Harnessing Clinical Psychiatric Data with an Electronic Assessment Tool (OPCRIT+): The Utility of Symptom Dimensions
National Institute for Health Research Specialist Biomedical Research Centre for Mental Health at the South London and Maudsley National Health Service Foundation Trust and The Institute of Psychiatry, King's College London, London, United Kingdom.PLoS ONE (Impact Factor: 3.23). 09/2013; 8(3):e58790. DOI: 10.1371/journal.pone.0058790
Progress in personalised psychiatry is dependent on researchers having access to systematic and accurately acquired symptom data across clinical diagnoses. We have developed a structured psychiatric assessment tool, OPCRIT+, that is being introduced into the electronic medical records system of the South London and Maudsley NHS Foundation Trust which can help to achieve this. In this report we examine the utility of the symptom data being collected with the tool. Cross-sectional mental state data from a mixed-diagnostic cohort of 876 inpatients was subjected to a principal components analysis (PCA). Six components, explaining 46% of the variance in recorded symptoms, were extracted. The components represented dimensions of mania, depression, positive symptoms, anxiety, negative symptoms and disorganization. As indicated by component scores, different clinical diagnoses demonstrated distinct symptom profiles characterized by wide-ranging levels of severity. When comparing the predictive value of symptoms against diagnosis for a variety of clinical outcome measures (e.g. 'Overactive, aggressive behaviour'), symptoms proved superior in five instances (R(2) range: 0.06-0.28) whereas diagnosis was best just once (R(2)∶0.25). This report demonstrates that symptom data being routinely gathered in an NHS trust, when documented on the appropriate tool, have considerable potential for onward use in a variety of clinical and research applications via representation as dimensions of psychopathology.
Harnessing Clinical Psychiatric Data with an Electronic
Assessment Tool (OPCRIT
): The Utility of Symptom
Philip James Brittain
*, Sarah Elizabeth Margaret Lobo
, James Rucker
, Myanthi Amarasinghe
Anantha Padmanabha Pillai Anilkumar
, Martin Baggaley
, Pallavi Banerjee
, Jenny Bearn
, Matthew Butler
, Colin Donald Campbell
, Anthony James Cleare
, Luiz Dratcu
, Fiona Gaughran
, Matthew Goldin
, Annika Henke
, Nikola Kern
, Abdallah Krayem
, Ronan McIvor
, Humphrey Needham-Bennett
, Stuart Newman
, Dele Olajide
, Ranga Rao
, Ijaz Ur Rehman
, Gertrude Seneviratne
, Daniel Stahl
, Sajid Suleman
, John Tully
, David Veale
, Robert Stewart
, Peter McGuffin
, Simon Lovestone
, Gunter Schumann
1 National Institute for Health Research Specialist Biomedical Research Centre for Mental Health at the South London and Maudsley National Health Service Foundation
Trust and The Institute of Psychiatry, King’s College London, London, United Kingdom, 2 Medic al Research Council Social, Genetic and Developmental Psychiatry Centre,
Institute of Psychiatry, King’s College London, London, United Kingdom, 3 Institute of Psychiatry, King’s College London, London, United Kingdom, 4 Department of
Forensic and Developmental Neuroscience, Institute of Psychiatry, King’s College London, London, United Kingdom, 5 National Psychosis Service, South London and
Maudsley National Health Service Foundation Trust, Bethlem Royal Hospital, London, United Kingdom, 6 South London and Maudsley National Health Service Foundation
Trust, Bethlem Royal Hospital, London, United Kingdom
Progress in personalised psychiatry is dependent on researchers having access to systematic and accurately acquired
symptom data across clinical diagnoses. We have developed a structured psychiatric assessment tool, OPCRIT+, that is being
introduced into the electronic medical records system of the South London and Maudsley NHS Foundation Trust which can
help to achieve this. In this report we examine the utility of the symptom data being collected with the tool. Cross-sectional
mental state data from a mixed-diagnostic cohort of 876 inpatients was subjected to a principal components analysis (PCA).
Six components, explaining 46% of the variance in recorded symptoms, were extracted. The components represented
dimensions of mania, depression, positive symptoms, anxiety, negative symptoms and disorganization. As indicated by
component scores, different clinical diagnoses demonstrated distinct symptom profiles characterized by wide-ranging
levels of severity. When comparing the predictive value of symptoms against diagnosis for a variety of clinical outcome
measures (e.g. ‘Overactive, aggressive behaviour’), symptoms proved superior in five instances (R
range: 0.06–0.28) whereas
diagnosis was best just once (R
:0.25). This report demonstrates that symptom data being routinely gathered in an NHS
trust, when documented on the appropriate tool, have considerable potential for onward use in a variety of clinical and
research applications via representation as dimensions of psychopathology.
