Evidence map›Paper›PMID 38810787›Full record

ArticleJournal of affective disorders2024

At-home, telehealth-supported ketamine treatment for depression: Findings from longitudinal, machine learning and symptom network analysis of real-world data.

David S Mathai, Thomas D Hull, Leonardo Vando, Matteo Malgaroli

Abstract read
In one paragraph

Article in Journal of affective disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

David S MathaiThe Johns Hopkins University School of Medicine, Center for Psychedelic and Consciousness Research, Department of Psychiatry and Behavioral Sciences, Baltimore, MD, United States of America; Sattva Medicine - Psychiatry/Psychotherapy Practice, Miami, FL, United States of America.
Thomas D HullInstitute for Psycholinguistics and Digital Health, United States of America.
Leonardo VandoMindbloom, Orlando, FL, United States of America.
Matteo MalgaroliNYU Grossman School of Medicine, Department of Psychiatry, New York, NY, United States of America. Electronic address: matteo.malgaroli@nyulangone.org.

Funding

Deep Learning Based Natural Language Processing Markers of Anxiety and DepressionK23MH134068 · NIMH · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Matteo Malgaroli · 2023 to 2026
$982k
NIMH NIH HHS K23 MH134068
6 · The paper itself

Abstract

backgroundImproving 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.

methodsIn 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).

resultsA 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 fixed-dose procedure for ketamine administration.

conclusionsAt-home, telehealth-supported ketamine administration was largely safe, well-tolerated, and associated 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.

Indexed as

Antidepressive AgentsKetamineMachine LearningTelemedicineAdultDepressionFemaleHumansLongitudinal StudiesMajor Depressive DisorderMaleMiddle AgedTreatment OutcomeAntidepressive AgentsKetamineDepressionKetamineMachine learningNetwork analysisPsychedelicTelehealth

Identifiers

PMID38810787
PMCPMC11284959

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.