Negative symptoms in schizophrenia: a study in a large clinical sample of patients using a novel automated method

The relationship between negative symptoms and MATRICS neurocognitive domains: A meta-analysis and systematic review

BACKGROUND

Negative symptoms (NS) are a core symptom domain in schizophrenia spectrum disorders and are associated with poorer social and vocational functioning, and with increased likelihood and durations of hospital admission. NS are not well understood, limiting available interventions. However, numerous studies have reported associations between neurocognitive domains and NS severity. Thus, one promising area in understanding NS is in relation to neurocognition. Currently, the specificity of the relationship between NS and neurocognition is unknown, meaning that there is no consensus regarding which neurocognitive domain is most strongly associated with NS. There is a need to systematically examine the relationship between NS and various neurocognitive domains within study samples.

METHODS

A systematic search of Ovid PsycINFO, Ovid MEDLINE and Web of Science was performed for articles published since 2004 (year of MATRICS Consensus publication). Inclusion criteria were: 1) individuals with schizophrenia spectrum disorders, first episode psychosis or clinical high risk 2) assessed all six MATRICS neurocognitive domains (processing speed, attention, working memory, verbal learning & memory, visual learning & memory, reasoning & problem solving), 3) reported correlations between all six MATRICS neurocognitive domains and global NS. A three-level random effects hierarchical meta-analysis was performed to assess the relationship between NS (global, expressive, and experiential dimensions) and the six MATRICS neurocognitive domains.

RESULTS

21 studies were included in the review (n = 3619). All MATRICS neurocognitive domains had small significant correlations with global NS (r = -0.16 to -0.20, p < 0.0001). This relationship was significantly moderated by diagnosis and the moderating effect of sex/ gender trended on significance. Analysis of a subset of the studies revealed that MATRICS neurocognitive domains also had small significant correlations with the two NS dimensions, expressive and experiential. Correlations were stronger with the expressive NS dimension.

CONCLUSIONS

This review is novel in assessing the relationship between multiple neurocognitive domains and NS within the same sample, by synthesizing close to two decades of research. Our results suggest that there is a non-specific relationship between neurocognition and NS, and that expressive NS may have a stronger relationship with neurocognitive functioning-based on the MATRICS classification of neurocognition and the neurocognitive assessments used in the included studies. This has implications on our understanding of NS and neurocognition, as well as their treatments. As we gain better understanding of the directionality of the NS-cognition relationship, it could suggest that NS, particularly in the expressive domain, could be improved by targeting cognition globally or that neurocognitive treatments could be more effective if NS are addressed first. Further implications of these results are discussed.

A Study of Neurological Soft Signs and Cognition in Schizophrenia

INTRODUCTION

Neurological soft signs (NSS) are delicate neurological abnormalities that comprise deficits in motor coordination, problems with the sequencing of complex motor acts, and sensory integration difficulties. These are nonspecific with no specific localization in the brain. NSS are found in many patients with Schizophrenia. Cognitive dysfunctions are also present in more than two-thirds of patients with Schizophrenia. This study aims at assessing the NSS and its association with cognitive impairment in patients with Schizophrenia.

METHODS

 A total of 100 Schizophrenia patients were included in the study. The Heidelberg scale was used for assessing the NSS. The Montreal Cognitive Assessment Scale (MoCA) for cognitive impairment, the Positive and Negative Syndrome Scale (PANSS) for Schizophrenia, and the Brief Psychiatric Rating Scale (BPRS) were used to assess the severity. Statistical analysis was performed by Pearson's Chi-square test, Kruskal-Wallis test, Wilcoxon rank tests and Spearman rank correlation along with mean and standard deviation.

RESULTS

 NSS were present in 68% (N=68) of the patients with motor coordination being maximally affected. Cognitive impairment was found in 73% (N=73) of patients with a MoCA score <26. Patients with predominant negative symptoms had higher NSS scores and lower MoCA scores. A "statistically significant" correlation was observed between cognitive impairment and NSS. Most patients with NSS and impaired cognition were in the "markedly ill" category of BPRS.

CONCLUSION

 A significant association was observed between cognitive deficits, negative symptoms, and NSS in Schizophrenia. NSS and cognitive dysfunctions are integral parts of Schizophrenia symptom domains and need to be assessed as the negative symptoms and severity of illness are associated with NSS, especially problems with motor coordination and cognitive dysfunctions.

Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review

OBJECTIVE

Natural language processing (NLP) combined with machine learning (ML) techniques are increasingly used to process unstructured/free-text patient-reported outcome (PRO) data available in electronic health records (EHRs). This systematic review summarizes the literature reporting NLP/ML systems/toolkits for analyzing PROs in clinical narratives of EHRs and discusses the future directions for the application of this modality in clinical care.

METHODS

We searched PubMed, Scopus, and Web of Science for studies written in English between 1/1/2000 and 12/31/2020. Seventy-nine studies meeting the eligibility criteria were included. We abstracted and summarized information related to the study purpose, patient population, type/source/amount of unstructured PRO data, linguistic features, and NLP systems/toolkits for processing unstructured PROs in EHRs.

RESULTS

Most of the studies used NLP/ML techniques to extract PROs from clinical narratives (n = 74) and mapped the extracted PROs into specific PRO domains for phenotyping or clustering purposes (n = 26). Some studies used NLP/ML to process PROs for predicting disease progression or onset of adverse events (n = 22) or developing/validating NLP/ML pipelines for analyzing unstructured PROs (n = 19). Studies used different linguistic features, including lexical, syntactic, semantic, and contextual features, to process unstructured PROs. Among the 25 NLP systems/toolkits we identified, 15 used rule-based NLP, 6 used hybrid NLP, and 4 used non-neural ML algorithms embedded in NLP.

CONCLUSIONS

This study supports the potential utility of different NLP/ML techniques in processing unstructured PROs available in EHRs for clinical care. Though using annotation rules for NLP/ML to analyze unstructured PROs is dominant, deploying novel neural ML-based methods is warranted.

The relations between socio-demographic information and negative symptoms, mental health, and quality of life: a latent profile analysis with psychotic patients in Kosovo

The current study aims to identify meaningful psychotic patients' profiles by examining certain combinations of patient's demographic and socio-economic variables (sex, age, marital status, number of children, cohabitant and level of education). Moreover, we aim to assess whether there is any significant effect of class membership (profile) on negative symptoms, health state, and quality of life among psychotic patients. A convenience sample of 103 patients (age: M = 22, SD = 1.75), was drawn from the clinical populations of Kosovo. Demographic and socio-economic data was obtained through individual interviews, meanwhile a battery of questionnaires was used to assess negative symptoms, mental health, and quality of life of patients. The 4-class solution was selected as the best fitting model and used in subsequent analyses. Results indicated a significant effect of class membership on health state, quality of life and negative symptoms. Practical implications are discussed.

Long-term outcomes of the recovery approach in a high-security mental health setting: a 20 year follow-up study

Background

This study examined the outcomes of a descriptive, longitudinal cohort consisting of 241 patients initially examined in a population study at the high secure State Hospital for Scotland and Northern Ireland in 1992-93. A partial follow-up focusing on patients with schizophrenia was conducted in 2000-01, followed by a comprehensive 20 year follow-up that began in 2014.

Aims

To explore what happens to patients who required high secure care during a 20 year follow-up period.

Method

Previously collected data were amalgamated with newly collected information to examine the recovery journey since baseline. Various sources were employed, including patient and keyworker interviews, case note reviews, and extraction from health and national records, and Police Scotland datasets.

Results

Over half of the cohort (56.0%) with available data resided outside secure services at some point during the follow-up period (mean 19.2 years), and only 12% of the cohort were unable to transition out of high secure care. The symptoms of psychosis improved, with statistically significant reductions observed in reported delusions, depression, and flattened affect. Reported sadness [according to the Montgomery-Åsberg Depression Rating Scale (MADRS)] at baseline, first, and 20 year follow-up interviews was negatively correlated with the questionnaire about the process of recovery (QPR) scores at the 20 year follow-up. However, qualitative data depicted progress and personal development. According to societal measures, there was little evidence of sustained social or functional recovery. The overall conviction rate post-baseline was 22.7%, with 7.9% violent recidivism. The cohort exhibited poor morbidity and mortality, with 36.9% of the cohort dying, primarily from natural causes (91%).

Conclusions

Overall, the findings showed positive outcomes in terms of movement out of high-security settings, symptom improvement, and low levels of recidivism. Notably, this cohort experienced a high rate of deaths and poor physical morbidity, along with a lack of sustained social recovery, particularly among those who had negotiated a path through services and who were current residents in the community. Social engagement, enhanced during residence in low secure or open ward settings, diminished significantly during the transition to the community. This is likely a result of self-protective measures adopted to mitigate societal stigma and the shift from a communal environment. Subjective depressive symptoms may impact broader aspects of recovery.

