Outcomes In Major Depressive Disorder Research Articles

Shared Genetic Liability and Causal Associations Between Major Depressive Disorder and Cardiovascular Diseases.

Major depressive disorder (MDD) is phenotypically associated with cardiovascular diseases (CVD). We aim to investigate mechanisms underlying relationships between MDD and CVD in the context of shared genetic variations. Polygenic overlap analysis was used to test genetic correlation and to analyze shared genetic variations between MDD and seven cardiovascular outcomes (coronary artery disease (CAD), heart failure, atrial fibrillation, stroke, systolic blood pressure, diastolic blood pressure, and pulse pressure measurement). Mendelian randomization analysis was used to uncover causal relationships between MDD and cardiovascular traits. By cross-trait meta-analysis, we identified a set of genomic loci shared between the traits of MDD and stroke. Putative causal genes for MDD and stroke were prioritized by fine-mapping of transcriptome-wide associations. Polygenic overlap analysis pointed toward substantial genetic variation overlap between MDD and CVD. Mendelian randomization analysis indicated that genetic liability to MDD has a causal effect on CAD and stroke. Comparison of genome-wide genes shared by MDD and CVD suggests 20q12 as a pleiotropic region conferring risk for both MDD and CVD. Cross-trait meta-analyses and fine-mapping of transcriptome-wide association signals identified novel risk genes for MDD and stroke, including RPL31P12, BORSC7, PNPT11, and PGF. Many genetic variations associated with MDD and CVD outcomes are shared, thus, pointing that genetic liability to MDD may also confer risk for stroke and CAD. Presented results shed light on mechanistic connections between MDD and CVD phenotypes.

Overview of a large transcranial magnetic stimulation patient outcomes database

Abstract While there is a clear evidence base from randomized clinical trials that demonstrate the efficacy of Transcranial Magnetic Stimulation (TMS) in the treatment of major depressive disorder (MDD), greater understanding of best practices in routine clinical practice is critically needed. This symposium will provide an in depth look at the first ten years of data collection of patient demographics, treatment parameters and outcomes from the NeuroStar® Advanced Therapy System Clinical Outcomes Registry. Methods: Registry data were collected at 103 practice sites. Of 7759 participants, 5010 patients were included in an intent-to-treat (ITT) sample, defined as a primary MDD diagnosis, age ≥ 18, and completion of the Patient Health Questionaire-9 (PHQ-9) before TMS and with at least one PHQ-9 assessment after baseline. Completers (N = 3,814) were responders or had received ≥ 20 sessions and had an end of acute treatment PHQ-9 assessment. CGI-S ratings were obtained in smaller samples. Results: In the total ITT and Completer samples, response (58–83%) and remission (28–62%) rates were notably high across self-report and clinician-administered assessments. Female patients and those treated with a larger number of pulses per session had superior clinical outcomes. Conclusions: The extent of clinical benefit reported by patients and clinicians following TMS in routine practice compares favorably with alternative interventions for treatment-resistant depression. Strong efficacy and the low side effect and medical risk profile suggest that TMS be evaluated as a first-line treatment for MDD. The findings demonstrate the power of creating a large registry of clinical outcomes in MDD. Keywords: TMS, treatment outcomes, registry

Post-traumatic stress disorder and childhood emotional abuse are markers of subthreshold bipolarity and worse treatment outcome in major depressive disorder.

Post-traumatic stress disorder (PTSD) and childhood maltreatment (CMT: parental neglect; emotional, physical and sexual abuse) have been linked to bipolar disorder but they are also common in major depressive disorder (MDD). Our objective was to investigate their association with the bipolar spectrum and antidepressant treatment outcome in 482 outpatients with DSM-IV MDD treated in the Combining Medications to Enhance Depression Outcomes trial for 28 weeks Bipolar spectrum score included age of onset <21 years, subthreshold hypomania (a period of elated or irritable mood with at least two concurrent hypomanic symptoms, which did not fulfill DSM criteria for hypomanic/manic episode) and depressive mixed state (DMX). PTSD subjects (n = 107; 22%) had more severe depression (P < 0.0001), work and social impairment (P = 0.0031), comorbid anxiety disorders (P < 0.0001) and increased suicidality (P = 0.0003). Bipolar spectrum score was higher with PTSD comorbidity (P = 0.0063) and childhood emotional abuse (P = 0.0001). PTSD comorbidity was associated with residual suicidality (P = 0.0218) after 6 weeks of antidepressant use whereas childhood emotional abuse [odds ratio (OR), 1.01-2.22], subthreshold hypomania (OR, 1.04-4.09) and DMX (OR, 1.00-4.19) were predictors of mood switch. These results corroborate the role of PTSD and childhood emotional abuse as markers of bipolar spectrum and prognostic factors during antidepressant treatment.

