) were used to generate patient 
stratification (clusters) at baseline and at 4 and 8 weeks based on total QIDS-CR and 
HDRS17 scores (see Fig 4). The Shapiro-
Wilk test was used to test whether the 
distribution of symptom severity scores is 
normal 
(Gaussian). 
Because 
total 
depression severity scores were not 
normally distributed (Fig. 4), we modeled 
the distribution of symptom severity 
scores as being composed of a mixture of 
Gaussians 
(k 
components 
of 
the 
distribution), 
where 
each 
Gaussian 
distribution represented a cluster of 
patients with similar symptom severity. 
We used an expectation-maximization 
(EM) algorithm that initially assumed only 
two (k=2, line 1 in Fig 4) components in the mixture (a single bell-shaped curve 
distribution) and gradually increased the number of components (multiple normal 
distributions) until an adequate fit of the data was achieved. For each value of k, we 
generated 10,000 samples (line 7 in Fig 4) using the parameters (mean and variance of a 
Gaussian component) estimated by the EM algorithm and compute p-values using 
Kolmogorov-Smirnov (line 8 in Fig 4) test. The process of increasing the components 
stopped at the first instance of p>0.05 (line 9 of Fig 4, i.e., test in line 8 fails to reject the 
null hypothesis that the estimated distribution and actual distribution of total depression 
severity scores are similar); otherwise, k was incremented by 1 (line 12 of Fig 4), and the 
process reverted back to line 5 of Fig 4. Under the assumption that the distribution was a 
mixture of k components 
(line 10 in Fig 4), patients 
were assigned to a cluster 
based on the components 
to which their scores 
belonged. Our solution 
yielded k=3 clusters, as 
shown 
in 
Fig 
5, 
at 
baseline, 4 weeks, and 8 
weeks.  The 3 clusters of 
patients were labeled A1, 
A2, and A3; the clusters 
at 4 weeks were labeled 
B1, B2, and B3; and the clusters at 8 weeks were labeled C1, C2, and C3. At each time-
point, the numeral 1 represented the mildest symptom cluster, 3 represented the most 
severe symptom cluster, and 2 represented an intermediate symptom cluster. Crucially, 
the clusters at 8 weeks (C1, C2, and C3) were shown to be ecologically (clinically) valid-
- all patients in C1 achieved remission, all patients in C3 failed to achieve remission or 
response, and 87% of patients in C2 achieved response without remission. 
Fig. 4: Patient stratification algorithm 
Fig. 5: (a) shows the inference of mixtures comprising the distribution 
of symptom severity scores. (b) shows distributions of symptom 
severity within clusters inferred using the sufficient statistics of
components inferred in (a). 

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Fig. 6: Symptom dynamics, total depression severity. 
 
Fig 3 Aim 1 – Modeling symptom dynamics pathways 
Probabilistic Graphical Models (PGMs) with the forward algorithm were used to model 
how total QIDS-CR and HDRS17 scores changed during citalopram/escitalopram 
treatment by identifying the path each patient took starting from a given baseline cluster 
to a cluster at 4 weeks, and then from 4 weeks to 8 and 12 weeks. Using this approach, 
the nodes of the graph corresponded to symptom clusters at each time-point. We 
computed the likelihood of each 
path using the recursive forward 
algorithm by defining the graph as 
a hidden Markov model. The path 
likelihood of arriving at any other 
node was computed as a recursive 
sum of the product of the path 
likelihood 
of 
arriving 
at 
the 
precursor 
node, 
the 
transition 
probability from the precursor 
node to the current node (i.e., 
proportion of patients who moved 
between the two nodes), and the 
fraction of patients who achieved 
response status at the present node 
relative to their baseline severity. 
The paths for the pairs of baseline 
and 4- or 8-week clusters that had the highest likelihood scores were identified as the 
“most likely” paths, which we then referred to as symptom dynamics paths. The 
symptom dynamic paths (shown in Fig. 6 for women in PGRN-AMPS and ISPC) and 
distributions of total depression scores on the paths replicated in the PGRN-AMPS and 
STAR*D datasets (for QIDS-CR), and in the PGRN-AMPS and ISPC datasets (for 
HDRS17), including the most likely response status on these paths at 4 and 8 weeks. We 
thus demonstrated that a compact representation of antidepressant response was achieved 
by capturing a set of 9 significant trajectories of treatment response – as opposed to over 
1,300 trajectories in Fig. 1. 
 
Fig 3 Aim 1 – Extracting core depressive symptoms 
To extract homogeneous patterns of citalopram/escitalopram response on the symptom 
dynamic paths, core depressive symptoms were defined using three criteria: (1) similar 
response patterns at all time-points, (2) low inter-individual variability, and (3) 
statistically distinct patterns of response at 4 weeks between symptom dynamic paths 
originating from a baseline cluster. Hierarchical clustering with complete linkage was 
used to identify individual depression scale items with similar rating patterns (clustered 
together with a common parent in the tree, other than the common parent of the entire 
hierarchy) within patient clusters at baseline, 4, and 8 weeks. Then, symptom clusters 
were identified wherein clinician ratings for each of the scale items at baseline had a 
nonzero median and low inter-individual variability (the chi-square test for the 
distribution of clinician ratings is significant after multiple comparisons, with the null 

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hypothesis being that the distributions of ratings for that item are equal). Finally, the 
Kolmogorov-Smirnov test were used to determine if there were significant differences in 
the distributions of core symptom scores at 4 weeks between the symptom dynamic paths 
leading to non-response (<50% reduction in QIDS-CR or HDRS17 scores), response 
(>50% reduction in QIDS-CR or HDRS scores), and remission (QIDS-CR score ≤ 5, 
HDRS17 score ≤ 7) at 8 weeks, from a given baseline cluster. In the individual and 
combined datasets, 5 items in the QIDS-CR (sad mood, concentration/decision-making, 
self-outlook, involvement, and energy/fatigability) and 4 items in the HDRS17 (sad 
mood, psychic anxiety, guilt feelings/delusions, and work/activities) met the core 
depressive symptom criteria. 
 
