Prediction of psychosis in prodrome: development and validation of a simple, personalized risk calculator

Association between serum cytokines and timeframe for conversion from clinical high-risk to psychosis

AIM

Although many studies have explored the link between inflammatory markers and psychosis, there is a paucity of research investigating the temporal progression in individuals at clinical high-risk (CHR) who eventually develop full psychosis. To address this gap, we investigated the correlation between serum cytokine levels and Timeframe for Conversion to Psychosis (TCP) in individuals with CHR.

METHODS

We enrolled 53 individuals with CHR who completed a 5-year follow-up with a confirmed conversion to psychosis. Granulocyte macrophage-colony stimulating factor (GM-CSF), interleukin (IL)-1β, 2, 6, 8, 10, tumor necrosis factor-α (TNF-α), and vascular endothelial growth factor (VEGF) levels were measured at baseline and 1-year. Correlation and quantile regression analyses were performed.

RESULTS

The median TCP duration was 14 months. A significantly shorter TCP was associated with higher levels of TNF-α (P = 0.022) and VEGF (P = 0.016). A negative correlation was observed between TCP and TNF-α level (P = 0.006) and VEGF level (P = 0.04). Quantile regression indicated negative associations between TCP and GM-CSF levels below the 0.5 quantile, IL-10 levels below the 0.3 quantile, IL-2 levels below the 0.25 quantile, IL-6 levels between the 0.65 and 0.75 quantiles, TNF-α levels below the 0.8 quantile, and VEGF levels below the 0.7 quantile. A mixed linear effects model identified significant time effects for IL-10 and IL-2, and significant group effects for changes in IL-2 and TNF-α.

CONCLUSIONS

Our findings underscore that a more pronounced baseline inflammatory state is associated with faster progression of psychosis in individuals with CHR. This highlights the importance of considering individual inflammatory profiles during early intervention and of tailoring preventive measures for risk profiles.

Advancements and Future Directions in Prevention Based on Evaluation for Individuals With Clinical High Risk of Psychosis: Insights From the SHARP Study

BACKGROUND AND HYPOTHESIS

This review examines the evolution and future prospects of prevention based on evaluation (PBE) for individuals at clinical high risk (CHR) of psychosis, drawing insights from the SHARP (Shanghai At Risk for Psychosis) study. It aims to assess the effectiveness of non-pharmacological interventions in preventing psychosis onset among CHR individuals.

STUDY DESIGN

The review provides an overview of the developmental history of the SHARP study and its contributions to understanding the needs of CHR individuals. It explores the limitations of traditional antipsychotic approaches and introduces PBE as a promising framework for intervention.

STUDY RESULTS

Three key interventions implemented by the SHARP team are discussed: nutritional supplementation based on niacin skin response blunting, precision transcranial magnetic stimulation targeting cognitive and brain functional abnormalities, and cognitive behavioral therapy for psychotic symptoms addressing symptomatology and impaired insight characteristics. Each intervention is evaluated within the context of PBE, emphasizing the potential for tailored approaches to CHR individuals.

CONCLUSIONS

The review highlights the strengths and clinical applications of the discussed interventions, underscoring their potential to revolutionize preventive care for CHR individuals. It also provides insights into future directions for PBE in CHR populations, including efforts to expand evaluation techniques and enhance precision in interventions.

Cognitive functions following initiation of antipsychotic medication in adolescents and adults at clinical high risk for psychosis: a naturalistic sub group analysis using the MATRICS consensus cognitive battery

BACKGROUND

The effects of antipsychotic (AP) medications on cognitive functions in individuals at clinical high-risk (CHR) of psychosis are poorly understood. This study compared the effects of AP treatment on cognitive improvement in CHR adolescents and adults.

METHODS

A total of 327 CHR participants, with an age range of 13 to 45 years, who underwent baseline neuropsychological assessments and a 1-year clinical follow-up were included. Participants with CHR were categorized into four groups based on their age: adolescents (aged < 18) and adults (aged ≥ 18), as well as their antipsychotic medication status (AP+ or AP-). Therefore, the four groups were defined as Adolescent-AP-, Adolescent-AP+, Adult-AP-, and Adult-AP+.

RESULTS

During the follow-up, 231 CHR patients received AP treatment, 94 converted to psychosis, and 161 completed the 1-year follow-up. The Adolescent-AP+ group had more positive symptoms, lower general functions, and cognitive impairments than the Adolescent-AP- group at baseline, but no significant differences were observed among adults. The Adolescent-AP+ group showed a significant increase in the risk of conversion to psychosis (p < 0.001) compared to the Adolescent-AP- group. The Adult-AP+ group showed a decreasing trend in the risk of conversion (p = 0.088) compared to the Adult-AP- group. The Adolescent-AP- group had greater improvement in general functions (p < 0.001), neuropsychological assessment battery mazes (p = 0.025), and brief visuospatial memory test-revised (p = 0.020), as well as a greater decrease in positive symptoms (p < 0.001) at follow-up compared to the Adolescent-AP+ group. No significant differences were observed among adults.

