Models Predicting Psychosis in Patients With High Clinical Risk: A Systematic Review

Tunisian Adolescents at CHR for Psychosis: A Pilot Study of Cognitive Remediation in a LMIC

BACKGROUND

Clinical high risk (CHR) youth are known to exhibit cognitive deficits at similar levels to their more severally ill counter parts. Cognitive training (CT) programs offer a promising method for early intervention and the prevention of further cognitive decline in this vulnerable population. However, there are few structured CT intervention programs addressing the needs of CHR youth in LMICs of the Middle East.

METHODS

We conducted a study in the Child and Adolescent Psychiatry Department of Razi University Hospital. Patients were assessed by trained raters with the "Comprehensive Assessment of At-Risk Mental States" to confirm their CHR status. Cognitive Training (CT) was combined with the Neuropsychological Educational Approach to Remediation (CT-NEAR) as part of a social rehabilitation program. We enrolled 25 CHR patients and examined several domains of cognitive functioning and evaluated daily functioning prior to starting the intervention and after completion.

RESULTS

There were 20 patients who completed the study. The CT-NEAR group (n = 10) completed an average number 28.33 sessions over 12 weeks, which were matched for therapist time with the TAU group (n = 10). We found statistically significant improvements in CT-NEAR versus TAU in several cognitive domains; such as cognitive flexibility, memory-short and long-term, and verbal fluency. Also, CT-NEAR versus TAU patients improved in global functioning.

CONCLUSIONS

Our findings indicate that cognitive remediation versus TAU for Tunisian CHR youth is feasible and effective especially in improving cognitive functioning when delivered in a social rehabilitation context (Bridging Group) and extends to global level of functioning.

Systematic review of clinical prediction models for psychosis in individuals meeting At Risk Mental State criteria

Objectives

This study aims to review studies developing or validating a prediction model for transition to psychosis in individuals meeting At Risk Mental State (ARMS) criteria focussing on predictors that can be obtained as part of standard clinical practice. Prediction of transition is crucial to facilitating identification of patients who would benefit from cognitive behavioural therapy and, conversely, those that would benefit from less costly and less-intensive regular mental state monitoring. The review aims to determine whether prediction models rated as low risk of bias exist and, if not, what further research is needed within the field.

Design

Bibliographic databases (PsycINFO, Medline, EMBASE, CINAHL) were searched using index terms relating to the clinical field and prognosis from 1994, the initial year of the first prospective study using ARMS criteria, to July 2024. Screening of titles, abstracts, and subsequently full texts was conducted by two reviewers independently using predefined criteria. Study quality was assessed using the Prediction model Risk Of Bias ASessment Tool (PROBAST).

Setting

Studies in any setting were included.

Primary and secondary outcome measures

The primary outcome for the review was the identification of prediction models considering transition risk and a summary of their risk of bias.

Results

Forty-eight unique prediction models considering risk of transition to psychosis were identified. Variables found to be consistently important when predicting transition were age, gender, global functioning score, trait vulnerability, and unusual thought content. PROBAST criteria categorised four unique prediction models as having an overall low-risk bias. Other studies were insufficiently powered for the number of candidate predictors or lacking enough information to draw a conclusion regarding risk of bias.

Conclusions

Two of the 48 identified prediction models were developed using current best practice statistical methodology, validated their model in independent data, and presented low risk of bias overall in line with the PROBAST guidelines. Any new prediction model built to evaluate the risk of transition to psychosis in people meeting ARMS criteria should be informed by the latest statistical methodology and adhere to the TRIPOD reporting guidelines to ensure that clinical practice is informed by the best possible evidence. External validation of such models should be carefully planned particularly considering generalisation across different countries.

Systematic review registration

https://www.crd.york.ac.uk/PROSPEROFILES/108488_PROTOCOL_20191127.pdf, identifier CRD42018108488.