Fig 3 Aim 2 – Predicting non-response/remission after 8 weeks of SSRI treatment 
We used random forests as a binary classifier to predict clinical outcomes at 8 weeks 
given a specific baseline cluster, using the baseline severity of core depressive symptoms 
and their changes at 4 weeks. AUC, PPV, NPV, sensitivity, and specificity were 
calculated as measures of algorithm performance, using the null information rate as a 
proxy for chance. To minimize the effects of overfit and information leak, nested cross-
validation (nested-CV) with 5 repeats was used to train the classifiers and maximize the 
area under the curve. In our preliminary study of citalopram/escitalopram-treated adults, 
the predictive accuracies for non-response ranged from 71 to 84% (p<0.01, AUC 0.85–
0.91) in women and from 89 to 92% (p<0.02, AUC 0.88−0.90) in men. For remission, 
predictive accuracies were 75–85% (p<0.04, AUC 0.84−0.89) in women and 63−95% 
(p<0.04, AUC 0.62–0.94) in men. 
 
Fig 3 Aim 3 – Electronic decision support tool development 
We have recently used data from our pilot work with SSRIs (and the SNRI, duloxetine) 
to build an interactive system (web-based decision support tool) that allows clinicians to 
input select demographic measures and depression severity scores, and obtain from the 
probabilistic graphical model output that conveys the predicted outcome and evidence 
supporting that prediction. The interactive system consists of a platform-independent 
front-end (i.e., it will work on desktops, laptops, smart phones and other hand-held 
devices) through which clinicians can input data and a back-end (e.g., a server hosting the 
ML framework and code) where results are computed. The front-end interacts with the 
trained prediction model in the server via a RESTful API that is integrated with iOS and 
Android mobile apps and HTTP-based websites, allowing clinicians to use a wide range 
of devices, including phones, tablets and workstations. The server is hosted at UIUC, 
which has vendor support from IBM Systems, Intel, and NVIDIA. Crucially, the analyses 
that provide the basis for the decision support tool’s functionality are retrospective in 
nature; thus, prospective validation is needed—which serves as the motivation for the 
current study.  
 
Additional Work – Integration of biological measures 
Moreover, the ML framework described here allows for the integration of biological 
measures in future studies—which serves as the motivation for the blood collection as 
part of this protocol. We tested the capability of a basic version of our ML algorithm 
(using total depression scale scores) to include functionally validated pharmacogenomics 

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biomarkers joined with clinical measures to predict remission and response using data 
from 1,030 Caucasian MDD outpatients. The data source for this project, again, consisted 
of the PGRN-AMPS (n = 398), STAR*D (n = 467), and ISPC (n = 165) trials. GWAS for 
PGRN-AMPS plasma metabolites associated with SSRI response (serotonin) and 
baseline MDD severity (kynurenine) identified SNPs in DEFB1 (rs5743467, rs2741130, 
rs2702877), ERICH3 (rs696692), AHR (rs17137566), and TSPAN5 (rs10516436), which 
were tested as predictors. Supervised machine learning methods trained using SNPs and 
total baseline depression scores predicted remission and response at 8 weeks with AUC > 
0.7 (p<0.04) in PGRN-AMPS patients, with comparable prediction accuracies >69% 
(p<0.05) in STAR*D and ISPC. None of the clinical or demographic predictor variables 
that were also tested in our ML algorithm were associated with depression severity at 
baseline or at 8 weeks. There was also a lack of a significant association between 
CYP2C19 metabolizer phenotypes with depression severity clusters at baseline and 8 
weeks. These results demonstrate that machine learning can achieve accurate and, 
importantly, replicable prediction of SSRI therapy response using total baseline 
depression severity combined with pharmacogenomic biomarkers (Athreya et al., in 
press). 
 
PARTICIPANTS 
 
Entry Criteria 
We will enroll 120 participants, both male and female.  They will meet the following 
criteria: 
 
(1) Outpatients with nonpsychotic MDD. Outpatient status assumes that the subject is not 
psychiatrically hospitalized or in an active suicidal crisis requiring hospitalization.  
(2) A total score of >10 on the QIDS-CR and on the QIDS-SR (equivalent to a HAMD17 
score of 13 [ids-qids.org, accessed on April 12, 2019]) given that when medication 
exceeds the effect of  placebo in primary care, participants have a 17-item HAMD score 
>12.  We added 2 HAMD points to take into account the possibility of measurement 
error. This is a very similar approach taken to the successful recruitment of subjects into 
the PGRN-AMPS trial. 
(3)  Antidepressant treatment is deemed appropriate by the study clinician.  
(4)  Adults who are between 18-64 years of age.   
 
(5)  Ability to provide informed consent 
(6)  Ability to understand English  
 
Exclusion Criteria 
The following will be exclusionary for participation in this study: 
(1)  A medical contraindication that precludes SSRI or SNRI treatment. 
(2) Presence of a general medical condition that, in the opinion of this study clinician, is 
the cause of their depressive symptoms, will be exclusionary. 
(3)  People with treatment-resistant depression will be excluded from participating. For 
this study, treatment resistance will be defined as failure to respond to two or more 
adequate therapeutic trials of SSRIs and at least on SNRI therapeutic trial (sufficient 

