Negative symptoms in schizophrenia: a study in a large clinical sample of patients using a novel automated method.
OBJECTIVE
To identify negative symptoms in the clinical records of a large sample of patients with schizophrenia using natural language processing and assess their relationship with clinical outcomes.


METHODS
Observational study using an anonymised electronic health record case register.


METHODS
South London and Maudsley NHS Trust (SLaM), a large provider of inpatient and community mental healthcare in the UK.


METHODS
7678 patients with schizophrenia receiving care during 2011.


METHODS
Hospital admission, readmission and duration of admission.


RESULTS
10 different negative symptoms were ascertained with precision statistics above 0.80. 41% of patients had 2 or more negative symptoms. Negative symptoms were associated with younger age, male gender and single marital status, and with increased likelihood of hospital admission (OR 1.24, 95% CI 1.10 to 1.39), longer duration of admission (β-coefficient 20.5 days, 7.6-33.5), and increased likelihood of readmission following discharge (OR 1.58, 1.28 to 1.95).


CONCLUSIONS
Negative symptoms were common and associated with adverse clinical outcomes, consistent with evidence that these symptoms account for much of the disability associated with schizophrenia. Natural language processing provides a means of conducting research in large representative samples of patients, using data recorded during routine clinical practice.
Department of Psychosis Studies, King's College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK.
Rashmi
http://orcid.org/0000-0002-9259-8788
R
Patel
Department of Psychological Medicine, King's College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK.
Nishamali
N
Jayatilleke
South London and Maudsley NHS Foundation Trust, Biomedical Research Centre Nucleus, London, UK.
Matthew
M
Broadbent
Department of Psychological Medicine, King's College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK.
Chin-Kuo
CK
Chang
Roche Products Limited, Welwyn Garden City, UK.
Nadia
N
Foskett
Department of Computer Science, The University of Sheffield, Portobello, Sheffield, UK.
Genevieve
G
Gorrell
Department of Psychological Medicine, King's College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK.
Richard D
RD
Hayes
Department of Psychological Medicine, King's College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK.
Richard
R
Jackson
Social Developmental and Genetic Psychiatry Department, King's College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK.
Caroline
C
Johnston
South London and Maudsley NHS Foundation Trust, Biomedical Research Centre Nucleus, London, UK.
Hitesh
H
Shetty
Department of Computer Science, The University of Sheffield, Portobello, Sheffield, UK.
Angus
A
Roberts
Department of Psychosis Studies, King's College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK.
Philip
P
McGuire
Department of Psychological Medicine, King's College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK.
Robert
R
Stewart
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001330:Electronic Data Processing; D057286:Electronic Health Records; D005260:Female; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data; D008131:London; D008297:Male; D008875:Middle Aged; D010359:Patient Readmission / Q000706:statistics & numerical data; D011379:Prognosis; D012559:Schizophrenia / Q000175:diagnosis*; D055815:Young Adult
electronic health records; natural language processing; negative symptoms; psychosis; schizophrenia; text mining