Citation: Brittain PJ, Lobo SEM, Rucker J, Amarasinghe M, Anilkumar APP, et al. (2013) Harnessing Clinical Psychiatric Data with an Electronic Assessment Tool
(OPCRIT+): The Utility of Symptom Dimensions. PLoS ONE 8(3): e58790. doi:10.1371/journal.pone.0058790
Editor: Christopher G. Davey, University of Melbourne, Australia
Received November 5, 2012; Accepted February 6, 2013; Publi shed March 8, 2013
Copyright: ß 2013 Brittain et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: Philip Brittain, Sarah Lobo, Myanthi Amarasinghe, Matthew Broadbent, Tony Cleare, Sophia Frangou, Janet Treasure, David Veale, Rob Stewart, Peter
McGuffin, Simon Lovestone, Matthew Hotopf and Gunter Schumann are part-funded by the National Institute for Health Research (NIHR) Biomedical Research
Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The funders had no role in study design, data collection and analysis,
decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: Philip.Brittain@kcl.ac.uk
Advances in personalized psychiatry depend on large-scale
biological sampling as well as researchers having ready access to
high-quality patient characterization information, including sys-
tematic and accurately acquired data on clinical signs and
symptoms. The OPCRIT program , which in the last 20 years
has been used extensively as a patient characterization tool, is
suitable for such a role. It contains a checklist constructed from the
operational criteria for the major psychiatric classificatory systems,
as well as a suite of proprietary algorithms which produce
Due to the extensive prior use in research and concise structure
of OPCRIT, we recently introduced ‘OPCRIT+’  into routine
use within a large mental health trust (The South London and
Maudsley NHS Foundation Trust – ‘SLaM’). OPCRIT+ is an
expansion of the original OPCRIT, incorporating patient history
and an increased diagnostic repertoire and sits within SLaM’s
electronic health record (ePJS), where all of the trust’s clinical
information is stored. OPCRIT+ acts as a data collection and
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diagnostic device, useable across a broad range of patient settings
and from which data suitable for a variety of clinical and research
applications are made available.
Although OPCRIT has most commonly been used to produce
diagnoses, one potential application of the symptom data
systematically acquired on OPCRIT+ will be to generate
dimensional representations of psychopathology. In such an
approach, a patient’s illness is represented by scores on clusters
of symptoms found to occur together in specific patient groups. A
number of studies have already used OPCRIT in this manner in
psychotic and affective disorders. Using principal components
analysis (PCA) or factor analysis, the extracted dimensions have
typically been found to represent mania, depression, positive
symptoms, disorganization and negative symptoms. Several studies
have also compared dimensional against categorical (diagnostic)
representations of illness in exploring associations with illness
characteristics and clinical outcome measures [3,4,5,6]. All of
these reported that a dimensional, or a dimensional and
categorical approach combined, was superior to a categorical
approach alone. This indicates the considerable research potential
offered from the use of the symptom data being recorded with
Whilst the introduction of such a tool into routine clinical
settings holds considerable promise, there are notable methodo-
logical differences between the previous use of OPCRIT and the
use of OPCRIT+ in routine clinical care. Typically, OPCRIT has
been completed by experienced psychopathology raters reviewing
medical notes whereas OPCRIT+ is mainly being completed by
junior doctors in busy inpatient units. Therefore, the viability and
potential utility of creating dimensional representations of
psychopathology from the symptom data being recorded on
OPCRIT+ cannot be assumed. In this paper we have set out to
examine this. First, we report a PCA which determined the
underlying dimensional structure of the symptom data. Next, using
component scores, we report on differences between clinical
diagnoses in terms of psychopathology represented by these
dimensions. Finally, to gain insight into the utility of this approach,
we detail the predictive power of component scores, in comparison
to clinical diagnosis, for a variety of clinical outcome measures.
Materials and Methods
All clinical data, stored on the forms used in this analysis, was
extracted from ePJS via the ‘Clinical Record Interactive Search’
system (‘CRIS’; ) which is a search engine and anonymization
portal allowing researchers access to patient data stored on the
electronic record. Ethical approval for CRIS as an anonymised
data resource for secondary analyses was provided by Oxfordshire
REC in 2008 (Reference 08/H0606/71), in accordance with the
Declaration of Helsinki, as well as by the Institute of Psychiatry’s
Institutional Review Board. Individual patient consent is therefore
not necessary for CRIS projects as all data is anonymized at the
point of extraction.
