Investigating racing thoughts in insomnia: A neglected piece of the mood-sleep puzzle?

Validity of the DSM-5 Mixed Features Specifier Interview

OBJECTIVES

To examine the reliability and validity of a semi-structured interview assessing the features of the DSM-5 mixed features specifier. Our goal was to develop an instrument that could be used for both diagnostic and severity measurement purposes.

METHODS

Four hundred fifty-nine psychiatric patients in a depressive episode were interviewed by a trained diagnostic rater who administered semi-structured interviews including the DSM-5 Mixed Features Specifier Interview (DMSI). We examined the inter-rater reliability and psychometric properties of the DMSI. The patients were rated on clinician rating scales of depression, anxiety, and irritability, and measures of psychosocial functioning, suicidality, and family history of bipolar disorder.

RESULTS

The DMSI had excellent joint-interview interrater reliability. More than twice as many patients met the DSM-5 mixed features specifier criteria during the week before the assessment than for the majority of the episode (9.4% vs. 3.9%). DMSI total scores were more highly correlated with a clinician-rated measure of manic symptoms than with measures of depression and anxiety. More patients with bipolar depression met the mixed features specifier than patients with MDD. Amongst patients with MDD, those with mixed features more frequently had a family history of bipolar disorder, were more frequently diagnosed with anxiety disorders, attention deficit disorder, and borderline personality disorder, more frequently had attempted suicide, and were more severely depressed, anxious, and irritable.

CONCLUSION

The DMSI is a reliable and valid measure of the presence of the DSM-5 mixed features specifier in depressed patients as well as the severity of the features of the specifier.

Spanish validation of the short version of the racing and crowded thoughts questionnaire (RCTQ-13)

BACKGROUND

The Racing and Crowded Thoughts Questionnaire (RCTQ-13) is the most widely used specific scale for the measurement of racing thoughts, but there is currently no Spanish version that allow the evaluation in Spanish-speaking patients. The objective of this study is to translate, adapt, and validate the RCTQ-13 in a Colombian population with affective disorders.

METHODS

The questionnaire was translated and back-translated, and corrections were implemented following a pilot test to improve comprehensibility. We included patients with Bipolar I Disorder and with Major depressive disorder seen in three centers in the city of Medellín, Colombia. We evaluate structural validity with confirmatory factor analysis, internal consistency, and test-retest reliability. Construct validity was also assessed with the comparison between euthymic, maniac, and depressive episodes and the correlation with worry, rumination, and mania scales. Responsiveness was measured 1 month after the first evaluation. Based on item response theory (IRT), we also estimated item difficulty, discrimination, and fit using a generalized partial credit model.

RESULTS

Two hundred fifty subjects were included. Confirmatory factor analysis revealed that the three-factor structure of the scale was appropriate. Internal consistency was adequate for the entire scale (Cronbach's alpha = 0.95, 95% CI: 0.94-0.96) and for each factor. Test-retest reliability was good (intraclass correlation coefficient = 0.82, 95%IC: 0.70-0.88). For construct validity, we observed differences between patients with different types of affective episodes, a moderate positive correlation with the Penn State Worry Scale (r = 0.55) and the Ruminative Response Scale (r = 0.42), and a low negative correlation with the Young Mania Rating Scale (r = - 0.10). Responsiveness was proved to be adequate. Under IRT, the response thresholds for the response options are organized for all items. The infit was adequate for all items and the outfit was acceptable.

CONCLUSIONS

The Spanish version of the RCTQ-13 is a reliable, valid, and responsive scale and can be used for the clinical assessment of the construct of racing and crowded thoughts in patients with the spectrum of affective disorders in whom this experience can be expressed with different nuances. Further research is needed to expand the relationship with rumination and worry.

Identifying the bridge between depression and mania: A machine learning and network approach to bipolar disorder

OBJECTIVES

Although the cyclic nature of bipolarity is almost by definition a network system, no research to date has attempted to scrutinize the relationship of the two bipolar poles using network psychometrics. We used state-of-the-art network and machine learning methodologies to identify symptoms, as well as relations thereof, that bridge depression and mania.

METHODS

Observational study that used mental health data (12 symptoms for depression and 12 for mania) from a large, representative Canadian sample (the Canadian Community Health Survey of 2002). Complete data (N = 36,557; 54.6% female) were analysed using network psychometrics, in conjunction with a random forest algorithm, to examine the bidirectional interplay of depressive and manic symptoms.

RESULTS

Centrality analyses pointed to symptoms relating to emotionality and hyperactivity as being the most central aspects of depression and mania, respectively. The two syndromes were spatially segregated in the bipolar model and four symptoms appeared crucial in bridging them: sleep disturbances (insomnia and hypersomnia), anhedonia, suicidal ideation, and impulsivity. Our machine learning algorithm validated the clinical utility of central and bridge symptoms (in the prediction of lifetime episodes of mania and depression), and suggested that centrality, but not bridge, metrics map almost perfectly onto a data-driven measure of diagnostic utility.

CONCLUSIONS

Our results replicate key findings from past network studies on bipolar disorder, but also extend them by highlighting symptoms that bridge the two bipolar poles, while also demonstrating their clinical utility. If replicated, these endophenotypes could prove fruitful targets for prevention/intervention strategies for bipolar disorders.

Psychopathological Dissection of Bipolar Disorder and ADHD: Focussing on Racing Thoughts and Verbal Fluency

In the present study, we propose a review and a synthesis of the work of our group about the phenomenology and the cognitive mechanisms of racing thoughts in bipolar disorder (BD) and ADHD. Contrary to the mainstream idea according to which racing thoughts are pathognomonic of BD, our work suggests that racing thoughts are enhanced in ADHD compared to hypomanic episodes of BD, whereas in euthymic episodes of BD self-reported racing thoughts are similar to the rates reported by healthy controls. Using verbal fluency tasks, we found many similarities between bipolar and ADHD subjects with one clear difference: lexical search strategy in hypomania is based on phonemic similarities rather than semantic-relatedness. However, this distinction observed in this cognitive task is certainly difficult to grasp during a clinical interview aiming to differentiate mild hypomania from combined ADHD presentation. The main landmark to distinguish them remains the episodic nature of bipolar disorders as opposed to the lifelong presentation of ADHD symptoms, a dichotomous view that is not so clear-cut in clinical practice.

Multidimensional variability in ecological assessments predicts two clusters of suicidal patients

The variability of suicidal thoughts and other clinical factors during follow-up has emerged as a promising phenotype to identify vulnerable patients through Ecological Momentary Assessment (EMA). In this study, we aimed to (1) identify clusters of clinical variability, and (2) examine the features associated with high variability. We studied a set of 275 adult patients treated for a suicidal crisis in the outpatient and emergency psychiatric departments of five clinical centers across Spain and France. Data included a total of 48,489 answers to 32 EMA questions, as well as baseline and follow-up validated data from clinical assessments. A Gaussian Mixture Model (GMM) was used to cluster the patients according to EMA variability during follow-up along six clinical domains. We then used a random forest algorithm to identify the clinical features that can be used to predict the level of variability. The GMM confirmed that suicidal patients are best clustered in two groups with EMA data: low- and high-variability. The high-variability group showed more instability in all dimensions, particularly in social withdrawal, sleep measures, wish to live, and social support. Both clusters were separated by ten clinical features (AUC = 0.74), including depressive symptoms, cognitive instability, the intensity and frequency of passive suicidal ideation, and the occurrence of clinical events, such as suicide attempts or emergency visits during follow-up. Initiatives to follow up suicidal patients with ecological measures should take into account the existence of a high variability cluster, which could be identified before the follow-up begins.