CONCLUSIONS

Early use of AP was not associated with a positive effect on cognitive function in CHR adolescents. Instead, the absence of AP treatment was associated with better cognitive recovery, suggesting that AP exposure might not be the preferred choice for cognitive recovery in CHR adolescents, but may be more reasonable for use in adults.

Combining Clinical With Cognitive or Magnetic Resonance Imaging Data for Predicting Transition to Psychosis in Ultra High-Risk Patients: Data From the PACE 400 Cohort

BACKGROUND

Multimodal modeling that combines biological and clinical data shows promise in predicting transition to psychosis in individuals who are at ultra-high risk. Individuals who transition to psychosis are known to have deficits at baseline in cognitive function and reductions in gray matter volume in multiple brain regions identified by magnetic resonance imaging.

METHODS

In this study, we used Cox proportional hazards regression models to assess the additive predictive value of each modality-cognition, cortical structure information, and the neuroanatomical measure of brain age gap-to a previously developed clinical model using functioning and duration of symptoms prior to service entry as predictors in the Personal Assessment and Crisis Evaluation (PACE) 400 cohort. The PACE 400 study is a well-characterized cohort of Australian youths who were identified as ultra-high risk of transitioning to psychosis using the Comprehensive Assessment of At Risk Mental States (CAARMS) and followed for up to 18 years; it contains clinical data (from N = 416 participants), cognitive data (n = 213), and magnetic resonance imaging cortical parameters extracted using FreeSurfer (n = 231).

RESULTS

The results showed that neuroimaging, brain age gap, and cognition added marginal predictive information to the previously developed clinical model (fraction of new information: neuroimaging 0%-12%, brain age gap 7%, cognition 0%-16%).

CONCLUSIONS

In summary, adding a second modality to a clinical risk model predicting the onset of a psychotic disorder in the PACE 400 cohort showed little improvement in the fit of the model for long-term prediction of transition to psychosis.

Heterogeneity of clinical symptomatology in pediatric patients at clinical high risk for psychosis

OBJECTIVE

Widespread use of diagnostic tools like the Structured Interview for Prodromal Symptoms (SIPS) has highlighted that youth at Clinical High Risk for Psychosis (CHR-P) present with heterogeneous symptomatology. This pilot study aims to highlight the range of clinical characteristics of CHR-P youth, investigate the role of the non-positive (negative, disorganization, and general) symptoms in risk assessment, and determine if specific profiles are associated with severe symptomatology.

METHODS

38 participants aged 7-18 were administered the SIPS and designated as CHR-P. Descriptive statistics and mean difference t-tests were used to describe the range in prevalence and severity of SIPS symptoms and to identify symptoms associated with greater overall symptomatology.

RESULTS

Participants who had a greater number of positive symptoms also had significantly more negative, disorganization, and general symptoms. A number of SIPS symptoms were associated with greater number of positive symptoms.

CONCLUSION

CHR-P youth represent a heterogeneous group, presenting with a wide range in clinical presentation as reflected in both the number of SIPS symptoms and their severity. Though the severity and duration of positive SIPS symptoms determines the CHR-P classification, high ratings on several of the other SIPS negative, disorganization, and general items may be useful indicators of elevated symptomatology.

[Willingness to expend effort for rewards in individuals at clinical high-risk for psychosis: a relationship with the severity and stability of negative symptoms]

OBJECTIVE

To identify the deficit in willingness to expend effort and its association with negative symptoms in the high-risk for psychosis (CHR) group.

MATERIAL AND METHODS

The study included young men: 45 patients, who met CHR criteria and were treated for a depressive episode, and 15 controls. All subjects completed a modified version of the Effort Expenditure for Rewards Task (EEfRT). The CHR group was assessed with the SOPS, SANS and HDRS at the beginning and at the end of treatment. EEfRT was performed only at the end of treatment.

RESULTS

The CHR group was significantly less likely to choose high effort tasks across reward probability and magnitude levels compared with the control group (all p<0.001). No significant correlations were found between the rate of selecting the high effort task and the negative syndrome domains of amotivation and diminished expression. The subgroups of CHR with stable and transient (i.e., with a reduction >50% during treatment) negative symptoms, which were identified by a cluster analysis, did not differ in the willingness to expend effort.

CONCLUSION

The study confirmed a decrease in the willingness to expend effort in the CHR group; however, this deficit was only weakly correlated with negative symptoms and persisted after the symptoms reduction during treatment, which requires future studies to investigate mechanisms underlying impaired effort expenditure for rewards in CHR.

Clinical significance of psychotic-like experiences across U.S. ethnoracial groups

BACKGROUND

Prevalence of psychotic-like experiences (PLEs) - reports of hallucinations and delusional thinking not meeting criteria for psychotic disorder - varies substantially across ethnoracial groups. What explains this range of PLE prevalence? Despite extensive research, the clinical significance of PLEs remains unclear. Are PLE prevalence and clinical severity differentially associated across ethnoracial groups?