Functional Connectivity Biomarkers in Schizophrenia

Schizophrenia is a debilitating neuropsychiatric disorder that affects approximately 1% of the population and poses a major public health problem. Despite over 100 years of study, the treatment for schizophrenia remains limited, partially due to the lack of knowledge about the neural mechanisms of the illness and how they relate to symptoms. The US Food and Drug Administration (FDA) and the National Institute of Health (NIH) have provided seven biomarker categories that indicate causes, risks, and treatment responses. However, no FDA-approved biomarkers exist for psychiatric conditions, including schizophrenia, highlighting the need for biomarker development. Over three decades, magnetic resonance imaging (MRI)-based studies have identified patterns of abnormal brain function in schizophrenia. By using functional connectivity (FC) data, which gauges how brain regions interact over time, these studies have differentiated patient subgroups, predicted responses to antipsychotic medication, and correlated neural changes with symptoms. This suggests FC metrics could serve as promising biomarkers. Here, we present a selective review of studies leveraging MRI-derived FC to study neural alterations in schizophrenia, discuss how they align with FDA-NIH biomarkers, and outline the challenges and goals for developing FC biomarkers in schizophrenia.

Recent Updates on Predicting Conversion in Youth at Clinical High Risk for Psychosis

PURPOSE OF REVIEW

This review highlights recent advances in the prediction and treatment of psychotic conversion. Over the past 25 years, research into the prodromal phase of psychotic illness has expanded with the promise of early identification of individuals at clinical high risk (CHR) for psychosis who are likely to convert to psychosis.

RECENT FINDINGS

Meta-analyses highlight conversion rates between 20 and 30% within 2-3 years using existing clinical criteria while research into more specific risk factors, biomarkers, and refinement of psychosis risk calculators has exploded, improving our ability to predict psychotic conversion with greater accuracy. Recent studies highlight risk factors and biomarkers likely to contribute to earlier identification and provide insight into neurodevelopmental abnormalities, CHR subtypes, and interventions that can target specific risk profiles linked to neural mechanisms. Ongoing initiatives that assess longer-term (> 5-10 years) outcome of CHR participants can provide valuable information about predictors of later conversion and diagnostic outcomes while large-scale international biomarker studies provide hope for precision intervention that will alter the course of early psychosis globally.

Prognostic models predicting transition to psychotic disorder using blood-based biomarkers: a systematic review and critical appraisal

Accumulating evidence suggests individuals with psychotic disorder show abnormalities in metabolic and inflammatory processes. Recently, several studies have employed blood-based predictors in models predicting transition to psychotic disorder in risk-enriched populations. A systematic review of the performance and methodology of prognostic models using blood-based biomarkers in the prediction of psychotic disorder from risk-enriched populations is warranted. Databases (PubMed, EMBASE and PsycINFO) were searched for eligible texts from 1998 to 15/05/2023, which detailed model development or validation studies. The checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) was used to guide data extraction from eligible texts and the Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias and applicability of the studies. A narrative synthesis of the included studies was performed. Seventeen eligible studies were identified: 16 eligible model development studies and one eligible model validation study. A wide range of biomarkers were assessed, including nucleic acids, proteins, metabolites, and lipids. The range of C-index (area under the curve) estimates reported for the models was 0.67-1.00. No studies assessed model calibration. According to PROBAST criteria, all studies were at high risk of bias in the analysis domain. While a wide range of potentially predictive biomarkers were identified in the included studies, most studies did not account for overfitting in model performance estimates, no studies assessed calibration, and all models were at high risk of bias according to PROBAST criteria. External validation of the models is needed to provide more accurate estimates of their performance. Future studies which follow the latest available methodological and reporting guidelines and adopt strategies to accommodate required sample sizes for model development or validation will clarify the value of including blood-based biomarkers in models predicting psychosis.

Predictors of transition in patients with clinical high risk for psychosis: an umbrella review