Antidepressant treatment outcomes in patients with and without comorbid physical or psychiatric disorders: A systematic review and meta-analysis

BackgroundMany patients with major depressive disorder (MDD) experience substantial impairment despite the availability of efficacious treatments. We performed a systematic review and meta-analysis to compare antidepressant outcomes in MDD with or without physical or psychiatric comorbidities. MethodsPubmed, EMBASE, and PsycInfo were searched up to May 14th, 2020 using keywords including MDD, antidepressant, medication, and comorbid. 1915 studies were reviewed. Studies that performed a direct and quantitative comparison of antidepressant effect in patients with MDD with or without comorbidities were included. Study characteristics and primary outcomes were extracted. Continuous and dichotomous variables were considered using standardized mean difference (SMD). Heterogeneity was measured using χ2 and I2 tests. Risk of bias was assessed using Cochrane Risk of Bias tool and NIH Quality Assessment Tool. Results26 studies met selection criteria. Studies of physical (6 studies; I2 = 57.69%, p = 0.04) and psychiatric comorbidities (20 studies; I2 = 75.75%, p < 0.001) were heterogeneous. When compared to patients with MDD without comorbidities, those with physical (SMD = -0.19, 95% CI: -0.30 to -0.08, p = 0.001; 1910 and 2905 patients with or without comorbidities) or psychiatric comorbidities (SMD = -0.20, 95% CI: -0.31 to -0.095, p < 0.001; 4308 and 6867 patients with or without comorbidities) had worse antidepressant outcomes. LimitationsOur limitations included aggregating the comorbidities into physical and psychiatric comorbidities and the high heterogeneity of the studies. ConclusionsOur review provides updated evidence demonstrating that patients with MDD and physical or psychiatric comorbidities experience worse antidepressant outcomes.

Hearing Rehabilitative Treatment for Older Adults With Comorbid Hearing Loss and Depression: Effects on Depressive Symptoms and Executive Function

ObjectivesRecent research has revealed important neural and psychiatric consequences of hearing loss (HL) in older adults. This pilot study examined the neural effects of HL and the impact of hearing aids on neuropsychiatric outcomes in major depressive disorder (MDD). DesignTwelve-week, double-blind, randomized controlled trial. Participants/InterventionN = 25 (≥60 years) with MDD and moderate-profound HL were randomized to receive hearing aids (100% gain targets) or sham hearing aids (flat 30 dB HL) in addition to psychiatric treatment-as-usual. MeasurementsDepressive symptoms (Hamilton Rating Scale for Depression [HRSD]), executive functioning (NIH Toolbox Flanker), integrity of auditory brain areas (structural MRI, diffusion tensor imaging). ResultsAt baseline, worse speech discrimination was associated with auditory cortical thinning (Left anterior transverse temporal gyrus: r = 0.755, p = 0.012) and lower integrity of the superior longitudinal fasciculus (FA: Left r = 0.772, p = 0.025, Right r = 0.782, p = 0.022). After 12-weeks, hearing aids were effective at improving hearing functioning (Hearing Handicap for the Elderly: active -12.47 versus sham -4.19, t = -2.64, df = 18, p = 0.016) and immediate memory (active +14.9 versus sham +5.7, t = 2.28, df = 16, p = 0.037). Moderate improvement was observed for hearing aids on executive functioning but did not reach statistical significance (Flanker: active +4.8 versus sham -2.4, t = 1.95, df = 15, p = 0.071). No significant effect on depression was found (HRSD: active -5.50 versus sham -7.32, t = 0.75, df = 19, p = 0.46). ConclusionsHL can affect brain regions important for auditory and cognitive processing, and hearing remediation may have beneficial effects on executive functioning in MDD. Future studies may evaluate whether impairment in cognitive control consequent to HL may be an important risk mechanism for MDD.

