ArticleTranslational psychiatry2020
Predicting treatment dropout after antidepressant initiation.
Article in Translational psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- Machine learning versus conventional clinical methods in guiding management of heart failure patients-a systematic review.Heart failure reviews · 2021Pooled it
- Trial
- Identifying Patients at Risk for Psychotherapy Dropout in Community Settings.JAMA network open · 2026Article
- A real-world analysis of antidepressant medications in US veterans aged 60 years and older: a comparative analysis.Journal of comparative effectiveness research · 2025Article
- Association between prescriber practices and major depression treatment outcomes.Journal of mood and anxiety disorders · 2024Article
- A randomized sham-controlled trial of high-dosage accelerated intermittent theta burst rTMS in major depression: study protocol.BMC psychiatry · 2024Article
- Discontinuation of antidepressant treatment: a retrospective cohort study on more than 20,000 participants.Annals of general psychiatry · 2023Article
- Bayesian network analysis of antidepressant treatment trajectories.Scientific reports · 2023Article
- Using Machine Learning to Predict Antidepressant Treatment Outcome From Electronic Health Records.Psychiatric research and clinical practice · 2023Article
- Participation of the Serotonergic System and Brain-Derived Neurotrophic Factor in the Antidepressant-like Effect of Flavonoids.International journal of molecular sciences · 2022Review
- Do clinicians follow heuristics in prescribing antidepressants?Journal of affective disorders · 2022Article
- Prediction of Dropout in a Randomized Controlled Trial of Adjunctive Light Treatment in Patients with Non-Seasonal Depression and Evening Chronotype.Clocks & sleep · 2022Article
- Efficiently identifying individuals at high risk for treatment resistance in major depressive disorder using electronic health records.Journal of affective disorders · 2022Article
- A Delphi-method-based consensus guideline for definition of treatment-resistant depression for clinical trials.Molecular psychiatry · 2022Review
- Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2021Review
- The promise of machine learning in predicting treatment outcomes in psychiatry.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2021Article
- How machine-learning recommendations influence clinician treatment selections: the example of the antidepressant selection.Translational psychiatry · 2021Article
- Comorbid Chronic Pain and Depression: Shared Risk Factors and Differential Antidepressant Effectiveness.Frontiers in psychiatry · 2021Article
- Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise As Much As Possible.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational ScienceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Antidepressants exhibit similar efficacy, but varying tolerability, in randomized controlled trials. Predicting tolerability in real-world clinical populations may facilitate personalization of treatment and maximize adherence. This retrospective longitudinal cohort study aimed to determine the extent to which incorporating patient history from electronic health records improved prediction of unplanned treatment discontinuation at index antidepressant prescription. Clinical data were analyzed from individuals from health networks affiliated with two large academic medical centers between March 1, 2008 and December 31, 2014. In total, the study cohorts included 51,683 patients with at least one International Classification of Diseases diagnostic code for major depressive disorder or depressive disorder not otherwise specified who initiated antidepressant treatment. Among 70,121 total medication changes, 16,665 (23.77%) of them were followed by failure to return; maximum risk was observed with paroxetine (27.71% discontinuation), and minimum with venlafaxine (20.78% discontinuation); Mantel-Haenzel χ
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.