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Journal of Aective Disorders 361 (2024) 198–208
Available online 27 May 2024
0165-0327/© 2024 Published by Elsevier B.V.
Research paper
At-home, telehealth-supported ketamine treatment for depression: Findings
from longitudinal, machine learning and symptom network analysis of
real-world data
David S. Mathai
a
,
b
, Thomas D. Hull
c
, Leonardo Vando
d
, Matteo Malgaroli
e
,
*
a
The Johns Hopkins University School of Medicine, Center for Psychedelic and Consciousness Research, Department of Psychiatry and Behavioral Sciences, Baltimore,
MD, United States of America
b
Sattva Medicine – Psychiatry/Psychotherapy Practice, Miami, FL, United States of America
c
Institute for Psycholinguistics and Digital Health, United States of America
d
Mindbloom, Orlando, FL, United States of America
e
NYU Grossman School of Medicine, Department of Psychiatry, New York, NY, United States of America
ARTICLE INFO
Keywords:
Ketamine
Depression
Psychedelic
Network analysis
Machine learning
Telehealth
ABSTRACT
Background: Improving safe and effective access to ketamine therapy is of high priority given the growing burden
of mental illness. Telehealth-supported administration of sublingual ketamine is being explored toward this goal.
Methods: In this longitudinal study, moderately-to-severely depressed patients received four doses of ketamine at
home over four weeks within a supportive digital health context. Treatment was structured to resemble methods
of therapeutic psychedelic trials. Patients receiving a second course of treatment were also examined. Symptoms
were assessed using the Patient Health Questionnaire (PHQ-9) for depression. We conducted preregistered
machine learning and symptom network analyses to investigate outcomes (osf.io/v2rpx).
Results: A sample of 11,441 patients was analyzed, demonstrating a modal antidepressant response from both
non-severe (n =6384, 55.8 %) and severe (n =2070, 18.1 %) baseline depression levels. Adverse events were
detected in 3.0–4.8 % of participants and predominantly neurologic or psychiatric in nature. A second course of
treatment helped extend improvements in patients who responded favorably to initial treatment. Improvement
was most strongly predicted by lower depression scores and age at baseline. Symptoms of Depressed mood and
Anhedonia sustained depression despite ongoing treatment.
Limitations: This study was limited by the absence of comparison or control groups and lack of a xed-dose
procedure for ketamine administration.
Conclusions: At-home, telehealth-supported ketamine administration was largely safe, well-tolerated, and asso-
ciated with improvement in patients with depression. Strategies for combining psychedelic-oriented therapies
with rigorous telehealth models, as explored here, may uniquely address barriers to mental health treatment.
1. Introduction
Ketamine, an N-methyl-D-aspartate (NMDA) receptor-mediated
dissociative drug, has received substantial attention in the last decade
as a breakthrough mental health intervention (Sanacora et al., 2017).
Though ketamine was approved for medical use by the United States
Food and Drug Administration (FDA) as an anesthetic in 1970, its psy-
chiatric value went largely unrecognized until 2000, when the rst
randomized controlled trial using a subanesthetic dose of ketamine for
the treatment of depression indicated positive results (Berman et al.,
2000). Numerous studies have since replicated and expanded upon these
ndings, providing evidence that ketamine can be effective in treating
depression and shows promise as a pharmacotherapy for a wider range
of mental health disorders (Walsh et al., 2021).
Despite interest in dissociative and psychedelic medicines as a novel
category of rapidly-acting mental health treatments (Johnston et al.,
2023; Lepow et al., 2023; Nayak et al., 2023; O’Donnell et al., 2023),
several issues have limited broader adoption of ketamine as a psychiatric
intervention. Most signicantly, ketamine has not yet been approved by
the FDA for use as an antidepressant. Racemic ketamine is widely
* Corresponding author at: One Park Avenue, 8th Floor, New York, NY 10016, United States of America.
E-mail address: matteo.malgaroli@nyulangone.org (M. Malgaroli).
Contents lists available at ScienceDirect
Journal of Affective Disorders
journal homepage: www.elsevier.com/locate/jad
https://doi.org/10.1016/j.jad.2024.05.131
Received 22 January 2024; Received in revised form 30 April 2024; Accepted 25 May 2024
Journal of Aective Disorders 361 (2024) 198–208
199
available as a generic medication, and its lack of patent protection has
deterred sponsorship of the large, resource-intensive trials that are
required for the approval of ketamine for a new therapeutic indication
(Wilkinson and Sanacora, 2017). Psychiatric applications of ketamine
are considered “off-label,” limiting treatment accessibility and imposing
signicant cost barriers because insurance coverage is rarely available.
Adoption is further complicated by a lack of consensus about optimal
models of treatment delivery, including with respect to routes of
administration, treatment setting, and the role of adjunctive psycho-
therapy or psychosocial support (Mathai et al., 2022a).
In 2019, the FDA approved intranasal esketamine (i.e., the patented
S-enantiomer that forms one half of ketamine as a racemic mixture) for
treatment-resistant depression. However, a growing body of evidence
suggests that ketamine may benet from an analogous safety prole,
superior efcacy and lower dropout rates when compared with esket-
amine (Bahji et al., 2021; Correia-Melo et al., 2020; Nikolin et al., 2023;
Singh et al., 2016). Financial analyses suggest that ketamine assisted
treatment is a more affordable investment for the mental healthcare
sector (Brendle et al., 2022; Dadiomov, 2020; Ottawa (ON): Canadian
Agency for Drugs and Technologies in Health, 2021). Accordingly,
improving safe access to generic, racemic ketamine is of high priority
given the high rates of partial- or non- response observed with currently
available antidepressants (Vasiliu, 2022). Oral or sublingual ketamine
could uniquely address this need at low cost (Andrade, 2023; Dutton
et al., 2023; Swainson and Khullar, 2020), particularly in cases where
treatment with esketamine is not readily available, and may be of
greater public interest than more invasive routes of ketamine adminis-
tration (Mathai et al., 2021).