Negative symptoms, striatal dopamine and model-free reward decision-making in schizophrenia

Negative symptoms, such as lack of motivation or social withdrawal, are highly prevalent and debilitating in patients with schizophrenia. Underlying mechanisms of negative symptoms are incompletely understood, thereby preventing the development of targeted treatments. We hypothesized that in patients with schizophrenia during psychotic remission, impaired influences of both model-based and model-free reward predictions on decision-making ('reward prediction influence', RPI) underlie negative symptoms. We focused on psychotic remission, because psychotic symptoms might confound reward-based decision-making. Moreover, we hypothesized that impaired model-based/model-free RPIs depend on alterations of both associative striatum dopamine synthesis and storage (DSS) and executive functioning. Both factors influence RPI in healthy subjects and are typically impaired in schizophrenia. Twenty-five patients with schizophrenia with pronounced negative symptoms during psychotic remission and 24 healthy controls were included in the study. Negative symptom severity was measured by the Positive and Negative Syndrome Scale negative subscale, model-based/model-free RPI by the two-stage decision task, associative striatum DSS by 18F-DOPA positron emission tomography and executive functioning by the symbol coding task. Model-free RPI was selectively reduced in patients and associated with negative symptom severity as well as with reduced associative striatum DSS (in patients only) and executive functions (both in patients and controls). In contrast, model-based RPI was not altered in patients. Results provide evidence for impaired model-free reward prediction influence as a mechanism for negative symptoms in schizophrenia as well as for reduced associative striatum dopamine and executive dysfunction as relevant factors. Data suggest potential treatment targets for patients with schizophrenia and pronounced negative symptoms.

Associations of remote mental healthcare with clinical outcomes: a natural language processing enriched electronic health record data study protocol

INTRODUCTION

People often experience significant difficulties in receiving mental healthcare due to insufficient resources, stigma and lack of access to care. Remote care technology has the potential to overcome these barriers by reducing travel time and increasing frequency of contact with patients. However, the safe delivery of remote mental healthcare requires evidence on which aspects of care are suitable for remote delivery and which are better served by in-person care. We aim to investigate clinical and demographic associations with remote mental healthcare in a large electronic health record (EHR) dataset and the degree to which remote care is associated with differences in clinical outcomes using natural language processing (NLP) derived EHR data.

METHODS AND ANALYSIS

Deidentified EHR data, derived from the South London and Maudsley (SLaM) National Health Service Foundation Trust Biomedical Research Centre (BRC) Case Register, will be extracted using the Clinical Record Interactive Search tool for all patients receiving mental healthcare between 1 January 2019 and 31 March 2022. First, data on a retrospective, longitudinal cohort of around 80 000 patients will be analysed using descriptive statistics to investigate clinical and demographic associations with remote mental healthcare and multivariable Cox regression to compare clinical outcomes of remote versus in-person assessments. Second, NLP models that have been previously developed to extract mental health symptom data will be applied to around 5 million documents to analyse the variation in content of remote versus in-person assessments.

ETHICS AND DISSEMINATION

The SLaM BRC Case Register and Clinical Record Interactive Search (CRIS) tool have received ethical approval as a deidentified dataset (including NLP-derived data from unstructured free text documents) for secondary mental health research from Oxfordshire REC C (Ref: 18/SC/0372). The study has received approval from the SLaM CRIS Oversight Committee. Study findings will be disseminated through peer-reviewed, open access journal articles and service user and carer advisory groups.

Increasing social and community participation in veterans living with schizophrenia: A treatment outcome study

People living with schizophrenia often face challenges engaging in social and community activities. A critical barrier is negative symptoms that reflect diminished feelings and thoughts that support social interaction. Several years ago, we began a process of specifying an intervention for individuals with schizophrenia and clinically meaningful negative symptoms that could be delivered in an integrated fashion with mental health services offered in VA medical centers with the primary focus of improving social and community engagement. In the present study, we examined the impact of a multi-component intervention to improve social and community participation in a group of Veterans living with schizophrenia and negative symptoms. We compared an intervention called Engaging in Community Roles and Experiences (EnCoRE) - a 12-week program of individual and group meetings that support learning and implementing skills with the goal of helping participants increase engagement in personally-relevant social and community activities - to an active wellness education control condition. Participants in both conditions attended on average of at least half of the groups that were offered, indicating that many individuals living with negative symptoms are willing to participate in an intervention to improve social and community participation. Although there were no significant differences on the two primary outcomes, those in EnCoRE showed better social and general functioning at post treatment and improved social motivational negative symptoms and decreases in perceived limitations at a 3-month follow-up. EnCoRE may be especially beneficial for participants who endorsed more dysfunctional attitudes about their abilities.

Examining the repeatable battery for the assessment of neuropsychological status validity indices in people with schizophrenia spectrum disorders

Objective: We examined the frequency of possible invalid test scores on the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) in patients with schizophrenia spectrum disorders, and whether there was an association between scores on the embedded RBANS performance validity tests (PVTs) and self-reported symptoms of apathy as measured by the Initiate Scale of the Behavior Rating Inventory of Executive Function-Adult Version (BRIEF-A). Methods: Participants included 250 patients (M = 24.4 years-old, SD = 5.7) with schizophrenia spectrum disorders. Base rates of RBANS Effort Index (EI), Effort Scale (ES), and Performance Validity Index (PVI) test scores were computed. Spearman correlations were used to examine the associations between the RBANS PVTs, the RBANS Index scores, and the BRIEF-A Initiate Scale. Regression analyses were used to investigate how well the RBANS PVTs predicted scores on the BRIEF-A Initiate Scale. Results: The frequency of invalid scores on the EI (>3) and the PVI (<42) in participants with schizophrenia spectrum disorders was 6%. The frequency of invalid ES scores (<12) was 28% in the patients compared to 15% in the U.S. standardization sample. There was a small significant correlation between the EI and the BRIEF-A Initiate Scale (rho=.158, p<.05). Conclusions: The rates of invalid scores were similar to previously published studies. Invalid scores on the BRIEF-A were uncommon. Apathy measured with the BRIEF-A Initiate Scale was not associated with performance on the RBANS validity measures or with measures of cognition.

Associations of presenting symptoms and subsequent adverse clinical outcomes in people with unipolar depression: a prospective natural language processing (NLP), transdiagnostic, network analysis of electronic health record (EHR) data

OBJECTIVE

To investigate the associations of symptoms of mania and depression with clinical outcomes in people with unipolar depression.

DESIGN

A natural language processing electronic health record study. We used network analysis to determine symptom network structure and multivariable Cox regression to investigate associations with clinical outcomes.

SETTING

The South London and Maudsley Clinical Record Interactive Search database.

PARTICIPANTS

All patients presenting with unipolar depression between 1 April 2006 and 31 March 2018.

EXPOSURE

(1) Symptoms of mania: Elation; Grandiosity; Flight of ideas; Irritability; Pressured speech. (2) Symptoms of depression: Disturbed mood; Anhedonia; Guilt; Hopelessness; Helplessness; Worthlessness; Tearfulness; Low energy; Reduced appetite; Weight loss. (3) Symptoms of mania or depression (overlapping symptoms): Poor concentration; Insomnia; Disturbed sleep; Agitation; Mood instability.

MAIN OUTCOMES

(1) Bipolar or psychotic disorder diagnosis. (2) Psychiatric hospital admission.

RESULTS

Out of 19 707 patients, at least 1 depression, overlapping or mania symptom was present in 18 998 (96.4%), 15 954 (81.0%) and 4671 (23.7%) patients, respectively. 2772 (14.1%) patients subsequently developed bipolar or psychotic disorder during the follow-up period. The presence of at least one mania (HR 2.00, 95% CI 1.85 to 2.16), overlapping symptom (HR 1.71, 95% CI 1.52 to 1.92) or symptom of depression (HR 1.31, 95% CI 1.07 to 1.61) were associated with significantly increased risk of onset of a bipolar or psychotic disorder. Mania (HR 1.95, 95% CI 1.77 to 2.15) and overlapping symptoms (HR 1.76, 95% CI 1.52 to 2.04) were associated with greater risk for psychiatric hospital admission than symptoms of depression (HR 1.41, 95% CI 1.06 to 1.88).

CONCLUSIONS

The presence of mania or overlapping symptoms in people with unipolar depression is associated with worse clinical outcomes. Symptom-based approaches to defining clinical phenotype may facilitate a more personalised treatment approach and better predict subsequent clinical outcomes than psychiatric diagnosis alone.