Data on 876 patients admitted to SLaM inpatient units between
May 2008 and November 2011 were used in this analysis. SLaM
operates 68 inpatient units across four main hospital sites. As the
introduction of OPCRIT+ within SLaM is an on-going process,
we could only use data from units where the form was currently in
use; this included: 1 addictions unit, 1 affective disorders unit, 1
eating disorders unit, 1 brain injury unit, 1 psychiatric triage
service, 4 forensic units and 8 ‘acute’ wards. For this analysis, ICD-
10 diagnosis was assigned by using the closest recorded clinical
diagnosis to when the assessment of symptoms with OPCRIT+
was made (mean difference: 82 days, S.D: 322). The distribution of
diagnoses and demographic information are detailed in Table 1.
Table 1. Distribution of ICD-10 clinical diagnoses and demographic information.
Diagnosis N (%) Median age Percent male
F00–09 Organic, including symptomatic, mental disorders 17 (1.9) 53 70.6
F06 Other mental disorders due to brain damage and dysfunction and to physical disease 8 (0.9) 49 62.5
F10–F19 Mental and behavioural disorders due to psychoactive substance use 314 (35.8) 41 65.3
F10 Mental and behavioural disorders due to use of alcohol 165 (18.8) 45 67.3
F20–F29 Schizophrenia, schizotypal and delusional disorders 292 (33.3) 37 75.7
F20 Schizophrenia 200 (22.8) 37.5 78
F30–F39 Mood (affective) disorders 143 (16.3) 44 60.1
F31 Bipolar affective disorder 67 (7.6) 47 50.7
F40–F48 Neurotic, stress-related and somatoform disorders 40 (4.6) 42 80
F43 Reaction to severe stress, and adjustment disorders 24 (2.7) 40 83.3
F50–F59 Behavioural syndromes associated with physiological disturbances and physical
38 (4.3) 28 0
F50 Eating disorders 38 (4.3) 28 0
F60–F69 Disorders of adult personality and behaviour 26 (3) 39.5 65.4
F60 Specific personality disorders 26 (3) 39.5 65.4
F70–F79 Mental retardation 6 (0.7) 35 66.7
F70 Mild mental retardation 5 (0.6) 32 80
Total 876 40 65.9
Rows provide details for all cases within 8 broad ICD ranges (in bold) and underneath each of these the accompanying largest two-digit subgroup within that range.
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ICD-10 form. Primary (used in this analysis) and secondary
ICD-10 clinical diagnoses are recorded on this form. Diagnoses
were recorded either at the two e.g. F20 or three-digit level e.g.
F20.2. Therefore, for the purposes of this analysis, we compressed
all diagnoses into the two digit level.
. Psychopathology present at, or near to, inpatient
admission was rated with the ‘Mental State Examination’ section
of OPCRIT+ . Only symptom data is detailed in this analysis,
as other sections required for OPCRIT+ to produce diagnoses
(e.g. ‘History of Presenting Complaint’) were not yet in use. The
majority of mental state examinations undertaken within SLaM
are done by junior doctors; as such, they were tasked with
The Mental State Examination section consists of a series of
free-text fields corresponding to the standard categories of a
mental state examination e.g. ‘Appearance & Behaviour’ under
each of which lie the original OPCRIT items e.g. ‘Agitated
activity’ and the items unique to OPCRIT+ e.g. ‘Anxiety levels
abnormal’. Raters typed their assessments, as a standard part of
the clinical documentation process, and then coded observed signs
and symptoms as ‘present’. Items not marked as such were
considered absent. All doctors received training in the use of the
form. OPCRIT has established reliability and validity  and
OPCRIT+, although only recently developed, has demonstrated
substantial inter-rater reliability . OPCRIT+ is available for
download via the following link: http://sgdp.iop.kcl.ac.uk/
HoNOS (Health of the nation outcome scales). The
HoNOS instrument  contains 12 items measuring behaviour,
impairment, symptoms and social functioning, each on a 0–4 scale
of severity. A HoNOS ‘total’ score is also produced. The scales
form part of the English Minimum Data Set for Mental Health
and as such are routinely completed for SLaM patients.