The use of telehealth presents an additional opportunity to expand
access to mental health care by reducing treatment barriers including
travel, time, and cost without substantially diminishing the quality of
treatment (Cuijpers et al., 2019; Guinart et al., 2021; Hull et al., 2022).
To this end, our group recently published an analysis of 1247 patients
who used an online medical service (www.mindbloom.com) for at-home
treatment with sublingual ketamine with access to a prescribing psy-
chiatric clinician and behavioral coach through a secure telehealth and
remote monitoring platform (Hull et al., 2022). The treatment protocol
consisted of four medication doses (initiated at ~5 mg/kg) over four
weeks with ongoing mental health support, given the anticipated safety
and benet of combining forms of psychosocial intervention with ke-
tamine, and as seen with investigational psychedelic therapies involving
MDMA and psilocybin (Dore et al., 2019; Mathai et al., 2022a; Tsang
et al., 2023). These results provided initial evidence for the safety and
effectiveness of at-home, sublingual ketamine telehealth for depression,
as demonstrated by a low incidence of adverse reactions (3.8 %), and
rates of response (62.8 %) and remission (32.6 %) that were consistent
with laboratory- and clinic-administered ketamine treatment.
The current study was based on ongoing data collection using the
same telehealth platform and focused on replication of the previous
study within a substantially larger patient sample (n >10,000) who
received at-home, telehealth-supported ketamine treatment for psychi-
atric care. This report also included preliminary data on patients who
received a second course of treatment with ketamine, used novel ma-
chine learning and symptom network analyses to examine treatment
outcomes, and presented previously unpublished data on medication
dosage. We had the following aims: 1) to assess clinical characteristics
and treatment response for depressed patients who received one course
of ketamine treatment; 2) to assess clinical characteristics and treatment
response for a subsample of depressed patients who received a second
course of treatment; 3) to explore if machine learning models could be
used to organize heterogeneous subgroups of patients and identify
predictors of treatment response; and 4) to use network analysis (Fon-
seca-Pedrero, 2017) to explore changes in individual symptoms of
depression. These aims seek to enhance our understanding of several
poorly understood features of ketamine as an intervention in real world
settings.
2. Methods
2.1. Participants
2.1.1. Patients
Participants were self-referred adult patients who presented to the
telehealth platform with a chief complaint of anxiety or depression and
were seeking treatment through the web-based service. Inclusion
criteria to access treatment through the platform consisted of: 1) being
18 years old or older, 2) having regular internet or cell phone access, 3)
receiving a depression or anxiety diagnosis from their selected licensed
clinician based on a video-based clinical intake interview, and 4) having
access to a safe, private environment for dosing sessions and a trusted
adult who could be physically present during treatment.
Exclusion criteria consisted of: 1) ketamine use disorder, 2) ketamine
allergy or hypersensitivity, 3) active psychotic, manic or mixed symp-
toms, 4) history of primary psychotic disorder, 5) active suicidal idea-
tion with method, intent, or plan in the past 3 months, 6) suicide attempt
within the past year, 7) uncontrolled hypertension, 8) congestive heart
failure or other impaired cardiac status, 9) severe and poorly-controlled
respiratory problems (e. g., chronic obstructive pulmonary disease), 10)
diagnosis of hyperthyroidism, 11) elevated intraocular pressure/glau-
coma, 12) pregnancy, nursing, or currently trying to become pregnant,
or 13) any other severe systemic disease, or other aspect of the patient’s
psychiatric history, outpatient support system or home environment
that would render at-home treatment psychologically unsafe in the
opinion of the prescribing psychiatric clinician. Clinicians also screened
for the following issues, and determined eligibility for treatment on a
case-by-case basis: 1) active moderate or severe substance use disorder,
and 2) history of severe trauma. When necessary, clinicians required
labs, EKG, coordination with external mental health providers, conr-
mation of regular, ongoing third-party therapeutic support, and/or
clearance from medical providers following a physical examination as
appropriate to ensure patients were appropriate for treatment.
Analysis was limited to patients with moderate-to-severe depression
as indicated by a baseline score ≥10 on the 9-item Patient Health
Questionnaire. Study owchart is reported in the Supplementary Ma-
terials (Supplementary Fig. 1).
2.1.2. Clinicians and guides
All clinicians in the provider network were Psychiatrists, Psychiatric-
Mental Health Nurse Practitioners (PMHNPs), or Physician Associates
(PAs) with psychiatry experience. Behavioral coaches, referred to as
“Guides,” were required to have coaching certication and/or to have
provided one-on-one behavioral coaching for more than one year with at
least 50 clients. For more information on prescribing clinicians and
behavioral coaches, see Supplement.
