Major Depressive Disorder (MDD) is one of the most common mental illnesses worldwide and is strongly associated with suicidality. Commonly used treatments for MDD with suicidality include crisis intervention, oral antidepressants (although risk of suicidal behavior is high among non-responders and during the first 10-14 days of the treatment) benzodiazepines and lithium. Although several interventions addressing suicidality exist, only few studies have characterized in detail patients with MDD and suicidality, including treatment, clinical course and outcomes. Patient Characteristics, Validity of Clinical Diagnoses and Outcomes Associated with Suicidality in Inpatients with Symptoms of Depression (OASIS-D)-study is an investigator-initiated trial funded by Janssen-Cilag GmbH.

For population 1 out of 3 OASIS-D populations, to assess the sub-population of patients with suicidality and its correlates in hospitalized individuals with MDD.

The ongoing OASIS-D study consecutively examines hospitalized patients at 8 German psychiatric university hospitals treated as part of routine clinical care. A sub-group of patients with persistent suicidality after >48 hours post-hospitalization are assessed in detail and a sub-group of those are followed for 6 months to assess course and treatment of suicidality associated with MDD. The present analysis focuses on a preplanned interim analysis of the overall hospitalized population with MDD.

Of 2,049 inpatients (age=42.5±15.9 years, females=53.2%), 68.0% had severe MDD without psychosis and 21.2% had moderately severe MDD, with 16.7% having treatment-resistant MDD. Most inpatients referred themselves (49.4%), followed by referrals by outpatient care providers (14.6%), inpatient care providers (9.0%), family/friends (8.5%), and ambulance (6.8%). Of these admissions, 43.1% represented a psychiatric emergency, with suicidality being the reason in 35.9%. Altogether, 72.4% had at least current passive suicidal ideation (SI, lifetime=87.2%), including passive SI (25.1%), active SI without plan (15.5%), active SI with plan (14.2%), and active SI with plan+intent (14.1%), while 11.5% had attempted suicide ≤2 weeks before admission (lifetime=28.7%). Drug-induced mental and behavioral disorders (19.6%) were the most frequent comorbid disorders, followed by personality disorders (8.2%). Upon admission, 64.5% were receiving psychiatric medications, including antidepressants (46.7%), second-generation antipsychotics (23.0%), anxiolytics (11.4%) antiepileptics (6.0%), and lithium (2.8%). Altogether, 9.8% reported nonadherence to medications within 6 months of admission.

In adults admitted for MDD, suicidality was common, representing a psychiatric emergency in 35.9% of patients. Usual-care treatments and outcomes of suicidality in hospitalized adults with MDD require further study.

None Declared

Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

Self-ratings of psychotic experiences might be biased by depressive symptoms.

Data from a large naturalistic multicentre trial on depressed inpatients (n = 488) who were assessed on a biweekly basis until discharge were analyzed. Self-rated psychotic symptoms as assessed with the 90-Item Symptom Checklist (SCL-90) were correlated with the SCL-90 total score, the SCL-90 depression score, the Beck Depression Inventory (BDI), the Hamilton Depression Rating Scale 21 item (HAMD-21) total score, the Montgomery Åsberg Depression Rating Scale (MADRS) total score and the clinician-rated paranoid-hallucinatory score of the Association for Methodology and Documentation in Psychiatry (AMDP) scale.

At discharge the SCL-90 psychosis score correlated highest with the SCL-90 depression score (0.78, P<0.001) and with the BDI total score (0.64, P<0.001). Moderate correlations were found for the MADRS (0.34, P<0.001), HAMD (0.37, P<0.001) and AMDP depression score (0.33, P<0.001). Only a weak correlation was found between the SCL-90 psychosis score and the AMDP paranoid-hallucinatory syndrome score (0.15, P<0.001). Linear regression showed that change in self-rated psychotic symptoms over the treatment course was best explained by a change in the SCL-90 depression score (P<0.001). The change in clinician-rated AMDP paranoid-hallucinatory score had lesser influence (P = 0.02).

In depressed patients self-rated psychotic symptoms correlate poorly with clinician-rated psychotic symptoms. Caution is warranted when interpreting results from epidemiological surveys using self-rated psychotic symptom questionnaires as indicators of psychotic symptoms. Depressive symptoms which are highly prevalent in the general population might influence such self-ratings.

According to cognitive theories of depression, negative biases affect most cognitive processes including perception. Such depressive perception may result not only from biased cognitive appraisal but also from automatic processing biases that influence the access of sensory information to awareness.

Twenty patients with major depressive disorder (MDD) and 20 healthy control participants underwent behavioural testing with a variant of binocular rivalry, continuous flash suppression (CFS), to investigate the potency of emotional visual stimuli to gain access to awareness. While a neutral, fearful, happy or sad emotional face was presented to one eye, high-contrast dynamic patterns were presented to the other eye, resulting in initial suppression of the face from awareness. Participants indicated the location of the face with a key press as soon as it became visible. The modulation of suppression time by emotional expression was taken as an index of unconscious emotion processing.

We found a significant difference in the emotional modulation of suppression time between MDD patients and controls. This difference was due to relatively shorter suppression of sad faces and, to a lesser degree, to longer suppression of happy faces in MDD. Suppression time modulation by sad expression correlated with change in self-reported severity of depression after 4 weeks.

Our finding of preferential access to awareness for mood-congruent stimuli supports the notion that depressive perception may be related to altered sensory information processing even at automatic processing stages. Such perceptual biases towards mood-congruent information may reinforce depressed mood and contribute to negative cognitive biases.