Assessments are usually made by nursing staff. HoNOS has
demonstrated good reliability . A cut-off point, for HoNOS
completion, of 14 days either side of the assessment of symptoms
was used (mean difference: 0.46 days, S.D: 5.33), reducing the
maximum sample size for analysis using these variables to 452. A
further 1.3% of data was missing, which was imputed using the
Ward stay form. Duration of inpatient episode was ascer-
tained from the ‘Ward stay’ form. These record admission and
discharge dates and are usually completed by administrative staff.
For the analysis using this variable, we only used subjects who
were admitted to one of seven acute wards, as the duration of stay
on many of the other wards e.g. an addictions unit, was likely to be
determined primarily by factors other than the presence of
symptoms e.g. a predefined period of detoxification. We also only
included subjects where the documentation of symptoms with
OPCRIT+ was made during the first ward stay of an admission i.e.
not if the assessment of symptoms was made on a ward they had
been transferred to. However, if a subject was subsequently
transferred to another ward, after their initial admission, this
subject was included. These factors reduced the maximum
number of subjects available for analysis with this variable to 252.
All analyses were undertaken using SPSS version 19. Figure 1
details the various steps in the analysis.
Principal Components Analysis (PCA). Individual OP-
CRIT+ items were entered into a PCA, a variable reduction
technique which maximizes the amount of variance accounted for
in the observed variables by a smaller group of variables called
components . Items unrelated to phenomenology were
excluded e.g. ‘source of rating’, as were items whose variance
was near zero i.e. scoring 0 for almost all subjects. In line with
previous studies [5,6], there were several instances where items
which had similar meaning were combined to form one variable.
These composite items were ‘Restricted or blunted affect’
(combining ‘Restricted affect’ and ‘Blunted affect’), ‘Sleep abnor-
mal’ (combining ‘Initial insomnia’, ‘Middle insomnia’, ‘Early
morning waking’ and ‘Excessive sleep’) and ‘Problems with
appetite and/or weight’ (combining ‘Poor appetite’, ‘Increased
appetite’, ‘Weight loss’ and ‘Weight gain’). A total of 43 items, for
each subject, entered the initial analysis as either 0 (symptom not
present) or 1 (symptom present). The number of components
extracted was based on examination of the scree plot, parallel
analysis (a Monte Carlo simulation method) and a requirement
that they be interpretable and clinically meaningful. Direct
oblimin rotation [11,12], which allows the extracted components
to correlate, was used to aid interpretation.
Component score estimation and their distribution
within diagnostic classes.
Component scores are values
indicating a person’s relative standing on a component. These
scores can be used to represent severity levels for each subject, on
each component, based on a sum of the weighted items which are
recorded as being present at the mental state examination e.g.
Subject 1 is recorded as having elevated mood+thoughts
racing+reduced need for sleep and is therefore more severely
manic than subject 2 who is only recorded as having pressured
speech. Scores were estimated using the Anderson-Rubin method
. Scores are produced based on a group mean centred on 0
with a standard deviation of 1. Scores for components 4–6 were
inverted as their initial loadings were negative. Thus, for all
components, higher scores represented greater symptom severity.
Figure 1. Flow chart detailing the four steps of the analysis and
the number of subjects included at each step.
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Table 2. Component loadings, after direct oblimin rotation, of the 39 symptoms extracted from the OPCRIT+ checklist.