2.2. Procedures
Data collection for this study was conducted as part of organizational
quality assurance and program management processes between January
21, 2021 and June 16, 2023. All patients and clinicians gave consent to
the use of their data in a de-identied, aggregate format for research
purposes as part of the user agreement before using the platform. Study
analytic procedures were approved as exempt by the institutional review
board at New York University (i21-01533). General procedures have
been described elsewhere (Hull et al., 2022) and are detailed in the
Supplement, including information on drug administration, treatment
monitoring and general precautions. Briey, patients completed a
standardized medical and psychiatric intake evaluation with a psychi-
atric clinician through live video conference to determine if inclusion/
exclusion criteria were met and appropriateness for treatment. Patients
meeting criteria for treatment were mailed a limited supply of ketamine
as needed for one course of treatment in the form of sublingual rapid
dissolve tablets (RDTs) divided over two separate shipments. Dosing was
D.S. Mathai et al.
Journal of Aective Disorders 361 (2024) 198–208
200
initiated at 300 mg to 600 mg of ketamine RDT to approximate a weight-
based dose of 5 mg/kg and adjusted using an ongoing, individualized
dose discovery process that was informed by initial tolerability and
targeted a mild to moderate level of dissociation for medication sessions.
When taking ketamine, patients were asked to hold the tablets under the
tongue or between the cheek and gums, without swallowing for 7 min,
after which they were instructed to spit out all saliva. One course of
treatment consisted of four medication sessions over four weeks with the
availability of ongoing clinical, psychosocial, and peer support, orga-
nized within a structure of preparation, dosing, and integration that is
considered best practice for psychedelic medicine trials (Johnson et al.,
2008).
Patients had the option to elect an additional round of treatment.
This required an additional consultation with the prescribing clinician
and a determination of whether treatment was still appropriate, after
which patients could be prescribed a second round of ketamine resem-
bling the procedures above.
2.2.1. Assessments
Clinical assessments were used throughout treatment to track prog-
ress and facilitate care (Hull et al., 2022). Measures included the 9-item
Patient Health Questionnaire (PHQ-9; (Kroenke and Spitzer, 2002)), the
7-item Generalized Anxiety Disorder questionnaire (GAD-7; (Spitzer
et al., 2006)), the Columbia Suicide Severity Rating Scale (C-SSRS;
(Posner et al., 2011)), the Alcohol Use Disorders Identication Test
(AUDIT; (Bush et al., 1998)), the Drug Abuse Screening Test 10- item
(DAST-10; (Villalobos-Gallegos et al., 2015)). Follow-up symptom
measures for the rst round of treatment were administered after the
second and fourth medication sessions, along with an adverse event self-
report measure, which asked, “Have you noticed any issues with your
physical or mental health since beginning treatment?” Measures for the
second round of treatment were administered before the rst medication
session and then after the fourth medication session. Any adverse events
reported to the clinician or guide outside of this measure were recorded
in the electronic health record (EHR) and were identied for inclusion in
analysis as well. Dropout was measured by the number of individuals
who canceled ongoing treatment or who were removed from treatment
by the clinician due to adverse events or noncompliance.
2.3. Analyses
Analytic procedures for the study were pre-registered on the Open
Science Framework (osf.io/v2rpx) and consisted of four steps. First, we
assessed the treatment characteristics in the sample. Our analysis
involved calculating clinical metrics and adverse events based on com-
plete data. We adopted this approach to replicate previous ndings (Hull
et al., 2022) and to compare our results with other studies (Alnefeesi
et al., 2022). We also examined these metrics in the subgroup of in-
dividuals undergoing a second round of treatment, as a preliminary
nding on treatment maintenance. Second, we examined the treatment
outcome trajectories. Our analysis involved using unsupervised machine
learning to identify patterns of depression over time. We adopted this
approach to distinguish subgroups of patients who were improving and
not improving (Hull et al., 2020; Malgaroli et al., 2024), based on an
intent-to-treat analysis. Third, we identied which patient and treat-
ment characteristics increased the likelihood of improvement following
treatment. Our analysis utilized supervised and explainable machine
learning methods. We chose this approach due to the capability of these
methods to identify and rank important features associated with the
amelioration of depression (Lee et al., 2018; Schultebraucks et al.,
2021a). Fourth, we identied which symptoms of depression played a
role in treatment non-response. Our analysis utilized panel network
models to longitudinally explore changes in individual symptoms over
treatment. We chose this approach due to the ability of network analysis
to pinpoint the most salient symptoms that sustain depression (Mal-
garoli et al., 2021).
2.3.1. Clinical metrics and adverse events
We assessed clinical and patient characteristics descriptively and
measured treatment response, dened as a ≥50 % reduction in symptom
score on the PHQ-9 from baseline to session 4 of treatment (week 0–4).
We also measured clinically signicant change (CSC; (Jacobson and
Truax, 1991)) dened as a reduction of ≥5 points and a score that began
above the clinical threshold of 10 and fell below it at follow-up, remis-
sion dened as beginning above the clinical threshold at baseline and
having a follow-up score of <5 (Coley et al., 2020), changes in GAD-7
score, deterioration dened as a reliable increase in score of ≥5 points
(Jacobson and Truax, 1991), and adverse events (AEs). PHQ-9 and GAD-
7 outcomes were presented for individuals with complete survey re-
sponses at each timepoint of observation in order to facilitate interpre-
tation and comparison with the prior study.
AEs were measured after initiation of treatment, and those reported
from automated patient questionnaires were reviewed and manually
coded by system organ class using the Common Terminology Criteria for
Adverse Events (CTCAE) Version 5.0 (33). Incident report forms and
clinician logs were analyzed for AEs of special interest (i.e., intense
dissociation and sedation) and serious adverse events (SAEs). SAEs were
dened in accordance with ICH Good Clinical Practice criteria (34).
