Mood instability is a common feature of mental health disorders and is associated with poor clinical outcomes

Abstract

ObjectivesMood instability is a clinically important phenomenon but has received relatively little research attention. The objective of this study was to assess the impact of mood instability on clinical outcomes in a large sample of people receiving secondary mental healthcare.DesignObservational study using an anonymised electronic health record case register.SettingSouth London and Maudsley NHS Trust (SLaM), a large provider of inpatient and community mental healthcare in the UK.Participants27 704 adults presenting to SLaM between April 2006 and March 2013 with a psychotic, affective or personality disorder.ExposureThe presence of mood instability within 1 month of presentation, identified using natural language processing (NLP).Main outcome measuresThe number of days spent in hospital, frequency of hospital admission, compulsory hospital admission and prescription of antipsychotics or non-antipsychotic mood stabilisers over a 5-year follow-up period.ResultsMood instability was documented in 12.1% of people presenting to mental healthcare services. It was most frequently documented in people with bipolar disorder (22.6%), but was common in people with personality disorder (17.8%) and schizophrenia (15.5%). It was associated with a greater number of days spent in hospital (β coefficient 18.5, 95% CI 12.1 to 24.8), greater frequency of hospitalisation (incidence rate ratio 1.95, 1.75 to 2.17), greater likelihood of compulsory admission (OR 2.73, 2.34 to 3.19) and an increased likelihood of prescription of antipsychotics (2.03, 1.75 to 2.35) or non-antipsychotic mood stabilisers (2.07, 1.77 to 2.41).ConclusionsMood instability occurs in a wide range of mental disorders and is not limited to affective disorders. It is generally associated with relatively poor clinical outcomes. These findings suggest that clinicians should screen for mood instability across all common mental health disorders. The data also suggest that targeted interventions for mood instability may be useful in patients who do not have a formal affective disorder.

Data science for mental health: a UK perspective on a global challenge

“…Recent enhancements include the development of natural language processing applications to derive structured information from the text fields present in the electronic mental health record. These include recorded diagnoses, cognitive test scores, pharmacotherapy and symptom profiles [38][39][40][41][42] .…”

Affective instability, childhood trauma and major affective disorders

“…Exposure to such events was particularly linked to current experiences of anger independent of mood state in the current study. This link is important as childhood trauma in bipolar disorder is associated with rapid cycling, suicidal behaviour, earlier onset (Garno et al, 2005;Leverich and Post, 2006;Post et al, 2014), post traumatic stress disorder (Maniglio, 2013) increased number of depressive episodes (Etain et al, 2013), and more severe psychosis (Alvarez et al, 2011) Children who are subjected to traumatic events, show increased affective reactivity to stress and a diminished ability to regulate emotions (D'Andrea et al, 2012) and this may be due to induced changes in limbic reactivity or deficits in default mode network connectivity (Dvir et al, 2014). Data from a large (N=1065) prospective study of adolescents suggests emotional dysregulation leads to a multitude of psychiatric pathology but that it was not primarily a consequence of psychopathology (McLaughlin et al, 2011).…”

“…AI is associated with suicidal thinking (Marwaha et al, 2013), hospitalisation and antipsychotic use (Patel et al, 2015) even after controlling for the effects of diagnosis. AI has also been shown to be important in depression.…”

The revolution of personalized psychiatry: will technology make it happen sooner?

“…Evidence indicates that the availability of huge amounts of 'noisy' data may counterbalance the absence of optimal quality, a subject of considerable debate. Promising proof exists that processing techniques can be effectively used to extract and code relevant information from unstructured data, e.g., applying natural language analytic tools to clinical notes (Perlis et al 2012;Castro et al 2015;McCoy et al 2015;Patel et al 2015b). For example, in a study by Perlis et al, the application of natural language processing technique to information provided by EMR clinical notes allowed to classify current mood state of inpatients with a billing diagnosis of major depressive disorder and define their longitudinal outcomes (Perlis et al 2012).…”