Elevated mood .79 2.03 2.03 .04 2.03 .00 .62
Increased self-esteem .77 2.02 2.07 .03 .01 .07 .57
Thoughts racing .74 .00 2.06 .01 .08 2.06 .57
Excessive activity .73 2.05 2.02 2.05 2.01 2.11 .60
Reckless activity .70 2.06 2.03 2.04 2.16 2.03 .54
Reduced need for sleep .68 .04 .13 .05 .02 .22 .44
Pressured speech .65 2.03 2.07 .05 .13 2.19 .53
Irritable mood .38 .08 .07 2.02 .00 2.11 .19
Loss of energy/tiredness 2.12 .75 2.07 2.00 2.11 2.04 .60
Loss of pleasure 2.11 .74 2.04 2.06 2.06 2.1 .58
Poor concentration .13 .68 2.08 2.02 2 .13 2.12 .54
Dysphoria .03 .66 .13 2
.01 .01 2.08 .46
Suicidal ideation 2.04 .64 .19 .01 .14 .08 .46
Excessive self-reproach 2.09 .54 2.05 2.09 2.12 2.05 .36
Sleep abnormal .20 .50 .06 2.03 .09 .24 .37
Problems with appetite
2.05 .41 2.03 .03 .03 .14 .20
Altered libido .26 .39 2.09 2.13 2.05 2.02 .28
2.02 .03 .68 2.01 .00 .06 .46
Third person auditory
.04 .02 .60 .07 2.00 2 .01 .36
Thought insertion 2.04 2.02 .59 .06 2.08 2.04 .36
.07 2.04 .58 .01 .00 2.29 .50
Visual hallucinations 2.01 2.03 .55 2.13 .00 .15 .32
Delusions of influence .02 .05 .48 .03 .11 2.26 .33
2.02 2.02 .47 2.10 .02 .08 .22
2.02 .03 .44 .06 2.11 .00 .21
symptoms during anxiety
2.04 2.06 2.01
.87 2.00 .01 .74
Recurrent abrupt attacks
of severe anxiety
2.04 2.05 2.02
.81 2.00 2.00 .63
Anxiety levels abnormal 2.00 .05 .04
.81 .05 2.01 .67
Prominent, excessive free-
.01 .12 .02
.65 .00 2.02 .48
Negative formal thought
2.04 2.16 .04 .00
.83 .05 .67
Slowed activity .06 .12 2.04 .03
.75 .17 .58
Restricted or blunted affect 2.08 .22 .06 2.05
.57 2.03 .44
Lack of self-care .01 .06 .10 .09
.37 2.29 .30
Speech incoherent .07 .04 2.09 2.04 .01
Positive formal thought
.11 2.04 .04 .00 .13
Speech difficult to
.02 .01 2.15 .00 2.11
Bizarre delusions .00 2.01 .25 .02 .04
Bizarre behaviour .23 2.17 .12 2.07 2.24
Distractibility .27 2.09 .16 2.10 2.21
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For each component, median scores and the proportion of high
scorers (above the upper tertile) were calculated and differences
between six of the most frequent diagnoses across the ICD
spectrum (F10, F20, F31, F43, F50, and F60) were examined with
non-parametric tests of difference (median and chi-squared tests).
The relationship of component scores and clinical
diagnosis to clinical outcome measures.
For each clinical
outcome measure (HoNOS ratings and duration of ward stay) we
ran three regression models: a full model (Diagnosis+Symptoms;
D+S) and two nested models (Diagnosis only; D and Symptoms
only; S). Logistic, ordinal or linear regression was used where
appropriate. So as to meet cell count assumptions, each model
used only a limited number of the more frequent diagnoses (range:
4–9; n: 203–361) with schizophrenia being used as the reference
diagnosis in each case. For the same reason, HoNOS items were
collapsed into three categories (i.e. 0, 1–2, 3–4) for use in ordinal
regression and into a binary rating (i.e. 0, 1–4) for use in logistic
regression. Significant models only were compared using the
likelihood ratio test. There were four possible conclusions for each
clinical outcome measure: 1) D+S.D AND S, meaning a
combination of both predictors is best 2) D+S. D but = S,
meaning a symptoms only model provides the best fit 3) D+S.S
but = D, meaning a diagnosis only model provides the best fit and
4) D+S,D AND S, meaning a combination of the two does not
provide a better solution than either alone. In this case, we
compared the D and S models separately using Akaike’s
Information Criterion .
Component Structure and Correlations
Inspection of the scree plot and Monte Carlo simulation showed
that between 5 and 7 components could be extracted. Examina-
tion of the items loading to each component (table 2) suggested
that the 6 component solution was superior. These can be
considered as representing dimensions of mania, depression,
positive symptoms, anxiety, negative symptoms and disorganiza-
tion. All items had good face validity in relation to their
component e.g. ‘Elevated mood’ is a symptom of mania. Primary
loadings were all .0.30 with the majority . 0.40. Secondary
loadings were all ,0.25, except in six instances. This solution
explained 46% of the overall variance in the data (the sum of the
‘Percent of variance explained’). Four items, ‘agitated activity’,
‘grandiose delusions’, ‘lack of insight’ and ‘inappropriate affect’
were excluded from the final analysis as each one either cross-
loaded on more than one component or did not account for a
substantial proportion (.0.30) of any components variance.
Correlations between component scores, as indicated by
Spearman’s rank coefficients, were generally low (table 3). Only
a positive correlation between negative and disorganization
symptom scores approached a moderate effect size .