We also examined the subsample of patients who received a second
round of treatment. We conducted a sensitivity analysis to examine
differences between the Round 1 and 2 samples, and a cohort analysis to
separate trajectories for patients who did and did not achieve treatment
response after Round 1 of treatment.
2.3.2. Outcome trajectories
Depression trajectories over treatment were identied using Latent
Growth Mixture Modeling (LGMM) via MPLUS version 8 (Muth´
en and
Muth´
en, 2017). LGMM is an unsupervised machine learning method
that teases heterogeneity by identifying sub-groups of individuals who
share similar patterns (i.e., PHQ-9 scores over time). We implemented
an intent-to-treat approach for the LGMM by including all patients who
had baseline data available, and handled cases of missing data through
Full Information Maximum Likelihood (FIML) for the LGMM estimation.
The number of trajectories was determined by comparing nested un-
conditional models with increasing classes based on recommendation
from the literature (Nylund et al., 2007).
2.3.3. Improvement features: machine learning analyses
We used supervised machine learning to identify characteristics
discerning patients more likely to be on an improving symptoms tra-
jectory (Schultebraucks et al., 2021b). We used a Random Forest clas-
sication algorithm (Breiman, 2001) with default hyperparameters from
the caret R package (Kuhn, 2008). Candidate variables of interest
included demographics, baseline depression, ketamine dosage, and AEs
at week 2. Training for the model consisted of nested-cross validation
minimizing cross-entropy loss, with 10 inner- and 10 outer- folds via
nestedcv (Lewis et al., 2023). Nested-cross validations offer more robust
models for medical applications (Bates et al., 2023). The inner folds ne
tune the model’s hyperparameter, while the outer folds offer unseen test
data for model evaluation. We categorized trajectories into a binary
classication problem to improve model interpretability. Random
undersampling of the majority class was performed to rebalance class
distribution in the data.
We interpreted feature importance in the classication model using
SHapley Additive exPlanations (SHAP) values (Lundberg and Lee,
2017). Derived from game theory, SHAP values provide accurate and
consistent local estimations to interpret the importance of each variable.
SHAP values were generated using the package fastshap (Jethani et al.,
2022). Large absolute SHAP values indicate important features for the
model, with values closer to zero suggesting less signicance. The sign of
SHAP values indicates the direction of a feature’s impact on the pre-
diction (i.e., high or low variable scores).
D.S. Mathai et al.
Journal of Aective Disorders 361 (2024) 198–208
201
2.3.4. Non-response features: temporal panel network analyses
We examined symptoms patterns over treatment in non-responders
using network analysis (Borsboom et al., 2021). The network
approach identies highly inuential symptoms sustaining depression
by examining their interactions (Malgaroli et al., 2021). Specically, we
explored symptom pathways sustaining non-response over treatment to
identify treatment adjuncts. We estimated temporal networks using
panel graphical vector-autoregression (panel-GVAR) from the package
Psychonetrics v0.11.5 (Epskamp, 2020). In a panel-GVAR temporal
network, directed edges at time t are contingent upon all nodes both at t
and at the preceding time t-1. In addition to estimating within-person
temporal effects, the panel GVAR model also computes contempora-
neous and between-subjects effects, which can be used to estimate
corresponding networks. As panel-GVAR models are based on the
assumption of stationarity (Epskamp, 2020), we limited our analysis to
patients on chronic trajectories, given relative stability of symptoms in
treatment non-response.
We calculated panel-GVAR including symptoms of depression from
baseline to week 4 using FIML. We rst estimated a base model in which
all edges were included. Subsequently, we estimated a pruned model
and a step-up model using a signicance threshold of
α
=0.05 for the
pruning or addition of individual edges respectively. We then identied
the best tting panel-GVAR based on Bayesian Information Criteria
(BIC).
We used expected inuence (Robinaugh et al., 2016) to identify the
symptom in the temporal network that might play the greatest role in
the activation and maintenance of depression over treatment. Expected
inuence in temporal networks is composed of In-Expected Inuence
and Out-Expected Inuence. In-Expected Inuence predicts the effect of
other nodes on the activation of a specic symptom. Out-Expected In-
uence represents the inuence of a symptom in activating the rest of
the network.
3. Results
3.1. Sample characteristics
The sample consisted of 11,441 patients from ages 19 to 88, with an
average of 42.0 years (SD =10.5). Of these, 54.7 % were women (n =
6233), and 6.8 % (n =778) lived in areas classied by the Center for
Medicare and Medicaid Services as rural.
3.2. Clinical metrics and adverse events (AEs)
Table 1 presents the descriptive clinical outcomes for the entire
sample for both depression and anxiety symptoms among individuals
who completed survey responses at each timepoint. Average tablet
dosage per session was 590 mg (SD =245 mg), or 7.3 mg/kg based on an
average participant weight of 81.2 kg.
AEs were reported by 323 of 7496 patients (4.3 %) with valid re-
sponses after session 2 and by 242 of 5085 patients (4.8 %) after session
4. Across timepoints, the most common AEs were memory impairment
(0.6–1.1 %), suicidal ideation (0.6–0.7 %), abdominal pain (0.4–0.6 %),
dysuria (0.2–0.5 %), hypertension (0.1–0.4 %), chest discomfort (0–0.4
%), headache (0–0.4 %), dyspnea (0.2–0.4 %), and cravings (0.2–0.4 %).
