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A clinical risk stratification tool for predicting treatment resistance in major depressive disorder.

ABSTRACT: Early identification of depressed individuals at high risk for treatment resistance could be helpful in selecting optimal setting and intensity of care. At present, validated tools to facilitate this risk stratification are rarely used in psychiatric practice.Data were drawn from the first two treatment levels of a multicenter antidepressant effectiveness study in major depressive disorder, the STAR*D (Sequenced Treatment Alternatives to Relieve Depression) cohort. This cohort was divided into training, testing, and validation subsets. Only clinical or sociodemographic variables available by or readily amenable to self-report were considered. Multivariate models were developed to discriminate individuals reaching remission with a first or second pharmacological treatment trial from those not reaching remission despite two trials.A logistic regression model achieved an area under the receiver operating characteristic curve exceeding .71 in training, testing, and validation cohorts and maintained good calibration across cohorts. Performance of three alternative models with machine learning approaches--a naïve Bayes classifier and a support vector machine, and a random forest model--was less consistent. Similar performance was observed between more and less severe depression, men and women, and primary versus specialty care sites. A web-based calculator was developed that implements this tool and provides graphical estimates of risk.Risk for treatment resistance among outpatients with major depressive disorder can be estimated with a simple model incorporating baseline sociodemographic and clinical features. Future studies should examine the performance of this model in other clinical populations and its utility in treatment selection or clinical trial design.

SUBMITTER: Perlis RH

PROVIDER: S-EPMC3690142 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

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A clinical risk stratification tool for predicting treatment resistance in major depressive disorder.

Perlis Roy H RH

Biological psychiatry 20130204 1

<h4>Background</h4>Early identification of depressed individuals at high risk for treatment resistance could be helpful in selecting optimal setting and intensity of care. At present, validated tools to facilitate this risk stratification are rarely used in psychiatric practice.<h4>Methods</h4>Data were drawn from the first two treatment levels of a multicenter antidepressant effectiveness study in major depressive disorder, the STAR*D (Sequenced Treatment Alternatives to Relieve Depression) coh ...[more]

PMID: 23380715

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Project description:Background: Major Depressive Disorder (MDD) is a moderately heritable disorder with a high lifetime prevalence. At present, laboratory blood tests to support MDD diagnosis are not available. Methods: We used a classifier approach on blood gene expression profiles of a unique set of non-medicated subjects (MDD patients and controls) to select genes of which expression is predictive for disease status. To reveal blood gene expression changes related to MDD disease, we applied a powerful ex vivo stimulus to the blood, i.e. incubation with lipopolysaccharide (LPS; 10 ng/ml blood). Results: Based on LPS-stimulated blood gene expression using whole-genome microarrays in 42 subjects (primary cohort; 21 MDD patients (mean age 42.3 years), 21 healthy controls (mean age 41.9 years)), we identified a set of genes (CAPRIN1, CLEC4A, KRT23, MLC1, PLSCR1, PROK2, ZBTB16) that serves as a molecular signature of MDD. These findings were validated for the primary cohort using an independent quantitative PCR method (P = 0.007). The difference between depressive patients and controls was confirmed (P = 0.019) in a replication cohort of 13 patients with MDD (mean age 42.8 years) and 14 controls (mean age 45.6 years). The MDD-signature score comprised of expression levels of 7 genes could discriminate depressive patients from controls with sensitivity of 76.9% and specificity of 71.8%. Conclusions: We show for the first time that molecular analysis of stimulated blood cells can be used as an endophenotype for MDD diagnosis, which is a milestone in establishing biomarkers for neuropsychiatric disorders with moderate heritability in general. Our results may provide a new entry point for following and predicting treatment outcome, as well as prediction of severity and recurrence of MDD. In total, 33 MDD patients and 34 healthy controls were analyzed using basal gene expression in whole blood, and gene expression from whole blood that was stimulated with LPS for 5-6 h, using microarrays. Patients were arbitrarily selected from all patients to serve as primary cohort (nMDD = 21 (MDD01-MDD21); nControls = 21 (Con01-Con21)), or replication cohort (nMDD = 12 (MDD22-MDD35); nControls = 13 (Con22-Con37)) using microarrays. This submission does not include Samples CON21_LPS or CON30_LPS.