NeuroBlu, an electronic health record (EHR) trusted research environment (TRE) to support mental healthcare analytics with real-world data

PURPOSE

NeuroBlu is a real-world data (RWD) repository that contains deidentified electronic health record (EHR) data from US mental healthcare providers operating the MindLinc EHR system. NeuroBlu enables users to perform statistical analysis through a secure web-based interface. Structured data are available for sociodemographic characteristics, mental health service contacts, hospital admissions, International Classification of Diseases ICD-9/ICD-10 diagnosis, prescribed medications, family history of mental disorders, Clinical Global Impression-Severity and Improvement (CGI-S/CGI-I) and Global Assessment of Functioning (GAF). To further enhance the data set, natural language processing (NLP) tools have been applied to obtain mental state examination (MSE) and social/environmental data. This paper describes the development and implementation of NeuroBlu, the procedures to safeguard data integrity and security and how the data set supports the generation of real-world evidence (RWE) in mental health.

PARTICIPANTS

As of 31 July 2021, 562 940 individuals (48.9% men) were present in the data set with a mean age of 33.4 years (SD: 18.4 years). The most frequently recorded diagnoses were substance use disorders (1 52 790 patients), major depressive disorder (1 29 120 patients) and anxiety disorders (1 03 923 patients). The median duration of follow-up was 7 months (IQR: 1.3 to 24.4 months).

FINDINGS TO DATE

The data set has supported epidemiological studies demonstrating increased risk of psychiatric hospitalisation and reduced antidepressant treatment effectiveness among people with comorbid substance use disorders. It has also been used to develop data visualisation tools to support clinical decision-making, evaluate comparative effectiveness of medications, derive models to predict treatment response and develop NLP applications to obtain clinical information from unstructured EHR data.

FUTURE PLANS

The NeuroBlu data set will be further analysed to better understand factors related to poor clinical outcome, treatment responsiveness and the development of predictive analytic tools that may be incorporated into the source EHR system to support real-time clinical decision-making in the delivery of mental healthcare services.

Psychometric properties of the BIRT Motivation Questionnaire (BMQ), a self-measure of avolition in individuals with schizophrenia

AIMS

Avolition defined as a lack of interest or engagement in goal-directed behavior plays a key role in everyday functioning in schizophrenia and is considered as one of the main contributors to the burden of disease. The aim of this study was to 1) validate the self-report BIRT Motivation Questionnaire (BMQ-S) seldom used before in schizophrenia 2) examine the degree of agreement between the BMQ-S and its informant-report version 3) to assess its ability to predict real-world outcome at 12 month follow-up.

METHODS

One hundred and twenty-two (51.9% inpatients) adults with a diagnosis of schizophrenia were included. Exploratory Factor analysis was performed on the BMQ-S to identify the underlying structure. Real life functioning was measured with the Global Assessment of Functioning scale (GAF). Convergent validity was assessed with the Scale for Assessment of Negative Symptom (SANS) and the Lille Apathy Rating Scale (LARS).

RESULTS

The main psychometric properties of the BMQ-S (internal consistency, test-retest reliability) were satisfactory. Exploratory factorial analysis revealed a 4-factor model which explained 76% of the overall variance. The BMQ-S correlated significantly with the LARS and the SANS avolition subscore suggesting adequate convergent validity. The correlation between the BMQ-S and the clinician-report version was 0.48. The global score and in particular the Initiation/disorganisation dimension was a significant predictor of global functioning at 12-months even when adjusted for age, chlorpromazine intake and depression.

CONCLUSION

Our findings indicate that the BMQ-S has satisfactory psychometric properties and that schizophrenia patients can reliably assess their lack of motivation. Self-evaluation of avolition should be considered in the overall prediction of real-world functioning in schizophrenia.

Defining negative symptoms remission in schizophrenia using the Brief Negative Symptom Scale

INTRODUCTION

This study aimed to propose criteria for negative symptoms remission (NSR) in schizophrenia using the Brief Negative Symptom Scale (BNSS).

MATERIAL AND METHODS

274 participants were assessed on the Positive and Negative Syndrome Scale (PANSS), BNSS and Social and Occupational Functioning Assessment Scale (SOFAS). Two criteria for NSR on the BNSS were proposed - NSR based on the BNSS domains scores (NSR_{BNSS_DOMAINS}) and NSR based on 5 key items of the BNSS (NSR_{BNSS_5ITEMS})_. A SOFAS score of 61 and above was considered as functional remission (FR). Logistic regressions were run to examine the association between FR and NSR. Receiver operating characteristic (ROC) curve analysis was performed for the NSR criteria on FR. Kappa agreement statistic was used to evaluate the agreement between the two NSR criteria.

RESULTS

Eighty-nine (32.5%) participants fulfilled NSR_{BNSS_DOMAINS} criterion whereas 70 (25.6%) participants fulfilled NSR_{BNSS_5ITEMS} criterion. The two NSR criteria had substantial agreement (Kappa statistic=0.797) with each other. Sixty-one (25.3%) participants were in FR. FR was significantly associated with NSR, irrespective of the criterion used. To predict FR, the Area Under the Curve for NSR_{BNSS_DOMAINS} and NSR_{BNSS_5ITEMS} were 0.761 (CI: 0.696-0.826, p<0.001) and 0.723 (CI: 0.656-0.790, p<0.001), respectively. Hence, both NSR criteria demonstrated a fair ability to discriminate between functional remitters and non-remitters.

CONCLUSIONS

Depending on the setting and needs, clinicians and researchers might employ either the full BNSS or an abbreviated 5-item BNSS scale to identify NSR in schizophrenia. More research is needed to further examine the validity of these criteria in schizophrenia.

The Latent Structure of Negative Symptoms in the General Population in Adolescence and Emerging Adulthood

Negative symptoms predict adverse outcomes within psychotic disorders, in individuals at high-risk for psychosis, and in young people in the community. There is considerable interest in the dimensional structure of negative symptoms in clinical samples, and accumulating evidence suggests a 5-factor structure. Little is known about the underlying structure of negative symptoms in young people despite the importance of this developmental stage for mental health. We used confirmatory factor analysis to test the structure of parent-reported negative symptoms at mean ages 16.32 (SD 0.68, N = 4974), 17.06 (SD 0.88, N = 1469) and 22.30 (SD 0.93, N = 5179) in a community sample. Given previously reported associations between total negative symptoms and genome-wide polygenic scores (GPS) for major depressive disorder (MDD) and schizophrenia in adolescence, we assessed associations between individual subdomains and these GPSs. A 5-factor model of flat affect, alogia, avolition, anhedonia, and asociality provided the best fit at each age and was invariant over time. The results of our linear regression analyses showed associations between MDD GPS with avolition, flat affect, anhedonia, and asociality, and between schizophrenia GPS with avolition and flat affect. We showed that a 5-factor structure of negative symptoms is present from ages 16 to 22 in the community. Avolition was most consistently associated with polygenic liability to MDD and schizophrenia, and alogia was least associated. These findings highlight the value of dissecting negative symptoms into psychometrically derived subdomains and may offer insights into early manifestation of genetic risk for MDD and schizophrenia.

Application of Artificial Intelligence in Community-Based Primary Health Care: Systematic Scoping Review and Critical Appraisal

BACKGROUND

Research on the integration of artificial intelligence (AI) into community-based primary health care (CBPHC) has highlighted several advantages and disadvantages in practice regarding, for example, facilitating diagnosis and disease management, as well as doubts concerning the unintended harmful effects of this integration. However, there is a lack of evidence about a comprehensive knowledge synthesis that could shed light on AI systems tested or implemented in CBPHC.

OBJECTIVE

We intended to identify and evaluate published studies that have tested or implemented AI in CBPHC settings.

METHODS

We conducted a systematic scoping review informed by an earlier study and the Joanna Briggs Institute (JBI) scoping review framework and reported the findings according to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis-Scoping Reviews) reporting guidelines. An information specialist performed a comprehensive search from the date of inception until February 2020, in seven bibliographic databases: Cochrane Library, MEDLINE, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), ScienceDirect, and IEEE Xplore. The selected studies considered all populations who provide and receive care in CBPHC settings, AI interventions that had been implemented, tested, or both, and assessed outcomes related to patients, health care providers, or CBPHC systems. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Two authors independently screened the titles and abstracts of the identified records, read the selected full texts, and extracted data from the included studies using a validated extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. A third reviewer also validated all the extracted data.

RESULTS

We retrieved 22,113 documents. After the removal of duplicates, 16,870 documents were screened, and 90 peer-reviewed publications met our inclusion criteria. Machine learning (ML) (41/90, 45%), natural language processing (NLP) (24/90, 27%), and expert systems (17/90, 19%) were the most commonly studied AI interventions. These were primarily implemented for diagnosis, detection, or surveillance purposes. Neural networks (ie, convolutional neural networks and abductive networks) demonstrated the highest accuracy, considering the given database for the given clinical task. The risk of bias in diagnosis or prognosis studies was the lowest in the participant category (4/49, 4%) and the highest in the outcome category (22/49, 45%).