Distribution of Component Scores within ICD-10 Clinical
Median component scores differed significantly between the
different diagnoses detailed in table 4 (F06 and F70 were excluded
to meet cell count assumptions) for all symptom dimensions except
anxiety (Median tests. Mania: X
= 35.263, p,.001, Depression:
= 48.202, p,.001, Positive: X
= 107.128, p,.001, Negative:
= 60.261, p,.001, Disorganization: X
= 119.557, p,.001,
= 5.805, p = 0.326) with the same split occurring in
relation to the proportions of individuals scoring about the upper
tertile (Chi-squared tests. Mania: X
= 48.614, p,.001, Depres-
= 27.710, p,.001, Positive: X
= 73.180, p,.001, Neg-
= 43.465, p,.001, Disorganization: X
p,.001, Anxiety: X
= 10.476, p = 0.063).
Table 3. Component scores Spearman’s correlations.
Mania Depression Positive Anxiety Negative Disorganization
Depression .21** 1.000
Positive .26** .08* 1.000
Anxiety .19** .06 .03 1.000
Negative 2.02 .00 2.01 .00 1.000
Disorganization .13** 2.20** .11** 2.07* .43** 1.000
*Correlation is significant at the 0.05 level.
**Correlation is significant at the 0.01 level.
Table 2. Cont.
Percent of variance
13.5 11 7.5 5.5 5 3.5
Loadings greater than 0.3 are printed in bold. A six-component solution, with their interpretations, is presented. Item commun alities and the percent of variance
explained by each component are also presented.
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Association of Component Scores and Clinical Diagnosis
to Clinical Outcome Measures
The likelihood ratio test revealed that there were four measures
(Overactive, aggressive behaviour; Non-accidental, self-injury;
Problems with hallucinations/delusions and Problems with
depressed mood) where symptoms alone provided the best fitting
model and one measure (Duration of inpatient episode) where
diagnosis alone provided the best fit (see table 5). Thus, although
was higher in the combined model for all of these measures,
removing the diagnoses as a predictor (or symptoms, in the case of
‘Duration of inpatient episode’) did not significantly reduce the fit
of the model and thus the smaller model was chosen for reasons of
parsimony. ‘Problems with activities of daily living’ was only
significantly associated with the symptoms model. R
these models was generally low (range: 0.06–0.28). Depression and
disorganization were the most frequent significant predictors.
Anxiety was not a significant predictor in any of the models. There
were a further eight clinical outcome measures which were not
significantly associated with any of the three models.
In this analysis, using a newly developed electronic assessment
tool (OPCRIT+), we identified a six-component symptom
structure underlying the psychopathology recorded in a large,
mixed-diagnostic, inpatient cohort. Using component scores to
indicate severity, we demonstrated distinct symptom profiles across
different clinical diagnoses for five of the six components.
Furthermore, these severity scores provided significant predictive
Table 4. Median and interquartile range Anderson-Rubin component scores and proportion of individuals with high scores (above
the upper tertile) as a function of clinical ICD diagnostic category.
ICD-10 diagnostic category Mania Depressive
F06 Other mental disorders due to brain damage and
dysfunction and to physical disease
2.40/1.44/37 2.47/.99/25 2.48/.59/37 2.39/1.66/37 2.06/.64/50 .13/.50/87
F10 Mental and behavioural disorders due to use of alcohol 2.34/.14/17 2.24/1.22/38 2.49/.10/14 2.37/.15/26 2.43/.18/15 2.48/.28/8
F20 Schizophrenia 2.25/.66/48 2.70/.54/18 .07/1.52/60 2.38/.19/31 2.05/.99/53 .09/1.58/62
F31 Bipolar affective disorder .07/3.12/67 2.34/1.55/39 2.42/.47/30 2.33/.20/49 2.26/.88/42 2.28/.87/40
F43 Reaction to severe stress, and adjustment disorders 2.32/.24/29 .65/1.62/71 2.33/.99/37 2.37/.55/33 2.38/.84/42 2.43/.50/12
F50 Eating disorders 2.46/.13/13 2.24/1.76/37 2.53/.07/3 2.37/.80/45 2.40/.43/24 2.38/.26/11
F60 Specific personality disorders 2.34/.32/31 .07/1.77/50 .32/.98/69 2.36/.69/46 2.44/.47/19 2.40/.58/23
F70 Mild mental retardation 2.46/.23/20 2.72/.78/0 2.53/1.00/40 2.37/.14/20 2.38/.37/20 2.09/.78/60
Diagnoses listed are the largest two-digit subgroups within each broad ICD range (e.g. F06/F00–09). Figures are in the format of Median/Interquartile range/Proportion
of individuals with high scores.