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antidepressant dose, for 6 weeks or longer) during the current depressive episode.  
Failure to tolerate a therapeutic trial of a given medication (resulting in 
discontinuation due to adverse effects) will not be counted as exclusionary. Persons 
who have failed to respond to two or more adequately designed and executed 
therapeutic trials of SSRIs but have no history of least one failure to respond to SNRI 
treatment during the current depressive episode will be eligible to receive treatment 
with an SNRI in this study. 
(4) Diagnosis of schizophrenia, schizoaffective disorder, bipolar I or II disorder, or 
bipolar disorder NOS (including other specified or other unspecified bipolar 
disorders) or a primary psychiatric condition that requires a different initial treatment 
than an antidepressant.   
(5) Currently taking an antidepressant medication with subtherapeutic results in terms of 
antidepressive efficacy and unwilling to undergo a medication taper and 
discontinuation prior to initiation of a study drug from this protocol.  The subject will 
be closely monitored by the study clinician during the medication taper and 
discontinuation phase. The design of the medication taper will be at the discretion of 
the study clinician.  Subjects who cannot be safely tapered from their medication or 
who experience adverse effects during the taper that make further tapering infeasible 
will be excluded from the study.   
(6) Use of antidepressant medication primarily for management of nicotine dependence, 
chronic pain, migraine prophylaxis or other diagnoses.   
(7) Active substance use disorder. Persons in sustained full remission (> 12 months) and 
a negative urine drug of abuse screen at the screening visit will be considered eligible. 
Note: An additional urine drug screen will not be necessary for individuals with a 
negative urine drug screen documented in the medical record where the date of 
testing occurred within 12 weeks (84 days) of the screening/baseline study visit. 
However, study clinicians can still obtain a urine drug screen based on their clinical 
judgement even in participants with a negative drug screen within the 12 weeks 
preceding the screening/baseline study visit. 
(8) Trazadone, melatonin, low-dose quetiapine (<100 mg QHS), z-drugs (zolpidem, 
zopiclone, eszopiclone, etc.), ramelteon, and diphenhydramine may be used as rescue 
medications for insomnia. Benzodiazepines may be used for treatment of anxiety, and 
atomoxetine may be used for the treatment of attention deficit disorder.  Study 
subjects currently on antipsychotic medications (e.g., typical and atypical 
antipsychotic drugs, other than low-dose quetiapine for insomnia) and mood 
stabilizing agents (e.g., lithium, carbamazepine, valproate, lamotrigine) are not 
eligible for the study 
(9) Pregnant subjects and those who are currently breastfeeding and who plan to continue 
breastfeeding will be excluded. 
(10) 
Persons currently undergoing ECT, TMS or DBS as acute series or for 
maintenance. 
(11) 
Patients currently psychiatrically hospitalized or in an active suicidal crisis 
requiring hospitalization in the opinion of the study clinician.   
 

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As an additional stipulation, individuals whose total QIDS-CR and total QIDS-SR scores 
are 10 or higher at the screening visit but decrease (improve) to total scores less than 10 
on either the QIDS-CR or QIDS-SR at the baseline visit will be excluded. 
 
METHODS 
 
1.  Patient Identification and Recruitment   
Patient recruitment will be focused on the Mayo Clinic Rochester and Mayo Clinic 
Florida outpatient practices of the Department of Psychiatry and Psychology as well as 
Mayo Departments of Family Medicine, Preventive and Occupational Health and 
Community Internal Medicine and other medical providers also treat a large number of 
patients with MDD, so coordination will also occur with these departments.  Study 
coordinators (SC) will coordinate recruitment efforts through daily contact with clinicians 
conducting new psychiatric evaluations.   
 
2. Screening Visit 
At the screening visit, potential study subjects will: 
(1) Complete a brief form ascertaining basic demographic information and clinical 
information including prior course of the illness, prior suicide attempts, family 
history of MDD or bipolar disorder, current general medical illnesses, history of 
bone marrow or liver transplant or blood transfusion within the previous 6 weeks, 
and prior history of treatment during the current major depressive episode; and 
(2) Complete a QIDS-SR. 
 
At the screening visit, the study coordinator will: 
(1) Obtain informed consent. Patients will have the opportunity to have the Mayo 
IRB patient advocate or a significant other present during the consent procedure 
if desired; 
(2) Review the clinical and demographic information provided by the patient (see [1] 
above), including responses to items about prior course of the illness, prior 
suicide attempts, family history of MDD or bipolar disorder, current general 
medical illnesses, history of bone marrow or liver transplant or blood transfusion 
within the previous 6 weeks, and prior history of treatment during the current 
major depressive episode. The information regarding psychiatric and family 
history gathered via this process will be entered as a Research Note in the 
electronic health record to ensure that adequate information is available for 
follow-up after completion of the study; 
(3) Measure the patient’s height and weight; 
(4) Ask participants for their permission to be contacted in the future to obtain 
additional information for the study;  
(5) Complete the Structured Clinical Interview for DSM-IV (SCID) [First et al, 1995] 
to confirm a diagnosis of MDD; 
(6) Administer the QIDS-C16; and 
(7) Review the study inclusion/exclusion criteria.  
 

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Role of the study clinician: The study clinician will review the information provided by 
the patient and reviewed by the study coordinator and conduct a basic clinical interview 
to confirm eligibility for study participation. Safety issues, including suicidal ideation and 
risk for harming others, will be assessed.  
 
When appropriate after a negative pregnancy test and/or urine drug of abuse screen, the 
study coordinator will invite subjects to participate in the study if entry criteria are met.  
If the subject agrees, he/she will receive treatment as described below.  If the subject 
declines to participate, he/she will receive clinical care as usual from their primary 
physician or psychiatrist.   
 
3. Research Evaluation Treatment Schedule   
Participants selected for inclusion in the study will be seen at weeks 0 (baseline), 4, and 
8, and telephone interviews will be scheduled at 2 and 24 weeks. For those subjects who 
are unable to attend the follow-up visits as scheduled, we will contact them by phone to 
complete the QIDS-CR and Frequency, Intensity, and Burden of Side Effects Ratings 
(FIBSER)/Antidepressant Side-Effect Checklist (ASEC) (Uher et al. 2009; Wisniewski et 
al., 2006). A summary of assessments and rating scales by follow-up time point is 
presented below in Table 1. 
 
