Symptom clusters as predictors of late response to antidepressant treatment

Using Machine Learning to Predict Remission in Patients With Major Depressive Disorder Treated With Desvenlafaxine

BACKGROUND

Major depressive disorder (MDD) is a common and burdensome condition that has low rates of treatment success for each individual treatment. This means that many patients require several medication switches to achieve remission; selecting an effective antidepressant is typically a sequential trial-and-error process. Machine learning techniques may be able to learn models that can predict whether a specific patient will respond to a given treatment, before it is administered. This study uses baseline clinical data to create a machine-learned model that accurately predicts remission status for a patient after desvenlafaxine (DVS) treatment.

METHODS

We applied machine learning algorithms to data from 3,399 MDD patients (90% of the 3,776 subjects in 11 phase-III/IV clinical trials, each described using 92 features), to produce a model that uses 26 of these features to predict symptom remission, defined as an 8-week Hamilton Depression Rating Scale score of 7 or below. We evaluated that learned model on the remaining held-out 10% of the data (n = 377).

RESULTS

Our resulting classifier, a trained linear support vector machine, had a holdout set accuracy of 69.0%, significantly greater than the probability of classifying a patient correctly by chance. We demonstrate that this learning process is stable by repeatedly sampling part of the training dataset and running the learner on this sample, then evaluating the learned model on the held-out instances of the training set; these runs had an average accuracy of 67.0% ± 1.8%.

CONCLUSIONS

Our model, based on 26 clinical features, proved sufficient to predict DVS remission significantly better than chance. This may allow more accurate use of DVS without waiting 8 weeks to determine treatment outcome, and may serve as a first step toward changing psychiatric care by incorporating clinical assistive technologies using machine-learned models.

Machine Learning-Based Definition of Symptom Clusters and Selection of Antidepressants for Depressive Syndrome

The current polythetic and operational criteria for major depression inevitably contribute to the heterogeneity of depressive syndromes. The heterogeneity of depressive syndrome has been criticized using the concept of language game in Wittgensteinian philosophy. Moreover, “a symptom- or endophenotype-based approach, rather than a diagnosis-based approach, has been proposed” as the “next-generation treatment for mental disorders” by Thomas Insel. Understanding the heterogeneity renders promise for personalized medicine to treat cases of depressive syndrome, in terms of both defining symptom clusters and selecting antidepressants. Machine learning algorithms have emerged as a tool for personalized medicine by handling clinical big data that can be used as predictors for subtype classification and treatment outcome prediction. The large clinical cohort data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D), Combining Medications to Enhance Depression Outcome (CO-MED), and the German Research Network on Depression (GRND) have recently began to be acknowledged as useful sources for machine learning-based depression research with regard to cost effectiveness and generalizability. In addition, noninvasive biological tools such as functional and resting state magnetic resonance imaging techniques are widely combined with machine learning methods to detect intrinsic endophenotypes of depression. This review highlights recent studies that have used clinical cohort or brain imaging data and have addressed machine learning-based approaches to defining symptom clusters and selecting antidepressants. Potentially applicable suggestions to realize machine learning-based personalized medicine for depressive syndrome are also provided herein.

Combining machine learning algorithms for prediction of antidepressant treatment response

OBJECTIVES

Predictors 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 PROCEDURES

Treatment 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.

RESULTS

Clustering 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.

CONCLUSION

Our 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.

Clustering patients by depression symptoms to predict venlafaxine ER antidepressant efficacy: Individual patient data analysis

OBJECTIVE

To identify clusters of patients with major depressive disorder (MDD) based on the baseline 17-item Hamilton Rating Scale for Depression (HAM-D₁₇) items and to evaluate the efficacy of venlafaxine extended release (VEN) vs placebo, and the potential effect of dose on efficacy, in each cluster.

METHODS

Cluster analysis was performed to identify clusters based on standardized HAM-D₁₇ item scores of individual patient data at baseline from 9 double-blind, placebo-controlled studies of VEN for MDD. Change from baseline in HAM-D₁₇ total score was analyzed using a mixed-effects model for repeated measures for each cluster; response and remission rates at week 8 were analyzed using logistic regression. Discontinuation rates were also evaluated in each cluster.

RESULTS

In 2599 patients, 3 patient clusters were identified, characterized as High modified Core (mCore) Symptoms/High Anxiety (cluster 1), High mCore Symptoms/Medium Anxiety (cluster 2), and Medium mCore Symptoms/Medium Anxiety (cluster 3). Significant effects of VEN vs placebo were observed on change from baseline in HAM-D₁₇ total score at week 8 for both clusters 1 and 2 (both P < 0.001), but not for cluster 3. In cluster 3, a significant treatment effect of VEN was observed at week 8 in the lower-dose subgroup but not in the higher-dose subgroup. All-cause discontinuation rates were significantly higher in placebo than VEN in each cluster.

CONCLUSIONS

Three unique clusters of patients were identified differing in baseline mCore symptoms and anxiety. Cluster membership may predict efficacy outcomes and contribute to dose effects in patients treated with VEN.

CLINICAL TRIALS REGISTRATION

NCT01441440; other studies included in this analysis were conducted before the requirement to register clinical studies took effect.

Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review

BACKGROUND

No previous study has comprehensively reviewed the application of machine learning algorithms in mood disorders populations. Herein, we qualitatively and quantitatively evaluate previous studies of machine learning-devised models that predict therapeutic outcomes in mood disorders populations.

METHODS

We searched Ovid MEDLINE/PubMed from inception to February 8, 2018 for relevant studies that included adults with bipolar or unipolar depression; assessed therapeutic outcomes with a pharmacological, neuromodulatory, or manual-based psychotherapeutic intervention for depression; applied a machine learning algorithm; and reported predictors of therapeutic response. A random-effects meta-analysis of proportions and meta-regression analyses were conducted.