Repetitive transcranial magnetic stimulation treatment of major depressive disorder and comorbid chronic pain: response rates and neurophysiologic biomarkers.

Major depressive disorder (MDD) and chronic pain are highly comorbid, and pain symptoms are associated with a poorer response to antidepressant medication treatment. It is unclear whether comorbid pain also is associated with a poorer response to treatment with repetitive transcranial magnetic stimulation (rTMS). 162 MDD subjects received 30 sessions of 10 Hz rTMS treatment administered to the left dorsolateral prefrontal cortex (DLPFC) with depression and pain symptoms measured before and after treatment. For a subset of 96 patients, a resting-state electroencephalogram (EEG) was recorded at baseline. Clinical outcome was compared between subjects with and without comorbid pain, and the relationships among outcome, pain severity, individual peak alpha frequency (PAF), and PAF phase-coherence in the EEG were examined. 64.8% of all subjects reported pain, and both depressive and pain symptoms were significantly reduced after rTMS treatment, irrespective of age or gender. Patients with severe pain were 27% less likely to respond to MDD treatment than pain-free individuals. PAF was positively associated with pain severity. PAF phase-coherence in the somatosensory and default mode networks was significantly lower for MDD subjects with pain who failed to respond to MDD treatment. Pain symptoms improved after rTMS to left DLPFC in MDD irrespective of age or gender, although the presence of chronic pain symptoms reduced the likelihood of treatment response. Individual PAF and baseline phase-coherence in the sensorimotor and midline regions may represent predictors of rTMS treatment outcome in comorbid pain and MDD.

Combining psychopharmacotherapy and psychotherapy is not associated with better treatment outcome in major depressive disorder - evidence from the European Group for the Study of Resistant Depression

Despite plenty of effective antidepressant (AD) treatments, the outcome of major depressive disorder (MDD) is often unsatisfactory, probably due to improvable exploitation of available therapies. This European, cross-sectional, naturalistic multicenter study investigated the frequency of additional psychotherapy in terms of a manual-driven psychotherapy (MDP) in 1410 adult in- and outpatients with MDD, who were primarily treated with AD psychopharmacotherapy. Socio-demographic and clinical patterns were compared between patients receiving both treatments and those lacking concomitant MDP. In a total of 1279 MDD patients (90.7%) with known status of additional MDP, those undergoing a psychopharmacotherapy-MDP combination (31.2%) were younger, higher educated, more often employed and less severely ill with lower odds for suicidality as compared to patients receiving exclusively psychopharmacotherapy (68.8%). They experienced an earlier mean age of MDD onset, melancholic features, comorbid asthma and migraine and received lower daily doses of their first-line ADs. While agomelatine was more often established in these patients, MDD patients without MDP received selective serotonin reuptake inhibitors more frequently. These two patient groups did not differ in terms of response, non-response and treatment resistant depression (TRD). Accordingly, the employment of additional MDP could not be related to better treatment outcomes in MDD. The fact that MDP was applied in a minority of patients with rather beneficial socio-demographic and clinical characteristics might reflect inferior accessibility of these psychotherapeutic techniques for socially and economically disadvantaged populations.