METHODS

We examined the lifetime prevalence and clinical significance of PLEs across ethnoracial groups in the Collaborative Psychiatric Epidemiology Surveys (N = 11 139) using the Composite International Diagnostic Interview (CIDI) psychosis symptom screener. Outcomes included mental healthcare use (inpatient, outpatient), mental health morbidity (self-perceived poor/fair mental health, suicidal ideation or attempts), and impairment (role interference). Individuals with outcome onsets prior to PLE onset were excluded. We also examined associations of PLEs with CIDI diagnoses. Cox proportional-hazards regression and logistic regression modeling identified associations of interest.

RESULTS

Contrary to previous reports, only Asian Americans differed significantly from other U.S. ethnoracial groups, reporting lower lifetime prevalence (6.7% v. 8.0-11.9%) and mean number (0.09 v. 0.11-0.18) of PLEs. In multivariate analyses, PLE clinical significance showed limited ethnoracial variation among Asian Americans, non-Caribbean Latinos, and Afro-Caribbeans. In other groups, mental health outcomes showed significant ethnoracial clustering by outcome (e.g. hospitalization and role interference with Caribbean-Latino origin), possibly due to underlying differences in psychiatric disorder chronicity or treatment barriers.

CONCLUSIONS

While there is limited ethnoracial variation in U.S. PLE prevalence, PLE clinical significance varies across U.S. ethnoracial groups. Clinicians should consider this variation when assessing PLEs to avoid exaggerating their clinical significance, contributing to mental healthcare disparities.

Comprehensive review of multidimensional biomarkers in the ShangHai At Risk for Psychosis (SHARP) program for early psychosis identification

Psychosis is recognized as one of the largest contributors to nonfatal health loss, and early identification can largely improve routine clinical activity by predicting the psychotic course and guiding treatment. Clinicians have used the clinical high-risk for psychosis (CHR) paradigm to better understand the risk factors that contribute to the onset of psychotic disorders. Clinical factors have been widely applied to calculate the individualized risks for conversion to psychosis 1-2 years later. However, there is still a dearth of valid biomarkers to predict psychosis. Biomarkers, in the context of this paper, refer to measurable biological indicators that can provide valuable information about the early identification of individuals at risk for psychosis. The aim of this paper is to critically review studies assessing CHR and suggest possible biomarkers for application of prediction. We summarized the studies on biomarkers derived from the findings of the ShangHai at Risk for Psychosis (SHARP) program, including those that are considered to have the most potential. This comprehensive review was conducted based on expert opinions within the SHARP research team, and the selection of studies and results presented in this paper reflects the collective expertise of the team in the field of early psychosis identification. The three dimensions with potential candidates include neuroimaging dimension of brain structure and function, electrophysiological dimension of event-related potentials (ERPs), and immune dimension of inflammatory cytokines and complement proteins, which proved to be useful in supporting the prediction of psychosis from the CHR state. We suggest that these three dimensions could be useful as risk biomarkers for treatment optimization. In the future, when available for the integration of multiple dimensions, clinicians may be able to obtain a comprehensive report with detailed information of psychosis risk and specific indications about preferred prevention.

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.

Dynamic Prediction of Outcomes for Youth at Clinical High Risk for Psychosis: A Joint Modeling Approach

Importance

Leveraging the dynamic nature of clinical variables in the clinical high risk for psychosis (CHR-P) population has the potential to significantly improve the performance of outcome prediction models.

Objective

To improve performance of prediction models and elucidate dynamic clinical profiles using joint modeling to predict conversion to psychosis and symptom remission.

Design, Setting, and Participants

Data were collected as part of the third wave of the North American Prodrome Longitudinal Study (NAPLS 3), which is a 9-site prospective longitudinal study. Participants were individuals aged 12 to 30 years who met criteria for a psychosis-risk syndrome. Clinical, neurocognitive, and demographic variables were collected at baseline and at multiple follow-up visits, beginning at 2 months and up to 24 months. An initial feature selection process identified longitudinal clinical variables that showed differential change for each outcome group across 2 months. With these variables, a joint modeling framework was used to estimate the likelihood of eventual outcomes. Models were developed and tested in a 10-fold cross-validation framework. Clinical data were collected between February 2015 and November 2018, and data were analyzed from February 2022 to December 2023.

Main Outcomes and Measures

Prediction models were built to predict conversion to psychosis and symptom remission. Participants met criteria for conversion if their positive symptoms reached the fully psychotic range and for symptom remission if they were subprodromal on the Scale of Psychosis-Risk Symptoms for a duration of 6 months or more.

Results

Of 488 included NAPLS 3 participants, 232 (47.5%) were female, and the mean (SD) age was 18.2 (3.4) years. Joint models achieved a high level of accuracy in predicting conversion (balanced accuracy [BAC], 0.91) and remission (BAC, 0.99) compared with baseline models (conversion: BAC, 0.65; remission: BAC, 0.60). Clinical variables that showed differential change between outcome groups across a 2-month span, including measures of symptom severity and aspects of functioning, were also identified. Further, intra-individual risks for each outcome were more negatively correlated when using joint models (r = -0.92; P < .001) compared with baseline models (r = -0.50; P < .001).

Conclusions and Relevance

In this study, joint models significantly outperformed baseline models in predicting both conversion and remission, demonstrating that monitoring short-term clinical change may help to parse heterogeneous dynamic clinical trajectories in a CHR-P population. These findings could inform additional study of targeted treatment selection and could move the field closer to clinical implementation of prediction models.

