Harnessing Clinical Psychiatric Data with an Electronic Assessment Tool (OPCRIT+): The Utility of Symptom Dimensions (original) (raw)
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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 (R2 range: 0.06–0.28) whereas diagnosis was best just once (R2:0.25). 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.
PLoS ONE, 2013
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.
British Journal of Psychiatry, 2011
BackgroundThe increasingly large sample size requirements of modern adult mental health research suggests the need for a data collection and diagnostic application that can be used across a broad range of clinical and research populations.AimsTo develop a data collection and diagnostic application that can be used across a broad range of clinical and research settings.MethodWe expanded and redeveloped the OPCRIT system into a broadly applicable diagnostic and data-collection package and carried out an interrater reliability study of this new tool.ResultsOPCRIT+ performed well in an interrater reliability study with relatively inexperienced clinicians, giving a combined, weighted kappa of 0.70 for diagnostic reliability.ConclusionsOPCRIT+ showed good overall interrater reliability scores for diagnoses. It is now incorporated in the electronic patient record of the Maudsley and associated hospitals. OPCRIT+ can be downloaded free of charge at http://sgdp.iop.kcl.ac.uk/opcritplus.
Creating a map of psychiatric patients based on psychopathological symptom profiles
European Archives of Psychiatry and Clinical Neuroscience, 2009
Background In the current debate about the categorical or dimensional classification of mental disorders many fruitful methods to illustrate one or the other aspect are employed, and suggestions are made to combine the two perspectives. Methods We present such an approach to combine both perspectives at the same time. Based on psychopathological AMDP-symptom profiles, a map of psychiatric patients was calculated by robust nonmetric multidimensional scaling (NMDS). Results The sample from the Ludwig-Maximilians University in Munich included the records of patients, who were admitted and discharged in 2002 and 2003 with a diagnosis of either paranoid schizophrenia, (F20.00, N = 24), bipolar affective disorder, current episode manic without psychotic symptoms (F31.1, N = 32) or severe depressive episode without psychotic symptoms (F32.2, N = 78). In the resulting map of patients we found a clear categorical distinction according to the diagnostic groups, but also high regression values of AMDP-syndromes (manic syndrome: r = 0.83, depressive syndrome: r = 0.68, and paranoid-hallucinatory syndrome, r = 0.62). Discussion The map of psychiatric patients presents an approach to consider the categorical and dimensional aspects at the same time. We were able to identify meaningful delineations between diagnostic clusters as well as continuous transitions. This method allows the whole psychopathological profile of each individual patient to be considered and also to identify misdiagnosed cases at a glance.
The Camden & Islington Research Database: Using electronic mental health records for research
PloS one, 2018
Electronic health records (EHRs) are widely used in mental health services. Case registers using EHRs from secondary mental healthcare have the potential to deliver large-scale projects evaluating mental health outcomes in real-world clinical populations. We describe the Camden and Islington NHS Foundation Trust (C&I) Research Database which uses the Clinical Record Interactive Search (CRIS) tool to extract and de-identify routinely collected clinical information from a large UK provider of secondary mental healthcare, and demonstrate its capabilities to answer a clinical research question regarding time to diagnosis and treatment of bipolar disorder. The C&I Research Database contains records from 108,168 mental health patients, of which 23,538 were receiving active care. The characteristics of the patient population are compared to those of the catchment area, of London, and of England as a whole. The median time to diagnosis of bipolar disorder was 76 days (interquartile range: 1...
Psychological Medicine, 2009
Background. There is good evidence that psychotic symptoms segregate into symptom dimensions. However, it is still unclear how these dimensions are associated with risk indicators and other clinical variables, and whether they have advantages over categorical diagnosis in clinical practice. We investigated symptom dimensions in a first-onset psychosis sample and examined their associations with risk indicators and clinical variables. We then examined the relationship of categorical diagnoses to the same variables.
