SynthesisThe British journal of psychiatry : the journal of mental science2019
Predicting antidepressant response by monitoring early improvement of individual symptoms of depression: individual patient data meta-analysis.
Synthesis in The British journal of psychiatry : the journal of mental science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses 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
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Does early non-improvement predict treatment failure in pharmacotherapy for obsessive-compulsive disorder? A diagnostic test accuracy meta-analysis with individual participant data.Psychological medicine · 2025Pooled it
- Convergent functional effects of antidepressants in major depressive disorder: a neuroimaging meta-analysis.Molecular psychiatry · 2025Pooled it
- The Relationship of Early Sleep Improvement With Response to Pharmacotherapy in Unipolar Psychotic Depression.Journal of clinical psychopharmacologyTrial
- Developing Clinically Interpretable Neuroimaging Biotypes in Psychiatry.Biological psychiatry · 2026Review
- Contribution of Longitudinal Mobile Health Measures in the Dynamic Track of Patients With Major Depressive Disorder: Multiple Centers, Prospective Cohort Study Using Functional Data Analysis and Machine Learning.JMIR mHealth and uHealth · 2026Article
- Article
- (Es)ketamine as a treatment for depressive episodes with psychotic features: systematic review.BJPsych open · 2025Review
- Article
- Dorsal raphe dopaminergic neurons target CaMKIITranslational psychiatry · 2024Article
- A distinctive subcortical functional connectivity pattern linking negative affect and treatment outcome in major depressive disorder.Translational psychiatry · 2024Article
- Electroacupuncture alleviated depression-like behaviors in ventromedial prefrontal cortex of chronic unpredictable mild stress-induced rats: Increasing synaptic transmission and phosphorylating dopamine transporter.CNS neuroscience & therapeutics · 2023Article
- Venlafaxine XR treatment for older patients with major depressive disorder: decision trees for when to change treatment.Evidence-based mental health · 2022Article
- Perceived helpfulness of treatment for specific phobia: Findings from the World Mental Health Surveys.Journal of affective disorders · 2021Article
- Reading, Conducting, and Developing Systematic Review and Individual Patient Data Meta-Analyses in Psychiatry for Treatment Issues.Frontiers in psychiatry · 2021Article
- Genetic Markers for Later Remission in Response to Early Improvement of Antidepressants.International journal of molecular sciences · 2020Article
- Prognosis and improved outcomes in major depression: a review.Translational psychiatry · 2019Review
- Re-evaluation of Significance and the Implications of Placebo Effect in Antidepressant Therapy.Frontiers in psychiatry · 2019Article
- Leveraging Machine Learning Approaches for Predicting Antidepressant Treatment Response Using Electroencephalography (EEG) and Clinical Data.Frontiers in psychiatry · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundImprovement in depression within the first 2 weeks of antidepressant treatment predicts good outcomes, but non-improvers can still respond or remit, whereas improvers often do not.AimsWe aimed to investigate whether early improvement of individual depressive symptoms better predicts response or remission.
methodWe obtained individual patient data of 30 trials comprising 2184 placebo-treated and 6058 antidepressant-treated participants. Primary outcome was week 6 response; secondary outcomes were week 6 remission and week 12 response and remission. We compared models that only included improvement in total score by week 2 (total improvement model) with models that also included improvement in individual symptoms.
resultsFor week 6 response, the area under the receiver operating characteristic curve and negative and positive predictive values of the total improvement model were 0.73, 0.67 and 0.74 compared with 0.77, 0.70 and 0.71 for the item improvement model. Model performance decreased for week 12 outcomes. Of predicted non-responders, 29% actually did respond by week 6 and 43% by week 12, which was decreased from the baseline (overall) probabilities of 51% by week 6 and 69% by week 12. In post hoc analyses with continuous rather than dichotomous early improvement, including individual items did not enhance model performance.
conclusionsExamining individual symptoms adds little to the predictive ability of early improvement. Additionally, early non-improvement does not rule out response or remission, particularly after 12 rather than 6 weeks. Therefore, our findings suggest that routinely adapting pharmacological treatment because of limited early improvement would often be premature.Declaration of interestNone.
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.