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Mood instability and clinical outcomes in mental health disorders: A natural language processing (NLP) study

Published online by Cambridge University Press:  23 March 2020

R. Patel ,
T. Lloyd ,
R. Jackson ,
M. Ball ,
H. Shetty ,
M. Broadbent ,
J.R. Geddes ,
R. Stewart ,
P. McGuire  and
M. Taylor
R. Patel*
Affiliation:
Institute of Psychiatry, Psychology and Neuroscience, Department of Psychosis Studies, London, United Kingdom
T. Lloyd
Affiliation:
Institute of Psychiatry, Psychology and Neuroscience, Department of Psychosis Studies, London, United Kingdom
R. Jackson
Affiliation:
Institute of Psychiatry, Psychology and Neuroscience, Department of Psychological Medicine, London, United Kingdom
M. Ball
Affiliation:
Institute of Psychiatry, Psychology and Neuroscience, Department of Psychological Medicine, London, United Kingdom
H. Shetty
Affiliation:
South London and Maudsley NHS Foundation Trust, Biomedical Research Centre Nucleus, London, United Kingdom
M. Broadbent
Affiliation:
South London and Maudsley NHS Foundation Trust, Biomedical Research Centre Nucleus, London, United Kingdom
J.R. Geddes
Affiliation:
University of Oxford, Department of Psychiatry, Oxford, United Kingdom
R. Stewart
Affiliation:
Institute of Psychiatry, Psychology and Neuroscience, Department of Psychological Medicine, London, United Kingdom
P. McGuire
Affiliation:
Institute of Psychiatry, Psychology and Neuroscience, Department of Psychosis Studies, London, United Kingdom
M. Taylor
Affiliation:
Institute of Psychiatry, Psychology and Neuroscience, Department of Psychosis Studies, London, United Kingdom
*
*Corresponding author.

Abstract

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Introduction

Mood instability is an important problem but has received relatively little research attention. Natural language processing (NLP) is a novel method, which can used to automatically extract clinical data from electronic health records (EHRs).

Aims

To extract mood instability data from EHRs and investigate its impact on people with mental health disorders.

Methods

Data on mood instability were extracted using NLP from 27,704 adults receiving care from the South London and Maudsley NHS Foundation Trust (SLaM) for affective, personality or psychotic disorders. These data were used to investigate the association of mood instability with different mental disorders and with hospitalisation and treatment outcomes.

Results

Mood instability was documented in 12.1% of people included in the study. It was most frequently documented in people with bipolar disorder (22.6%), but was also common in personality disorder (17.8%) and schizophrenia (15.5%). It was associated with a greater number of days spent in hospital (B coefficient 18.5, 95% CI 12.1–24.8), greater frequency of hospitalisation (incidence rate ratio 1.95, 1.75–2.17), and an increased likelihood of prescription of antipsychotics (2.03, 1.75–2.35).

Conclusions

Using NLP, it was possible to identify mood instability in a large number of people, which would otherwise not have been possible by manually reading clinical records. Mood instability occurs in a wide range of mental disorders. It is generally associated with poor clinical outcomes. These findings suggest that clinicians should screen for mood instability across all common mental health disorders. The data also highlight the utility of NLP for clinical research.

Disclosure of interest

The authors have not supplied their declaration of competing interest.

Type
EW433
Information
European Psychiatry , Volume 33 , Issue S1: Abstracts of the 24th European Congress of Psychiatry , March 2016 , pp. s224
Copyright
Copyright © European Psychiatric Association 2014
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