Project description:Selecting a course of treatment in psychiatry remains a trial-and-error process, and this long-standing clinical challenge has prompted an increased focus on predictive models of treatment response using machine learning techniques. Electroencephalography (EEG) represents a cost-effective and scalable potential measure to predict treatment response to major depressive disorder. We performed separate meta-analyses to determine the ability of models to distinguish between responders and non-responders using EEG across treatments, as well as a performed subgroup analysis of response to transcranial magnetic stimulation (rTMS), and antidepressants (Registration Number: CRD42021257477) in Major Depressive Disorder by searching PubMed, Scopus, and Web of Science for articles published between January 1960 and February 2022. We included 15 studies that predicted treatment responses among patients with major depressive disorder using machine-learning techniques. Within a random-effects model with a restricted maximum likelihood estimator comprising 758 patients, the pooled accuracy across studies was 83.93% (95% CI: 78.90-89.29), with an Area-Under-the-Curve (AUC) of 0.850 (95% CI: 0.747-0.890), and partial AUC of 0.779. The average sensitivity and specificity across models were 77.96% (95% CI: 60.05-88.70), and 84.60% (95% CI: 67.89-92.39), respectively. In a subgroup analysis, greater performance was observed in predicting response to rTMS (Pooled accuracy: 85.70% (95% CI: 77.45-94.83), Area-Under-the-Curve (AUC): 0.928, partial AUC: 0.844), relative to antidepressants (Pooled accuracy: 81.41% (95% CI: 77.45-94.83, AUC: 0.895, pAUC: 0.821). Furthermore, across all meta-analyses, the specificity (true negatives) of EEG models was greater than the sensitivity (true positives), suggesting that EEG models thus far better identify non-responders than responders to treatment in MDD. Studies varied widely in important features across models, although relevant features included absolute and relative power in frontal and temporal electrodes, measures of connectivity, and asymmetry across hemispheres. Predictive models of treatment response using EEG hold promise in major depressive disorder, although there is a need for prospective model validation in independent datasets, and a greater emphasis on replicating physiological markers. Crucially, standardization in cut-off values and clinical scales for defining clinical response and non-response will aid in the reproducibility of findings and the clinical utility of predictive models. Furthermore, several models thus far have used data from open-label trials with small sample sizes and evaluated performance in the absence of training and testing sets, which increases the risk of statistical overfitting. Large consortium studies are required to establish predictive signatures of treatment response using EEG, and better elucidate the replicability of specific markers. Additionally, it is speculated that greater performance was observed in rTMS models, since EEG is assessing neural networks more likely to be directly targeted by rTMS, comprising electrical activity primarily near the surface of the cortex. Prospectively, there is a need for models that examine the comparative effectiveness of multiple treatments across the same patients. However, this will require a thoughtful consideration towards cumulative treatment effects, and whether washout periods between treatments should be utilised. Regardless, longitudinal cross-over trials comparing multiple treatments across the same group of patients will be an important prerequisite step to both facilitate precision psychiatry and identify generalizable physiological predictors of response between and across treatment options.

Project description:ImportancePsilocybin shows promise as a treatment for major depressive disorder (MDD).ObjectiveTo evaluate the magnitude, timing, and durability of antidepressant effects and safety of a single dose of psilocybin in patients with MDD.Design, setting, and participantsIn this phase 2 trial conducted between December 2019 and June 2022 at 11 research sites in the US, participants were randomized in a 1:1 ratio to receive a single dose of psilocybin vs niacin placebo administered with psychological support. Participants were adults aged 21 to 65 years with a Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition diagnosis of MDD of at least 60 days' duration and moderate or greater symptom severity. Exclusion criteria included history of psychosis or mania, active substance use disorder, and active suicidal ideation with intent. Participants taking psychotropic agents who otherwise met inclusion/exclusion criteria were eligible following medication taper. Primary and secondary outcomes and adverse events (AEs) were assessed at baseline (conducted within 7 days before dosing) and at 2, 8, 15, 29, and 43 days after dosing.InterventionsInterventions were a 25-mg dose of synthetic psilocybin or a 100-mg dose of niacin in identical-appearing capsules, each administered with psychological support.Main outcomes and measuresThe primary outcome was change in central rater-assessed Montgomery-Asberg Depression Rating Scale (MADRS) score (range, 0-60; higher scores indicate more severe depression) from baseline to day 43. The key secondary outcome measure was change in MADRS score from baseline to day 8. Other secondary outcomes were change in Sheehan Disability Scale score from baseline to day 43 and MADRS-defined sustained response and remission. Participants, study site personnel, study sponsor, outcome assessors (raters), and statisticians were blinded to treatment assignment.ResultsA total of 104 participants (mean [SD] age, 41.1 [11.3] years; 52 [50%] women) were randomized (51 to the psilocybin group and 53 to the niacin group). Psilocybin treatment was associated with significantly reduced MADRS scores compared with niacin from baseline to day 43 (mean difference,-12.3 [95% CI, -17.5 to -7.2]; P <.001) and from baseline to day 8 (mean difference, -12.0 [95% CI, -16.6 to -7.4]; P < .001). Psilocybin treatment was also associated with significantly reduced Sheehan Disability Scale scores compared with niacin (mean difference, -2.31 [95% CI, 3.50-1.11]; P < .001) from baseline to day 43. More participants receiving psilocybin had sustained response (but not remission) than those receiving niacin. There were no serious treatment-emergent AEs; however, psilocybin treatment was associated with a higher rate of overall AEs and a higher rate of severe AEs.Conclusions and relevancePsilocybin treatment was associated with a clinically significant sustained reduction in depressive symptoms and functional disability, without serious adverse events. These findings add to increasing evidence that psilocybin-when administered with psychological support-may hold promise as a novel intervention for MDD.Trial registrationClinicalTrials.gov Identifier: NCT03866174.

Dataset Information

A clinical risk stratification tool for predicting treatment resistance in major depressive disorder.

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A clinical risk stratification tool for predicting treatment resistance in major depressive disorder.

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