Speech characteristics yield important clues about motor function: Speech variability in individuals at clinical high-risk for psychosis

BACKGROUND AND HYPOTHESIS

Motor abnormalities are predictive of psychosis onset in individuals at clinical high risk (CHR) for psychosis and are tied to its progression. We hypothesize that these motor abnormalities also disrupt their speech production (a highly complex motor behavior) and predict CHR individuals will produce more variable speech than healthy controls, and that this variability will relate to symptom severity, motor measures, and psychosis-risk calculator risk scores.

STUDY DESIGN

We measure variability in speech production (variability in consonants, vowels, speech rate, and pausing/timing) in N = 58 CHR participants and N = 67 healthy controls. Three different tasks are used to elicit speech: diadochokinetic speech (rapidly-repeated syllables e.g., papapa…, pataka…), read speech, and spontaneously-generated speech.

STUDY RESULTS

Individuals in the CHR group produced more variable consonants and exhibited greater speech rate variability than healthy controls in two of the three speech tasks (diadochokinetic and read speech). While there were no significant correlations between speech measures and remotely-obtained motor measures, symptom severity, or conversion risk scores, these comparisons may be under-powered (in part due to challenges of remote data collection during the COVID-19 pandemic).

CONCLUSION

This study provides a thorough and theory-driven first look at how speech production is affected in this at-risk population and speaks to the promise and challenges facing this approach moving forward.

Development and Validation of the Negative Symptom Inventory-Psychosis Risk

BACKGROUND AND HYPOTHESES

Early identification and prevention of psychosis is limited by the availability of tools designed to assess negative symptoms in those at clinical high-risk for psychosis (CHR). To address this critical need, a multi-site study was established to develop and validate a clinical rating scale designed specifically for individuals at CHR: The Negative Symptom Inventory-Psychosis Risk (NSI-PR).

STUDY DESIGN

The measure was developed according to guidelines recommended by the NIMH Consensus Conference on Negative Symptoms using a transparent, iterative, and data-driven process. A 16-item version of the NSI-PR was designed to have an overly inclusive set of items and lengthier interview to support the ultimate intention of creating a new briefer measure. Psychometric properties of the 16-item NSI-PR were evaluated in a sample of 218 CHR participants.

STUDY RESULTS

Item-level analyses indicated that men had higher scores than women. Reliability analyses supported internal consistency, inter-rater agreement, and temporal stability. Associations with measures of negative symptoms and functioning supported convergent validity. Small correlations with positive, disorganized, and general symptoms supported discriminant validity. Structural analyses indicated a 5-factor structure (anhedonia, avolition, asociality, alogia, and blunted affect). Item response theory identified items for removal and indicated that the anchor range could be reduced. Factor loadings, item-level correlations, item-total correlations, and skew further supported removal of certain items.

CONCLUSIONS

These findings support the psychometric properties of the NSI-PR and guided the creation of a new 11-item NSI-PR that will be validated in the next phase of this multi-site scale development project.

Alterations in facial expressions in individuals at risk for psychosis: a facial electromyography approach using emotionally evocative film clips

BACKGROUND

Negative symptoms such as blunted facial expressivity are characteristic of schizophrenia. However, it is not well-understood if and what abnormalities are present in individuals at clinical high-risk (CHR) for psychosis.

METHODS

This experimental study employed facial electromyography (left zygomaticus major and left corrugator supercilia) in a sample of CHR individuals (N = 34) and healthy controls (N = 32) to detect alterations in facial expressions in response to emotionally evocative film clips and to determine links with symptoms.

RESULTS

Findings revealed that the CHR group showed facial blunting manifested in reduced zygomatic activity in response to an excitement (but not amusement, fear, or sadness) film clip compared to controls. Reductions in zygomatic activity in the CHR group emerged in response to the emotionally evocative peak period of the excitement film clip. Lower zygomaticus activity during the excitement clip was related to anxiety while lower rates of change in zygomatic activity during the excitement video clip were related to higher psychosis risk conversion scores.

CONCLUSIONS

Together, these findings inform vulnerability/disease driving mechanisms and biomarker and treatment development.

Detecting at-risk mental states for psychosis (ARMS) using machine learning ensembles and facial features

AIMS

Our study aimed to develop a machine learning ensemble to distinguish "at-risk mental states for psychosis" (ARMS) subjects from control individuals from the general population based on facial data extracted from video-recordings.

METHODS

58 non-help-seeking medication-naïve ARMS and 70 healthy subjects were screened from a general population sample. At-risk status was assessed with the Structured Interview for Prodromal Syndromes (SIPS), and "Subject's Overview" section was filmed (5-10 min). Several features were extracted, e.g., eye and mouth aspect ratio, Euler angles, coordinates from 51 facial landmarks. This elicited 649 facial features, which were further selected using Gradient Boosting Machines (AdaBoost combined with Random Forests). Data was split in 70/30 for training, and Monte Carlo cross validation was used.