RESULTS

We identified 639 records: 75 full-text publications were assessed for eligibility; 26 studies (n=17,499) and 20 studies (n=6325) were included in qualitative and quantitative review, respectively. Classification algorithms were able to predict therapeutic outcomes with an overall accuracy of 0.82 (95% confidence interval [CI] of [0.77, 0.87]). Pooled estimates of classification accuracy were significantly greater (p < 0.01) in models informed by multiple data types (e.g., composite of phenomenological patient features and neuroimaging or peripheral gene expression data; pooled proportion [95% CI] = 0.93[0.86, 0.97]) when compared to models with lower-dimension data types (pooledproportion=0.68[0.62,0.74]to0.85[0.81,0.88]).

LIMITATIONS

Most studies were retrospective; differences in machine learning algorithms and their implementation (e.g., cross-validation, hyperparameter tuning); cannot infer importance of individual variables fed into learning algorithm.

CONCLUSIONS

Machine learning algorithms provide a powerful conceptual and analytic framework capable of integrating multiple data types and sources. An integrative approach may more effectively model neurobiological components as functional modules of pathophysiology embedded within the complex, social dynamics that influence the phenomenology of mental disorders.

Feature Selection and Classification of Electroencephalographic Signals: An Artificial Neural Network and Genetic Algorithm Based Approach

Feature selection is an important step in many pattern recognition systems aiming to overcome the so-called curse of dimensionality. In this study, an optimized classification method was tested in 147 patients with major depressive disorder (MDD) treated with repetitive transcranial magnetic stimulation (rTMS). The performance of the combination of a genetic algorithm (GA) and a back-propagation (BP) neural network (BPNN) was evaluated using 6-channel pre-rTMS electroencephalographic (EEG) patterns of theta and delta frequency bands. The GA was first used to eliminate the redundant and less discriminant features to maximize classification performance. The BPNN was then applied to test the performance of the feature subset. Finally, classification performance using the subset was evaluated using 6-fold cross-validation. Although the slow bands of the frontal electrodes are widely used to collect EEG data for patients with MDD and provide quite satisfactory classification results, the outcomes of the proposed approach indicate noticeably increased overall accuracy of 89.12% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.904 using the reduced feature set.

Fatty acid metabolism and its longitudinal relationship with the hypothalamic-pituitary-adrenal axis in major depression: Associations with prospective antidepressant response

BACKGROUND

Metabolism of dietary fatty acids (FAs), and its relationship with the hypothalamic-pituitary-adrenal (HPA)-axis, have been found to be altered in major depressive disorder (MDD). Moreover, indications exist that these factors are associated with antidepressant-response. If we better understand these associations, we might identify novel targets for add-on therapy to increase antidepressant-response, and/or early indicators to improve response prediction.

OBJECTIVE

To determine whether alterations in FA-metabolism, and their relationship with the HPA-axis, are associated with prospective response to the antidepressant paroxetine in MDD.

DESIGN

We first compared 70 initially unmedicated MDD-patients with 51 age- and gender-matched controls at study-entry, regarding salivary cortisol and erythrocyte membrane FAs [omega-3 docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), FA-chain length, -unsaturation and -peroxidizability]. Subsequently, we treated patients with 6 weeks 20mg/day selective serotonin reuptake inhibitor paroxetine. After 6 weeks, we continued this treatment in responders (i.e. showing ≥50% decrease in Hamilton depression rating scale-score), and randomized non-responders to a 6-week, double-blind, placebo-controlled dose-escalation up to 50mg/day. We repeated cortisol and FA-measures in patients after 6 and 12 weeks.

RESULTS

Compared to controls, patients showed higher FA-chain length, FA-unsaturation and FA-peroxidation, and more negative relationships of FA-unsaturation and FA-peroxidation with cortisol. Moreover, these negative relationships were associated with paroxetine nonresponse. Nonresponse was also associated with low DHA, which was related to low fatty fish intake. Furthermore, early responders showed initial low FA-chain length, FA-peroxidation and EPA that increased during the study, while non-responders exhibited opposite patterns.

CONCLUSIONS

FA-metabolism alterations, and their relationship with cortisol, are associated with prospective paroxetine response in MDD, and may therefore form an early indicator of treatment effectiveness. Moreover, dietary fatty fish intake may improve antidepressant response through an effect on FA-metabolism.

Evidence-based guidelines for treating depressive disorders with antidepressants: A revision of the 2008 British Association for Psychopharmacology guidelines

A revision of the 2008 British Association for Psychopharmacology evidence-based guidelines for treating depressive disorders with antidepressants was undertaken in order to incorporate new evidence and to update the recommendations where appropriate. A consensus meeting involving experts in depressive disorders and their management was held in September 2012. Key areas in treating depression were reviewed and the strength of evidence and clinical implications were considered. The guidelines were then revised after extensive feedback from participants and interested parties. A literature review is provided which identifies the quality of evidence upon which the recommendations are made. These guidelines cover the nature and detection of depressive disorders, acute treatment with antidepressant drugs, choice of drug versus alternative treatment, practical issues in prescribing and management, next-step treatment, relapse prevention, treatment of relapse and stopping treatment. Significant changes since the last guidelines were published in 2008 include the availability of new antidepressant treatment options, improved evidence supporting certain augmentation strategies (drug and non-drug), management of potential long-term side effects, updated guidance for prescribing in elderly and adolescent populations and updated guidance for optimal prescribing. Suggestions for future research priorities are also made.

Hamilton depression rating subscales to predict antidepressant treatment outcome in the early course of treatment

BACKGROUND

Hamilton depression rating scale (HAMD) subscales provide an economic alternative for the full scale; however, their ability to detect onset of improvement in the early course of treatment (EI) has not yet been researched. The present study investigated in patients with major depression (MD) whether the subscales are a comparable option to predict treatment remission in the early course of treatment.

METHODS

Based on data from 210 MD patients of a 6-week randomised, placebo-controlled trial comparing mirtazapine (MIR) and paroxetine (PAR), the discriminative and predictive validity of EI for (stable) remission at treatment end was evaluated for seven subscales and the HAMD17 in the total and in treatment subgroups (MIR vs. PAR). Receiver operating characteristics (ROC) curves (at week 2) and the Clinical Global Impression scales (CGI) (at study endpoint) were used to validate the 20% EI criterion for the subscales.