See Supplementary Tables S1–6 for full AE data.
3.3. Adverse events of special interest (AESI)
Intense dissociation and related experiences: Clinician reports and
cancellation forms indicated 12 patients (0.1 %) who had treatment
discontinuation (either self-initiated or initiated by treatment team) due
to intense dissociation or other psychologically overwhelming drug
experiences. Self-report AE forms indicated two patients with ashbacks
and re-experiencing of prior trauma as a result of ketamine
administration.
Sedation: Clinician reports indicated two patients who experienced a
depressed level of consciousness resulting in Emergency Department
(ED) visits. Both recovered spontaneously and were discharged from the
ED without the need for signicant medical intervention or
hospitalization.
3.4. Serious adverse events (SAEs) and treatment discontinuation
SAEs occurred in 6 patients, were all psychiatric by classication and
consisted of severe depression (n =1), suicidal ideation (n =1), suicidal
behavior (n =2), and psychosis (n =2), requiring inpatient admission in
all but one of these cases. A total of 46 patients (0.4 %) had treatment
discontinuation (either self-initiated or initiated by treatment team) due
to AEs, generally for psychiatric reasons. The leading reasons for
discontinuation were psychologically overwhelming experiences (n =
12), anxiety (n =5), and agitation (n =3). Treatment was discontinued
for two individuals because of mania (n =1) and hypomania (n =1).
Non-psychiatric reasons for discontinuation included vomiting (n =3),
syncope (n =3), and headache (n =3).
3.5. Cohort analysis for patients electing a second round of treatment
Two cohort pathway analyses were conducted to examine the effect
of an additional round of treatment on clinical outcomes.
The rst for participants who achieved Clinically Signicant Change
(CSC) at session 4 (n =3020) in the rst round of treatment and then
elected to continue and receive a second round (n =1241). Of these, 84
% of patients either maintained (if PHQ-9 scores remained ≤9 at the
beginning of Round 2; 66 %) or recovered (if PHQ-9 scores increased to
>9 at the beginning of Round 2; 18 %) CSC with a second round of
Table 1
Clinical characteristics for full sample.
PHQ-9 observations Available Mean (SD) Cohen’s d
(95 % CI)
Response rate Remission rate Clin. sig. change Deteriorated
Baseline 11,441 15.5 (4.1) – – – – –
Session 2 7400 9.4 (5.5) 1.15 (1.12–1.18) 42.3 % 18.1 % 48.9 % 0.9 %
Session 4 4918 7.9 (5.2) 1.46 (1.41–1.50) 56.4 % 28.1 % 61.4 % 0.7 %
GAD-7 observations Available Mean (SD) Cohen’s d
(95 % CI)
Response rate Remission rate Clin. sig. change Deteriorated
Baseline 7776 15.2 (3.3) – – – – –
Session 2 5074 9.0 (5.4) 1.18 (1.14–1.21) 43.7 % 20.4 % 50.2 % 0.7 %
Session 4 3348 7.6 (5.1) 1.46 (1.41–1.51) 56.1 % 28.8 % 62.2 % 0.4 %
Note: Response rate dened as 50 % or larger reduction in symptoms. Remission dened as nal symptom score below 5. Clinically signicant change as moving below
the clinical threshold (score of <10) AND improving at least 5 points. Deterioration as worsening of symptoms by 5 or more points. Abbreviations: PHQ-9 =Patient
Health Questionnaire; GAD-7 =Generalized Anxiety Disorder questionnaire.
D.S. Mathai et al.
Journal of Aective Disorders 361 (2024) 198–208
202
treatment. The rest became symptomatic with PHQ-9 scores >9.
The second for participants who did not achieve CSC at session 4 (n
=1898) in the rst round of treatment and then elected to continue and
receive a second round (n =665). From this cohort, 66 % of participants
had PHQ-9 scores >9 at the beginning of Round 2, and 28 % of those met
criteria for CSC with a second round of treatment.
Dosage was higher on average than the rst round treatment at a
mean of 758 mg (SD =297 mg; Cohen’s d difference of 0.45, 95 % CI,
0.40–0.49), or 9.3 mg/kg based on average participant weight. Patients
electing an additional treatment round were also more likely to be older
and currently in outpatient therapy, and less likely to be divorced,
separated, or widowed, live in a rural area, or have higher baseline
scores on the DAST-10 or AUDIT than those who did not elect to
continue for another round (see Table 2 for all comparisons and odds
ratios). AEs were reported in 25 of 829 patients (3.0 %) with valid re-
sponses who pursued a second round of treatment.
3.6. Outcome trajectories
Fig. 1 displays the four trajectories identied to characterize
depression symptoms patterns from baseline to Session 4. LGMM model
t information is reported in Supplementary Table S7. Two Improvement
trajectories were identied, with PHQ-9 scores ameliorating below the
clinical cutoff by the nal observation. The rst was Improvement, not
severe (n =6384, 55.8 %), the modal trajectory consisting of patients
with moderate baseline depression, and the Improvement, severe trajec-
tory (n =2070, 18.1 %), characterized by more severe depressive
symptoms at baseline. Two Chronic trajectories indicative of non-
response to treatment were also identied: the Chronic, not severe (n
=2101, 18.4 %), and the Chronic, severe (n =886, 7.4 %), with the latter
characterized by severe baseline depression scores. In both Chronic
trajectories, patients continued to endorse symptoms that did not sub-
side over the course of treatment, remaining at moderate to severe
depression levels. Sensitivity analysis examined survey non-adherence
(See Supplementary Table S8), indicating that individuals not
responding to surveys at week 2 or 4 were more likely to be assigned to
the Improvement, not severe group compared to all other trajectories.