RESULTS

Final model reached 83 % of mean F1-score, and balanced accuracy of 85 %. Mean area under the curve for the receiver operator curve classifier was 93 %. Convergent validity testing showed that two features included in the model were significantly correlated with Avolition (SIPS N2 item) and expression of emotion (SIPS N3 item).

CONCLUSION

Our model capitalized on short video-recordings from individuals recruited from the general population, effectively distinguishing between ARMS and controls. Results are encouraging for large-screening purposes in low-resource settings.

Illness Phase as a Key Assessment and Intervention Window for Psychosis

The phenotype of schizophrenia, regardless of etiology, represents the most studied psychotic disorder with respect to neurobiology and distinct phases of illness. The early phase of illness represents a unique opportunity to provide effective and individualized interventions that can alter illness trajectories. Developmental age and illness stage, including temporal variation in neurobiology, can be targeted to develop phase-specific clinical assessment, biomarkers, and interventions. We review an earlier model whereby an initial glutamate signaling deficit progresses through different phases of allostatic adaptation, moving from potentially reversible functional abnormalities associated with early psychosis and working memory dysfunction, and ending with difficult-to-reverse structural changes after chronic illness. We integrate this model with evidence of dopaminergic abnormalities, including cortical D1 dysfunction, which develop during adolescence. We discuss how this model and a focus on a potential critical window of intervention in the early stages of schizophrenia impact the approach to research design and clinical care. This impact includes stage-specific considerations for symptom assessment as well as genetic, cognitive, and neurophysiological biomarkers. We examine how phase-specific biomarkers of illness phase and brain development can be incorporated into current strategies for large-scale research and clinical programs implementing coordinated specialty care. We highlight working memory and D1 dysfunction as early treatment targets that can substantially affect functional outcome.

Do the Components of Attenuated Positive Symptoms Truly Represent One Construct?

BACKGROUND AND HYPOTHESES

Psychosis-risk inventories, like the Structured Interview for Psychosis-Risk Syndromes (SIPS), utilize symptom components and coalesce the information into a single-severity rating. These components include frequency, duration, in-the-moment conviction, retrospective insight, distress, and effect on social/role functioning. While combining components distills a great deal of important information into one practical symptom rating, this approach may mask important details of the greater clinical picture.

STUDY DESIGN

Individuals at clinical high risk for psychosis (n = 115) were assessed with the SIPS Score Separable Components (SSSC) scale, created to accompany the SIPS positive items by dividing each item into the 7 components identified above. The latent structure of the SSSC was identified with an exploratory factor analysis (EFA). The factors were followed up with validation analyses including hypothesized cognitive, functioning, and symptom measures. Finally, clinical utility analyses were conducted to understand relationships between psychosis risk and common comorbidities.

STUDY RESULTS

EFA revealed that the SSSC had 3 interpretable factors with the appropriate fit (rmsr = 0.018, TLI = 0.921): Conviction (in-the-moment conviction, retrospective insight), Distress-Impairment (distress, social/role functioning), and Frequency/Duration (frequency, duration). Conviction was minimally valid, Distress-Impairment had excellent validity, and Frequency/Duration was not related to any of the candidate validators. Conviction significantly predicted elevated psychosis risk. Distress-Impairment was related to common comorbid symptoms. Notably, the factors associated more strongly with clinical features than the traditional SIPS scores.

CONCLUSIONS

The SSSC offers a supplemental approach to single-severity ratings, providing useful clinical insight, mechanistic understanding, and the potential for better capturing heterogeneity in this population.

Self-reported Gesture Interpretation and Performance Deficits in Individuals at Clinical High Risk for Psychosis

BACKGROUND AND HYPOTHESIS

Deficits in performing and interpreting communicative nonverbal behaviors, such as gesture, have been linked to varied psychopathology and dysfunction. Some evidence suggests that individuals at risk for psychosis have deficits in gesture interpretation and performance; however, individuals with internalizing disorders (eg, depression) may have similar deficits. No previous studies have examined whether gesture deficits in performance and interpretation are specific to those at risk for psychosis. Additionally, the underlying mechanisms (eg, cognition) and consequences (eg, functioning) of these deficits are poorly understood.

STUDY DESIGN

This study examined self-reported gesture interpretation (SRGI) and performance (SRGP) in those at clinical high risk for psychosis (CHR; N = 88), those with internalizing disorders (INT; N = 51), and healthy controls (HC; N = 53). Participants completed questionnaires, clinical interviews, and neurocognitive tasks.

STUDY RESULTS

Results indicated that the CHR group was characterized by significantly lower SRGI scores than the HC or INT groups (d = 0.41); there were no differences among groups in SRGP. Within CHR participants, greater deficits in SRGP were associated with lower verbal learning and memory (r = -.33), but not general intelligence or processing speed. Furthermore, gesture deficits were associated with higher cross-sectional risk for conversion to a full psychotic disorder in the CHR group.

CONCLUSIONS

Overall, these findings suggest that specific subdomains of gesture may reflect unique vulnerability for psychosis, self-report may be a viable assessment tool in understanding these phenomena, and gesture dysfunction may signal risk for transition to psychosis.