RESULTS

Only the Evans6 and Toronto7 subscale had almost the same predictive value as the HAMD17 (e.g., sensitivities stable remission Evans6/Toronto7: 96/95% vs. 96% HAMD17). The optimal cut-off for EI to predict remission was just below 20% for most subscales and slightly over 20% for stable remission.

LIMITATIONS

Study sample representativeness, non-independence of subscales, missing external validation criterion, lack of control group.

CONCLUSIONS

The Evans6 and Toronto7 subscales are valuable alternatives in situations, where economic aspects play a larger role. A sum score reduction of ≥20% as definition for EI seems also appropriate for the HAMD subscales, in the total as well as in the antidepressant subgroups.

Neural Network Based Response Prediction of rTMS in Major Depressive Disorder Using QEEG Cordance

OBJECTIVE

The combination of repetitive transcranial magnetic stimulation (rTMS), a non-pharmacological form of therapy for treating major depressive disorder (MDD), and electroencephalogram (EEG) is a valuable tool for investigating the functional connectivity in the brain. This study aims to explore whether pre-treating frontal quantitative EEG (QEEG) cordance is associated with response to rTMS treatment among MDD patients by using an artificial intelligence approach, artificial neural network (ANN).

METHODS

The artificial neural network using pre-treatment cordance of frontal QEEG classification was carried out to identify responder or non-responder to rTMS treatment among 55 MDD subjects. The classification performance was evaluated using k-fold cross-validation.

RESULTS

The ANN classification identified responders to rTMS treatment with a sensitivity of 93.33%, and its overall accuracy reached to 89.09%. Area under Receiver Operating Characteristic (ROC) curve (AUC) value for responder detection using 6, 8 and 10 fold cross validation were 0.917, 0.823 and 0.894 respectively.

CONCLUSION

Potential utility of ANN approach method can be used as a clinical tool in administering rTMS therapy to a targeted group of subjects suffering from MDD. This methodology is more potentially useful to the clinician as prediction is possible using EEG data collected before this treatment process is initiated. It is worth using feature selection algorithms to raise the sensitivity and accuracy values.

The impacts of migraine and anxiety disorders on painful physical symptoms among patients with major depressive disorder

Background

No study has simultaneously investigated the impacts of migraine and anxiety disorders on painful physical symptoms (PPS) among patients with major depressive disorder (MDD). The study aimed to investigate this issue.

Methods

This open-label study enrolled 155 outpatients with MDD, who were then treated with venlafaxine 75 mg per day for four weeks. Eighty-five participants with good compliance completed the treatment. Migraine was diagnosed according to the International Classification of Headache Disorders. MDD and anxiety disorders were diagnosed using the Structured Clinical Interview for DSM-IV-TR. The visual analog scale (VAS) was used to evaluate the severity of eight PPS. Multiple linear and logistic regressions were used to investigate the impacts of migraine and anxiety disorders on PPS.

Results

Compared with patients without migraine, patients with migraine had a greater severity of PPS at baseline and post-treatment. After controlling for demographic variables and depressive severity, migraine independently predicted the intensities of eight PPS at baseline and four PPS post-treatment. Moreover, migraine independently predicted poorer treatment responses of chest pain and full remission of pains in the head, chest, neck and/or shoulder. Anxiety disorders predicted less full remission of pains in the abdomen and limbs.

Conclusion

Migraine and anxiety disorders have negative impacts on PPS among patients with MDD. Integrating the treatment of migraine and anxiety disorders into the management of depression might help to improve PPS and the prognosis of MDD.

Analysis of Brain Functional Changes in High-Frequency Repetitive Transcranial Magnetic Stimulation in Treatment-Resistant Depression

Repetitive transcranial magnetic stimulation (rTMS) is a treatment procedure that uses magnetic fields to stimulate nerve cells in the brain, and is associated with significant improvements in clinical symptoms of major depressive disorder (MDD). The effect of rTMS treatment on the brain can be evaluated by cordance, a quantitative electroencephalography (QEEG) method that extracts information from absolute and relative power of EEG spectra. In this study, to analyze brain functional changes, pre- and post-rTMS, QEEG data were collected from 6 frontal electrodes (Fp1, Fp2, F3, F4, F7, and F8) in 2 slow bands (delta and theta) for 55 MDD subjects. To examine brain changes, cordance scores were determined, using repeated-measures analysis of variance (ANOVA). High-frequency rTMS was associated with cordance decrease in left frontal and right prefrontal regions in both delta and theta for nonresponders; it was associated with cordance increase in all right and left frontal electrodes, except F8, for responders.

Ant Colony Optimization Based Feature Selection Method for QEEG Data Classification

OBJECTIVE

Many applications such as biomedical signals require selecting a subset of the input features in order to represent the whole set of features. A feature selection algorithm has recently been proposed as a new approach for feature subset selection.

METHODS

Feature selection process using ant colony optimization (ACO) for 6 channel pre-treatment electroencephalogram (EEG) data from theta and delta frequency bands is combined with back propagation neural network (BPNN) classification method for 147 major depressive disorder (MDD) subjects.

RESULTS

BPNN classified R subjects with 91.83% overall accuracy and 95.55% subjects detection sensitivity. Area under ROC curve (AUC) value after feature selection increased from 0.8531 to 0.911. The features selected by the optimization algorithm were Fp1, Fp2, F7, F8, F3 for theta frequency band and eliminated 7 features from 12 to 5 feature subset.

CONCLUSION

ACO feature selection algorithm improves the classification accuracy of BPNN. Using other feature selection algorithms or classifiers to compare the performance for each approach is important to underline the validity and versatility of the designed combination.