CONCLUSIONS

We observed variabilities in reporting the participants, types of AI methods, analyses, and outcomes, and highlighted the large gap in the effective development and implementation of AI in CBPHC. Further studies are needed to efficiently guide the development and implementation of AI interventions in CBPHC settings.

Cognitive Impairments in Schizophrenia: A Study in a Large Clinical Sample Using Natural Language Processing

Background: Cognitive impairments are a neglected aspect of schizophrenia despite being a major factor of poor functional outcome. They are usually measured using various rating scales, however, these necessitate trained practitioners and are rarely routinely applied in clinical settings. Recent advances in natural language processing techniques allow us to extract such information from unstructured portions of text at a large scale and in a cost effective manner. We aimed to identify cognitive problems in the clinical records of a large sample of patients with schizophrenia, and assess their association with clinical outcomes. Methods: We developed a natural language processing based application identifying cognitive dysfunctions from the free text of medical records, and assessed its performance against a rating scale widely used in the United Kingdom, the cognitive component of the Health of the Nation Outcome Scales (HoNOS). Furthermore, we analyzed cognitive trajectories over the course of patient treatment, and evaluated their relationship with various socio-demographic factors and clinical outcomes. Results: We found a high prevalence of cognitive impairments in patients with schizophrenia, and a strong correlation with several socio-demographic factors (gender, education, ethnicity, marital status, and employment) as well as adverse clinical outcomes. Results obtained from the free text were broadly in line with those obtained using the HoNOS subscale, and shed light on additional associations, notably related to attention and social impairments for patients with higher education. Conclusions: Our findings demonstrate that cognitive problems are common in patients with schizophrenia, can be reliably extracted from clinical records using natural language processing, and are associated with adverse clinical outcomes. Harvesting the free text from medical records provides a larger coverage in contrast to neurocognitive batteries or rating scales, and access to additional socio-demographic and clinical variables. Text mining tools can therefore facilitate large scale patient screening and early symptoms detection, and ultimately help inform clinical decisions.

Defining negative symptoms remission in schizophrenia using the Brief Negative Symptom Scale

INTRODUCTION

This study aimed to propose criteria for negative symptoms remission (NSR) in schizophrenia using the Brief Negative Symptom Scale (BNSS).

MATERIAL AND METHODS

274 participants were assessed on the Positive and Negative Syndrome Scale (PANSS), BNSS and Social and Occupational Functioning Assessment Scale (SOFAS). Two criteria for NSR on the BNSS were proposed - NSR based on the BNSS domains scores (NSRBNSS_DOMAINS) and NSR based on 5 key items of the BNSS (NSRBNSS_5ITEMS). A SOFAS score of 61 and above was considered as functional remission (FR). Logistic regressions were run to examine the association between FR and NSR. Receiver operating characteristic (ROC) curve analysis was performed for the NSR criteria on FR. Kappa agreement statistic was used to evaluate the agreement between the two NSR criteria.

RESULTS

Eighty-nine (32.5%) participants fulfilled NSRBNSS_DOMAINS criterion whereas 70 (25.6%) participants fulfilled NSRBNSS_5ITEMS criterion. The two NSR criteria had substantial agreement (Kappa statistic=0.797) with each other. Sixty-one (25.3%) participants were in FR. FR was significantly associated with NSR, irrespective of the criterion used. To predict FR, the Area Under the Curve for NSRBNSS_DOMAINS and NSRBNSS_5ITEMS were 0.761 (CI: 0.696-0.826, p<0.001) and 0.723 (CI: 0.656-0.790, p<0.001), respectively. Hence, both NSR criteria demonstrated a fair ability to discriminate between functional remitters and non-remitters.

CONCLUSIONS

Depending on the setting and needs, clinicians and researchers might employ either the full BNSS or an abbreviated 5-item BNSS scale to identify NSR in schizophrenia. More research is needed to further examine the validity of these criteria in schizophrenia.

Machine Learning and Natural Language Processing in Mental Health: Systematic Review

BACKGROUND

Machine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining.

OBJECTIVE

The primary aim of this systematic review was to summarize and characterize, in methodological and technical terms, studies that used machine learning and NLP techniques for mental health. The secondary aim was to consider the potential use of these methods in mental health clinical practice.

METHODS

This systematic review follows the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) guidelines and is registered with PROSPERO (Prospective Register of Systematic Reviews; number CRD42019107376). The search was conducted using 4 medical databases (PubMed, Scopus, ScienceDirect, and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, and mental disorder. The exclusion criteria were as follows: languages other than English, anonymization process, case studies, conference papers, and reviews. No limitations on publication dates were imposed.

RESULTS

A total of 327 articles were identified, of which 269 (82.3%) were excluded and 58 (17.7%) were included in the review. The results were organized through a qualitative perspective. Although studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into 3 categories: patients included in medical databases, patients who came to the emergency room, and social media users. The main objectives were to extract symptoms, classify severity of illness, compare therapy effectiveness, provide psychopathological clues, and challenge the current nosography. Medical records and social media were the 2 major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than transparent functioning classifiers. Python was the most frequently used platform.

CONCLUSIONS

Machine learning and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new information, and only one major category of the population (ie, social media users) is an imprecise cohort. Moreover, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, machine learning and NLP techniques provide useful information from unexplored data (ie, patients' daily habits that are usually inaccessible to care providers). Before considering It as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner. Machine learning and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice.

Gender differences in clinical presentation and illicit substance use during first episode psychosis: a natural language processing, electronic case register study

OBJECTIVE

To determine whether gender differences in symptom presentation at first episode psychosis (FEP) remain even when controlling for substance use, age and ethnicity, using natural language processing applied to electronic health records (EHRs).

DESIGN, SETTING AND PARTICIPANTS

Data were extracted from EHRs of 3350 people (62% male patients) who had presented to the South London and Maudsley NHS Trust with a FEP between 1 April 2007 and 31 March 2017. Logistic regression was used to examine gender differences in the presentation of positive, negative, depressive, mania and disorganisation symptoms.

EXPOSURES FOR OBSERVATIONAL STUDIES

Gender (male vs female).

MAIN OUTCOMES AND MEASURES

Presence of positive, negative, depressive, mania and disorganisation symptoms at initial clinical presentation.

RESULTS

Eight symptoms were significantly more prevalent in men (poverty of thought, negative symptoms, social withdrawal, poverty of speech, aggression, grandiosity, paranoia and agitation). Conversely, tearfulness, low energy, reduced appetite, low mood, pressured speech, mood instability, flight of ideas, guilt, mutism, insomnia, poor concentration, tangentiality and elation were more prevalent in women than men. Negative symptoms were more common among men (OR 1.85, 95% CI 1.33 to 2.62) and depressive and manic symptoms more common among women (OR 0.30, 95% CI 0.26 to 0.35). After adjustment for illicit substance use, the strength of associations between gender and negative, manic and depression symptoms increased, whereas gender differences in aggression, agitation, paranoia and grandiosity became insignificant.

CONCLUSIONS

There are clear gender differences in the clinical presentation of FEP. Our findings suggest that gender can have a substantial influence on the nature of clinical presentation in people with psychosis, and that this is only partly explained by exposure to illicit substance use.

An evaluation of symptom domains in the 2 years before pregnancy as predictors of relapse in the perinatal period in women with severe mental illness

BACKGROUND

Symptoms may be more useful prognostic markers for mental illness than diagnoses. We sought to investigate symptom domains in women with pre-existing severe mental illness (SMI; psychotic and bipolar disorder) as predictors of relapse risk during the perinatal period.

METHODS

Data were obtained from electronic health records of 399 pregnant women with SMI diagnoses from a large south London mental healthcare provider. Symptoms within six domains characteristically associated with SMI (positive, negative, disorganization, mania, depression, and catatonia) recorded in clinical notes 2 years before pregnancy were identified with natural language processing algorithms to extract data from text, and associations investigated with hospitalization during pregnancy and 3 months postpartum.

RESULTS

Seventy-six women (19%) relapsed during pregnancy and 107 (27%) relapsed postpartum. After adjusting for covariates, disorganization symptoms showed a positive association at borderline significance with relapse during pregnancy (adjusted odds ratio [aOR] = 1.36; 95% confidence interval [CI] = 0.99-1.87 per unit increase in number of symptoms) and depressive symptoms negatively with relapse postpartum (0.78; 0.62-0.98). Restricting the sample to women with at least one recorded symptom in any given domain, higher disorganization (1.84; 1.22-2.76), positive (1.50; 1.07-2.11), and manic (1.48; 1.03-2.11) symptoms were associated with relapse during pregnancy, and disorganization (1.54; 1.08-2.20) symptom domains were associated with relapse postpartum.

CONCLUSIONS

Positive, disorganization, and manic symptoms recorded in the 2 years before pregnancy were associated with increased risk of relapse during pregnancy and postpartum. The characterization of routine health records from text fields is relatively transferrable and could help inform predictive risk modelling.