Functional connectivity evidence for state-independent executive function deficits in patients with major depressive disorder

BackgroundPersistent neurocognitive deficits are often associated with poor outcomes of major depressive disorder (MDD). Executive dysfunction is the most common cognitive deficit in MDD. However, it remains unclear which subcomponent of executive dysfunction is state-independent with distinct neural substrates. MethodsA comprehensive neurocognitive test battery was used to assess four subcomponents of executive function (working memory, inhibition, shifting, and verbal fluency) in 95 MDD patients and 111 matched healthy controls (HCs). After 6 months of paroxetine treatment, 56 patients achieved clinical remission (rMDD) and completed the second-time neurocognitive test. Network-based statistics analysis was utilized to explore the changes in functional connectivity (FC). ResultsCompared with the HCs, all the four subcomponents of MDD patients were significantly impaired. After treatment, there was a significant improvement in working memory, inhibition, and verbal fluency in the rMDD group. And shifting and verbal fluency of the rMDD group remained impaired compared with the HCs. Fifteen functional connections were interrupted in the MDD group, and 11 connections remained in a disrupted state after treatment. Importantly, verbal fluency was negatively correlated with the disrupted FC between the right dorsal prefrontal cortex and the left inferior parietal lobule in patients with MDD and remitted MDD. LimitationsThe correlation analysis of the association between cognitive impairment and connectivity alterations precluded us from making causal inferences. ConclusionsVerbal fluency is the potential state-independent cognitive deficit with distinct neural basis in patients with MDD.

Relations Among Individualized Brain Systems Patch Sizes and Diagnosis, Symptoms, and Treatment Outcomes in Major Depressive Disorder: Preliminary Results

Individualized brain mapping promises to provide a significant advance for neural models of, and treatments for, major depressive disorder (MDD). Developments in computational neuroimaging methods facilitate new precision in neural characterization at the person-specific level. Here, we describe preliminary results that link brain systems defined at the individual level – “individualized brain systems” – to MDD, including in relation to diagnosis, symptoms, and treatment outcomes.

Pretreatment qEEG biomarkers for predicting pharmacological treatment outcome in major depressive disorder: Independent validation from the NeuroPharm study

Several electroencephalogram (EEG) biomarkers for prediction of drug response in major depressive disorder (MDD) have been proposed, but validations in larger independent datasets are missing. In the current study, we investigated the prognostic value of previously suggested EEG biomarkers. We gathered data that matched prior studies in terms of EEG methodology, clinical criteria for MDD, and statistical approach as closely as possible. The NeuroPharm study is a non-randomized and open label prospective clinical trial. One hundred antidepressant free patients with MDD were enrolled in the study and 79 (57 female) were included in the per-protocol analysis. The biomarkers candidates for cross-validation were derived from prior studies such as iSPOT-D and EMBARC and include frontal and occipital alpha power and asymmetry and delta and theta activity at anterior cingulate cortex (ACC). The alpha asymmetry, reported in two out of six prior studies, could be partially validated. We found that in female patients, larger right than left frontal alpha power prior to drug treatment was associated with better clinical outcome 8 weeks later. Moreover, female non-responder had higher central left alpha power relative to the right. In contrast to prior reports, we found that lower theta activity at ACC was present in remitters and was associated with greater improvement at week 8. We provide evidence that in women with MDD, alpha asymmetry seems to be the most promising EEG biomarker for prediction of treatment response.Registration number: NCT02869035.

Depression Associated with Reduced Heart Rate Variability Predicts Outcome in Adult Congenital Heart Disease.

In adult congenital heart disease (ACHD), major depressive disorder (MDD) represents a frequent comorbidity. In non-CHD, adverse outcome is predicted by MDD and heart rate variability (HRV), whereas in ACHD their prognostic relevance is unknown. We prospectively evaluated 171 patients (age 35.6 ± 11.4 years; male 42.7%, mean observation time 54.7 ± 14.9 months). Binary regression analysis calculated the association between MDD and HRV. Cox proportional survival analysis estimated their impact on decompensated heart failure and all-cause mortality (HF/death), supraventricular and ventricular tachycardia (SVT/VT), and hospitalization due to unexpected cardiac causes. Exclusively MDD with moderate/severe symptoms showed significantly lower HRV as derived from frequency-domain analysis (Symindex) (p = 0.013). In multivariate Cox regression analysis, patients stratified according to the lower quartile of the Symindex comorbid with MDD (n = 16) exhibited poorer prognosis regarding HF/death (Hazard Ratio (HR): 7.04 (95%CI:(1.87–26.5)), SVT/VT (HR: 4.90 (95%CI:1.74–9.25)) and hospitalization (HR: 3.80 (95%CI:1.36–10.6)). An additional independent predictor was N-terminal pro-B-type natriuretic peptide elevation (p < 0.001), indicating advanced HF and heart disease complexity (p < 0.001). Autonomic nervous system dysfunction measured by altered HRV is considered to be one of the pathways linking MDD and adverse outcomes in cardiac diseases. Our results exceed the existing literature by demonstrating that MDD with decreased HRV is associated with poorer prognosis in ACHD.

