Development of a stage-dependent prognostic model to predict psychosis in ultra-high-risk patients seeking treatment for co-morbid psychiatric disorders

Progression from being at-risk to psychosis: next steps

Over the past 20 years there has been a great deal of research into those considered to be at risk for developing psychosis. Much has been learned and studies have been encouraging. The aim of this paper is to offer an update of the current status of research on risk for psychosis, and what the next steps might be in examining the progression from CHR to psychosis. Advances have been made in accurate prediction, yet there are some methodological issues in ascertainment, diagnosis, the use of data-driven selection methods and lack of external validation. Although there have been several high-quality treatment trials the heterogeneity of this clinical high-risk population has to be addressed so that their treatment needs can be properly met. Recommendations for the future include more collaborative research programmes, and ensuring they are accessible and harmonized with respect to criteria and outcomes so that the field can continue to move forward with the development of large collaborative consortiums as well as increased funding for multisite projects.

QSAR without borders

Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.

Spanish validation of the self-evaluation of negative symptoms scale SNS in an adolescent population

BACKGROUND

Negative symptoms (NS) may be observed in the general population in an attenuated form and in high-risk mental states. However, they have been less studied in the general population than positive symptoms, in spite of their importance at the insidious onset of schizophrenia and their appearance before positive symptoms. This study aimed to analyze the empirical structure of the Spanish version of the Self-Evaluation of Negative Symptoms (SNS) Scale and find its psychometric properties and invariance of measurement across sex and age in a sample of adolescents.

METHODS

The sample consisted of 4521 adolescents (53.6% female) from 11 to 18 years of age.

RESULTS

Confirmatory Factor Analysis of the SNS confirmed an internal structure of five first-order factors by the characteristic dimensions of NS: avolition, social withdrawal, diminished emotional range, anhedonia, alogia, and one second-order factor which includes the total NS score. Multi-group confirmatory factor analysis showed that the scale was invariant across sex and age. Total scale reliability was adequate. A strong relationship was found between the SNS with depressive symptomatology, moderate with ideas of reference and low with aberrant salience.

CONCLUSION

The results back use of the Spanish version of the SNS scale for detection of NS in the general population of adolescents.

Prediction, Psychosis, and the Cerebellum

An increasingly influential hypothesis posits that many of the diverse symptoms of psychosis can be viewed as reflecting dysfunctional predictive mechanisms. Indeed, to perceive something is to take a sensory input and make a prediction of the external source of that signal; thus, prediction is perhaps the most fundamental neural computation. Given the ubiquity of prediction, a more challenging problem is to specify the unique predictive role or capability of a particular brain structure. This question is relevant when considering recent claims that one aspect of the predictive deficits observed in psychotic disorders might be related to cerebellar dysfunction, a subcortical structure known to play a critical role in predictive sensorimotor control and perhaps higher-level cognitive function. Here, we review evidence bearing on this question. We first focus on clinical, behavioral, and neuroimaging findings suggesting cerebellar involvement in psychosis and, specifically, schizophrenia. We then review a relatively novel line of research exploring whether computational models of cerebellar motor function can also account for cerebellar involvement in higher-order human cognition, and in particular, language function. We end the review by highlighting some key gaps in these literatures, limitations that currently preclude strong conclusions regarding cerebellar involvement in psychosis.

Predictors of Transition to Psychosis in Individuals at Clinical High Risk

PURPOSE OF REVIEW

Current research is examining predictors of the transition to psychosis in youth who are at clinical high risk based on attenuated psychotic symptoms (APS). Determining predictors of the development of psychosis is important for an improved understanding of mechanisms as well as the development of preventative strategies. The purpose is to review the most recent literature identifying predictors of the transition to psychosis in those who are already assessed as being at risk.

RECENT FINDINGS

Multidomain models, in particular, integrated models of symptoms, social functioning, and cognition variables, achieve better predictive performance than individual factors. There are many methodological issues; however, several solutions have now been described in the literature. For youth who already have APS, predicting who may go on to later develop psychosis is possible. Several studies are underway in large consortiums that may overcome some of the methodological concerns and develop improved means of prediction.