Table 1. Assessments and scales by follow-up time point. 
Assessment 
Baseline 
Week 2 
(telephone) 
Week 4 
Week 8 
Week 24 
(telephone) 
QIDS-CR 
• 
• 
• 
• 
• 
QIDS-SR 
• 
 
• 
• 
 
HAMD-17 
• 
• 
• 
• 
• 
CGI-S 
• 
 
• 
• 
 
CGI-I 
 
 
• 
• 
 
C-SSRS* 
• 
 
• 
• 
 
PETS 
 
 
• 
• 
 
FIBSER/ASEC 
 
• 
• 
• 
• 
Side effects† 
 
• 
• 
• 
• 
Vital signs 
• 
 
• 
• 
 
ACE/MOSSS 
• 
 
 
 
 
HCG/UDS‡ 
• 
 
 
 
 
* Refers to the C-SSRS screen version. 
† Refers to spontaneous ascertainment of adverse effects by subject report. 
‡ A urine pregnancy test (HCG) and/or urine drug screen will be ordered at baseline. The 
HCG will be ordered only for women under age 45 years. 
 
 
Prior to Baseline Visit.   
Some enrollees who are already taking antidepressants will undergo a medication taper 
and discontinuation prior to initiation of a study drug from this protocol.  The rate of 
tapering will be determined by the study clinician, with the goal of taper completion 
within 2-8 weeks, depending on the current dose and antidepressant selected. Once the 

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medication is fully tapered and discontinued, patients will be eligible for their baseline 
study visit, when study medication will be initiated. For patients taking fluoxetine prior to 
study entry, the medication will be able to be stopped without tapering in most cases. 
 
Baseline Visit.  
Note: If medication allows, the screening and baseline visits can occur on the same day. 
In other words, for patients will are identified by clinicians as needing to start 
antidepressants, this will be the first study visit for those participants. 
 
(1) Clinical ratings: Before each visit with study clinicians, subjects will complete a 
QIDS-SR, ACE, and MOSSS; and the study coordinator will complete a QIDS-CR, 
17-item Hamilton Depression Rating Scale (HAMD17) (Hamilton 1967), and 
Columbia-Suicide Severity Rating Scale (C-SSRS) screening version (Posner et al., 
2011). QIDS-SR and –CR scale scores will be made available to study clinicians, as 
the latter will serve as a guide for clinical decisions about changing the dose of study 
drugs. At the end of the face to face visit, study clinicians will provide a CGI-S score 
(Guy 1976). 
(2) Initial data entry into clinical decision support tool: Study clinicians will enter 
information about subject age, sex, QIDS-CR, and HAMD17 scores. This information 
will be used in conjunction with changes in depressive symptom scores at the week 4 
face to face study visit to generate an output that specifies the prognoses of achieving 
eventual remission, response, and non-response at week 8. 
(3) Sample collection: At the baseline visit, we will obtain blood for DNA using two 
EDTA tubes, DNA isolation-50ng/ml that will be sent to the BAP Lab for storage. All 
venipunctures will be performed using standard techniques.    
(4) Vital signs:  At the baseline visit, subjects will have their blood pressure, heart rate, 
height, and body weight measured. 
(5) Study drug initiation: Study drugs will include all SSRI and SNRI antidepressants 
available for clinical use in the U.S. All study drugs will be initiated at standard 
doses, as shown in Table 2. For drugs with multiple indications, the doses chosen for 
this study are based on those recommended by the manufacturer. 
(6) Scheduling next study visit(s): The study coordinator will schedule the week 2 
telephone follow-up visit and the week 4 face to face study visit. 
 
Table 2. Study drugs and standard starting doses and titration guidelines 
Study drug name 
Starting dose 
(mg/day) 
Titration guideline (optional) 
Citalopram (Celexa) 
20  
Increase to 40 mg if non-responder at wk 4 
Escitalopram (Lexapro) 
10 
Increase to 20 mg if non-responder at wk 4 
Fluoxetine 
20 
Increase to 40 mg if non-responder at wk 4 
Fluvoxamine 
50 
Increase to 100mg if non-responder at wk 4 
Paroxetine 
20 
Increase to 40 mg if non-responder at wk 4 
Sertraline 
50 
Increase to 100 mg if non-responder at wk 4 
 
 
 
Desvenlafaxine 
50 
Increase to 100 mg if non-responder at wk 4 
Duloxetine 
60* 
Increase to 90 mg if non-responder at wk 4 
Levomilnacipran 
40† 
Increase to 80 mg if non-responder at wk 4 

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Venlafaxine 
75 
Increase to 150 mg if non-responder at wk 4 
* The total duloxetine dose can be given once daily, usually in the morning, or on a BID schedule (e.g., 30 
mg BID). The study clinician may start the medication at a dose of 30 mg once daily and increase after 7 
days as tolerated to an initial dose of 60 mg daily (or 30 mg BID).  
† Levomilnacipran is initiated at a dose of 20 mg once daily for 2 days, then increased to 40 mg once daily. 
 
Telephone Visit at Week 2. 
At week 2, the study coordinator will conduct a telephone visit with each subject that will 
serve as an interim check regarding, mainly, tolerability of study drugs. Although they 
are regarded as self-report measures, the FIBSER and ASEC will be administered 
telephonically by the study coordinator, and spontaneous report of adverse effects will be 
elicited via open questioning. In addition, the study coordinator will administer the 
QIDS-CR and HAMD17, enter this information into the clinical decision support tool 
interface, record the support tool output (probability of a non-response/remission at 8 
weeks) on a standard form, and confirm scheduling of the next study visit at week 4.  
 
Although study clinicians will not be needed under routine circumstances for the 
telephone visit at week 2, they will be available for support when it is requested by study 
coordinators, or for special circumstances. Such circumstances include, but are not 
limited to, the following: 
(1) The research coordinator will alert a study clinician if a patient expresses a wish to 
die (suicidal ideation), or if there are responses of 1 or higher on item 12 of the QIDS-
CR or on item 3 of the HAMD17; 
(2) The research coordinator will alert a study clinician if a patient complains of 
distressing medication side-effects, or if there is a FIBSER item 3 score of 4 (marked 
impairment) or higher. 
  