PloS one, 2016
The use of Electronic Health Records databases for medical research has become mainstream. In the UK, increasing use of Primary Care Databases is largely driven by almost complete computerisation and uniform standards within the National Health Service. Electronic Health Records research often begins with the development of a list of clinical codes with which to identify cases with a specific condition. We present a methodology and accompanying Stata and R commands (pcdsearch/Rpcdsearch) to help researchers in this task. We present severe mental illness as an example. We used the Clinical Practice Research Datalink, a UK Primary Care Database in which clinical information is largely organised using Read codes, a hierarchical clinical coding system. Pcdsearch is used to identify potentially relevant clinical codes and/or product codes from word-stubs and code-stubs suggested by clinicians. The returned code-lists are reviewed and codes relevant to the condition of interest are select...
Computerized assessment of common mental disorders in primary care: effect on clinical outcome
Family Practice, 1996
Objective. A randomized controlled trial was conducted to examine the clinical effectiveness of providing general practitioners (GPs) with the results of a self-administered computerized assessment of common mental disorders. Method. Attenders at a general practice in a deprived inner city area of South London were identified using case finding questionnaires. Six hundred and eighty-one subjects were randomly allocated to three groups which differed in the information provided to the GP: 1) no additional information was given to the GP; 2) the results of the 12 item General Health Questionnaire (GHQ) were given to the GP (the GHQ is a paper and pencil questionnaire that assesses common mental disorders); 3) the results of a self-administered computerized assessment (PROQSY) of common mental disorders were provided for the GP. Results. Clinical outcome was assessed using the 12-item GHQ. Consultations with the GP, prescriptions and referrals within and outside the practice were also recorded. The group in whom the GP received the results of the computerized assessment showed a modest clinical improvement, relative to the other two groups after 6 weeks. There was no difference in clinical outcome between the groups at 6 months. There appeared to be no increase in consultations or prescriptions in the computerized assessment group. Conclusions. Self-administered computerized assessments for psychiatric disorder have potential as a means of improving the clinical outcome of patients in primary care. It is likely that the effectiveness of the approach would be greatly increased by linking the results of computerized assessments to clinical practice guidelines, tailored to the individual patient by means of computerized technology.
Schizophrenia Research, 2018
Classifications of psychotic disorders are moving towards utilizing dimensional symptom domains as the preferred mechanism for describing psychotic symptomatology. The ICD-11 has proposed six symptom domains (Positive symptoms, Negative symptoms, Depressive symptoms, Manic symptoms, Psychomotor symptoms, and Cognitive symptoms) that would be rated in addition to providing a psychotic disorder diagnosis. This study investigated clinicians' use of dichotomous versus multi-point scales for rating these six domains. Global mental health professionals (n = 273) rated case vignettes using both a 2-point and 4-point version of a rating scale for the six domains. Clinicians were more accurate using the 2-point scale in absolute terms, but after correcting for chance guessing and disagreements, the two versions of the scale were equally accurate. Clinicians believed the 2-point scale would be easier to use, although they also indicated that the 4-point scale would provide richer clinical information. Participants were able to detect the presence of psychotic symptom domains in the vignettes with good reliability with no special training using either scale. We recommend that clinicians and researchers use the version of the scale that best matches their purpose (i.e., to maximize accuracy or enhance case description). Future work should develop the implementation characteristics of the scale to improve its potential for global application.
Psychiatry research, 2016
Raw data were used from five studies of adults with mental illnesses (N=4,480) in an attempt to identify a psychiatric symptoms factor structure, as measured by the Positive and Negative Syndrome Scale or the Brief Psychiatric Rating Scale, that was generalizable across participant characteristics. First, the fit of four extant models was tested via confirmatory factor analysis (CFA), then exploratory factor analyses (EFA) were conducted with a 50% random sample, followed by a CFA with the remaining 50% to confirm the EFA factor structure. Measurement invariance of the factor structure was also examined across diagnosis, sex, race, age, and hospitalization status. The extant models were not generalizable to these data. However, a 4-factor (Affective, Positive, Negative, Disorganized Cognitive Processing) model was identified that retained all items and showed invariance across participant characteristics. It is possible to obtain a psychiatric symptoms factor structure that is gener...