Depression symptom clusters and their predictive value for treatment outcomes: results from an individual patient data meta-analysis of duloxetine trials

We evaluated individual patient data from phase II to IV clinical trials of duloxetine in major depressive disorder (MDD) (34 studies, 13,887 patients). Our goal was to identify clusters of patients with similar depressive symptom patterns at baseline, as measured by the 17-item Hamilton Depression Rating Scale (HAMD-17), and to investigate their respective predictive value of outcomes as measured by the HAMD-17 total score. Five clusters were identified at baseline: 1) "Lack of insight"; 2) "Sleep/sexual/somatic"; 3) "Typical MDD"; 4) "Gastrointestinal/weight loss"; and 5) "Mild MDD". However, it should be noted that cluster descriptors are not mutually exclusive. Analyses of the HAMD-17 total score results over time were performed using the 18 randomized placebo and/or actively controlled studies representing 6723 patients. At the end of acute treatment (ranging from 4 to 36 weeks), different levels of effect sizes for active therapy (64.5% duloxetine) vs. placebo were detected by cluster. In 3 out of 5 clusters (representing about 80% of the patients), the effect size was significantly different from 0, in favor of active therapy. The effect size was largest in those clusters with severe somatic symptoms ("Sleep/sexual/somatic" cluster [-0.4170], and "Gastrointestinal/weight loss" cluster [-0.338]). In conclusion, our cluster analysis identified 5 clinically relevant MDD patient clusters with specific mean treatment outcomes. Identification of MDD clusters may help to improve outcomes by adapting MDD treatment to particular clinical profiles.

Cognitive and behavioural therapy for insomnia (CBT-I) in psychiatric populations: a systematic review

Insomnia is highly co-morbid with psychiatric disorders, making it a frequent issue in treatment planning in psychiatric clinics. Research has also shown that although insomnia may originally precede or be a consequence of a psychiatric disorder, insomnia likely becomes semi-independent, and may exacerbate those disorders if it is not addressed, leading to reduced treatment response. Cognitive behavioural therapy for insomnia (CBT-I) is now recommended as the first line of treatment of primary insomnia. The research reviewed below indicates that CBT-I in patients with co-morbid depression, anxiety, post-traumatic stress disorder (PTSD), and substance abuse disorders is generally effective for insomnia and sometimes the co-morbid disorder as well. Although more research is needed before definitive recommendations can be made, it appears as though CBT-I is a viable approach to treating the patient with co-morbid insomnia and psychiatric disorders.

Family history of major depression and residual symptoms in responder and non-responder depressed patients

BACKGROUND

Little is known about the extent to which a family history of major depression (MD) affects residual depressive symptoms in responder and non-responder patients suffering from MD.

METHODS

Nine hundred eighty-six patients with MD were recruited within the context of a large multicenter project. Information about the family history of MD, as well as about total depressive symptoms and specific depressive clusters, was collected and analyzed.

RESULTS

No significant difference was observed in overall depressive symptoms between patients with and those without a family history of MD. However, non-responder patients with a family history of MD showed significantly higher scores in core symptoms as compared with responder patients without a family history of MD.

CONCLUSIONS

Non-responder MD patients with a positive family history of MD could represent a slightly different sub-group of MD patients with more consistent core depressive symptoms as compared with responder patients without a family history of MD. However, taking into account the retrospective assessment of data, the use of positive or negative family history as a dichotomous indicator of familial loading and the cross-sectional design of the present study, further research is needed to draw more definitive conclusions.

Inhibition of glycine transporter-I as a novel mechanism for the treatment of depression

BACKGROUND

Antidepressants, aiming at monoaminergic neurotransmission, exhibit delayed onset of action, limited efficacy, and poor compliance. Glutamatergic neurotransmission is involved in depression. However, it is unclear whether enhancement of the N-methyl-D-aspartate (NMDA) subtype glutamate receptor can be a treatment for depression.

METHODS

We studied sarcosine, a glycine transporter-I inhibitor that potentiates NMDA function, in animal models and in depressed patients. We investigated its effects in forced swim test, tail suspension test, elevated plus maze test, novelty-suppressed feeding test, and chronic unpredictable stress test in rats and conducted a 6-week randomized, double-blinded, citalopram-controlled trial in 40 patients with major depressive disorder. Clinical efficacy and side effects were assessed biweekly, with the main outcomes of Hamilton Depression Rating Scale, Global Assessment of Function, and remission rate. The time course of response and dropout rates was also compared.

RESULTS

Sarcosine decreased immobility in the forced swim test and tail suspension test, reduced the latency to feed in the novelty-suppressed feeding test, and reversed behavioral deficits caused by chronic unpredictable stress test, which are characteristics for an antidepressant. In the clinical study, sarcosine substantially improved scores of Hamilton Depression Rating Scale, Clinical Global Impression, and Global Assessment of Function more than citalopram treatment. Sarcosine-treated patients were much more likely and quicker to remit and less likely to drop out. Sarcosine was well tolerated without significant side effects.

CONCLUSIONS

Our preliminary findings suggest that enhancing NMDA function can improve depression-like behaviors in rodent models and in human depression. Establishment of glycine transporter-I inhibition as a novel treatment for depression waits for confirmation by further proof-of-principle studies.

SSRI versus bupropion effects on symptom clusters in suicidal depression: post hoc analysis of a randomized clinical trial

OBJECTIVE

Identifying the depression symptoms most closely associated with suicidal thoughts and which medications provide the fastest depression relief may help suicide prevention.

METHOD

Post hoc analysis of data from a randomized, double-blind, 8-week clinical trial of the selective serotonin reuptake inhibitor paroxetine controlled release (n = 36) versus the norepinephrine-dopamine reuptake inhibitor bupropion extended release (n = 38) was conducted in patients with DSM-IV major depressive disorder and past suicide attempt or current suicidal thoughts. Treatment effects on Hamilton Depression Rating Scale (HDRS) and Beck Depression Inventory symptom clusters were compared. We hypothesized that paroxetine would demonstrate a superior effect on nonsuicidal, affective/cognitive depression symptom clusters that our prior work found to be associated with suicidal thoughts and attempts. Data were collected from February 2005 to January 2010.