During these circumstances, a study clinician will assess the clinical situation via the 
telephone with the patient and initiate appropriate next-step management. This may 
include, but not necessarily be limited to, stopping study medication, changing the dose 
of study medication, providing urgent or more frequent face to face clinical visits, or 
sending the patient to the emergency room/activating emergency response. Such 
decisions will be at the discretion of the study clinician. Additional details are provided 
below under the section entitled, SUICIDE RISK MANAGEMENT PLAN. 
 
Face to Face Visit at Week 4. 
(1) Clinical ratings: At the week 4 visit, subjects will complete a QIDS-SR, FIBSER, 
ASEC, and the Patient Experience with Treatment and Self-management scale 
(PETS); and the study coordinator will complete a QIDS-CR, HAMD17, and C-
SSRS. All of these scale scores will be made available to study clinicians. At the end 
of the face to face visit, study clinicians will provide CGI-S and CGI-I scores. 
(2) Determination of response status: For this study, a positive antidepressive response 
will be defined as a reduction (improvement) in QIDS-CR total score of 50% or 
more, relative to their baseline total QIDS-CR score.  Subjects who achieve this 
threshold of improvement will be considered positive responders. Those who do not 
will be considered non-responders. This will serve as a clinical guide to dosing, as 
described in item (5) below. Some responders will be further classified as having 

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achieved symptomatic remission, which will be defined in this study as a QIDS-CR 
total score of 5 or lower (Rush et al., 2006). 
(3) Quantification of the probability of non-response/remission at 8 weeks:  In order to 
ensure the blinding of the clinical decision support tool prediction, the study 
coordinator will enter the week 4 QIDS-CR and HAMD17 core symptom scores into 
the clinical decision support tool. Using the information entered at baseline and at 
week 4, the tool will generate an output that specifies the prognoses of achieving 
eventual remission, response, and non-response at week 8. The study coordinator will 
record the output from the decision support tool on a standard form. Study clinicians 
will complete a short form in which they will select the outcome that they believe 
will be most likely at 8 weeks (non-response, response without remission, remission). 
(4) Vital signs:  At the week 4 visit, subjects will have their blood pressure, heart rate, 
and body weight measured. 
(5) Dosing of study drugs: Study clinicians will be allowed to adjust the dose of study 
drug at the week 4 visit (but changing medications or adding adjunctive medication, 
will not be allowed). The decision to adjust the dose of study drug will be made at the 
discretion of the study clinician based on their judgment, in collaboration with the 
patient in a shared decision making framework, Table 2 provides guidance for the 
adjustment of the dose of study drugs). 
(6) Scheduling next study visit: The research coordinator will schedule the week 8 face 
to face study visit, and will remind the subject that the next visit (week 8) will be the 
last face to face visit as part of this research. 
 
Face to Face Visit at Week 8. 
(1) Clinical ratings: At the week 8 visit, subjects will complete a QIDS-SR, FIBSER, 
ASEC, and PETS; and the study coordinator will complete a QIDS-CR, HAMD17, 
and C-SSRS. All of these scale scores will be made available to study clinicians. At 
the end of the face to face visit, study clinicians will provide CGI-S and CGI-I 
scores. 
(2) Determination of response status: At week 8, subjects will be classified as having 
non-response (those who have not achieved a > 50% reduction from baseline in 
QIDS-CR total score), response without remission (> 50% reduction in QIDS-CR 
total score from baseline, but total score still adds up to > 5), or remission (QIDS-CR 
total score < 5). This will again serve as a clinical guide to dosing, as described in 
item (4) below.  
(3) Vital signs:  At the week 8 visit, subjects will have their blood pressure, heart rate, 
and body weight measured. 
(4) Dosing of study drugs: Subjects will be reminded that the week 8 visit will be the last 
face to face study visit. Further treatment will be provided by the subject’s regular 
health care provider(s). For the participants who are classified as having symptomatic 
remission at week 8, no dose adjustment will typically be needed. For persons who 
have not achieved remission at week 8 (including non-responders) an increase in the 
dose of the antidepressant, augmentation/combination therapy, or therapeutic switch 
should be considered. Depending on the specific situation, study clinicians may elect 
to initiate this process themselves in close coordination with the subject’s regular 
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regular health care provider(s). Otherwise, study clinicians may provide specific 
next-step treatment recommendations to the subject so that they can discuss them 
with their regular health care provider.  
(5) Subsequent follow-up: The research coordinator will schedule the week 24 telephone 
study visit. 
 
Telephone Visit at Week 24. 
At week 24, the study coordinator will conduct a telephone visit with each subject that 
will serve as final check regarding depressive symptom severity and subsequent 
treatment that they have received, using a standardized form. This telephone follow up 
visit will also provide an opportunity to answer any questions that subjects may have 
about their participation in the study. During this telephone follow-up visit, the study 
coordinator will administer the QIDS-CR, HAMD17, FIBSER, and ASEC, and will 
ascertain spontaneous reports of adverse effects of current treatments.  
 
As was the case for the telephone visit at week 2, study clinicians will not be needed 
under routine circumstances; however, they will be available for support when it is 
requested by study coordinators, or for special circumstances. Again, such circumstances 
include, but are not limited to, the following: 
(1) The research coordinator will alert a study clinician if a patient expresses a wish to die 
(suicidal ideation), or if there are responses of 1 or higher on item 12 of the QIDS-CR or 
on item 3 of the HAMD17; 
(2) The research coordinator will alert a study clinician if a patient complains of 
distressing medication side-effects, or if there is a FIBSER item 3 score of 4 (marked 
impairment) or higher. 
  