Personalized Medicine Using Neuroimmunological Biomarkers in Depressive Disorders.

Major depressive disorder (MDD) is associated with increased suicidal risk and reduced productivity at work. Neuroimmunology, the study of the immune system and nervous system, provides further insight into the pathogenesis and outcome of MDD. Cytokines are the main modulators of neuroimmunology, and their levels are somewhat entangled in depressive disorders as they affect depressive symptoms and are affected by antidepressant treatment. The use of cytokine-derived medication as a treatment option for MDD is currently a topic of interest. Although not very promising, cytokines are also considered as possible prognostic or diagnostic markers for depression. The machine learning approach is a powerful tool for pattern recognition and has been used in psychiatry for finding useful patterns in data that have translational meaning and can be incorporated in daily clinical practice. This review focuses on the current knowledge of neuroimmunology and depression and the possible use of machine learning to widen our understanding of the topic.

Selective Serotonin Reuptake Inhibitor Pharmaco-Omics: Mechanisms and Prediction.

Selective serotonin reuptake inhibitors (SSRIs) are a standard of care for the pharmacotherapy of patients suffering from Major Depressive Disorder (MDD). However, only one-half to two-thirds of MDD patients respond to SSRI therapy. Recently, a “multiple omics” research strategy was applied to identify genetic differences between patients who did and did not respond to SSRI therapy. As a first step, plasma metabolites were assayed using samples from the 803 patients in the PGRN-AMPS SSRI MDD trial. The metabolomics data were then used to “inform” genomics by performing a genome-wide association study (GWAS) for plasma concentrations of the metabolite most highly associated with clinical response, serotonin (5-HT). Two genome-wide or near genome-wide significant single nucleotide polymorphism (SNP) signals were identified, one that mapped near the TSPAN5 gene and another across the ERICH3 gene, both genes that are highly expressed in the brain. Knocking down TSPAN5 and ERICH3 resulted in decreased 5-HT concentrations in neuroblastoma cell culture media and decreased expression of enzymes involved in 5-HT biosynthesis and metabolism. Functional genomic studies demonstrated that ERICH3 was involved in clathrin-mediated vesicle formation and TSPAN5 was an ethanol-responsive gene that may be a marker for response to acamprosate pharmacotherapy of alcohol use disorder (AUD), a neuropsychiatric disorder highly co-morbid with MDD. In parallel studies, kynurenine was the plasma metabolite most highly associated with MDD symptom severity and application of a metabolomics-informed pharmacogenomics approach identified DEFB1 and AHR as genes associated with variation in plasma kynurenine levels. Both genes also contributed to kynurenine-related inflammatory pathways. Finally, a multiply replicated predictive algorithm for SSRI clinical response with a balanced predictive accuracy of 76% (compared with 56% for clinical data alone) was developed by including the SNPs in TSPAN5, ERICH3, DEFB1 and AHR. In summary, application of a multiple omics research strategy that used metabolomics to inform genomics, followed by functional genomic studies, identified novel genes that influenced monoamine biology and made it possible to develop a predictive algorithm for SSRI clinical outcomes in MDD. A similar pharmaco-omic research strategy might be broadly applicable for the study of other neuropsychiatric diseases and their drug therapy.