Using Natural Language Processing on Electronic Health Records to Enhance Detection and Prediction of Psychosis Risk

BACKGROUND

Using novel data mining methods such as natural language processing (NLP) on electronic health records (EHRs) for screening and detecting individuals at risk for psychosis.

METHOD

The study included all patients receiving a first index diagnosis of nonorganic and nonpsychotic mental disorder within the South London and Maudsley (SLaM) NHS Foundation Trust between January 1, 2008, and July 28, 2018. Least Absolute Shrinkage and Selection Operator (LASSO)-regularized Cox regression was used to refine and externally validate a refined version of a five-item individualized, transdiagnostic, clinically based risk calculator previously developed (Harrell's C = 0.79) and piloted for implementation. The refined version included 14 additional NLP-predictors: tearfulness, poor appetite, weight loss, insomnia, cannabis, cocaine, guilt, irritability, delusions, hopelessness, disturbed sleep, poor insight, agitation, and paranoia.

RESULTS

A total of 92 151 patients with a first index diagnosis of nonorganic and nonpsychotic mental disorder within the SLaM Trust were included in the derivation (n = 28 297) or external validation (n = 63 854) data sets. Mean age was 33.6 years, 50.7% were women, and 67.0% were of white race/ethnicity. Mean follow-up was 1590 days. The overall 6-year risk of psychosis in secondary mental health care was 3.4 (95% CI, 3.3-3.6). External validation indicated strong performance on unseen data (Harrell's C 0.85, 95% CI 0.84-0.86), an increase of 0.06 from the original model.

CONCLUSIONS

Using NLP on EHRs can considerably enhance the prognostic accuracy of psychosis risk calculators. This can help identify patients at risk of psychosis who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes.

Making the Most of Big Data in Plastic Surgery: Improving Outcomes, Protecting Patients, Informing Service Providers

ABSTRACT

In medicine, "big data" refers to the interdisciplinary analysis of high-volume, diverse clinical and lifestyle information on large patient populations. Recent advancements in data storage and electronic record keeping have enabled the expansion of research in this field. In the United Kingdom, Big data has been highlighted as one of the government's "8 Great Technologies," and the Medical Research Council has invested more than £100 million since 2012 in developing the Health Data Research UK infrastructure. The recent Royal College of Surgeons Commission of the Future of Surgery concluded that analysis of big data is one of the 4 most likely avenues to bring some of the most innovative changes to surgical practice in the 21st century.In this article, we provide an overview of the nascent field of big data analytics in plastic and highlight how it has the potential to improve outcomes, increase safety, and aid service planning.We outline the current resources available, the emerging role of big data within the subspecialties of burns, microsurgery, skin and breast cancer, and how these data can be used. We critically review the limitations and considerations raised with big data, offer suggestions regarding database optimization, and suggest future directions for research in this exciting field.

The Potential of Research Drawing on Clinical Free Text to Bring Benefits to Patients in the United Kingdom: A Systematic Review of the Literature

Background: The analysis of clinical free text from patient records for research has potential to contribute to the medical evidence base but access to clinical free text is frequently denied by data custodians who perceive that the privacy risks of data-sharing are too high. Engagement activities with patients and regulators, where views on the sharing of clinical free text data for research have been discussed, have identified that stakeholders would like to understand the potential clinical benefits that could be achieved if access to free text for clinical research were improved. We aimed to systematically review all UK research studies which used clinical free text and report direct or potential benefits to patients, synthesizing possible benefits into an easy to communicate taxonomy for public engagement and policy discussions. Methods: We conducted a systematic search for articles which reported primary research using clinical free text, drawn from UK health record databases, which reported a benefit or potential benefit for patients, actionable in a clinical environment or health service, and not solely methods development or data quality improvement. We screened eligible papers and thematically analyzed information about clinical benefits reported in the paper to create a taxonomy of benefits. Results: We identified 43 papers and derived five themes of benefits: health-care quality or services improvement, observational risk factor-outcome research, drug prescribing safety, case-finding for clinical trials, and development of clinical decision support. Five papers compared study quality with and without free text and found an improvement of accuracy when free text was included in analytical models. Conclusions: Findings will help stakeholders weigh the potential benefits of free text research against perceived risks to patient privacy. The taxonomy can be used to aid public and policy discussions, and identified studies could form a public-facing repository which will help the health-care text analysis research community better communicate the impact of their work.

Primary and Secondary Negative Symptoms in Schizophrenia

The negative symptoms of schizophrenia include volitional (motivational) impairment manifesting as avolition, anhedonia, social withdrawal, and emotional disorders such as alogia and affective flattening. Negative symptoms worsen patients' quality of life and functioning. From the diagnostic point of view, it is important to differentiate between primary negative symptoms, which are regarded as an integral dimension of schizophrenia, and secondary negative symptoms occurring as a result of positive symptoms, comorbid depression, side effects of antipsychotics, substance abuse, or social isolation. If secondary negative symptoms overlap with primary negative symptoms, it can create a false clinical impression of worsening deficit symptoms and disease progression, which leads to the choice of incorrect therapeutic strategy with excessive dopamine blocker loading. Different longitudinal trajectories of primary and secondary negative symptoms in different schizophrenia stages are proposed as an important additional discriminating factor. This review and position paper focuses primarily on clinical aspects of negative symptoms in schizophrenia, their definition, phenomenology, factor structure, and classification. It covers the historical and modern concepts of the paradigm of positive and negative symptoms in schizophrenia, as well as a detailed comparison of the assessment tools and psychometric tests used for the evaluation of negative symptoms.

Self-evaluation of negative symptoms in adolescent and young adult first psychiatric episodes

BACKGROUND

Negative Symptoms (blunted affect, alogia, anhedonia, avolition, and asociality) are usually described in schizophrenia but they are also present in other psychiatric disorders. The diagnosis and prognosis relevance of negative symptoms (NS) self-assessment during a first psychiatric episode is still unknown.

AIMS

To determine (i) the rate of self-assessed NS in a first psychiatric episode among adolescents and young adults compared to control subjects; and (ii), whether there is a difference in the prevalence of NS between schizophrenia and major depressive disorder first episodes.

METHODS

The population included patients aged 15-25 years, with no psychiatric history and no history of medication. A dimensional evaluation was assessed during hospitalization, including depressive (Hamilton Depression Scale), psychotic symptoms (Prodromal Questionnaire, 16 items) and the self-evaluation of negative symptoms (SNS scale). Prospective categorical diagnoses were updated 6 months after hospitalization. The population included 117 individuals (58 patients and 59 healthy controls).

RESULTS

Among healthy individuals, 47.5% reported at least one NS, the most reported being amotivation. After binary logistic regression, Negative Symptoms (SNS score) were associated with a diagnostic of psychiatric disorder at the 6-months follow-up (OR = 1.163, p = .001), whereas depressive symptoms and psychotic experiences were not. A SNS threshold allowed to screen first episode patients and SZ patients in the general population (assessed with ROC curve). A high prevalence of self-reported NS was observed across diagnostic boundaries in first psychiatric episodes, with a mean SNS score of 19.3 ± 7.1 for schizophrenic disorders and 20.7 ± 8.6 for depressive disorders. The prevalence of NS was not significantly different between depressive disorders and schizophrenic disorders (p > .05).

CONCLUSION

NS are an important transnosographic dimension during first psychiatric episodes among adolescents and young adults. Negative symptoms self-assessment with the SNS scale is relevant during a first psychiatric episode.

Cariprazine in the management of negative symptoms of schizophrenia: state of the art and future perspectives

In schizophrenia, dopaminergic hyperactivity in the mesolimbic regions, or possibly even selectively so in the dorsal striatum, seems to cause the emergence of psychotic symptoms, whereas dopaminergic hypoactivity in cortical regions underlies the negative symptoms and cognitive deficits. Managing the negative symptoms is a major current challenge in the treatment of schizophrenia with a dearth of novel modalities to address this clinical issue. Cariprazine is a novel second-generation antipsychotic that specifically targets the D3 receptor mainly associated to negative symptoms. The review summarizes the main issues regarding negative symptom management and the role of cariprazine treatment.

Toward the Development of Data Governance Standards for Using Clinical Free-Text Data in Health Research: Position Paper

BACKGROUND

Clinical free-text data (eg, outpatient letters or nursing notes) represent a vast, untapped source of rich information that, if more accessible for research, would clarify and supplement information coded in structured data fields. Data usually need to be deidentified or anonymized before they can be reused for research, but there is a lack of established guidelines to govern effective deidentification and use of free-text information and avoid damaging data utility as a by-product.

OBJECTIVE

This study aimed to develop recommendations for the creation of data governance standards to integrate with existing frameworks for personal data use, to enable free-text data to be used safely for research for patient and public benefit.