During these circumstances, a study clinician will assess the clinical situation via the 
telephone with the patient and initiate appropriate next-step management. This may 
include, but not necessarily be limited to, stopping study medication, changing the dose 
of study medication, providing urgent or more frequent face to face clinical visits, or 
sending the patient to the emergency room/activating emergency response. Such 
decisions will be at the discretion of the study clinician. Additional details are provided 
below under the section entitled, SUICIDE RISK MANAGEMENT PLAN. 
 
CLINICAL DECISION SUPPORT TOOL 
 
Overview. 
We have built an interactive web-based system that allows clinicians to input select 
demographic measures and depression severity scores, and obtain from the probabilistic 
graphical model output (see PRELIMINARY STUDIES) that conveys the predicted 
outcome and evidence supporting that prediction. The interactive system will consist of a 
platform-independent front-end (i.e., it will work on desktops, laptops, smart phones and 
other hand-held devices) through which clinicians can input data and a back-end (e.g., a 
server hosting the artificial intelligence/machine learning framework and code—again, 
see PRELIMINARY STUDIES) where results are computed. The front-end will interact 
with the trained prediction model in the server via a RESTful API that is integrated with 

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iOS and Android mobile apps and HTTP-based websites, allowing clinicians to use a 
wide range of devices, including phones, tablets and workstations. The server is hosted at 
UIUC, which has vendor support from IBM Systems, Intel, and NVIDIA.  
 
Subject Confidentiality. 
The tool will be accessed via password, and only study clinicians and research 
coordinators will be provided such access codes (passwords). The only inputs into the 
tool made by study clinicians or research coordinators will be unique study subject ID 
numbers (unique to each participant), subject sex, and scores for the QIDS-CR and 
HAMD-17. No protected health information will be entered into the tool, such as subject 
names, addresses, telephone numbers, social security numbers, Mayo Clinic patient 
numbers, or any other data that could serve as identifiers. 
 
PREMATURE DISCONTINUATION FROM THE STUDY 
Subjects who are prematurely discontinued from the study will then receive standard 
treatment from a primary physician or psychiatrist with different medications and/or 
therapeutic modalities of treatment (e.g. psychotherapy, etc.), if necessary.  Patients do 
have the option to switch to another antidepressant at the end of the 8 week trial if they 
have financial or insurance concerns. And as specified earlier, all patients who complete 
the 8 weeks of the medication trial will be contacted by phone at week 24. 
 
A subject will be withdrawn from the study for any of the following reasons: 
• 
Loss to follow-up, defined as the failure to keep scheduled face to face or telephone 
study visits despite a minimum of 3 attempts (e.g., missed the week 4 study visit as 
originally scheduled but reachable by phone, then failed to show for a rescheduled 
visit, and then failed once more to show after rescheduling the visit a second time) or 
failure to contact a subject who misses a study visit despite a minimum of 3 attempts 
(e.g., failed to show for the week 4 study visit as originally scheduled, then 3 
messages left to reschedule the visit with all calls unreturned). 
• 
Withdrawal of consent 
• 
Death 
• 
Positive response between the screening visit and baseline visit without benefit of 
study drug (described earlier) 
• 
Violation of protocol procedures, as per investigators’ judgment 
• 
Any rating of CGI-I or 5 (much worse) or 6 (very much worse) 
• 
Emergence of worsening psychotic or perceptual changes or mania, as determined by 
the study clinician 
• 
The investigator believes it is in the best interest of the subject to discontinue the 
study (e.g., for safety or tolerability reasons such as an adverse event) 
• 
The subject becomes pregnant 
 
If a subject withdraws from the study for reasons other than withdrawal of consent, an 
early termination visit will be conducted at the time of discontinuation.  Reason(s) for 
withdrawal will be documented in the subjects’ study documents as a brief clinical note. 
 

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Collected samples (blood draw obtained at baseline) will be retained and used in 
accordance with the subjects’ original separate informed consent for research samples.  
The subject may withdraw consent for research samples, in which case the sample will be 
destroyed and no further testing will take place.   
 
SUICIDE RISK MANAGEMENT PLAN 
Suicide risk will be assessed in multiple domains both with brief surveys filled out by 
patients along with clinical interview and assessment according to the procedures noted 
above. Suicide specific items included in outcomes measures include suicide ratings in 
the HAMD17 and the QIDS. For more extensive assessment and analysis, the C-SSRS 
screening version will be used. Finally, patients will be asked in an open-ended manner if 
they have concerns related to their safety. There are other safety features that pertain to 
suicide risk in this protocol. Participants will be contacted by telephone at week two by 
the research coordinator to ensure medication compliance and patient safety. The 
research assistants will be trained to notify a study clinician at the time of the clinical 
visits or the phone interviews if the subject endorses any suicidal ideation or plans. After 
week 8 of the study, the subject will receive ongoing care from their regular health care 
provider(s) and will be off of the medication protocol, although we anticipate that the 
majority will continue to be treated with the study drugs. 
 
If there are concerns raised that are related to suicide risk assessed by 
investigator/research assistants the patient will then be assessed by a study clinician for 
acute suicidal risk and suitability for continuation in the study.  Specifically, this 
consultant will assess the patient and make determination of whether the patient needs 
more in-depth assessment, additional appointments to ensure or more closely monitor 
their safety, or emergent hospitalization. This will also apply if patients are having 
worsening of depressive symptoms without an increase in thoughts of suicide. 
 
As an added precaution, subjects will be given listings of numbers to call if there are 
concerns in between assessments by research coordinators. These numbers will include 
study coordinators at each respective study site, Emergency Department 
Information/Triage Desk number (904-953-2000 in Jacksonville; 507-255-5385 in 
Rochester), and the National Suicide Prevention Lifeline at 1-800-273-TALK.  
 