Table 5. Diagnosis only (D), symptoms only (S) and models containing both sets of predictors (D+S) and their associations with
various clinical outcome measures.
Clinical outcome measure D S D+S Best model Predictors
Overactive, aggressive behaviour .09** .14*** .17*** S D, M, Di
Non-accidental, self-injury .13*** .16*** .19*** S D, Di
Problem drinking or drug taking .02 .04 .07 n/a
Cognitive problems .02 .04 .06 n/a
Physical illness or disability problems .01 .02 .02 n/a
Problems with hallucinations/delusions .15*** .28*** .33*** S P, D, N, Di
Problems with depressed mood .11*** .16*** .20*** S M, D
Other mental and behavioural problems .02 .01 .03 n/a
Problems with relationships .01 .01 .03 n/a
Problems with activities of daily living .02 .06* .09 S
Problems with living conditions .01 .02 .03 n/a
Problems with occupation and activities .02 .03 .05 n/a
HoNOS Total .03 .02 .06 n/a
Duration of inpatient episode
.25*** .18*** .29*** D F10, F32, F60, F43, F23
Columns 2–4 report Nagelkerke’s Pseudo R
where linear regression is used) for each model and overall model significance (*significant at the ,0.05 level,
**significant at the ,0.01 level, ***significant at the ,0.001 level). Column 5 details the best fitting model based on the likelihood ratio test (p,0.05) or the non-
significance of other models in the comparison
. Column 6 details, in descending order of significance, predictors in the best model with a p-value of ,0.1. M = Mania,
D = Depression, P = Positive symptoms, A = Anxiety, N = Negative symptoms, Di = Disorganization, FXX = ICD10 diagnostic category.
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value, which was more informative than diagnosis, for a range of
clinical outcome measures.
The component structure we extracted is similar to those
reported in studies using the original OPCRIT for this purpose
[3,4,5,6,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]. In fact,
the five most commonly reported components (or factors) in those
studies were also extracted in our PCA: mania, depression,
negative symptoms, disorganization and positive symptoms
(although the specific OPCRIT items associated with these
components varies somewhat across studies). This similarity
occurred despite the fact that over half of the patients in our
study belonged to diagnostic categories outside the psychotic and
affective spectrum, from where cohorts in the other studies were
drawn. One notable difference in our component structure
however, was the extraction of an ‘anxiety’ component. This
occurred due to the additional items in OPCRIT+ allowing the
diagnosis of anxiety spectrum disorders.
The extracted components explained 46% of the variance in the
symptom data being recorded. This is at the lower end of the
range seen in the studies cited above (mean: 52.2% range: 39–
71%). There are a number of possible explanations for this. For
example, it may be because our PCA contained ratings from a
large number of doctors, whereas those in the cited studies
typically contained far fewer raters. Alternatively, it could have
resulted from the addition of patients whose primary diagnosis was
outside the psychotic and affective spectrum and who may have
presented with more heterogeneous symptom profiles. Despite
this, the successful extraction of an underlying component
structure is a vital first step in onward use of the data.
Following the PCA, we created component scores for all
subjects to indicate severity levels on each of the six symptom
dimensions. We then investigated the distributions of these scores
as a function of clinical diagnosis. There were distinct distribu-
tions, by diagnosis, for five out of the six components, demon-
strated by different median scores and proportions of ‘high-
scorers’. Scores on the anxiety dimension did not differ in these
respects, indicating that doctors were rating all in-patients as
having similar levels of anxiety. Different distributions of
symptoms between diagnoses would be expected and support
the construct validity of measuring symptom severity in this way. It
is notable though, from inspection of the median and inter-quartile
range figures, that there was substantial symptom heterogeneity
within diagnoses. This variability, in its most extreme form meant
that, for example, there were patients with a diagnosis of F10
‘Mental and behavioural disorders due to use of alcohol’ in the
upper and lower 5% of scores on four out of the six dimensions
(positive symptoms, mania, depression and anxiety).