METHODS

We outlined data protection legislation and regulations relating to the United Kingdom for context and conducted a rapid literature review and UK-based case studies to explore data governance models used in working with free-text data. We also engaged with stakeholders, including text-mining researchers and the general public, to explore perceived barriers and solutions in working with clinical free-text.

RESULTS

We proposed a set of recommendations, including the need for authoritative guidance on data governance for the reuse of free-text data, to ensure public transparency in data flows and uses, to treat deidentified free-text data as potentially identifiable with use limited to accredited data safe havens, and to commit to a culture of continuous improvement to understand the relationships between the efficacy of deidentification and reidentification risks, so this can be communicated to all stakeholders.

CONCLUSIONS

By drawing together the findings of a combination of activities, we present a position paper to contribute to the development of data governance standards for the reuse of clinical free-text data for secondary purposes. While working in accordance with existing data governance frameworks, there is a need for further work to take forward the recommendations we have proposed, with commitment and investment, to assure and expand the safe reuse of clinical free-text data for public benefit.

A transnosographic approach of negative symptoms pathophysiology in schizophrenia and depressive disorders

BACKGROUND

Negative Symptoms (blunted affect, alogia, anhedonia, avolition and asociality) are observed in schizophrenia but also in depressive disorders.

OBJECTIVE

To gather cognitive, neuroanatomical, neurofunctional and neurobiological knowledge of negative symptoms in studies on schizophrenia, depressive disorder, and transnosographic studies.

RESULTS

Blunted affect in schizophrenia is characterized by amygdala hyperactivation and frontal hypoactivation, also found in depressive disorder. Mirror neurons, may be related to blunted affect in schizophrenia. Alogia may be related to cognitive dysfunction and basal ganglia area impairments in schizophrenia. Data surrounding alogia in depressive disorder is scarce; wider speech deficits are often studied instead. Consummatory Anhedonia may be less affected than Anticipatory Anhedonia in schizophrenia. Anhedonia is associated with reward impairments and altered striatal functions in both diagnostics. Amotivation is associated with Corticostriatal Hypoactivation in both disorders. Anhedonia and amotivation are transnosographically associated with dopamine dysregulation. Asociality may be related to oxytocin.

CONCLUSION

Pathophysiological hypotheses are specific to each dimension of negative symptoms and overlap across diagnostic boundaries, possibly underpinning the observed clinical continuum.

Are Negative Symptoms Merely the "Real World" Consequences of Deficits in Social Cognition?

Many investigations have demonstrated that negative symptoms and social cognitive deficits in schizophrenia play a large role in determining functional outcomes and ultimately long-term prognosis. Given this, there is increasing interest in understanding the relationship between these two symptom domains, particularly since studies have consistently found moderate to large associations between them. This shared variance raises a key question: to what degree do these two categories of symptoms arise from overlapping or identical changes in brain function? In other words, do some or all negative symptoms represent merely the downstream effects of social cognition deficits on daily functioning? In this commentary, the evidence for and against this possibility, limitations of currently validated empirical measurements of these symptoms, and directions for further investigation of this hypothesis are discussed. Understanding the shared and distinct mechanisms of these disabling deficits will have important implications for the design of novel, personalized treatments for psychotic illness.

Anhedonia across borders: Transdiagnostic relevance of reward dysfunction for noninvasive brain stimulation endophenotypes

INTRODUCTION

Anhedonia is a transdiagnostic psychopathological dimension, consisting in the impaired ability to experience pleasure. In order to further our understanding of its neural correlates and to explore its potential relevance as a predictor of treatment response, in this article we systematically reviewed studies involving anhedonia and neuromodulation interventions, across different disorders.

METHODS

We included seven studies fulfilling inclusion/exclusion criteria and involving different measures of anticipatory and consummatory anhedonia, as well as different noninvasive brain stimulation interventions (transcranial magnetic stimulation and transcranial direct current stimulation). Studies not exploring hedonic measures or not involving neuromodulation intervention were excluded.

RESULTS

All the included studies entailed the use of rTMS protocols in one of the diverse prefrontal targets. The limited amount of studies and the heterogeneity of stimulation protocols did not allow to draw any conclusion with regard to the efficacy of rTMS in the treatment of transnosographic anhedonia. A potential for anhedonia in dissecting possible endophenotypes of different psychopathological conditions preliminarily emerged.

CONCLUSIONS

Anhedonia is an underexplored condition in neuromodulation trials. It may represent a valuable transdiagnostic dimension that requires further examination in order to discover new clinical predictors for treatment response.

Negative symptoms in first-episode psychosis: Clinical correlates and 1-year follow-up outcomes in London Early Intervention Services

AIM

Negative symptoms (NS) have been associated with poor outcome and remain difficult to treat in patients with psychosis. This study examined the association of NS with clinical features at first presentation to mental health services for psychosis and with outcomes at 1-year follow-up.

METHODS

Clinical data were utilized from five London Early Intervention Services (EIS) included in the MiData audit database. The sample comprised 484 first-episode psychosis patients with complete Positive and Negative Syndrome Scale data at baseline and 1-year follow-up. Multiple imputation (N = 50) was conducted to account for missing follow-up data.

RESULTS

Baseline NS were associated with male gender (B = -1.63, P < .05), younger age at onset (B = -.15, P <. 05), a higher level of impairment on the Global Assessment of Functioning (disability) Scale at baseline (B = -.19, P <. 010), an absence of reported substance misuse prior to baseline assessment (B = -3.05, P <. 001) and unemployment at baseline (B = -.93, P <. 01). At 1-year follow-up, NS at presentation were associated with worse Global Assessment of Functioning Scale for symptom (B = -.28, P < .01) and disability (B = -.27, P <. 05) and with hospital admission (OR = 1.06, P < .01).

CONCLUSIONS

Negative symptoms at presentation to EIS were associated with worse functioning at entry and poorer outcomes 1 year later. Future research is required to better understand the aetiology and trajectories of NS in early psychosis and propose novel targeted interventions.

Transdiagnostic dimensions of psychopathology at first episode psychosis: findings from the multinational EU-GEI study

BACKGROUND

The value of the nosological distinction between non-affective and affective psychosis has frequently been challenged. We aimed to investigate the transdiagnostic dimensional structure and associated characteristics of psychopathology at First Episode Psychosis (FEP). Regardless of diagnostic categories, we expected that positive symptoms occurred more frequently in ethnic minority groups and in more densely populated environments, and that negative symptoms were associated with indices of neurodevelopmental impairment.

METHOD

This study included 2182 FEP individuals recruited across six countries, as part of the EUropean network of national schizophrenia networks studying Gene-Environment Interactions (EU-GEI) study. Symptom ratings were analysed using multidimensional item response modelling in Mplus to estimate five theory-based models of psychosis. We used multiple regression models to examine demographic and context factors associated with symptom dimensions.

RESULTS

A bifactor model, composed of one general factor and five specific dimensions of positive, negative, disorganization, manic and depressive symptoms, best-represented associations among ratings of psychotic symptoms. Positive symptoms were more common in ethnic minority groups. Urbanicity was associated with a higher score on the general factor. Men presented with more negative and less depressive symptoms than women. Early age-at-first-contact with psychiatric services was associated with higher scores on negative, disorganized, and manic symptom dimensions.

CONCLUSIONS

Our results suggest that the bifactor model of psychopathology holds across diagnostic categories of non-affective and affective psychosis at FEP, and demographic and context determinants map onto general and specific symptom dimensions. These findings have implications for tailoring symptom-specific treatments and inform research into the mood-psychosis spectrum.

Negative Symptoms in Early-Onset Psychosis and Their Association With Antipsychotic Treatment Failure

The prevalence of negative symptoms (NS) at first episode of early-onset psychosis (EOP), and their effect on psychosis prognosis is unclear. In a sample of 638 children with EOP (aged 10-17 y, 51% male), we assessed (1) the prevalence of NS at first presentation to mental health services and (2) whether NS predicted eventual development of multiple treatment failure (MTF) prior to the age of 18 (defined by initiation of a third trial of novel antipsychotic due to prior insufficient response, intolerable adverse-effects or non-adherence). Data were extracted from the electronic health records held by child inpatient and community-based services in South London, United Kingdom. Natural Language Processing tools were used to measure the presence of Marder Factor NS and antipsychotic use. The association between presenting with ≥2 NS and the development of MTF over a 5-year period was modeled using Cox regression. Out of the 638 children, 37.5% showed ≥2 NS at first presentation, and 124 (19.3%) developed MTF prior to the age of 18. The presence of NS at first episode was significantly associated with MTF (adjusted hazard ratio 1.62, 95% CI 1.07-2.46; P = .02) after controlling for a number of potential confounders including psychosis diagnostic classification, positive symptoms, comorbid depression, and family history of psychosis. Other factors associated with MTF included comorbid autism spectrum disorder, older age at first presentation, Black ethnicity, and family history of psychosis. In EOP, NS at first episode are prevalent and may help identify a subset of children at higher risk of responding poorly to antipsychotics.