3.7. Improvement features
We ran a random forest classier to predict Improvement trajectories
membership from patient baseline characteristics, ketamine dosage, and
session 2 AEs (n =11,441). The nal model achieved an overall Accu-
racy of 0.804, and the Area Under the ROC Curve was 0.813, indicating
good discriminative ability between Improvement and Chronic groups.
The model Precision was 0.865, and Recall (or sensitivity) was 0.886,
with a F1 score (harmonic mean of precision and recall) of 0.875.
Further model characteristics are reported in Supplementary Figs. S2
and S3.
SHAP (SHapley Additive exPlanations) values were used to evaluate
the impact of patients and treatment characteristics in the model’s
prediction. The higher the SHAP value of a feature, the higher the
importance in predicting likelihood of an Improvement trajectory. Ex-
amination of SHAP values (Fig. 2) suggests that depression symptoms
endorsed at baseline (PHQ-9 scores) was the most important feature,
indicating that lower initial scores were associated with higher likeli-
hood of improvement. The age of the patients was also predictive of
improvement, with a trend toward younger age increasing probability of
improvement. Other demographic factors including patients’ body mass
index (higher) and gender (female) also contributed to the model’s
predictions, albeit to a lesser extent. Interestingly, ketamine dosage,
while having a lower median SHAP value, showed some of the highest
individual SHAP values, indicating specic cases where medication
dosage was highly inuential.
3.8. Non-response features
The temporal network is estimated only for non-responder Chronic
patients (n =2987) using a panel-GVAR model. Model t information is
reported in Supplementary Table S9. Panel-GVAR also allows to esti-
mate two additional networks aggregated across all time points: a
within-subjects contemporaneous and a between-subject networks,
which are reported in Supplementary Figures 4 and 5. Temporal
Network stability metrics are reported in Supplementary Figs. S6 and S7.
The temporal network edges (Fig. 3) show mutual activations be-
tween Depressed Mood and Anhedonia. The two symptoms have the
highest Out-Expected Inuence (Fig. 4), suggesting their importance in
sustaining the network of depressive treatments over time. Taken
together, results from the temporal network indicate the inuential role
of Mood and Anhedonia in the maintenance of depression despite
treatment.
4. Discussion
In this longitudinal study of at-home ketamine for depression, we
found ongoing evidence for the safety and potential for improvement
associated with treatment administered within a carefully supportive
digital health context. Patients examined here received one or more
courses of treatment, each consisting of four medication sessions with
sublingual racemic ketamine over four weeks along with structured
clinical, psychosocial, and peer support. To our knowledge, this is the
largest safety and effectiveness study of any ketamine derivative or
psychedelic-oriented intervention to date.
Preregistered analyses showed rates of clinically signicant change,
response, remission, deterioration, and adverse events comparable to
those previously reported in a smaller sample with similar characteris-
tics (Hull et al., 2022). Using a weight-based exible-dosing procedure,
patients were started on an initial dose of 300–600 mg ketamine RDT
and titrated based on response to an average dose of 590 mg across
treatment. This mean falls within the higher range of dosages previously
reported for oral or sublingual ketamine when swallowed (Andrade,
2019; Hassan et al., 2022; Nu˜
nez et al., 2020) but is difcult to compare
because of differences in drug administration (e.g., lower bioavailability
with spitting vs swallowing). It is likewise difcult to compare dosages
here to those involving other routes of ketamine administration.
Of note, these procedures were unique relative to studies of intra-
venous ketamine or intranasal esketamine, which have generally pur-
sued a dose optimization strategy that minimizes the subjective
experience of dissociation (Mathai et al., 2022a). However, there has
been little evidence to date that drug-induced dissociation negatively
impacts therapeutic outcomes (Hull et al., 2022), with most studies
Table 2
Differences for patients electing a maintenance round.
95% Confidence interval
Odds Ratio p Lower bound Upper bound
Gender (female) 1.025 0.571 0.941 1.117
Age (older) 1.020 < .001 1.015 1.025
Divorced/Separated/Widowed (yes) 0.860 0.046 0.741 0.997
Rural zip code (yes) 0.842 0.049 0.709 0.999
BMI 1.001 0.778 0.994 1.008
Inpatient, History (yes) 1.077 0.230 0.954 1.215
Outpatient therapy, History (yes) 1.120 0.149 0.960 1.305
Outpatient therapy, Currently (yes) 1.152 0.002 1.054 1.259
AUDIT 0.985 0.002 0.975 0.995
DAST-10 0.954 0.015 0.919 0.991
CSSRS 0.886 0.225 0.729 1.077
PHQ-9 Baseline 1.000 0.893 0.994 1.007
GAD-7 Baseline 1.001 0.823 0.994 1.008
Abbreviations: p =P-value; BMI =Body Mass Index; AUDIT =Alcohol Use
Disorder Identication Test; DAST-10 =Drug Abuse Screening Test; CSSRS =
Columbia Suicide Severity Rating Scale; PHQ-9 =Patient Health Question-
naire; GAD-7 =Generalized Anxiety Disorder questionnaire.
D.S. Mathai et al.