Measuring brain glucose metabolism in order to predict response to antidepressant or placebo: A randomized clinical trial

There is critical need for a clinically useful tool to predict antidepressant treatment outcome in major depressive disorder (MDD) to reduce suffering and mortality. This analysis sought to build upon previously reported antidepressant treatment efficacy prediction from 2-[18F]-fluorodeoxyglucose - Positron Emission Tomography (FDG-PET) using metabolic rate of glucose uptake (MRGlu) from dynamic FDG-PET imaging with the goal of translation to clinical utility. This investigation is a randomized, double-blind placebo-controlled trial. All participants were diagnosed with MDD and received an FDG-PET scan before randomization and after treatment. Hamilton Depression Rating Scale (HDRS-17) was completed in participants diagnosed with MDD before and after 8 weeks of escitalopram, or placebo. MRGlu (mg/(min*100 ml)) was estimated within the raphe nuclei, right insula, and left ventral Prefrontal Cortex in 63 individuals. Linear regression was used to examine the association between pretreatment MRGlu and percent decrease in HDRS-17. Additionally, the association between percent decrease in HDRS-17 and percent change in MRGlu between pretreatment scan and post-treatment scan was examined. Covariates were treatment type (SSRI/placebo), handedness, sex, and age. Depression severity decrease (n = 63) was not significantly associated with pretreatment MRGlu in the raphe nuclei (β = -2.61e-03 [-0.26, 0.25], p = 0.98), right insula (β = 0.05 [-0.23, 0.32], p = 0.72), or ventral prefrontal cortex (β = 0.06 [-0.23, 0.34], p = 0.68) where β is the standardized estimated coefficient, with a 95% confidence interval, or in whole brain voxelwise analysis (family-wise error correction, alpha = 0.05). MRGlu percent change was not significantly associated with depression severity decrease (n = 58) before multiple comparison correction in the RN (β = 0.20 [-0.07, 0.47], p = 0.15), right insula (β = 0.24 [-0.03, 0.51], p = 0.08), or vPFC (β = 0.22 [-0.06, 0.50], p = 0.12). We propose that FDG-PET imaging does not indicate a clinically relevant biomarker of escitalopram or placebo treatment response in heterogeneous major depressive disorder cohorts. Future directions include focusing on potential biologically-based subtypes of major depressive disorder by implementing biomarker stratified designs.

Ketamine’s modulation of cerebro-cerebellar circuitry during response inhibition in major depression

Patients with major depressive disorder (MDD) exhibit impaired control of cognitive and emotional systems, including deficient response selection and inhibition. Though these deficits are typically attributed to abnormal communication between macro-scale cortical networks, altered communication with the cerebellum also plays an important role. Yet, how the circuitry between the cerebellum and large-scale functional networks impact treatment outcome in MDD is not understood. We thus examined how ketamine, which elicits rapid therapeutic effects in MDD, modulates cerebro-cerebellar circuitry during response-inhibition using a functional imaging NoGo/Go task in MDD patients (N = 46, mean age: 39.2, 38.1% female) receiving four ketamine infusions, and healthy controls (N = 32, mean age:35.2, 71.4% female). We fitted psychophysiological-interaction (PPI) models for a functionally-derived cerebellar-seed and extracted average PPI in three target functional networks, frontoparietal (FPN), sensory-motor (SMN) and salience (SN) networks. Time and remission status were then evaluated for each of the networks and their network-nodes. Follow-up tests examined whether PPI-connectivity differed between patient remitter/non-remitters and controls. Results showed significant decreases in PPI-connectivity after ketamine between the cerebellum and FPN (p < 0.001) and SMN networks (p = 0.008) in remitters only (N = 20). However, ketamine-related changes in PPI-connectivity between the cerebellum and the SN (p = 0.003) did not vary with remitter status. Cerebellar-FPN, -SN PPI values at baseline were also associated with treatment outcome. Using novel methodology to quantify the functional coupling of cerebro-cerebellar circuitry during response-inhibition, our findings highlight that these loops play distinct roles in treatment response and could potentially serve as novel biomarkers for fast-acting antidepressant therapies in MDD.

Psychopharmacology Algorithms for Major Depressive Disorder: Current Status.