RESULTS

There was a treatment main effect on HDRS psychic depression (depressed mood, guilt, retardation, helpless, hopeless, worthless) (estimate = -2.2; 95% CI, -3.2 to -1.1; t67.16 = -4.01; P < .001), one of the clusters most strongly correlated to suicidal ideation. The net drug effect demonstrated that mean psychic depression score was 2.2 points lower after 1 week of paroxetine compared to bupropion treatment. The significance level of this effect was P < .001 at weeks 1 and 2, P = .012 at week 3 and P = .051 at week 4. Results for other depression scale factors were nonsignificant (P > .05).

CONCLUSIONS

The results require replication but suggest a pathway by which selective serotonin reuptake inhibitor treatment may exert a stronger effect compared with norepinephrine-dopamine reuptake inhibitor treatment on reduction of suicidal thoughts during initial weeks of pharmacotherapy in these higher risk patients.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT00429169.

Percentage reduction of depression severity versus absolute severity after initial weeks of treatment to predict final response or remission

AIM

Percentage reduction of depression severity has been used to predict both response and remission of major depression. We aimed to compare the accuracy to predict response or remission by percentage reduction of depression score or depression score after initial weeks of treatment.

METHODS

The subjects were 126 depressed inpatients who received 20 mg/day fluoxetine for 6 weeks. Symptom severity was assessed using the 17-item Hamilton Depression Rating Scale (HAMD-17). Response was defined as a reduction of 50% or more of the HAMD-17. Remission was defined as a score of ≤7 of the HAMD-17. At weeks 1, 2, 3 and 4, the percentages of HAMD-17 score reduction, the percentages of mood cluster score reduction, HAMD-17 scores, and mood cluster scores were regarded as potential predictors. The receiver operating characteristic curve was applied to determine the cut-off point of predictors at weeks 1, 2, 3, and 4.

RESULTS

One-hundred and seven patients completed the 6-week trial. At weeks 1, 2, 3, and 4, percentages of HAMD-17 score reduction or HAMD-17 scores were the best predictors of responder or remitters, respectively. Using the percentage of HAMD-17 score reduction at each assessment as a predictor of response generated a larger area under the curve than other predictors. Conversely, applying the absolute HAMD-17 score at each assessment as a predictor of remission had the largest area under the curve.

CONCLUSION

Applying percentage of reduction in depression severity during the early weeks of treatment can predict response, and it is reasonable to apply depression severity to predict remission.

A randomized, double-blind placebo-controlled trial of oral creatine monohydrate augmentation for enhanced response to a selective serotonin reuptake inhibitor in women with major depressive disorder

OBJECTIVE

Antidepressants targeting monoaminergic neurotransmitter systems, despite their immediate effects at the synaptic level, usually require several weeks of administration to achieve clinical efficacy. The authors propose a strategy of adding creatine monohydrate (creatine) to a selective serotonin reuptake inhibitor (SSRI) in the treatment of patients with major depressive disorder. Such augmentation may lead to a more rapid onset of antidepressant effects and a greater treatment response, potentially by restoring brain bioenergetics at the cellular level.

METHOD

Fifty-two women with major depressive disorder were enrolled in an 8-week double-blind placebo-controlled clinical trial and randomly assigned to receive escitalopram in addition to either creatine (5 g/day, N=25) or placebo (N=27). Efficacy was primarily assessed by changes in the Hamilton Depression Rating Scale (HAM-D) score.

RESULTS

In comparison to the placebo augmentation group, patients receiving creatine augmentation showed significantly greater improvements in HAM-D score, as early as week 2 of treatment. This differential improvement favoring creatine was maintained at weeks 4 and 8. There were no differences between treatment groups in the proportion of patients who discontinued treatment prematurely (creatine: N=8, 32.0%; placebo: N=5, 18.5%) or in the overall frequency of all reported adverse events (creatine: 36 events; placebo: 45 events).

CONCLUSIONS

The current study suggests that creatine augmentation of SSRI treatment may be a promising therapeutic approach that exhibits more rapid and efficacious responses in women with major depressive disorder.

Predictors of remission in depression to individual and combined treatments (PReDICT): study protocol for a randomized controlled trial

BACKGROUND

Limited controlled data exist to guide treatment choices for clinicians caring for patients with major depressive disorder (MDD). Although many putative predictors of treatment response have been reported, most were identified through retrospective analyses of existing datasets and very few have been replicated in a manner that can impact clinical practice. One major confound in previous studies examining predictors of treatment response is the patient's treatment history, which may affect both the predictor of interest and treatment outcomes. Moreover, prior treatment history provides an important source of selection bias, thereby limiting generalizability. Consequently, we initiated a randomized clinical trial designed to identify factors that moderate response to three treatments for MDD among patients never treated previously for the condition.

METHODS/DESIGN

Treatment-naïve adults aged 18 to 65 years with moderate-to-severe, non-psychotic MDD are randomized equally to one of three 12-week treatment arms: (1) cognitive behavior therapy (CBT, 16 sessions); (2) duloxetine (30-60 mg/d); or (3) escitalopram (10-20 mg/d). Prior to randomization, patients undergo multiple assessments, including resting state functional magnetic resonance imaging (fMRI), immune markers, DNA and gene expression products, and dexamethasone-corticotropin-releasing hormone (Dex/CRH) testing. Prior to or shortly after randomization, patients also complete a comprehensive personality assessment. Repeat assessment of the biological measures (fMRI, immune markers, and gene expression products) occurs at an early time-point in treatment, and upon completion of 12-week treatment, when a second Dex/CRH test is also conducted. Patients remitting by the end of this acute treatment phase are then eligible to enter a 21-month follow-up phase, with quarterly visits to monitor for recurrence. Non-remitters are offered augmentation treatment for a second 12-week course of treatment, during which they receive a combination of CBT and antidepressant medication. Predictors of the primary outcome, remission, will be identified for overall and treatment-specific effects, and a statistical model incorporating multiple predictors will be developed to predict outcomes.

DISCUSSION

The PReDICT study's evaluation of biological, psychological, and clinical factors that may differentially impact treatment outcomes represents a sizeable step toward developing personalized treatments for MDD. Identified predictors should help guide the selection of initial treatments, and identify those patients most vulnerable to recurrence, who thus warrant maintenance or combination treatments to achieve and maintain wellness.