Journal of Aective Disorders 361 (2024) 198–208
203
conveying a neutral or positive relationship between dissociation and
antidepressant efcacy for ketamine (Mathai et al., 2020, 2023b).
Furthermore, it has been proposed that clinical outcomes with disso-
ciative interventions might be optimized beyond the effects of the
molecule alone within therapeutic frameworks that value and support
the occurrence of psychologically meaningful drug experiences (Mathai
et al., 2022a). Here, a exible-dosing strategy was used targeting a mild
to moderate level of dissociation with ketamine and found high rates of
Fig. 1. Outcome trajectories of Patient Health Questionnaire (PHQ-9) depression scores (n =11,441).
Fig. 2. Feature importance for nested cross-validated Random Forest model of Improvement using SHAP values dot plot (n =11,441).
Patient characteristics are ranked on the Y axis based on feature importance for the model (i.e., highest mean absolute SHAP values). Dots represent the attribution
for each feature per patient, with values ranging from red (lower) to blue (higher). The X axis shows the effect of the feature in increasing (right) or decreasing (left)
the likelihood of depression Improvement trajectories membership. Abbreviations: SHAP =SHapley Additive exPlanations; PHQ-9 =Patient Health Questionnaire;
AUDIT =Alcohol Use Disorder Identication Test; DAST-10 =Drug Abuse Screening Test; CSSRS =Columbia Suicide Severity Rating Scale. (For interpretation of the
references to colour in this gure legend, the reader is referred to the web version of this article.)
D.S. Mathai et al.
Journal of Aective Disorders 361 (2024) 198–208
204
clinical response and tolerability, with only 12 of 11,441 patients with
discontinuation of treatment due to intense dissociation or other psy-
chologically overwhelming drug experiences.
Adverse events that occurred were predominantly neurologic or
psychiatric in nature, including reports of memory impairment, suicidal
ideation, headache, drug cravings, depression, and anxiety. All serious
adverse events were psychiatric. With the exception of drug cravings,
these events were consistent with established risks for commonly pre-
scribed antidepressant medications (Gosmann et al., 2023; Jakobsen
et al., 2017; Patel et al., 2015; Preda et al., 2001). Unlike conventional
pharmacotherapies, ketamine is known for its prominent subjective ef-
fects that mimic intoxication (Mathai et al., 2022b), with signicant
qualitative experiences that can range from more pleasant to chal-
lenging (Breeksema et al., 2023, 2022; Mollaahmetoglu et al., 2021;
Sumner et al., 2021). Risks of drug craving and misuse with ketamine
have been identied previously, including the potential for a ketamine
use disorder (Chubbs et al., 2022; Vines et al., 2022), but are mitigated
in clinical settings that emphasize judicious prescribing and appropriate
supervision of patients (Swainson et al., 2022). Together, these ndings
highlight the importance of ketamine administration within contexts
that are equipped to provide behavioral support for subjective drug
experiences that occur (Mathai et al., 2023a) and to adequately manage
relevant psychiatric risks, as demonstrated here.
Some have argued for increased regulation of ketamine (Harding,
2023; Wilkinson et al., 2024), drawing comparisons to the use of a
formal Risk Evaluation Mitigation Strategy (REMS) with esketamine,
because of concerns for abuse, excessive dissociation, sedation, and
respiratory depression. Furthermore, at least one case of at-home keta-
mine use involving excessive sedation and respiratory depression
requiring serious medical intervention has been reported (Johnson et al.,
2024). Importantly, these events of interest occurred minimally in our
sample, were monitored closely when they did occur, and at no point
met criteria for a serious adverse event. While the question of optimal
regulation remains, our ndings do not support the need for a REMS-
type program for ketamine when administered at subanesthetic dos-
ages, with clear instructions for use, and with the degree of supervision
detailed here. Rather than providing signicant clinical benet, such
programs are likely to increase administrative burden on healthcare
systems, while decreasing access to treatment (Wilson and Milne, 2011).
The clinical approach used here offers an example of how a rigorous
telehealth program might be used to decrease the sizeable costs of
psychedelic therapies (Hull et al., 2022; Williams et al., 2021) while
maintaining a high quality of care.
Another critical area of study has involved extending the duration of
benet with ketamine, which has led to therapeutic strategies such as
repeated medication administration (Kryst et al., 2020; McMullen et al.,
2021; Phillips et al., 2019). The antidepressant benets of a single dose
of ketamine are virtually undetectable by two weeks, but even with
repeated dosing, symptom relapse occurs at high rates within the rst
month of treatment cessation (Smith-Apeldoorn et al., 2022). Findings
from our cohort analysis support the value of ongoing treatment among
patients who demonstrate a favorable response to ketamine initially, as a
way of maintaining improvement. For patients without response to an
initial course of treatment, only a minority went on to achieve signi-
cant change pursuing a second trial, suggesting that the benets of
ongoing administration of ketamine may be diminished relative to risk
for most of these individuals. This is especially worth considering given
both the limited quality of data on the risks of long-term ketamine use
(Meshkat et al., 2023; Nikayin et al., 2022), and our ndings that the
amount of ketamine administered tends to increase over time using
exible-dosing procedures. Importantly, even when maintenance
Fig. 3. Temporal network of depression symptoms over treatment in non-
responder Chronic patients (n =2987). Nodes in the temporal network repre-
sent individual depressive symptoms, while edges represent predictive temporal
connections between symptoms. Blue and red edges represent positive and
negative association respectively, with thicker edges representing stronger
connections. Curved edges indicate the autoregressive stability of a symptom
over treatment. (For interpretation of the references to colour in this gure
legend, the reader is referred to the web version of this article.)