Multivariate joint models for the dynamic prediction of psychosis in individuals with clinical high risk

This study attempted to construct and validate dynamic prediction via multivariate joint models and compare the prognostic performance of these models to both static and univariate joint models. Individuals with clinical high risk(CHR)(n = 289) were recruited and re-assessed for positive symptoms, general functions, and conversion to psychosis at 2-months, 1-year, and 2-years to develop the dynamic models. A multivariate joint model of positive psychotic symptoms was assessed using the Structured Interview for Prodromal Symptoms(SIPSp) and general function assessed by global assessment of functioning scores(GAFs) with time-to-conversion to psychosis. The area under the receiver operating characteristic(ROC) curve(AUC) was used to test the accuracy of the models. Among 298 CHR individuals, 68 converted to psychosis within 2 years after the initial assessments. Multivariate joint models showed that declining GAFs and increasing SIPSp corresponded to significant and trending to significantly increased risk of psychosis onset and had much higher prognostic accuracy (cross-validated AUC=0.9) compared to the static model(AUC=0.6) and univariate joint models(cross-validated AUC=0.6-0.8). Our results showed that multivariate joint models could be highly efficient in forecasting psychosis onset for CHR individuals. Longitudinal assessments for psychopathology and general functions can be useful for dynamically predicting the prognosis of the pre-morbid phase of psychosis.

Reduced willingness to expend effort for rewards is associated with risk for conversion and negative symptom severity in youth at clinical high-risk for psychosis

BACKGROUND

Schizophrenia (SZ) is typically preceded by a prodromal (i.e. pre-illness) period characterized by attenuated positive symptoms and declining functional outcome. Negative symptoms are prominent among individuals at clinical high-risk (CHR) for psychosis (i.e. those with prodromal syndromes) and predictive of conversion to illness. Mechanisms underlying negative symptoms are unclear in the CHR population.

METHODS

The current study evaluated whether CHR participants demonstrated deficits in the willingness to expend effort for rewards and whether these impairments are associated with negative symptoms and greater risk for conversion. Participants included 44 CHR participants and 32 healthy controls (CN) who completed the Effort Expenditure for Reward Task (EEfRT).

RESULTS

Compared to CN, CHR participants displayed reduced likelihood of exerting high effort for high probability and magnitude rewards. Among CHR participants, reduced effort expenditure was associated with greater negative symptom severity and greater probability of conversion to a psychotic disorder on a cross-sectional risk calculator.

CONCLUSIONS

Findings suggest that effort-cost computation is a marker of illness liability and a transphasic mechanism underlying negative symptoms in the SZ spectrum.

The 3-year progression of clinically significant psychotic-like experiences in a general adult population in Lagos, Nigeria

PURPOSE

The study assessed the 3-year progression of clinically significant psychotic-like experience (CS-PLE) symptoms in an adult general population in terms of stability or remission of symptoms and transition to psychosis.

METHODS

Participants (n = 1292) aged 18-65 years with CS-PLE were assessed at baseline for sociodemographic details, family history of mental illness, functioning status, common mental disorders, alcohol, and substance use disorders. Three years later they were reassessed for diagnosis of psychosis, presence or remission of PLE symptoms, and contact with mental health services.

RESULTS

The mean age of the participants at baseline in years was 36.56 (SD = 11.66) and there were 855 (66.2%) females. By the 3rd year follow-up, 95 (7.3%) had transited to psychosis, while 850 (65.5%) had persistent CS-PLE symptoms and the rest 347 (27.2%) were in remission. Only history of mental illness in the immediate family (HR 4.81, 95% CI 1.40-16.47, P = 0.013) and regular use of cannabis at less than 18 years of age (HR 0.65, 95% CI 0.55-0.77, P < 0.001) were the independent predictors of conversion to psychosis at 3 years.

CONCLUSION

The rate of TTP in the non-clinical population with elevated risk may be lower than that earlier reported in the western literature. Interventions aimed at preventing transition to psychosis in high risk groups must pay attention to early onset users of cannabis and those with family history of mental illness.

Comparing a Computerized Digit Symbol Test to a Pen-and-Paper Classic

Background and Hypothesis

Processing speed dysfunction is a core feature of psychosis and predictive of conversion in individuals at clinical high risk (CHR) for psychosis. Although traditionally measured with pen-and-paper tasks, computerized digit symbol tasks are needed to meet the increasing demand for remote assessments. Therefore we: (1) assessed the relationship between traditional and computerized processing speed measurements; (2) compared effect sizes of impairment for progressive and persistent subgroups of CHR individuals on these tasks; and (3) explored causes contributing to task performance differences.

Study Design

Participants included 92 CHR individuals and 60 healthy controls who completed clinical interviews, the Brief Assessment of Cognition in Schizophrenia Symbol Coding test, the computerized TestMyBrain Digit Symbol Matching Test, a finger-tapping task, and a self-reported motor abilities measure. Correlations, Hedges' g, and linear models were utilized, respectively, to achieve the above aims.