Diagnosis of a clinical high-risk (CHR) state enables timely treatment of individuals at risk for a psychotic disorder, thereby contributing to improving illness outcomes. However, only a minority of patients diagnosed with CHR will make the transition to overt psychosis. To identify patients most likely to benefit from early intervention, several studies have investigated characteristics that distinguish CHR patients who will later develop a psychotic disorder from those who will not. We aimed to summarize evidence from systematic reviews and meta-analyses on predictors of transition to psychosis in CHR patients, among characteristics and biomarkers assessed at baseline. A systematic search was conducted in Pubmed, Scopus, PsychInfo and Cochrane databases to identify reviews and meta-analyses of studies that investigated specific baseline predictors or biomarkers for transition to psychosis in CHR patients using a cross-sectional or longitudinal design. Non-peer-reviewed publications, gray literature, narrative reviews and publications not written in English were excluded from analyses. We provide a narrative synthesis of results from all included reviews and meta-analyses. For each included publication, we indicate the number of studies cited in each domain and its quality rating. A total of 40 publications (21 systematic reviews and 19 meta-analyses) that reviewed a total of 272 original studies qualified for inclusion. Baseline predictors most consistently associated with later transition included clinical characteristics such as attenuated psychotic and negative symptoms and functioning, verbal memory deficits and the electrophysiological marker of mismatch negativity. Few predictors reached a level of evidence sufficient to inform clinical practice, reflecting generalizability issues in a field characterized by studies with small, heterogeneous samples and relatively few transition events. Sample pooling and harmonization of methods across sites and projects are necessary to overcome these limitations.

Predictive validity of psychosis risk models when applied to adolescent psychiatric patients

BACKGROUND

Several multivariate algorithms have been developed for predicting psychosis, as attempts to obtain better prognosis prediction than with current clinical high-risk (CHR) criteria. The models have typically been based on samples from specialized clinics. We evaluated the generalizability of 19 prediction models to clinical practice in an unselected adolescent psychiatric sample.

METHODS

In total, 153 adolescent psychiatric patients in the Helsinki Prodromal Study underwent an extensive baseline assessment including the SIPS interview and a neurocognitive battery, with 50 participants (33%) fulfilling CHR criteria. The adolescents were followed up for 7 years using comprehensive national registers. Assessed outcomes were (1) any psychotic disorder diagnosis (n = 18, 12%) and (2) first psychiatric hospitalization (n = 25, 16%) as an index of overall deterioration of functioning.

RESULTS

Most models improved the overall prediction accuracy over standard CHR criteria (area under the curve estimates ranging between 0.51 and 0.82), although the accuracy was worse than that in the samples used to develop the models, also when applied only to the CHR subsample. The best models for transition to psychosis included the severity of positive symptoms, especially delusions, and negative symptoms. Exploratory models revealed baseline negative symptoms, low functioning, delusions, and sleep problems in combination to be the best predictor of psychiatric hospitalization in the upcoming years.

CONCLUSIONS

Including the severity levels of both positive and negative symptomatology proved beneficial in predicting psychosis. Despite these advances, the applicability of extended psychosis-risk models to general psychiatric practice appears limited.

Ethical Implications of the Use of Language Analysis Technologies for the Diagnosis and Prediction of Psychiatric Disorders

Recent developments in artificial intelligence technologies have come to a point where machine learning algorithms can infer mental status based on someone's photos and texts posted on social media. More than that, these algorithms are able to predict, with a reasonable degree of accuracy, future mental illness. They potentially represent an important advance in mental health care for preventive and early diagnosis initiatives, and for aiding professionals in the follow-up and prognosis of their patients. However, important issues call for major caution in the use of such technologies, namely, privacy and the stigma related to mental disorders. In this paper, we discuss the bioethical implications of using such technologies to diagnose and predict future mental illness, given the current scenario of swiftly growing technologies that analyze human language and the online availability of personal information given by social media. We also suggest future directions to be taken to minimize the misuse of such important technologies.

Cognitive dysfunction in schizophrenia: An expert group paper on the current state of the art

Cognitive impairment in schizophrenia represents one of the main obstacles to clinical and functional recovery. This expert group paper brings together experts in schizophrenia treatment to discuss scientific progress in the domain of cognitive impairment to address cognitive impairments and their consequences in the most effective way. We report on the onset and course of cognitive deficits, linking them to the alterations in brain function and structure in schizophrenia and discussing their role in predicting the transition to psychosis in people at risk. We then address the assessment tools with reference to functioning and social cognition, examining the role of subjective measures and addressing new methods for measuring functional outcomes including technology based approaches. Finally, we briefly review treatment options for cognitive deficits, focusing on cognitive remediation programs, highlighting their effects on brain activity and conclude with the potential benefit of individualized integrated interventions combing cognitive remediation with other approaches.