Major depressive disorder (MDD) is one of the leading causes of disability worldwide, and a considerable portion of depressed patients does not respond well to available treatment strategies. Algorithm-based treatment may contribute to improving the MDD outcomes and have the potential to homogenize the pharmacological treatment of MDD patients, facilitating outcome research and cost-effectiveness analysis. This chapter provides a critical review of the available literature on the use of treatment algorithms for the management of MDD. The main available algorithms, their effectiveness, and challenges and limitations associated with their development are discussed. Finally, we provide a discussion of the future direction of algorithm-based treatments for MDD.

Toward Targeted ECT: The Interdependence of Predictors of Treatment Response in Depression Further Explained.

Several clinical variables assumed to be predictive of electroconvulsive therapy (ECT) outcome in major depressive disorder show substantial interrelations. The current study tries to disentangle this interdependence to distill the most important predictors of treatment success to help improve patient-treatment matching. We constructed a conceptual framework of interdependence capturing age, episode duration, and treatment resistance, all variables associated with ECT outcome, and the clinical symptoms of what we coin core depression, ie, depression with psychomotor agitation, retardation, psychotic features, or a combination of the three. The model was validated in a sample of 73 patients with a major depressive episode according to DSM-5 treated twice weekly with ECT (August 2015-January 2018) using path analyses, with the size and direction of all direct and indirect paths being estimated using structural equation modeling. Reduction in Montgomery-Asberg Depression Rating Scale (MADRS) scores during treatment was the ECT outcome measure. The baseline presence of psychomotor agitation, retardation, and/or psychotic symptoms strongly correlated with beneficial ECT outcome (z = 0.84 [SE = 0.17]; P < .001), and the association between age and the effect of ECT appears to be mediated by their presence (z = 0.53 [SE = 0.18]; P = .004). There was no direct correlation between age and ECT response (P = .479), but there was for episode duration and ECT outcome (z = -0.38 [SE = 0.08]; P < .001). ECT is a very effective treatment option for severe depressive disorder, especially for patients suffering from severe depression characterized by the presence of psychomotor agitation, psychomotor retardation, psychotic symptoms, or a combination of these 3 features, with the chance of a beneficial outcome being reduced in patients with a longer episode duration. Age may heretofore have been given too much weight in ECT decision making. ClinicalTrials.gov Identifier: NCT02562846.

Combining machine learning algorithms for prediction of antidepressant treatment response.

ObjectivesPredictors for unfavorable treatment outcome in major depressive disorder (MDD) applicable for treatment selection are still lacking. The database of a longitudinal multicenter study on 1079 acutely depressed patients, performed by the German research network on depression (GRND), allows supervised and unsupervised learning to further elucidate the interplay of clinical and psycho‐sociodemographic variables and their predictive impact on treatment outcome phenotypes.Experimental ProceduresTreatment response was defined by a change of HAM‐D 17‐item baseline score ≥50% and remission by the established threshold of ≤7, respectively, after up to eight weeks of inpatient treatment. After hierarchical symptom clustering and stratification by treatment subtypes (serotonin reuptake inhibitors, tricyclic antidepressants, antipsychotic, and lithium augmentation), prediction models for different outcome phenotypes were computed with random forest in a cross‐center validation design. In total, 88 predictors were implemented.ResultsClustering revealed four distinct HAM‐D subscores related to emotional, anxious, sleep, and appetite symptoms, respectively. After feature selection, classification models reached moderate to high accuracies up to 0.85. Highest accuracies were observed for the SSRI and TCA subgroups and for sleep and appetite symptoms, while anxious symptoms showed poor predictability.ConclusionOur results support a decisive role for machine learning in the management of antidepressant treatment. Treatment‐ and symptom‐specific algorithms may increase accuracies by reducing heterogeneity. Especially, predictors related to duration of illness, baseline depression severity, anxiety and somatic symptoms, and personality traits moderate treatment success. However, prospectives application of machine learning models will be necessary to prove their value for the clinic.