Study Results

Task performance was strongly correlated (r = 0.505). A similar degree of impairment was seen between progressive (g = -0.541) and persistent (g = -0.417) groups on the paper version. The computerized task uniquely identified impairment for progressive individuals (g = -477), as the persistent group performed similarly to controls (g = -0.184). Motor abilities were related to the computerized version, but the paper version was more related to symptoms and psychosis risk level.

Conclusions

The paper symbol coding task measures impairment throughout the CHR state, while the computerized version only identifies impairment in those with worsening symptomatology. These results may be reflective of sensitivity differences, an artifact of existing subgroups, or evidence of mechanistic differences.

Age-related changes in self-reported psychotic experiences in clinical help-seeking population: From 15 to 45 years

AIMS

Psychotic experiences differ with age. It is currently unknown whether there were specific patterns and associations between the presentation of psychotic experiences and age. This study aimed to explore age-related differences (15-45 years) in self-reported psychotic experiences in a large-scale clinical population.

METHODS

A total of 2542 consecutive new patients aged 15-45 years were recruited on their first visit to the Shanghai Mental Health Center and screened with the PRIME Screen-Revised (PS-R). According to the clinical diagnostic information of patients from their outpatient medical records compiled by their clinicians, four diagnostic categories were applied: 1) psychotic disorder; 2) mood disorder; 3) anxiety disorder and 4) others.

RESULTS

The PS-R scores of self-reported psychotic experiences declined with age, except for two age ranges: ≤18 years for overall sample (≤18 vs. 19-34 years: t = 5.531, df = 2202, p < .001) and 37-40 years for female sample (37-40 vs. >40 years: t = 1.985, df = 138, p = .049), which showed upward trends, contrary to those of others. There were no significant differences in self-reported psychotic experiences between age groups in patients with psychotic disorders, while significant age differences were found in all nonpsychotic patients.

CONCLUSION

These findings support the view that frequent PS-R screening demonstrated that psychotic experiences decline with age in the clinical population. Early detection of psychosis should focus on not only adolescents but also women aged >36 years.

Prediction of psychosis: model development and internal validation of a personalized risk calculator

BACKGROUND

Over the past two decades, early detection and early intervention in psychosis have become essential goals of psychiatry. However, clinical impressions are insufficient for predicting psychosis outcomes in clinical high-risk (CHR) individuals; a more rigorous and objective model is needed. This study aims to develop and internally validate a model for predicting the transition to psychosis within 10 years.

METHODS

Two hundred and eight help-seeking individuals who fulfilled the CHR criteria were enrolled from the prospective, naturalistic cohort program for CHR at the Seoul Youth Clinic (SYC). The least absolute shrinkage and selection operator (LASSO)-penalized Cox regression was used to develop a predictive model for a psychotic transition. We performed k-means clustering and survival analysis to stratify the risk of psychosis.

RESULTS

The predictive model, which includes clinical and cognitive variables, identified the following six baseline variables as important predictors: 1-year percentage decrease in the Global Assessment of Functioning score, IQ, California Verbal Learning Test score, Strange Stories test score, and scores in two domains of the Social Functioning Scale. The predictive model showed a cross-validated Harrell's C-index of 0.78 and identified three subclusters with significantly different risk levels.

CONCLUSIONS

Overall, our predictive model showed a predictive ability and could facilitate a personalized therapeutic approach to different risks in high-risk individuals.

Motion energy analysis during speech tasks in medication-naïve individuals with at-risk mental states for psychosis

Movement abnormalities are commonly observed in schizophrenia and at-risk mental states (ARMS) for psychosis. They are usually detected with clinical interviews, such that automated analysis would enhance assessment. Our aim was to use motion energy analysis (MEA) to assess movement during free-speech videos in ARMS and control individuals, and to investigate associations between movement metrics and negative and positive symptoms. Thirty-two medication-naïve ARMS and forty-six healthy control individuals were filmed during speech tasks. Footages were analyzed using MEA software, which assesses movement by differences in pixels frame-by-frame. Two regions of interest were defined—head and torso—and mean amplitude, frequency, and coefficient of variability of movements for them were obtained. These metrics were correlated with the Structured Interview for Prodromal Syndromes (SIPS) symptoms, and with the risk of conversion to psychosis—inferred with the SIPS risk calculator. ARMS individuals had significantly lower mean amplitude of head movement and higher coefficients of movement variability for both head and torso, compared to controls. Higher coefficient of variability was related to higher risk of conversion. Negative correlations were seen between frequency of movement and most SIPS negative symptoms. All positive symptoms were correlated with at least one movement variable. Movement abnormalities could be automatically detected in medication-naïve ARMS subjects by means of a motion energy analysis software. Significant associations of movement metrics with symptoms were found, supporting the importance of movement analysis in ARMS. This could be a potentially important tool for early diagnosis, intervention, and outcome prediction.

Prodromal phase: Differences in prodromal symptoms, risk factors and markers of vulnerability in first episode mania versus first episode psychosis with onset in late adolescence or adulthood

OBJECTIVE

This study was aimed at identifying differences in the prodromal symptoms and their duration, risk factors and markers of vulnerability in patients presenting a first episode mania (FEM) or psychosis (FEP) with onset in late adolescence or adulthood in order to guide tailored treatment strategies.