Fig. 4. Expected Inuence of depression symptoms in temporal network over
treatment for non-responder Chronic patients (n =2987).
D.S. Mathai et al.
Journal of Aective Disorders 361 (2024) 198–208
205
ketamine treatment is appropriate for patients, clinicians are advised to
actively monitor the risk-benet ratio and need for ongoing treatment,
the availability of alternative treatment options, and the potential for
tolerance, relapse, or recurrence (McIntyre et al., 2021).
As before (Hull et al., 2022), we identied several unique sub-
populations with different trajectories over the course of treatment.
Trajectories of improvement were most strongly predicted by lower
depression scores at baseline, as has been the case for many (Trivedi
et al., 2006; Van et al., 2008) but not all studies (Fournier et al., 2010;
Friedman et al., 2012) of antidepressant interventions. Other studies of
ketamine and esketamine have similarly found lower illness severity to
be predictive of response (Lucchese et al., 2021), and higher severity to
be predictive of non-response (Chen et al., 2021; Jesus-Nunes et al.,
2022).
Consistent with other studies of ketamine for depression, we also
observed that younger age increased the probability of improvement
(Chen et al., 2021; Turkoz et al., 2023). While not assessed here, it is
possible that younger adults have greater drug potencies or are more
sensitive to therapeutic effects driven by drug-induced changes in neu-
roplasticity (Strawn et al., 2023). Younger age has also been associated
with more positive expectations involving psychedelics (ˇ
Zuljevi´
c et al.,
2022) and greater intensity of acute psychedelic effects (Aday et al.,
2021), both of which may be relevant to therapeutic outcomes with
ketamine and warrant further study.
Lastly, using network analysis of individual depressive symptoms,
we found that mutual activation of depressed mood and anhedonia had a
substantial role in maintaining depression despite ongoing treatment.
This connection has been frequently demonstrated within symptom
networks of depression (Malgaroli et al., 2021) and, for ketamine
particularly, suggests the value of harnessing changes in neuroplasticity
with specic interventions that are geared toward behavioral activation,
and increasing engagement with activities and experiences that are
rewarding for patients (Hasler, 2020; Lepow et al., 2021; Phillips et al.,
2023; Serretti, 2023). Further research in this area is needed, but such
combinations may enhance medication response and even lead to more
durable forms of benet (Mathai et al., 2022a).
4.1. Limitations
This study has several key limitations. First, the high number of
participants with survey non-adherence at week 4, though expected for
real-world studies, may have been relevant to outcomes. To mitigate this
risk, we employed an intent-to-treat analysis and used likelihood-based
adjustment to account for missing data when calculating outcome tra-
jectories. Moreover, sensitivity analyses suggested a lower probability of
poor clinical response for survey non-adherent patients. Next, the
absence of a xed-dose procedure, which is often considered gold
standard for evaluation of dose response, may be seen as a drawback of
the study and a source of bias caused by non-random treatment
adjustment. However, it has also been argued that exible-dose studies
are more representative of actual clinical practice and risk/benet
considerations, since dose is often changed in accordance with patient
response (Lipkovich et al., 2008). Finally, the lack of comparison or
control groups makes it difcult to quantify the exact contribution of the
treatment to reported outcomes. While these data support the ecological
effectiveness (Singal et al., 2014) of sublingual ketamine through a
sample of over 10,000 treatment-seeking patients, further study of the
efcacy or comparative effectiveness of this intervention is warranted
through randomized controlled trials comparing sublingual ketamine to
placebo, other modes of ketamine administration, or conventional drug
or talk therapies for depression.
5. Conclusion
We found that at-home ketamine administration within a supportive
digital health infrastructure was largely safe, well-tolerated, and
associated with improvement in patients with depression. Strategies for
combining telehealth with ketamine and similar psychedelic therapies,
as explored here, may uniquely address barriers to mental health
treatment and increase access to care.
CRediT authorship contribution statement
David S. Mathai: Writing – review & editing, Writing – original
draft, Methodology, Formal analysis. Thomas D. Hull: Writing – review
& editing, Writing – original draft, Visualization, Validation, Software,
Methodology, Formal analysis, Conceptualization. Leonardo Vando:
Writing – review & editing, Funding acquisition, Data curation. Matteo
Malgaroli: Writing – review & editing, Writing – original draft, Super-
vision, Software, Project administration, Methodology, Formal analysis,
Conceptualization.
Declaration of competing interest
David S. Mathai and Matteo Malgaroli have no relevant commercial
or nancial relationships to disclose. Thomas D. Hull received minor
consulting fees from Mindbloom. Leonardo Vando is an employee of
Mindbloom. Data for this research were provided by the online medical
service, which had no involvement in the study design or formal analysis
for this manuscript.
Acknowledgements
The authors would like to thank the patients and support staff who
made this work possible. We would especially like to thank Kristin
Arden, Michael Petegorsky, Heidi Chang, and Jack Swain for their
assistance in moving this project forward.
Funding
Matteo Malgaroli’s research was supported by the National Institute
of Mental Health (NIMH) through grant # K23MH134068. Article open
access processing fees were paid for by the online medical service. The
content is solely the responsibility of the authors and does not neces-
sarily represent the ofcial views of the NIMH.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.jad.2024.05.131.
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