Predictors of Placebo Response in Pharmacological Clinical Trials of Negative Symptoms in Schizophrenia: A Meta-regression Analysis

We conducted a meta-regression analysis of all double-blind, randomized, placebo-controlled clinical trials (DBRCTs) reporting effects of drug and placebo on negative symptoms in people with stable schizophrenia and predominant or prominent negative symptoms to assess predictors of placebo response in these individuals. We used Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines for systematic reviews and meta-analyses to conduct a systematic literature search to identify DBRCTs assessing treatment efficacy on negative symptoms, as primary outcome, in patients with stable schizophrenia and predominant or prominent negative symptoms. We used Cohen's d, with 95% CIs, as the effect size measure for placebo response, based on negative symptom change scores from baseline to endpoint (range 4 to 24 wk) in the placebo-treated group. We included 18 DBRCTs from 17 publications, assessing the effect of 13 drugs vs placebo on negative symptoms and comprising 998 patients, in the meta-regression analyses. Overall, drugs showed greater efficacy than placebo in reducing negative symptoms, with small effect size (Cohen's d: 0.208, P = .020). Placebo response was significant (P < .001) and clinically relevant (Cohen's d: 2.909), but there was significant heterogeneity and high risk of publication bias. Multivariable meta-regression analyses showed that larger numbers of arms in the trial, larger numbers of study sites and industry sponsorship were significant moderators of placebo response in this population. Our results suggest that some clinical trial design and operational factors affect the level of placebo response in such studies, thus highlighting the need for designs better suited to assess these outcomes.

[Personality disorders and schizophrenic defect (problem of comorbidity)]

AIM

To test the main hypothesis that the deficit phenomena in schizophrenia act not in the 'pure' form, but in the form of aggravating personality characteristics, forming so-called 'common' syndromes with personality disorders (PD).

MATERIAL AND METHODS

The results of the psychopathological study (with the use of psychometric methods) of deficit disorders in a sample of 170 patients with schizophrenia and schizophrenia spectrum disorders (63 men, 107 women) are presented in relation to the abnormal structure of premorbid personality (PD of clusters A, B, C). An analysis of negative symptoms according to the comparability of defect to the profile of premorbid personality made it possible to distinguish three groups of deficit states associated with PD - 'common syndromes': defensive schizoidy by the type of deficit schizoid and expansive schizoidy by the type of 'verschroben' (cluster A); pathological hysterical infantilism, malignant hysteria and defective erotomania (cluster B); pseudo-psychasthenia and pathological rationalism (cluster C).

RESULTS

It has been found that the symptomatology of 'common syndromes' is subject to patterns reflecting the dichotomy of the basic defect. This pattern is valid not only for one single cluster of PD, but extends to all psychopathy-like disorders, regardless of their affiliation with a particular cluster. The pathocharacterological component of the 'common syndromes' coexisting with the deficit symptom complexes is subject to the basic deficit component of the defect and is separated into polar dimensions (defensive-expansive) within specific clusters of PD, and then unified in accordance with the dichotomy of schizophrenic defect in categories with the predominance of emotional or apathoabulic disorders.

CONCLUSION

Psychopathy-like symptom complexes in the space of 'common syndromes' can be qualified as a psychopathological construct secondary to basic deficit disorders, and their isolation as an independent entity of negative disorders appears to be unjustified.

Is paliperidone palmitate more effective than other long-acting injectable antipsychotics?

BACKGROUND

Paliperidone palmitate is one of the most widely prescribed long-acting injectable (LAI) antipsychotics in the UK. However, it is relatively expensive and there are few data comparing its effectiveness to that of other LAI antipsychotics. We sought to address this issue by analyzing a large anonymized electronic health record (EHR) dataset from patients treated with LAI antipsychotics.

METHODS

EHR data were obtained from 1281 patients in the South London and Maudsley NHS Foundation Trust (SLaM) who started treatment with a LAI antipsychotic between 1 April 2011 and 31 January 2015. The number of days spent as a psychiatric inpatient and the number of admissions to a psychiatric hospital were analyzed in each of the 3 years before and after LAI prescription.

RESULTS

Patients treated with paliperidone palmitate (n = 430; 33.6%) had a greater number of inpatient days and a greater number of admissions in the year prior to treatment than those treated with other LAI antipsychotics. Nevertheless, in the 3 years after initiation there were no significant differences between paliperidone and the other LAI antipsychotics in the number of days as an inpatient (B coefficient 5.4 days, 95% confidence interval (CI) -57.3 to 68.2, p = 0.86) or number of hospital admissions (Incidence rate ratio 1.07, 95% CI 0.62 to 1.83, p = 0.82).

CONCLUSION

Paliperidone palmitate was more likely to be prescribed in patients with more frequent and lengthy hospital admissions prior to initiation. However, the absence of differences in outcomes after initiation indicates that paliperidone palmitate was not more effective than other cheaper LAI antipsychotics.

High Throughput Phenotyping for Dimensional Psychopathology in Electronic Health Records

BACKGROUND

Relying on diagnostic categories of neuropsychiatric illness obscures the complexity of these disorders. Capturing multiple dimensional measures of neuropathology could facilitate the clinical and neurobiological investigation of cognitive and behavioral phenotypes.

METHODS

We developed a natural language processing-based approach to extract five symptom dimensions, based on the National Institute of Mental Health Research Domain Criteria definitions, from narrative clinical notes. Estimates of Research Domain Criteria loading were derived from a cohort of 3619 individuals with 4623 hospital admissions. We applied this tool to a large corpus of psychiatric inpatient admission and discharge notes (2010-2015), and using the same cohort we examined face validity, predictive validity, and convergent validity with gold standard annotations.

RESULTS

In mixed-effect models adjusted for sociodemographic and clinical features, greater negative and positive symptom domains were associated with a shorter length of stay (β = -.88, p = .001 and β = -1.22, p < .001, respectively), while greater social and arousal domain scores were associated with a longer length of stay (β = .93, p < .001 and β = .81, p = .007, respectively). In fully adjusted Cox regression models, a greater positive domain score at discharge was also associated with a significant increase in readmission risk (hazard ratio = 1.22, p < .001). Positive and negative valence domains were correlated with expert annotation (by analysis of variance [df = 3], R² = .13 and .19, respectively). Likewise, in a subset of patients, neurocognitive testing was correlated with cognitive performance scores (p < .008 for three of six measures).

CONCLUSIONS

This shows that natural language processing can be used to efficiently and transparently score clinical notes in terms of cognitive and psychopathologic domains.

Big data: what it can and cannot achieve

This article looks at the use of large health records datasets, typically linked with other data sources, and their use in mental health research. The most comprehensive examples of this kind of big data are typically found in Scandinavian countries however there are also many useful sources in the UK. There are a number of promising methodological innovations from studies using big data in UK mental health research, including: hybrid study designs, examples of data linkage and enhanced study recruitment. It is, though, important to be aware of the limitations of research using big data, particularly the various analysis pitfalls. We therefore caution against throwing out the methodological baby with the bathwater and argue that other data sources are equally valuable and ideally research should incorporate a range of data.

Rapid improvement in beliefs, mood, and performance following an experimental success experience in an analogue test of recovery-oriented cognitive therapy

BACKGROUND

Negative symptoms significantly contribute to disability and lack of community participation for low functioning individuals with schizophrenia. Cognitive therapy has been shown to improve negative symptoms and functional outcome in this population. Elucidation of the mechanisms of the therapy would lead to a better understanding of negative symptoms and the development of more effective interventions to promote recovery. The objective of this study was to determine (1) whether guided success at a card-sorting task will produce improvement in defeatist beliefs, positive beliefs about the self, mood, and card-sorting performance, and (2) whether these changes in beliefs and mood predict improvements in unguided card-sorting.

METHODS

Individuals with schizophrenia having prominent negative symptoms and impaired neurocognitive performance (N = 35) were randomized to guided success (n = 19) or a control (n = 16) condition.

RESULTS

Controlling for baseline performance, the experimental group performed significantly better, endorsed defeatist beliefs to a lesser degree, reported greater positive self-concept, and reported better mood than the control condition immediately after the experimental session. A composite index of change in defeatist beliefs, self-concept, and mood was significantly correlated with improvements in card-sorting.

CONCLUSIONS

This analogue study supports the rationale of cognitive therapy and provides a general therapeutic model in which experiential interventions that produce success have a significant immediate effect on a behavioral task, mediated by changes in beliefs and mood. The rapid improvement is a promising indicator of the responsiveness of this population, often regarded as recalcitrant, to cognitively-targeted behavioral interventions.