Negative impact of migraine on quality of life after 4 weeks of treatment in patients with major depressive disorder

AIM

The impact of migraine on health-related quality of life (HRQoL) among patients with major depressive disorder (MDD) after acute antidepressant treatment has not been addressed. The aim of the present study was to investigate whether or not the negative impact of migraine on HRQoL among outpatients with MDD continued to have an effect after 4 weeks of venlafaxine treatment.

METHODS

A total of 135 outpatients with MDD were enrolled, who were then treated with venlafaxine 75 mg per day for 4 weeks in the present open-label study. Migraine was diagnosed based on the International Classification of Headache Disorders (2nd edn). Changes in Short-Form 36 (SF-36) and Hamilton Depression Rating Scale (HAMD) scores were the outcome measures. Multiple linear regression was used to assess whether migraine was an independent factor predicting SF-36 score after treatment.

RESULTS

Seventy-two participants (18M/54F) completed the 4-week treatment. Subjects with migraine had a poorer HRQoL in terms of bodily pain and mental health at baseline. Subjects with and without migraine showed significant improvement in all SF-36 subscales and depression after treatment, but subjects with migraine still had a poorer HRQoL regarding bodily pain and physical functioning after treatment as compared with those without migraine. Migraine could predict a negative outcome after treatment in the subscales of physical functioning, role limitations-physical, and role limitations-emotional.

CONCLUSIONS

Migraine may have a negative impact on the improvement of partial SF-36 subscales, especially on functional recovery, after acute treatment among outpatients with MDD. Whether additional intervention besides antidepressant treatment for migraine is indicated may need further study.

Time course of improvement of different symptom clusters in patients with major depression and pain treated with duloxetine or placebo

OBJECTIVE

This post hoc analysis assessed improvements in a broad range of psychopathological dimensions and in interference of pain with functioning as well as the time course of these improvements in patients with major depressive disorder (MDD) and pain treated with duloxetine versus placebo.

RESEARCH DESIGN AND METHODS

Data were derived from an 8-week, double-blind, placebo-controlled study in adult outpatients with MDD and non-specific physical pain. Mean times between improvement in Brief Pain Inventory (BPI) pain severity and interference of pain with functioning, depression severity, and dimensions of the Symptom Checklist-90 Revised (SCL-90-R) subscales were evaluated by responder analysis.

RESULTS

For all SCL-90-R subscores, a higher percentage of duloxetine-treated patients reached responder status (50% improvement) as compared to placebo, of these anger/hostility and interpersonal sensitivity had the highest response rates. In the duloxetine-treated group, response for anger/hostility, phobic anxiety, psychoticism, and most items assessing interference of pain with functioning was reached earlier than response for pain severity. The times to response for Montgomery-Asberg Depression Rating Scale (MADRS) and for pain severity were similar. In the placebo-treated group, times to response for depression, anxiety, and MADRS were longer than response for pain severity.

CONCLUSIONS

Duloxetine, and to a lesser degree placebo, not only improved depressive symptomatology and pain severity but also a much broader range of psychopathological symptoms. Time courses of improvements were different for duloxetine and placebo, in that depression and interference of pain with functioning improved earlier than pain severity in duloxetine-treated patients but not in placebo-treated patients. These results suggest that time to response is a valuable means of characterizing treatment effects.

LIMITATIONS

Pain was only assessed as a symptom and no further clinical diagnosis for pain syndromes were performed. CLINICAL TRIAL REGISTRY ID: www.clinicaltrial.gov - NCT00191919.

Anxious depression and early changes in the HAMD-17 anxiety-somatization factor items and antidepressant treatment outcome

The objective of this study was to assess the relationship between early changes in anxiety/somatization symptoms and treatment outcome among major depressive disorder patients during a 12-week trial of fluoxetine. We also examined the relationship between anxious depression and treatment response. Five hundred and ten major depressive disorder patients received 12 weeks of fluoxetine with flexible dosing [target dosages: 10 mg/day (week 1), 20 mg/day (weeks 2-4), 40 mg/day (weeks 4-8), and 60 mg/day (weeks 5-12)]. We assessed the relationship between early changes in 17-item Hamilton Rating Scale for Depression (HAMD-17)-anxiety/somatization factor items and depressive remission, as well as whether anxious depression at baseline predicted remission at study endpoint. Baseline HAMD-17 scores were considered as covariates and the Bonferroni correction (P < or = 0.008) was used for multiple comparisons. Adjusting for baseline HAMD-17 scores, patients who experienced greater early improvement in somatic symptoms (gastrointestinal) were significantly more likely to attain remission (HAMD-17 <8) at endpoint than those without early improvement (P=0.006). Early changes in the remaining items did not predict remission, nor did anxious depression at baseline. In conclusion, among the anxiety/somatization factor items, only early changes in somatic symptoms (gastrointestinal) predicted remission. Future studies are warranted to further investigate this relationship as well as that between anxious depression and treatment outcome.

The change of prefrontal QEEG theta cordance as a predictor of response to bupropion treatment in patients who had failed to respond to previous antidepressant treatments

The aim of the study was to examine whether the reduction of theta prefrontal quantitative EEG (QEEG) cordance after one week of bupropion administration is a predictor of response to a 4-week treatment in patients that had failed to respond to previous antidepressant treatments.

METHOD

EEG data of 18 inpatients were monitored at baseline and after one week. QEEG cordance was computed at 3 frontal electrodes (Fp1, Fp2, Fz). Response to treatment was defined as a >/=50% reduction of MADRS score.

RESULTS

Nine of the eleven responders and one of the seven non-responders showed decreased prefrontal cordance value after the first week of treatment (p=0.01). Positive and negative predictive values of cordance reduction for the prediction of response to the treatment were 0.9 and 0.75, respectively.

CONCLUSION

Similar to other antidepressants, the reduction of prefrontal QEEG cordance might be helpful in the prediction of the acute outcome of bupropion treatment.