Social impairment and social language deficits in children and adolescents with and at risk for psychosis

INTRO

One of the more debilitating functional outcomes of schizophrenia-spectrum disorders is social impairment. Previous studies have identified impaired social functioning both in the prodromal phase of psychosis and after acute symptoms abate, suggesting that social impairment represents a core deficit in psychosis not directly linked to psychotic episodes or symptom severity. To date, research in this area has focused primarily on adult populations rather than children, and has not directly assessed social language in individuals across the psychosis continuum.

METHODS

81 youth ages 7-18 (N = 24 Typically Developing [TD], N = 36 Clinical High Risk [CHR], N = 21 Psychotic Disorder [PD]) were recruited. Youth participants were administered the Social Language Development Test (SLDT), and parent(s)/guardian(s) completed the Social Responsiveness Scale-II (SRS-II).

RESULTS

Social language ability was not associated with social impairment. PD participants performed significantly worse on the SLDT than TD participants. CHR and PD participants were both rated as having experienced significantly greater social impairment than TD participants on every subscale of the SRS-II.

DISCUSSION

Deficits in social language ability and social functioning are strong candidates for phenotypic markers of psychosis, and may be evident earlier in development than previous work has demonstrated. Additionally, the severity of social impairment did not differ between CHR and PD participants, further supporting that social cognitive deficits and social impairment, while related to symptom severity, are discrete deficits in individuals with and at risk for psychosis. These results highlight the importance of addressing social skills for individuals presenting in clinical settings with psychotic symptoms, including children.

CBT in the prevention of psychosis and other severe mental disorders in patients with an at risk mental state: A review and proposed next steps

Patients with an 'At risk mental state' (ARMS) for developing psychosis can be treated successfully with CBT to postpone and prevent the transition to a first psychotic episode. A characteristic of individuals that meet ARMS criteria is that they are still open for multiple explanations for extraordinary experiences. CBT aims to normalize extraordinary experiences with education and to prevent delusional explanations. The treatment is not only effective, but also cost-saving in averting psychosis as well as in reducing disability adjusted life years at 18- and 48-month follow-up. Profiling within the ARMS group results in a personalized treatment. The screening and early treatment for ARMS fulfills all the criteria of the World Health Organization and is ready to be routine screening and treatment in mental health care. At the same time, ARMS patients are complex patients with multi-morbid disorders. Especially childhood trauma is associated to ARMS status, together with co-morbid PTSD, depression, substance abuse and anxiety disorders. Psychotic symptoms appear to be severity markers in other non-psychotic disorders. Preventing psychosis in ARMS patients should be broadened to also address other disorders and aim to reduce chronicity of psychopathology and improve social functioning in general. Several mechanisms play a part in psychopathology in ARMS patients such as stress sensitivity as a result of adverse experiences, dopamine sensitivity that is associated with salience and aggravates several cognitive biases, dissociation mediating between trauma and hallucinations, and low self-esteem and self-stigma. New avenues to treat the complexity of ARMS patients will be proposed.

Early intervention for everyone? A review of cross-cultural issues and their treatment in ultra-high-risk (UHR) cohorts

AIM

Over the past 20 years, early management of psychosis has become both a research and policy priority. In Western countries, psychotic disorders appear more prevalent in migrant and minority ethnic groups than in native or dominant groups. Moreover, disparities exist in health conditions and access to care among immigrants and minority ethnic groups, compared with native-born and majority groups. Appropriate early detection tools are necessary for the different groups.

METHODS

This systematic review provides a synthesis of the assessment and discussion of transcultural issues in ultra-high-risk (UHR) cohorts. The Medline database was searched via PubMed for peer-reviewed articles published in English from 1995 to 2017. All 79 studies included are prospective UHR cohort studies that used the Comprehensive Assessment of At-Risk Mental States (CAARMS).

RESULTS

In UHR cohort studies that used the CAARMS, transcultural data (native language, ethnicity, place of birth, migration) are rarely collected, and inadequate ability to speak the dominant language is a common exclusion criterion. When they are included, the CAARMS scores differ between some minorities and the native-born majority group.