STATISTICAL PLAN 
Sample Size Determination and Power Estimate. 
We hypothesize the remission rate in the study cohort will be 45%, based on the 
remission rate (45.8%) reported for the Eligible for Analysis subset (n=463) in PGRN-
AMPS trial (Mrazek et al., 2014). We anticipate the clinical decision support’s prediction 
will have an overall accuracy of 80% and a sensitivity to predict remission of 95%.  
Under those assumptions, the study is investigating an off diagonal difference of 16% 
between the prediction of treatment response and the true (observed) remission rate at 8 
weeks. The study has 97% power to detect that difference using a McNemar’s test with 
120 total subjects included in the study at the alpha=0.05 level of significance. Thus if the 
clinical decision support tool is under/over calling the 8 week response rate (i.e., 
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high power to detect it. We estimate that about 30 subjects will drop out of the study or 
be lost to follow-up after the baseline visit. The target sample size of 120 subjects takes 
into account the estimated dropout rate.. 
  
Descriptive Analyses. 
Cohort clinical and demographic characteristics will be summarized using means and 
frequency (%), as required, for all consented participants and by analysis set (defined 
below). Data will also be visually inspected for measures of dispersion to assess potential 
outliers that warrant data clarification prior to the formal analyses.  
 
Primary Outcome Analysis. 
The main statistical objective is the quantification of agreement between the predicted 
outcome  at 4 weeks using the clinical decision tool and the observed outcome at 8 weeks 
based on QIDS-CR total scores at 8 weeks. Remission is defined as an 8-week QIDS-CR 
total score < 5.  In addition to the remission endpoint, a secondary ordinal endpoint 
consisting of remission, response (a50% reduction from baseline in QIDS-CR total score 
at 8 weeks but not meeting the remission definition) vs. non-response will be established.    
The week 4 predictions of each of these clinical outcomes will be provided by the tool as 
the probability of the single “most likely” outcome. The observed outcomes at 8 weeks 
will also be unitary (remission OR response without remission OR non-response). 
 
Agreement between the week 4 prediction of the outcome and the 8 week clinical 
outcome will be compared using McNemar’s test (or Bowker’s generalized test of 
symmetry for the ordinal secondary outcome). This will establish if there’s a significant 
difference between the predicted outcome, and the actual observed outcome at 8 weeks.  
Kappa statistics will also be calculated to illustrate the agreement between the new 
approach and the 8 week clinical outcome. In addition the sensitivity for prediction of 
remission and specificity of the clinical decision support tool will be calculated to 
illustrate the ability of the new approach to estimate the 8 week clinical outcome as well 
as to provide clinical interpretable summaries of the results of the McNemar test (e.g., 
examination of false positive and false negatives). 
 
We will conduct a descriptive analysis of the subjects in disagreement between predicted 
outcomes and actual outcomes. We will also assessing clinician prediction accuracy in 
this study, as well as the accuracy of the clinical decision support tool, although the 
objectives of this study and the reasons for developing the clinical decision support tool 
do not include comparing predictive accuracies of the decision support tool versus 
clinician assessment.  
 
Analysis Sets 
The primary analysis will be conducted under the intention to treat principle. Missing 8 
week clinical outcomes will be imputed for the primary analysis using worst case 
imputation (assumed discordance of the algorithm with clinical data), chained equations 
and random forest imputation strategies if necessary.  The results from these three 
methods will be compared qualitatively and reported separately in instances where the 
interpretation of the data varies between imputation strategies. In addition, a full analysis 

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set will also be constructed.  This analysis set will include all randomized subjects that 
have the clinical decision support tool prediction available at 4 weeks (i.e., participated in 
the first half of the protocol sufficiently to have a predicted 8 week data available). This 
full analysis set may also require imputation of the 8 week outcome in the event of 
missing data. If needed, the same trio of imputation strategies will be applied to this 
analysis set. A completers analysis will also be conducted. 
 
 
RATING SCALES AND INSTRUMENTS 
(1) HDRS-17:  A 17-item clinician rating of depressive symptoms, scored on a 4-point 
scale (0 to 4) (range 0–54). Anchors are provided for each of the numbered scale 
points. Higher scores represent higher levels of depression. Its psychometric 
properties have been studied extensively in adults (Cusin et al., 2010).  The HDRS-17 
in adults has high interrater reliability0.80-0.98) and high test-retest reliability (up to 
0.81) (Williams 1998). Validity of the HDRS ranges from 0.65 to 0.90 with global 
depression measures and other well validated clinician-rated measures such as the 
IDS-C and MADRS (Hamilton 2000). Scores of 7 of less after treatment is a 
generally accepted definition of depressive symptom remission (Frank, et al., 
1991). The following severity ranges for the HDRS-17 have been advocated:  no 
depression, or remission (0–7); mild depression(8–16); moderate depression (17–23); 
and severedepression (≥24) (Zimmerman et al., 2013). 
(2) QIDS-SR-16:  The QIDS-SR - 16 has highly acceptable psychometric properties, 
which supports the usefulness of this brief rating of depressive symptom severity in 
both clinical and research settings (Rush et al., 2003). Assessed prior to infusion with 
MADRS and on post-infusion day assessment. This will be used for secondary 
outcome measure of self-report remission of symptoms. 
(3) CGI:  Overall clinical judgment symptom severity (I) and change for a preceding 
phase (II). Dropout criteria will be any 1 rating of CGI II – 5 (much worse) or 6 (very 
much worse) (Guy 1979). 
(4) C-SSRS:  The screening version of the full-scale C-SSRS is a shorter version of the 
overall scale designed to assess suicidal ideation and behavior over recent months 
with triage categories.  
(5) FIBSER: The FIBSER is a standard self-reported side-effect measure that was 
designed to be easily adopted into clinical practice for patients receiving treatment for 
depression. Using data from STAR*D, the FIBSER as shown to be reliable, with high 
correlations between observations taken a short time apart, and correlations 
decreasing as time between observations increased (Wisniewski et al., 2006). There 
were also consistent relationships between items over time. The FIBSER has both 
face and construct validity.  
(6) ASEC:  The ASEC is a simple self-report measure that is used to describe adverse 
reactions to antidepressants. In a recent validation study, the ASEC and the 
psychiatrist-rated UKU Side Effect Rating scale were repeatedly administered to 811 
depressed adults who received open-label treatment with escitalopram or nortriptyline 
(Uher et al., 2009). There was good agreement between self-report and psychiatrists' 
ratings. 