Prediction of response to medication and cognitive therapy in the treatment of moderate to severe depression

A recent randomized controlled trial found nearly equivalent response rates for antidepressant medications and cognitive therapy in a sample of moderate to severely depressed outpatients. In this article, the authors seek to identify the variables that were associated with response across both treatments as well as variables that predicted superior response in one treatment over the other. The sample consisted of 180 depressed outpatients: 60 of whom were randomly assigned to cognitive therapy; 120 were assigned to antidepressant medications. Treatment was provided for 16 weeks. Chronic depression, older age, and lower intelligence each predicted relatively poor response across both treatments. Three prescriptive variables-marriage, unemployment, and having experienced a greater number of recent life events-were identified, and each predicted superior response to cognitive therapy relative to antidepressant medications. Thus, 6 markers of treatment outcome were identified, each of which might be expected to carry considerable clinical utility. The 3 prognostic variables identify subgroups that might benefit from alternative treatment strategies; the 3 prescriptive variables identify groups who appear to respond particularly well to cognitive therapy.

Low frequency (1-Hz), right prefrontal repetitive transcranial magnetic stimulation (rTMS) compared with venlafaxine ER in the treatment of resistant depression: a double-blind, single-centre, randomized study

BACKGROUND

Previous studies have shown effectiveness of repetitive transcranial magnetic stimulation (rTMS) in the treatment of depression. This double-blind study compared efficacy of l Hz rTMS over the right prefrontal dorsolateral cortex with venlafaxine ER in the treatment of resistant depression.

METHODS

A total of 60 inpatients with depressive disorder (DSM-IV criteria), who previously did not respond to at least one antidepressant treatment, were randomly assigned to 1 Hz rTMS with placebo and venlafaxine ER with sham rTMS for 4 weeks. The primary outcome measure was score change in the Montgomery-Asberg Depression Rating Scale (MADRS). We also used Clinical Global Impression (CGI) and Beck Depressive. Inventory-Short Form (BDI-SF). The response was defined as a >or=50% reduction of MADRS score.

RESULTS

There were no significant differences between treatment groups in MADRS (p=0.38), BDI-SF (p=0.56) and CGI (p=0.17) scores from baseline to endpoint. Response rates for rTMS (33%) and venlafaxine (39%) as well as remission (MADRS score<or=10 points) rates (19% vs. 23%) and drop-out rate did not differ between treatment groups. There were significant reductions of MADRS, CGI and BDI-SF scores in both groups.

LIMITATIONS

Small sample size. No placebo arm was included for ethical reasons, because both treatments have previously been reported to be more effective than placebo. Relatively short duration of antidepressant treatment.

CONCLUSION

The findings of this study suggest that, at least in the acute treatment, the right sided rTMS produces clinically relevant reduction of depressive symptomatology in patients with resistant depression comparable to venlafaxine ER. Larger sample sizes are required to confirm these results.

Fluoxetine and olanzapine combination therapy in treatment-resistant major depression: review of efficacy and safety data

BACKGROUND

There has been growing evidence supporting the use of atypical antipsychotic drugs as adjunctive treatments in patients with major depression who fail to respond adequately to antidepressants.

OBJECTIVE

To review the efficacy and safety data for one such combination, fluoxetine (FLX) + olanzapine (OLZ) in treatment-resistant depression (TRD).

METHODS

We reviewed published randomized, controlled acute-phase studies, as well as available long-term clinical studies.

RESULTS/CONCLUSIONS

In each acute-phase study (n = 5), FLX/OLZ group experienced rapid antidepressant effects and, in two of these studies, resulted in significantly greater improvement at study end point compared with antidepressant monotherapy. These effects were strongest when TRD was defined as having failed at least two antidepressant trials during the current depressive episode. FLX + OLZ was generally well tolerated; however, increases in body weight and prolactin levels with FLX + OLZ were greater than that of antidepressant monotherapy groups and were similar to OLZ monotherapy. However, changes in random total cholesterol were also greatest for FLX + OLZ and were greater in magnitude than that of OLZ or FLX monotherapy. Long-term effectiveness/safety data are sparse, and comparison trials and sequential treatment studies involving FLX + OLZ and other antidepressant-atypical antipsychotic combinations are lacking. Thus, the exact place of FLX + OLZ among other available options for TRD is difficult to determine.

Clinical practice recommendations for depression

OBJECTIVE

To provide clinically relevant evidence-based recommendations for the management of depression in adults that are informative, easy to assimilate and facilitate clinical decision making.

METHOD

A comprehensive literature review of over 500 articles was undertaken using electronic database search engines (e.g. MEDLINE, PsychINFO and Cochrane reviews). In addition articles, book chapters and other literature known to the authors were reviewed. The findings were then formulated into a set of recommendations that were developed by a multidisciplinary team of clinicians who routinely deal with mood disorders. The recommendations then underwent consultative review by a broader advisory panel that included experts in the field, clinical staff and patient representatives.

RESULTS

The clinical practice recommendations for depression (Depression CPR) summarize evidence-based treatments and provide a synopsis of recommendations relating to each phase of the illness. They are designed for clinical use and have therefore been presented succinctly in an innovative and engaging manner that is clear and informative.

CONCLUSION

These up-to-date recommendations provide an evidence-based framework that incorporates clinical wisdom and consideration of individual factors in the management of depression. Further, the novel style and practical approach should promote uptake and implementation.