CONCLUSIONS

This systematic review demonstrates barriers to the access to participation in early intervention research for migrants and ethnic minorities. This selection bias may result in lower validity for the CAARMS among these populations and thus in inadequate intervention programmes. Along with targeted studies, minorities' access to participation in UHR cohorts should be improved through 3 tools: interpreters at recruitment and for administration of CAARMS, a guide to cultural formulation and transcultural data collection.

Predicting early transition from sub-syndromal presentations to major mental disorders

BACKGROUND

Transition from at-risk state to full syndromal mental disorders is underexplored for unipolar and bipolar disorders compared with psychosis.

AIMS

Prospective, trans-diagnostic study of rates and predictors of early transition from sub-threshold to full syndromal mental disorder.

METHOD

One-year outcome of 243 consenting youth aged 15-25 years with a sub-syndromal presentation of a potentially severe mental disorder. Survival analysis and odds ratio (OR) for predictors of transition identified from baseline clinical and demographic ratings.

RESULTS

About 17% (n=36) experienced transition to a major mental disorder. Independent of syndromal diagnosis, transition was significantly more likely in individuals who were NEET (not in education, employment or training), in females and in those with more negative psychological symptoms (e.g. social withdrawal).

CONCLUSIONS

NEET status and negative symptoms are modifiable predictors of illness trajectory across diagnostic categories and are not specific to transition to psychosis.

DECLARATION OF INTEREST

I.B.H. has been a Commissioner in Australia's National Mental Health Commission since 2012. He was a board member of headspace: National Youth Mental Health Foundation until January 2012. He has led a range of community-based and pharmaceutical industry-supported depression awareness and education and training programmes. He has led projects for health professionals and the community supported by governmental, community agency and pharmaceutical industry partners (Wyeth, Eli Lilly, Servier, Pfizer, AstraZeneca) for the identification and management of depression and anxiety. He has received honoraria for presentations of his own work at educational seminars supported by a number of non-government organisations and the pharmaceutical industry (including Servier, Pfizer, AstraZeneca and Eli Lilly). He is a member of the Medical Advisory Panel for Medibank Private and also a board member of Psychosis Australia Trust. He leads an investigator-initiated study of the effects of agomelatine on circadian parameters (supported in part by Servier) and has participated in a multicentre clinical trial of the effects of agomelatine on sleep architecture in depression and a Servier-supported study of major depression and sleep disturbance in primary care settings.

COPYRIGHT AND USAGE

© The Royal College of Psychiatrists 2017. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) license.

Prediction of conversion to psychosis in individuals with an at-risk mental state: a brief update on recent developments

PURPOSE OF REVIEW

So far, only little more than one-third of individuals classified as being at-risk for psychosis have been shown to actually convert to frank psychosis during follow-up. There have therefore been enormous efforts to improve the accuracy of predicting this transition. We reviewed the most recent studies in the field with the aim to clarify whether accuracy of prediction has been improved by the different research endeavors and what could be done to further improve it, and/or what alternative goals research should pursue.

RECENT FINDINGS

A total of 56 studies published between May 2015 and December 2016 were included, of which eight were meta-analyses. New meta-analytical evidence confirms that established instruments for checking clinical risk criteria have an excellent clinical utility in individuals referred to high-risk services. Within a such identified group of ultra-high-risk (UHR) individuals, especially Brief Limited Intermittent Psychotic Symptoms and Attenuated Psychotic Symptoms seem to predict transition. Further assessments should be performed within the UHR individuals, as risk of transition seems particularly high in those with an even higher severity of certain symptoms such as suspiciousness or anhedonia, in those with lower global or social functioning, poor neurocognitive performance or cannabis abuse. Also, electroencephalography, neuroimaging and blood biomarkers might contribute to improving individual prediction. The most promising approach certainly is a staged multidomain assessment. Risk calculators to integrate all data for an individualized prediction are being developed.

SUMMARY

Prediction of psychosis is already possible with an excellent prognostic performance based on clinical assessments. Recent studies show that this accuracy can be further improved by using multidomain approaches and modern statistics for individualized prediction. The challenge now is the translation into the clinic with a broad clinical implementation.