We then investigated the predictive power of component scores
by following an existing literature whose aim has been to establish
the superiority of dimensional, categorical or combinatorial
representations of psychopathology. There were five clinical
outcome measures where dimensional representations of illness
alone provided the best model, whereas there was only one
measure where a categorical representation alone was best. There
were no measures where a combined approach provided the best
solution. The superiority of dimensional over categorical repre-
sentations of psychopathology, as demonstrated here, is in
agreement with other studies which have asked this question
using the original OPCRIT [3,4,5]; although one study concluded
that combinatorial approaches were best . It is important to
note however, in relation to the above observations, that we were
using ICD diagnoses collapsed to the 2-digit level (due to variation
in the way clinical diagnoses were documented). It may be, that at
the three digit level or higher (e.g. F10.52), categorical represen-
tations of psychopathology would exhibit greater predictive power
as well as less symptom heterogeneity.
Despite their overall superiority to diagnosis in this analysis, the
predictive value of the component scores, for this set of clinical
outcome variables, was only modest (indicated by low R
and eight measures having no association with the ‘symptoms
only’ model). It is therefore important that the utility of this
approach in other research realms (e.g. biomarker research) is
explored further, particularly as one intended use of the data will
be to characterize associated biological and neuroimaging
information being gathered in a Bioresource (Biobank) operated
by the trust and its partners. It may be that categorical or
combinatorial representations of psychopathology are more
appropriate for other research areas. Crucially though, via the
adoption of OPCRIT+ by SLaM, researchers will now have access
to both symptom and diagnosis data recorded in the clinic.
In summary, our analysis has demonstrated that using
OPCRIT+, symptom data being routinely recorded across a
broad diagnostic spectrum within inpatient settings can be reused
to represent severity levels on psychopathological dimensions. This
has been achieved despite the very different methodological
circumstances between our study and the previous use of
OPCRIT for this purpose. Symptom dimensions are applicable
across a variety of research and clinical applications and have the
potential to add significant explanatory power to many types of
The authors would like to thank Avi Reichenberg, Anbarasu Lourdusamy,
Martina Sattlecker and Harvey Wickham for their support and advice.
Conceived and designed the experiments: PJB G. Schumann. Performed
the experiments: PJB G. Schumann. Analyzed the data: PJB DS G.
Schumann. Contributed reagents/materials/analysis tools: SEML JR MA
AA M. Baggaley PB JB M. Broadbent M. Butler CDC AJC LD SF FG MG
AH NK AK FM RM HNB SN D. Olajide D. O’Flynn RR IR G.
Seneviratne DS SS J. Treasure J. Tully DV RS PM SL MH. Wrote the
paper: PJB SEML JR MA AA M. Baggaley PB JB M. Broadbent M. Butler
CDC AJC LD SF FG MG AH NK AK FM RM HNB SN D. Olajide D.
O’Flynn RR IR G. Seneviratne DS SS J. Treasure J. Tully DV RS PM SL
MH G. Schumann.
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- [Show abstract] [Hide abstract] ABSTRACT: Background: The limited evidence on the relationship between problem behaviours and symptoms of psychiatric disorders experienced by adults with intellectual disabilities leads to conflict about diagnostic criteria and confused treatment. This study examined the relationship between problem behaviours and other psychopathology, and compared the predictive validity of dimensional and categorical models experienced by adults with intellectual disabilities. Methods: Exploratory and confirmatory factor analyses appropriate for non-continuous data were used to derive, and validate, symptom dimensions using two clinical datasets (n=457; n=274). Categorical diagnoses were derived using DC-LD. Severity and 5-year longitudinal outcome was measured using a battery of instruments. Results: Five factors/dimensions were identified and confirmed. Problem behaviours were included in an emotion dysregulation-problem behaviour dimension that was distinct from the depressive, anxiety, organic and psychosis dimensions. The dimensional model had better predictive validity than categorical diagnosis. Conclusions: International classification systems should not include problem behaviours as behavioural equivalents in diagnostic criteria for depression or other psychiatric disorders. Investigating the relevance of emotional regulation to psychopathology may provide an important pathway for development of improved interventions. What this paper adds: There is uncertainty whether new onset problem behaviours or a change in longstanding problem behaviours should be considered as symptoms of depression or other types of psychiatric disorders in adults with intellectual disabilities. The validity of previous studies was limited by the use of pre-defined, categorical diagnoses or unreliable statistical methods. This study used robust statistical modelling to examine problem behaviours within a dimensional model of symptoms. We found that problem behaviours were included in an emotional dysregulation dimension and not in the dimension that included symptoms that are typical of depression. The dimensional model of symptoms had greater predictive validity than categorical diagnoses of psychiatric disorders. Our findings suggest that problem behaviours are a final common pathway for emotional distress in adults with intellectual disabilities so clinicians should not use a change in problem behaviours as a diagnostic criterion for depression, or other psychiatric disorders.