Olanzapine and fluoxetine combination therapy for treatment-resistant depression: review of efficacy, safety, and study design issues

Treatment-resistant depression (TRD) is a common occurrence in clinical practice. Up to 30% of patients with major depression do not respond to conventional antidepressant treatment, while a significantly greater number of patients experience only partial symptom reduction. Numerous strategies may be applied by the practicing clinician to overcome limitations in the effectiveness of antidepressant monotherapy, including combining drug treatment with evidence-supported psychotherapies, combining antidepressants (combination pharmacotherapy), and combining antidepressants with other non-antidepressant psychotropic medications (augmentation treatment). One such augmentation strategy, the combination of the selective serotonin reuptake inhibitor, fluoxetine (FLX), with the atypical antipsychotic drug, olanzapine (OLZ), is supported by the results of four randomized, double-blind, acute phase studies of patients who had responded inadequately to antidepressant monotherapy. In each study, the FLX/OLZ combination caused rapid reduction in Montgomery-Asberg Depression Rating scale scores, with two of the four studies showing significantly greater improvement than antidepressant monotherapy at study endpoint. Effects of the FLX/OLZ combination were strongest in cases where failure to respond to two antidepressants prior to randomization was established during the current depressive episode. The FLX/OLZ combination was well-tolerated; however, body weight gain and increases in prolactin were greater than that of the antidepressant monotherapy groups, and were comparable to that of OLZ monotherapy. While effective during acute-phase treatment, questions remain regarding the long-term efficacy and safety of FLX/OLZ relative to antidepressant monotherapy and other combination strategies. Efforts aimed at determining the placement of FLX/OLZ among the available options for addressing TRD are limited by lack of comparison and sequential treatment studies. Important aspects of study design and directions for future research are discussed.

Mourning and melancholia revisited: correspondences between principles of Freudian metapsychology and empirical findings in neuropsychiatry

Freud began his career as a neurologist studying the anatomy and physiology of the nervous system, but it was his later work in psychology that would secure his place in history. This paper draws attention to consistencies between physiological processes identified by modern clinical research and psychological processes described by Freud, with a special emphasis on his famous paper on depression entitled 'Mourning and melancholia'. Inspired by neuroimaging findings in depression and deep brain stimulation for treatment resistant depression, some preliminary physiological correlates are proposed for a number of key psychoanalytic processes. Specifically, activation of the subgenual cingulate is discussed in relation to repression and the default mode network is discussed in relation to the ego. If these correlates are found to be reliable, this may have implications for the manner in which psychoanalysis is viewed by the wider psychological and psychiatric communities.

Evidence-based guidelines for treating depressive disorders with antidepressants: a revision of the 2000 British Association for Psychopharmacology guidelines

A revision of the 2000 British Association for Psychopharmacology evidence-based guidelines for treating depressive disorders with antidepressants was undertaken to incorporate new evidence and to update the recommendations where appropriate. A consensus meeting involving experts in depressive disorders and their management was held in May 2006. Key areas in treating depression were reviewed, and the strength of evidence and clinical implications were considered. The guidelines were drawn up after extensive feedback from participants and interested parties. A literature review is provided, which identifies the quality of evidence to inform the recommendations, the strength of which are based on the level of evidence. These guidelines cover the nature and detection of depressive disorders, acute treatment with antidepressant drugs, choice of drug versus alternative treatment, practical issues in prescribing and management, next-step treatment, relapse prevention, treatment of relapse, and stopping treatment.

Celecoxib as an adjunct in the treatment of depressive or mixed episodes of bipolar disorder: a double-blind, randomized, placebo-controlled study

OBJECTIVE

To investigate whether the cox-2 inhibitor celecoxib has antidepressant effects in bipolar disorder (BD) patients during depressive or mixed phases.

METHODS

We studied 28 DSM-IV BD patients who were experiencing a depressive or mixed episode and were on a stable dose of a mood stabilizer or atypical antipsychotic medication. Subjects were randomized to receive 6 weeks of double-blind placebo or celecoxib (400 mg/day) treatment. Current mood stabilizer or antipsychotic medication remained at the same doses during the trial.

RESULTS

Intention-to-treat analysis showed that the patients receiving celecoxib had lower Hamilton Depression Rating Scale (HamD) scores after 1 week of treatment compared to the patients receiving placebo, but this difference was not statistically significant (p = 0.09). The improvement in the first week of treatment was statistically significant when the analysis included only the subjects who completed the full 6-week trial (p = 0.03). The two groups did not differ significantly on depressive or manic symptoms from the second week until the end of the trial. Celecoxib was well tolerated with the exception of two subjects who dropped out of the study due to rash.

CONCLUSIONS

Our findings suggest that adjunctive treatment with celecoxib may produce a rapid-onset antidepressant effect in BD patients experiencing depressive or mixed episodes.

Clinical Management of Symptom Clusters

OBJECTIVES

To discuss the current evidence for the management of symptoms in clusters and future directions in symptom cluster management research.

DATA SOURCES

Research and review articles.

CONCLUSION

Currently no research has been reported on management of formally identified symptom clusters in oncology. Several researchers have described psychoeducational and exercise interventions that have decreased multiple concurrent symptoms. Understanding common biological mechanisms that underlie symptom clusters, such as inflammatory pathways, may suggest more effective treatments for symptom clusters.

IMPLICATIONS FOR NURSING PRACTICE

Clinicians and researchers need to collaborate to better understand symptom clusters and develop effective interventions.

Dissecting the determinants of depressive disorders outcome: an in depth analysis of two clinical cases

Clinicians face everyday the complexity of depression. Available pharmacotherapies and psychotherapies improve patients suffering in a large part of subjects, however up to half of patients do not respond to treatment. Clinicians may forecast to a good extent if a given patient will respond or not, based on a number of data and sensations that emerge from face to face assessment. Conversely, clinical predictors of non response emerging from literature are largely unsatisfactory. Here we try to fill this gap, suggesting a comprehensive assessment of patients that may overcome the limitation of standardized assessments and detecting the factors that plausibly contribute to so marked differences in depressive disorders outcome. For this aim we present and discuss two clinical cases. Mr. A was an industrial manager who came to psychiatric evaluation with a severe depressive episode. His employment was demanding and the depressive episode undermined his capacity to manage it. Based on standardized assessment, Mr. A condition appeared severe and potentially dramatic. Mrs. B was a housewife who came to psychiatric evaluation with a moderate depressive episode. Literature predictors would suggest Mrs. B state as associated with a more favourable outcome. However the clinician impression was not converging with the standardized assessment and in fact the outcome will reverse the prediction based on the initial formal standard evaluation. Although the present report is based on two clinical cases and no generalizability is possible, a more detailed analysis of personality, temperament, defense mechanisms, self esteem, intelligence and social adjustment may allow to formalize the clinical impressions used by clinicians for biologic and pharmacologic studies.