Trial reportScientific reports2018
Antidepressant drug-specific prediction of depression treatment outcomes from genetic and clinical variables.
Trial report in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 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
36 citing papers in PubMed, 2 syntheses or guidelines pooled it, 81 citations in OpenAlex.
- Machine Learning for Comparative Antidepressant Selection in Major Depressive Disorder: Systematic Review.JMIR mental health · 2026Pooled it
- Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group.Molecular psychiatry · 2020Pooled it
- Validation-Aware Retrospective EEG Treatment-Response Modelling Using Chaotic Pattern of Prime Numbers Features: Segment-Level Separability and Subject-Wise Generalisation.Bioengineering (Basel, Switzerland) · 2026Article
- A clinically actionable framework for personalizing iCBT to improve depression outcomes.NPJ digital medicine · 2026Review
- Learning Outcomes That Maximally Differentiate Psychiatric Treatments.International journal of methods in psychiatric research · 2026Article
- Computational variant predictors for pharmacogenomics: from evaluation of single alleles to assessment of adverse drug reactions to antidepressants.The pharmacogenomics journal · 2026Article
- Data driven versus standard practice policies for personalized antidepressant treatment: a retrospective cohort study of electronic health records.BMC psychiatry · 2026Article
- Applying Machine Learning to Predict Complex Clinical Course in Youth With Eating Disorders.The International journal of eating disorders · 2026Article
- Machine Learning Model for Response to Internet-Delivered CBT vs Antidepressant Medication.JAMA network open · 2025Article
- Predicting Depression Therapy Outcomes Using EEG-Derived Amplitude Polar Maps.Brain sciences · 2025Article
- Pharmacogenomic augmented machine learning in electronic health record alerts: A health system-wide usability survey of clinicians.Clinical and translational science · 2024Article
- Predicting the outcome of psychotherapy for chronic depression by person-specific symptom networks.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2024Article
- Article
- Development of a multivariate prediction model for antidepressant resistant depression using reward-related predictors.Frontiers in psychiatry · 2024Article
- Deep phenotyping towards precision psychiatry of first-episode depression - the Brain Drugs-Depression cohort.BMC psychiatry · 2023Article
- Predicting treatment outcome in depression: an introduction into current concepts and challenges.European archives of psychiatry and clinical neuroscience · 2023Review
- The Precision in Psychiatry (PIP) study: Testing an internet-based methodology for accelerating research in treatment prediction and personalisation.BMC psychiatry · 2023Article
- A cultural-ecosocial systems view for psychiatry.Frontiers in psychiatry · 2023Article
- Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants.Journal of child psychology and psychiatry, and allied disciplines · 2022Article
- Creating sparser prediction models of treatment outcome in depression: a proof-of-concept study using simultaneous feature selection and hyperparameter tuning.BMC medical informatics and decision making · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors at 9 institutions in 7 countries.
Funding
Abstract
Individuals with depression differ substantially in their response to treatment with antidepressants. Specific predictors explain only a small proportion of these differences. To meaningfully predict who will respond to which antidepressant, it may be necessary to combine multiple biomarkers and clinical variables. Using statistical learning on common genetic variants and clinical information in a training sample of 280 individuals randomly allocated to 12-week treatment with antidepressants escitalopram or nortriptyline, we derived models to predict remission with each antidepressant drug. We tested the reproducibility of each prediction in a validation set of 150 participants not used in model derivation. An elastic net logistic model based on eleven genetic and six clinical variables predicted remission with escitalopram in the validation dataset with area under the curve 0.77 (95%CI; 0.66-0.88; p = 0.004), explaining approximately 30% of variance in who achieves remission. A model derived from 20 genetic variables predicted remission with nortriptyline in the validation dataset with an area under the curve 0.77 (95%CI; 0.65-0.90; p < 0.001), explaining approximately 36% of variance in who achieves remission. The predictive models were antidepressant drug-specific. Validated drug-specific predictions suggest that a relatively small number of genetic and clinical variables can help select treatment between escitalopram and nortriptyline.
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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.