Advancements in the treatment of depression are pivotal due to high levels of non-response and relapse. This study evaluated the role of personality pathology in the treatment of depression by testing whether maladaptive personality traits (1) predict changes in depression over treatment or vice versa, (2) change themselves over treatment, (3) change differentially depending on treatment with schema therapy (ST) or cognitive behavioural therapy (CBT), and (4) moderate the effectiveness of these treatments.

We included 193 depressed inpatients (53.4% women, Mage = 42.9, SD = 13.4) participating in an assessor-blind randomized clinical trial and receiving a 7-week course of ST or CBT. The research questions were addressed using multiple indicator latent change score models as well as multigroup structural equation models implemented in EffectLiteR.

Maladaptive traits did not predict changes in depressive symptoms at post-treatment, or vice versa. However, maladaptive trait domains decreased over treatment (standardized Δμ range: −0.38 to −0.89), irrespective of treatment with ST or CBT. Maladaptive traits at baseline did not moderate the effectiveness of these treatments.

Self-reported maladaptive personality traits can change during treatment of depression, but may have limited prognostic or prescriptive value, at least in the context of ST or CBT. These results need to be replicated using follow-up data, larger and more diverse samples, and informant-rated measures of personality pathology.

Despite innovative treatments, the impairment in real-life functioning in subjects with schizophrenia (SCZ) remains an unmet need in the care of these patients. Recently, real-life functioning in SCZ was associated with abnormalities in different electrophysiological indices. It is still not clear whether this relationship is mediated by other variables, and how the combination of different EEG abnormalities influences the complex outcome of schizophrenia.

The purpose of the study was to find EEG patterns which can predict the outcome of schizophrenia and identify recovered patients.

Illness-related and functioning-related variables were measured in 61 SCZ at baseline and after four-years follow-up. EEGs were recorded at the baseline in resting-state condition and during two auditory tasks. We performed Sparse Partial Least Square (SPLS) Regression, using EEG features, age and illness duration to predict clinical and functional features at baseline and follow up. Through a Linear Support Vector Machine (Linear SVM) we used electrophysiological and clinical scores derived from SPLS regression, in order to classify recovered patients at follow-up.

We found one significant latent variable (p<0.01) capturing correlations between independent and dependent variables at follow-up (RHO=0.56). Among individual predictors, age and illness-duration showed the highest scores; however, the score for the combination of the EEG features was higher than all other predictors. Within dependent variables, negative symptoms showed the strongest correlation with predictors. Scores resulting from SPLS Regression classified recovered patients with 90.1% of accuracy.

A combination of electrophysiological markers, age and illness-duration might predict clinical and functional outcome of schizophrenia after 4 years of follow-up.

The long-term outcomes of bipolar disorder range from lasting remission to chronic course or frequent recurrences requiring admissions. The distinction between bipolar I and II disorders has limited utility in outcome prediction. It is unclear to what extent the clinical course of bipolar disorder predicts long-term outcomes.

A representative sample of 191 individuals diagnosed with bipolar I or II disorder was recruited and followed for up to 5 years using a life-chart method. We previously described the clinical course over the first 18 months with dimensional course characteristics and latent classes. Now we test if these course characteristics predict long-term outcomes, including time ill (time with any mood symptoms) and hospital admissions over a second non-overlapping follow-up period in 111 individuals with available data from both 18 months and 5 years follow-ups.

Dimensional course characteristics from the first 18 months prospectively predicted outcomes over the following 3.5 years. The proportion of time depressed, the severity of depressive symptoms and the proportion of time manic predicted more time ill. The proportion of time manic, the severity of manic symptoms and depression-to-mania switching predicted a greater likelihood of hospital admissions. All predictions remained significant after controlling for age, sex and bipolar I v. II disorder.

Differential associations with long-term outcomes suggest that course characteristics may facilitate care planning with greater predictive validity than established types of bipolar disorders. A clinical course dominated by depressive symptoms predicts a greater proportion of time ill. A clinical course characterized by manic episodes predicts hospital admissions.

It has been proposed that non-steroidal anti-inflammatory drugs (NSAIDs) may interfere with the efficacy of antidepressants and contribute to treatment resistance in major depressive disorder (MDD). This effect requires replication and a test of whether it is specific to serotonin-reuptake inhibiting (SRI) antidepressants.

We tested the effect of concomitant medication with NSAIDs on the efficacy of escitalopram, a SRI antidepressant, and nortriptyline, a tricyclic antidepressant, among 811 subjects with MDD treated for up to 12 weeks in the GENDEP study. Effects of NSAIDs on improvement of depressive symptoms were tested in mixed-effect linear models. Effects on remission were tested in logistic regression. Age, sex, baseline severity and centre of recruitment were considered as potential confounding factors.

Ten percent (n=78) of subjects were taking NSAIDs during the antidepressant treatment. Older subjects were significantly more likely to take NSAIDs. After controlling for age, sex, centre of recruitment and baseline severity, concomitant medication with NSAIDs did not significantly influence the efficacy of escitalopram [β=0.035, 95% confidence interval (CI) −0.145 to 0.215, p=0.704] or nortriptyline (β=0.075, 95% CI −0.131 to 0.281, p=0.476). Although slightly fewer subjects who took NSAIDs reached remission [odds ratio (OR) 0.80, 95% CI 0.49–1.31, p=0.383], this non-significant effect was reversed after controlling for age, sex, baseline severity and recruitment centre effects (OR 1.04, 95% CI 0.61–1.77, p=0.882).

NSAIDs are unlikely to affect the efficacy of SRI or other antidepressants. Concurrent use of NSAIDs and antidepressants does not need to be avoided.

Symptom dimensions have not yet been comprehensively tested as predictors of the substantial heterogeneity in outcomes of antidepressant treatment in major depressive disorder.

We tested nine symptom dimensions derived from a previously published factor analysis of depression rating scales as predictors of outcome in 811 adults with moderate to severe depression treated with flexibly dosed escitalopram or nortriptyline in Genome-based Therapeutic Drugs for Depression (GENDEP). The effects of symptom dimensions were tested in mixed-effect regression models that controlled for overall initial depression severity, age, sex and recruitment centre. Significant results were tested for replicability in 3637 adult out-patients with non-psychotic major depression treated with citalopram in level I of Sequenced Treatment Alternatives to Relieve Depression (STAR*D).

The interest-activity symptom dimension (reflecting low interest, reduced activity, indecisiveness and lack of enjoyment) at baseline strongly predicted poor treatment outcome in GENDEP, irrespective of overall depression severity, antidepressant type and outcome measure used. The prediction of poor treatment outcome by the interest-activity dimension was robustly replicated in STAR*D, independent of a comprehensive list of baseline covariates.

Loss of interest, diminished activity and inability to make decisions predict poor outcome of antidepressant treatment even after adjustment for overall depression severity and other clinical covariates. The prominence of such symptoms may require additional treatment strategies and should be accounted for in future investigations of antidepressant response.