Real-World Implementation of Precision Psychiatry: A Systematic Review of Barriers and Facilitators

Background: Despite significant research progress surrounding precision medicine in psychiatry, there has been little tangible impact upon real-world clinical care. Objective: To identify barriers and facilitators affecting the real-world implementation of precision psychiatry. Method: A PRISMA-compliant systematic literature search of primary research studies, conducted in the Web of Science, Cochrane Central Register of Controlled Trials, PsycINFO and OpenGrey databases. We included a qualitative data synthesis structured according to the ‘Consolidated Framework for Implementation Research’ (CFIR) key constructs. Results: Of 93,886 records screened, 28 studies were suitable for inclusion. The included studies reported 38 barriers and facilitators attributed to the CFIR constructs. Commonly reported barriers included: potential psychological harm to the service user (n = 11), cost and time investments (n = 9), potential economic and occupational harm to the service user (n = 8), poor accuracy and utility of the model (n = 8), and poor perceived competence in precision medicine amongst staff (n = 7). The most highly reported facilitator was the availability of adequate competence and skills training for staff (n = 7). Conclusions: Psychiatry faces widespread challenges in the implementation of precision medicine methods. Innovative solutions are required at the level of the individual and the wider system to fulfil the translational gap and impact real-world care.

White matter microstructure and the clinical risk for psychosis: A diffusion tensor imaging study of individuals with basic symptoms and at ultra-high risk

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This DTI cross-sectional study compared UHR, basic symptom & control groups (n = 112).

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The splenium of UHR individuals exhibited differences in fractional anisotropy (FA).

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Basic symptoms alone were not associated with white matter microstructure changes.

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Large differences in FA & radial diffusivity were found in converters to psychosis.

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Regional FA was inversely correlated with the general psychopathology domain.

Background

Widespread white matter abnormalities are a frequent finding in chronic schizophrenia patients. More inconsistent results have been provided by the sparser literature on at-risk states for psychosis, i.e., emerging subclinical symptoms. However, considering risk as a homogenous construct, an approach of earlier studies, may impede our understanding of neuro-progression into psychosis.

Methods

An analysis was conducted of 3-Tesla MRI diffusion and symptom data from 112 individuals (mean age, 21.97 ± 4.19) within two at-risk paradigm subtypes, only basic symptoms (n = 43) and ultra-high risk (n = 37), and controls (n = 32). Between-group comparisons (involving three study groups and further split based on the subsequent transition to schizophrenia) of four diffusion-tensor-imaging-derived scalars were performed using voxelwise tract-based spatial statistics, followed by correlational analyses with Structured Interview for Prodromal Syndromes responses.

Results

Relative to controls, fractional anisotropy was lower in the splenium of the corpus callosum of ultra-high-risk individuals, but only before stringent multiple-testing correction, and negatively correlated with General Symptom severity among at-risk individuals. At-risk participants who transitioned to schizophrenia within 3 years, compared to those that did not transition, had more severe WM differences in fractional anisotropy and radial diffusivity (particularly in the corpus callosum, anterior corona radiata, and motor/sensory tracts), which were even more extensive compared to healthy controls.

Conclusions

These findings align with the subclinical symptom presentation and more extensive disruptions in converters, suggestive of severity-related demyelination or axonal pathology. Fine-grained but detectable differences among ultra-high-risk subjects (i.e., with brief limited intermittent and/or attenuated psychotic symptoms) point to the splenium as a discrete site of emerging psychopathology, while basic symptoms alone were not associated with altered fractional anisotropy.

Persistent and distressing psychotic-like experiences using adolescent brain cognitive development℠ study data