What can we learn about brain donors? Use of clinical information in human postmortem brain research

Postmortem studies on the human brain reside at the core of investigations on neurologic and psychiatric disorders. Ground-breaking advances continue to be made on the pathologic basis of many of these disorders, at molecular, cellular, and neural connectivity levels. In parallel, there is increasing emphasis on improving methods to extract relevant demographic and clinical information about brain donors and, importantly, translate it into measures that can reliably and effectively be incorporated in the design and data analysis of postmortem human investigations. Here, we review the main source of information typically available to brain banks and provide examples on how this information can be processed. In particular, we discuss approaches to establish primary and secondary diagnoses, estimate exposure to therapeutic treatment and substance abuse, assess agonal status, and use time of death as a proxy in investigations on circadian rhythms. Although far from exhaustive, these considerations are intended as a contribution to ongoing efforts from tissue banks and investigators aimed at establishing robust, well-validated methods for collecting and standardizing information about brain donors, further strengthening the scientific rigor of human postmortem studies.

Computational approaches to schizophrenia: A perspective on negative symptoms

Schizophrenia is a heterogeneous spectrum disorder often associated with detrimental negative symptoms. In recent years, computational approaches to psychiatry have attracted growing attention. Negative symptoms have shown some overlap with general cognitive impairments and were also linked to impaired motivational processing in brain circuits implementing reward prediction. In this review, we outline how computational approaches may help to provide a better understanding of negative symptoms in terms of the potentially underlying behavioural and biological mechanisms. First, we describe the idea that negative symptoms could arise from a failure to represent reward expectations to enable flexible behavioural adaptation. It has been proposed that these impairments arise from a failure to use prediction errors to update expectations. Important previous studies focused on processing of so-called model-free prediction errors where learning is determined by past rewards only. However, learning and decision-making arise from multiple cognitive mechanisms functioning simultaneously, and dissecting them via well-designed tasks in conjunction with computational modelling is a promising avenue. Second, we move on to a proof-of-concept example on how generative models of functional imaging data from a cognitive task enable the identification of subgroups of patients mapping on different levels of negative symptoms. Combining the latter approach with behavioural studies regarding learning and decision-making may allow the identification of key behavioural and biological parameters distinctive for different dimensions of negative symptoms versus a general cognitive impairment. We conclude with an outlook on how this computational framework could, at some point, enrich future clinical studies.

Treatment of negative symptoms: Where do we stand, and where do we go?

Negative symptoms, e.g. social withdrawal, reduced initiative, anhedonia and affective flattening, are notoriously difficult to treat. In this review, we take stock of recent research into treatment of negative symptoms by summarizing psychosocial as well as pharmacological and other biological treatment strategies. Major psychosocial approaches concern social skills training, cognitive behavior therapy for psychosis, cognitive remediation and family intervention. Some positive findings have been reported, with the most robust improvements observed for social skills training. Although cognitive behavior therapy shows significant effects for negative symptoms as a secondary outcome measure, there is a lack of data to allow for definite conclusions of its effectiveness for patients with predominant negative symptoms. With regard to pharmacological interventions, antipsychotics have been shown to improve negative symptoms, but this seems to be limited to secondary negative symptoms in acute patients. It has also been suggested that antipsychotics may aggravate negative symptoms. Recent studies have investigated glutamatergic compounds, e.g. glycine receptor inhibitors and drugs that target the NMDA receptor or metabotropic glutamate 2/3 (mGlu2/3) receptor, but no consistent evidence of improvement of negative symptoms was found. Finally, some small studies have suggested improvement of negative symptoms after non-invasive electromagnetic neurostimulation, but this has only been partly replicated and it is still unclear whether these are robust improvements. We address methodological issues, in particular the heterogeneity of negative symptoms and treatment response, and suggest avenues for future research. There is a need for more detailed studies that focus on different dimensions of negative symptoms.

Early clinical recovery in first-episode psychosis: Symptomatic remission and its correlates at 1-year follow-up

The aim was to gain more knowledge about early clinical recovery in first-episode psychosis (FEP). The interrelationship between symptomatic remission, poor global functioning and neurocognitive impairment was investigated. FEP participants (n =91) from the TOP study were investigated at baseline and 1-year follow-up. Symptomatic remission was defined by internationally standardized criteria. Poor global functioning was defined as GAF-F score ≤60. Neurocognitive impairment was defined as 1.5 standard deviation below healthy controls on a neuropsychological composite score. Finally, early clinical recovery was defined as symptomatic remission during the last 6 months and functional remission (1. GAF-F score ≥61, 2. at least 50% study/employment, and 3. living independently). At 1-year follow-up 26% were in symptomatic remission, predicted by duration of untreated psychosis and baseline positive symptoms. Significantly fewer in the symptomatic remission group had poor global functioning compared to the non-remission group, with no difference in the rate of neurocognitive impairment. Finally, 14% were considered in early clinical recovery. They had the same rate of neurocognitive impairment as the remaining group. These findings imply that symptomatic remission and early clinical recovery can already be identified at 1-year follow-up, and that this is relatively independent of neurocognitive impairment.

Global brain asymmetry is increased in schizophrenia and related to avolition

OBJECTIVE

Schizophrenia may be the result of a failure of the normal lateralization process of the brain. However, whole-brain asymmetry has not been assessed up to date. Here, we propose a novel measure of global brain asymmetry based on the Dice coefficient to quantify similarity between brain hemispheres.

METHOD

Global gray and white matter asymmetry was calculated from high-resolution T1 structural images acquired from 24 patients with schizophrenia and 26 healthy controls, age- and sex-matched. Some of the analyses were replicated in a much larger sample (n = 759) obtained from open-access online databases.

RESULTS

Patients with schizophrenia had more global gray matter asymmetry than controls. Additionally, increased gray matter asymmetry was associated with avolition, whereas the inverse relationship was found for anxiety at a trend level. These analyses were replicated in a larger sample and confirmed previous results.

CONCLUSION

Our findings suggest that global gray matter asymmetry is related to the concept of developmental stability and is a useful indicator of perturbations during neurodevelopment.

Big data and data repurposing - using existing data to answer new questions in vascular dementia research

Introduction

Traditional approaches to clinical research have, as yet, failed to provide effective treatments for vascular dementia (VaD). Novel approaches to collation and synthesis of data may allow for time and cost efficient hypothesis generating and testing. These approaches may have particular utility in helping us understand and treat a complex condition such as VaD.

Methods

We present an overview of new uses for existing data to progress VaD research. The overview is the result of consultation with various stakeholders, focused literature review and learning from the group’s experience of successful approaches to data repurposing. In particular, we benefitted from the expert discussion and input of delegates at the 9^th International Congress on Vascular Dementia (Ljubljana, 16-18^th October 2015).

Results

We agreed on key areas that could be of relevance to VaD research: systematic review of existing studies; individual patient level analyses of existing trials and cohorts and linking electronic health record data to other datasets. We illustrated each theme with a case-study of an existing project that has utilised this approach.

Conclusions

There are many opportunities for the VaD research community to make better use of existing data. The volume of potentially available data is increasing and the opportunities for using these resources to progress the VaD research agenda are exciting. Of course, these approaches come with inherent limitations and biases, as bigger datasets are not necessarily better datasets and maintaining rigour and critical analysis will be key to optimising data use.

What proportion of patients with psychosis is willing to take part in research? A mental health electronic case register analysis

OBJECTIVE

The proportion of people with mental health disorders who participate in clinical research studies is much smaller than for those with physical health disorders. It is sometimes assumed that this reflects an unwillingness to volunteer for mental health research studies. We examined this issue in a large sample of patients with psychosis.

DESIGN

Cross-sectional study.

SETTING

Anonymised electronic mental health record data from the South London and Maudsley NHS Foundation Trust (SLaM).

PARTICIPANTS

5787 adults diagnosed with a psychotic disorder.

EXPOSURE

Whether approached prior to 1 September 2014 for consent to be approached about research participation.

MAIN OUTCOME MEASURES

Number of days spent in a psychiatric hospital, whether admitted to hospital compulsorily, and total score on the Health of the Nation Outcome Scale (HoNOS) between 1 September 2014 and 28 February 2015 with patient factors (age, gender, ethnicity, marital status and diagnosis) and treating clinical service as covariates.

RESULTS

1187 patients (20.5% of the total sample) had been approached about research participation. Of those who were approached, 773 (65.1%) agreed to be contacted in future by researchers. Patients who had been approached had 2.3 fewer inpatient days (95% CI -4.4 to -0.3, p=0.03), were less likely to have had a compulsory admission (OR 0.65, 95% CI 0.50 to 0.84, p=0.001) and had a better HoNOS score (β coefficient -0.9, 95% CI -1.5 to -0.4, p=0.001) than those who had not. Among patients who were approached, there was no significant difference in clinical outcomes between those agreed to research contact and those who did not.

CONCLUSIONS

About two-thirds of patients with psychotic disorders were willing to be contacted about participation in research. The patients who were approached had better clinical outcomes than those who were not, suggesting that clinicians were more likely to approach patients who were less unwell.