Sequential Combination of Pharmacotherapy and Psychotherapy in Major Depressive Disorder

The sequential model emerged from the awareness that the persistence of residual symptoms and the frequent occurrence of psychiatric comorbidity were both associated with poor long-term outcome of major depressive disorder (MDD).To conduct an updated meta-analysis to examine the association of the sequential combination of pharmacotherapy and psychotherapy with reduced risk of relapse and recurrence in MDD.Keyword searches were conducted in PubMed, PsycInfo, Web of Science, and the Cochrane Library from inception of each database through November 2019. Reference lists from relevant studies were examined using the following keywords: sequential treatment, drugs and psychotherapy, combined treatment, continuation or maintenance, relapse or recurrence and prevention, and depress* or major depress*, selecting adults and randomized controlled trials as additional limits. Authors of selected articles were contacted if needed.Randomized clinical trials examining the effectiveness of the sequential use of psychotherapy following response to acute-phase pharmacotherapy in the treatment of adult remitted patients with MDD were selected independently by 2 reviewers.The methods used fulfilled the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. Data extraction and methodologic quality assessment were conducted independently by the reviewers. Examination of the pooled results was performed based on the random-effects model. Heterogeneity between study results and likelihood of significant publication bias were assessed. Sensitivity analyses and meta-regressions were also run.The primary outcome measures were relapse or recurrence rates of MDD, as defined by study investigators, at the longest available follow-up.Seventeen randomized clinical trials met criteria for inclusion in the meta-analysis, with 1 study yielding 2 comparisons (2283 patients overall, with 1208 patients in a sequential treatment arm and 1075 in a control arm). The pooled risk ratio for relapse/recurrence of MDD was 0.84 (95% CI, 0.74-0.94), suggesting a relative advantage in preventing relapse/recurrence for the sequential combination of treatments compared with control conditions.The results of this systematic review and meta-analysis indicate that the sequential integration of psychotherapy following response to acute-phase pharmacotherapy, alone or combined with antidepressant medication, was associated with reduced risk of relapse and recurrence in MDD. The preventive value of the sequential strategy relies on abatement of residual symptoms and/or increase in psychological well-being. The steps for implementing the sequential approach in remitted patients with recurrent MDD are provided.

The association of body mass index (BMI) with treatment outcomes in patients with major depressive disorder

BackgroundIn patients with major depressive disorder (MDD), poor antidepressant treatment response might be associated with an excessive body mass index (BMI). However, the impact of underweight on treatment response is unclear. Moreover, it has not been studied whether a continuous or categorical BMI measure should be used to predict of treatment response. MethodsPost-hoc analysis of data collected in a clinical trial including adults with MDD (n=202) reporting outcomes of antidepressant medication, i.e. paroxetine, mirtazapine or paroxetine+mirtazapine. Measures included baseline BMI (underweight=BMI <18.5, normal weight:=BMI:18.5-23.9, overweight=BMI≥24) and symptom severity (17-item-Hamilton Depression scale; HAMD-17) assessed at weeks 0, 2, 3, 4, 6 and 8.Univariate analyses were used to explore the effect of baseline BMI on HAMD-17 reduction, response (defined as ≥50% HAMD-17 reduction) and remission (defined as HAMD-17 ≤7) at endpoint. Pearson correlation were used to explore the relationship between body weight, BMI as continuous measure and HAMD-17 reduction. Logistic regression was used to determine the predictors for remission. Multiple linear regression was used to establish the correlation of BMI with change of HAMD-17. Results111 (55.0%) patients were normal weight, 20 (9.9%) were underweight, 71 (35.1%) were overweight. Underweight patients showed the best improvement to antidepressant treatment. Non-remitters had greater body weight and BMI than remitters (P<0.05). The reduction of HAMD-17 was correlated with baseline body weight (r=-0.16, P=0.032) and BMI (r=-0.19, P=0.012). Logistic regression found patients with BMI<24 to be 2 times (OR=1.958, 95%CI: 1.015, 3.774) remitters (P=0.045) than overweight patients. The multiple linear regression showed that the change of HAMD-17 total score decreased with increasing BMI (β=-0.32, P = 0.016). ConclusionWe confirmed that BMI can predict treatment outcomes in MDD. For the first time we found that underweight patients benefit most from antidepressant treatment. The findings may be useful to physicians in their decision regarding the choice of antidepressants according to BMI.