Childhood psychotic-like experiences (PLEs) are associated with a range of impairments; a subset of children experiencing PLEs will develop psychiatric disorders, including psychotic disorders. A potential distinguishing factor between benign PLEs versus PLEs that are clinically relevant is whether PLEs are distressing and/or persistent. The current study used three waves of Adolescent Brain Cognitive Development℠ (ABCD) study PLEs assessments to examine the extent to which persistent and/or distressing PLEs were associated with relevant baseline risk factors (e.g., cognition) and functioning/mental health service utilization domains. Four groups varying in PLE persistence and distress endorsement were created based on all available data in ABCD Release 3.0, with group membership not contingent on complete data: persistent distressing PLEs (n = 272), transient distressing PLEs (n = 298), persistent non-distressing PLEs (n = 221), and transient non-distressing PLEs (n = 536) groups. Using hierarchical linear models, results indicated youth with distressing PLEs, whether transient or persistent, showed delayed developmental milestones (β = 0.074, 95%CI:0.013,0.134) and altered structural MRI metrics (β = -0.0525, 95%CI:-0.100,-0.005). Importantly, distress interacted with PLEs persistence for the domains of functioning/mental health service utilization (β = 0.079, 95%CI:0.016,0.141), other reported psychopathology (β = 0.101, 95%CI:0.030,0.170), cognition (β = -0.052, 95%CI:0.-0.099,-0.002), and environmental adversity (β = 0.045, 95%CI:0.003,0.0.86; although no family history effects), with the interaction characterized by greatest impairment in the persistent distressing PLEs group. These results have implications for disentangling the importance of distress and persistence for PLEs with regards to impairments, including functional, pathophysiological, and environmental outcomes. These novel longitudinal data underscore that it is often only in the context of distress that persistent PLEs were related to impairments.

Identifying and changing cognitive vulnerability in the classroom: preliminary evaluation of CUES-Ed, a school-based universal cognitive behavioural early intervention service for 7-10 year olds

BACKGROUND

School-based early intervention may alleviate current emotional and behavioural problems, and, by targeting underlying vulnerability, safeguard children's future mental health. Improving on mixed outcomes to date is an international public health priority. CUES-Ed is a classroom-based, clinician-led, universal cognitive behavioural programme for primary school children, designed to promote emotional literacy and regulation. Additionally, CUES-Ed targets cognitive mechanisms implicated in the future development of mental disorder: stigmatising appraisals of emotional expression and of unusual perceptual experiences, and the tendency to jump-to-conclusions (JTC). We report here on fitness for purpose of our in-service assessment of cognitive vulnerability, and change in cognitive vulnerability following CUES-Ed and compared with a naturalistic waitlist.

METHODS

From 05/2017-11/2017, 960 children participated (900 CUES-Ed; 60 naturalistic waitlist). Assessments were completed in whole classes; 732 children provided pre-post data on all measures; 227 were missing data through absence or poor completion (n = 1 declined assessment).

RESULTS

Relationships between baseline cognitive vulnerability measures and their components were consistent with reliable and valid assessment. Cognitive vulnerability reduced from before to after CUES-Ed and compared with the naturalistic waitlist, for JTC (large effects) and stigmatising appraisals (small-medium effects), for all children (ESs pre-post: 0.2-1.0; between-group: 0.1-1.0) and vulnerable subgroups (ESs pre-post: 0.5-1.7; between-group: 0.2-2.0).

CONCLUSIONS

Targeted cognitive vulnerability mechanisms change following CUES-Ed. As stigmatising appraisals and JTC may increase vulnerability to future mental illness, findings suggest a promise in reducing future risk. A formally controlled research study, with longer-term follow-up, is required to test this. Limitations and implications for future evaluation are discussed.

Comparison of the touch-screen and traditional versions of the Corsi block-tapping test in patients with psychosis and healthy controls

BACKGROUND

Working memory (WM) refers to the capacity system for temporary storage and processing of information, which is known to depend on the integrity of the prefrontal cortex. Impairment in working memory is a core cognitive deficit among individuals with psychotic disorders. The Corsi block-tapping test is a widely-used instrument to assess visuospatial working memory. The traditional version is composed of 9 square blocks positioned on a physical board. In recent years, the number of digital instruments has increased significantly; several advantages might derive from the use of a digital version of the Corsi test.

METHODS

This study aimed to compare the digital and traditional versions of the Corsi test in 45 patients with psychotic disorders and 45 healthy controls. Both groups completed a neuropsychological assessment involving attention and working memory divided into the two conditions.

RESULTS

Results were consistent between the traditional and digital versions of the Corsi test. The digital version, as well as the traditional version, can discriminate between patients with psychosis and healthy controls. Overall, patients performed worse with respect to the healthy comparison group. The traditional Corsi test was positively related to intelligence and verbal working memory, probably due to a more significant effort to execute the test.

CONCLUSIONS

The digital Corsi might be used to enhance clinical practice diagnosis and treatment.The digital version can be administered in a natural environment in real-time. Further, it is easy to administer while ensuring a standard procedure.