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(7) MOSSS: We will use the eight-item modified MOSSS, which assesses the quality of 
social support in medical patients and has been shown to good psychometric 
properties that are similar to the full 19-item version of the scale (Moser et al., 2012). 
(8) ACE: The ACE questionnaire is a 10-item self-rated survey that assessed exposures 
to adverse childhood experiences that occurred prior to 18 years of age (Felitti et al., 
1998). The questionnaire consists of 10 main-stem yes/no questions, some of which 
have additional questions.  
(9) PETS: We will use the PETS scale as a patient-reported measure of treatment burden. 
The PETS consists of 78 items, divided into 15 domains that include learning about 
health conditions/care, medications, difficulty with medication taking, medical 
appointments, health monitoring, exercise/physical therapy, diet, use of medical 
equipment, interpersonal challenges, medical/healthcare expenses, confusion/concern 
about medical information, healthcare providers, difficulty with healthcare services, 
role functioning/social activity limitations, and physical/mental exhaustion. 
Individual scale items are rated on a 4- or 5-point Likert scale (e.g., very easy to very 
difficult, not at all to very much, strongly agree to strongly disagree, never to always). 
Recent validation work documented good internal consistency (alpha 0.79 – 0.95), 
with higher PETS scores associated with greater treatment burden, and significant 
correlation with more distress, less satisfaction with medications, lower self-efficacy, 
worse physical and mental health, and lower convenience of healthcare (p<0.001 for 
all correlations) (Eton et al., 2017). 
 
HUMAN SUBJECTS 
Common Adverse Effects. 
The most commonly observed side effects of SSRIs and SNRIs are lightheadedness, 
fainting, dizziness, confusion, hallucinations, rhinitis, dry mouth, tremor, nausea, 
decreased libido, ejaculation disorder (primarily ejaculatory delay), erectile dysfunction, 
impotence, fatigue, nausea, diarrhea (or constipation), sleep disorders (somnolence), 
agitation, restlessness, anxiety, and sweating (Goldstein & Goodnick 1998). Some 
SNRIs, venalfaxine in particular, are associated with modest bust statistically significant 
increases in supine diastolic blood pressure, though typically at doses above 300 mg/day 
(Thase 1998). According to data provided by the manufacturer, 1.4% of patients treated 
with extended-release venlafaxine experienced a ≥15 mm Hg increase in supine diastolic 
blood pressure during short-term treamtent. In a meta-analysis of 3,744 patients with 
MDD who received venlafaxine for 6 weeks, active treamtent did not adversely affect the 
control of blood pressure for patients with pre-existing high blood pressure or elevated 
baseline values (Thase 1998).  
 
Suicide Risk. 
Although SSRIs have been associated with an idiosyncratic increase in suicidality in a 
FDA meta-analysis, the increase in risk is restricted to individuals under the age of 24 
years--moreover, there is no significant increase in the risk of suicidal thinking in people 
aged 25 years and higher, and pharmacoepidemiological studies show a protective effect 
across the lifespan (Barbui et al., 2009; Brent 2016; Gibbons et al., 2012; Sharma et al., 
2016; Stone et al., 2009). This protocol features extensive assessment of suicide risk, 
including telephone contact early in the course of follow-up after starting study drugs 

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(week 2 telephone follow-up). These procedures, we believe, will mitigate the possible 
heightened risk of suicidality translating to suicide risk or behavior. Specific procedures 
for addressing suicide risk are described in detail above in the section entitled, SUICIDE 
RISK MANAGEMENT PLAN.  
 
Other Risks. 
Risks are also associated with a single venipuncture performed for DNA extraction at 
basleline, as well as the measurement of drug response by rating scales and 
questionnaires designed to assess depression severity and drug side effect frequency and 
intensity.  While every effort will be made to schedule venipuncture at the times of 
clinically-indicated venipuncture, this cannot always be guaranteed.   
 
Capacity for Consent. 
Another common concern with psychiatric patients is the ability to provide informed 
consent. However, people with at least moderately severe recurrent major depression who 
are treated as outpatients show few impairments in their decision-making capacities 
related to research (Appelbaum et al., 1999), and depressive symptom severity and 
capacity do not appear to be significantly correlated in medical patients (Casarett et al., 
2003). In our study, individuals with psychotic features and those whose depression is so 
severe that psychaitric hospitalization is indicated will be excluded. Individuals in acute 
suicidal crisis will also be excluded. All screening visits with study clinicians will include 
a judgment as the the capacity of subjects to provide valid consent.  If there is any 
question about whether or not the patient can provide valid informed consent for this 
research, the patient will not be enrolled in this study.   
 
Reproductive and Lactational Safety. 
Regarding childbearing potential, the safety of SSRIs to the fetus has been extensively 
investigated and, with the possible exception of paroxetine and fluoxetine, SSRIs are not 
thought to be associated with clinically significant teratogenic potential (Myles et al., 
2013; Wang et al, 2015). Far less is known about the reproductive safety of SNRIs 
(Richardson et al., 2019). In order to participate in this research, women of childbearing 
age must have a negative pregnancy test.  Pregnant women will be excluded. Evidence 
and/or expert consensus suggests some potential for harmful infant effects of study drugs 
when used during breastfeeding, although most antidepressants are expected to produce 
low levels in breast milk with no clinical significance (Kronenfeld et al., 2017).  
Nevertheless, women who are currently breastfeeding and who plan to continue 
breastfeeding will be excluded. 
 
BUDGET 
Budget is attached.  
 
CONSENT FORMS 
Draft is attached. 
 
 

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