METHODS

Patients with a FEM or FEP underwent a clinical assessment. Prodromes were evaluated with the Bipolar Prodrome Symptom Scale-Retrospective (BPSS-R). Chi-squared tests were conducted to assess specific prodromal symptoms, risk factors or markers of vulnerability between groups. Significant prodromal symptoms were entered in a stepwise forward logistic regression model. The probabilities of a gradual versus rapid onset pattern of the prodromes were computed with logistic regression models.

RESULTS

The total sample included 108 patients (FEM = 72, FEP = 36). Social isolation was associated with the prodromal stage of a FEP whilst Increased energy or goal-directed activity with the prodrome to a FEM. Physically slowed down presented the most gradual onset whilst Increased energy presented the most rapid. The presence of obstetric complications and difficulties in writing and reading during childhood were risk factors for FEP. As for markers of vulnerability, impairment in premorbid adjustment was characteristic of FEP patients. No specific risk factor or marker of vulnerability was identified for FEM.

CONCLUSION

Early characteristics differentiating FEP from FEM were identified. These findings might help shape early identification and preventive intervention programmes.

Dynamic networks of psychological symptoms, impairment, substance use, and social support: The evolution of psychopathology among emerging adults

BACKGROUND

Subthreshold/attenuated syndromes are established precursors of full-threshold mood and psychotic disorders. Less is known about the individual symptoms that may precede the development of subthreshold syndromes and associated social/functional outcomes among emerging adults.

METHODS

We modeled two dynamic Bayesian networks (DBN) to investigate associations among self-rated phenomenology and personal/lifestyle factors (role impairment, low social support, and alcohol and substance use) across the 19Up and 25Up waves of the Brisbane Longitudinal Twin Study. We examined whether symptoms and personal/lifestyle factors at 19Up were associated with (a) themselves or different items at 25Up, and (b) onset of a depression-like, hypo-manic-like, or psychotic-like subthreshold syndrome (STS) at 25Up.

RESULTS

The first DBN identified 11 items that when endorsed at 19Up were more likely to be reendorsed at 25Up (e.g., hypersomnia, impaired concentration, impaired sleep quality) and seven items that when endorsed at 19Up were associated with different items being endorsed at 25Up (e.g., earlier fatigue and later role impairment; earlier anergia and later somatic pain). In the second DBN, no arcs met our a priori threshold for inclusion. In an exploratory model with no threshold, >20 items at 19Up were associated with progression to an STS at 25Up (with lower statistical confidence); the top five arcs were: feeling threatened by others and a later psychotic-like STS; increased activity and a later hypo-manic-like STS; and anergia, impaired sleep quality, and/or hypersomnia and a later depression-like STS.

CONCLUSIONS

These probabilistic models identify symptoms and personal/lifestyle factors that might prove useful targets for indicated preventative strategies.

Metabolic disturbances, hemoglobin A1c, and social cognition impairment in Schizophrenia spectrum disorders

Social cognitive impairments are core features of schizophrenia spectrum disorders (SSD) and are associated with greater functional impairment and decreased quality of life. Metabolic disturbances have been related to greater impairment in general neurocognition, but their relationship to social cognition has not been previously reported. In this study, metabolic measures and social cognition were assessed in 245 participants with SSD and 165 healthy comparison subjects (HC), excluding those with hemoglobin A1c (HbA1c) > 6.5%. Tasks assessed emotion processing, theory of mind, and social perception. Functional connectivity within and between social cognitive networks was measured during a naturalistic social task. Among SSD, a significant inverse relationship was found between social cognition and cumulative metabolic burden (β = -0.38, p < 0.001) and HbA1c (β = -0.37, p < 0.001). The relationship between social cognition and HbA1c was robust across domains and measures of social cognition and after accounting for age, sex, race, non-social neurocognition, hospitalization, and treatment with different antipsychotic medications. Negative connectivity between affect sharing and motor resonance networks was a partial mediator of this relationship across SSD and HC groups (β = -0.05, p = 0.008). There was a group x HbA1c effect indicating that SSD participants were more adversely affected by increasing HbA1c. Thus, we provide the first report of a robust relationship in SSD between social cognition and abnormal glucose metabolism. If replicated and found to be causal, insulin sensitivity and blood glucose may present as promising targets for improving social cognition, functional outcomes, and quality of life in SSD.

Three prominent self-report risk measures show unique and overlapping utility in characterizing those at clinical high-risk for psychosis

Self-report questionnaires have been developed to efficiently assess psychosis risk and vulnerability. Despite this, the validity of these questionnaires for assessing specific positive symptoms in those at clinical high risk for psychosis (CHR) is unclear. Positive symptoms have largely been treated as a uniform construct in this critical population and there have been no reports on the construct validity of questionnaires for assessing specific symptoms. The present study examined the convergent, discriminant, and criterion validity of the Launay Slade Hallucination Scale-Revised (LSHS-R), Prodromal Questionnaire-Brief (PQB), and Community Assessment of Psychic Experiences positive scale (CAPE-P) using a multimethod approach. CHR individuals (N = 71) and healthy controls (HC; N = 71) completed structured clinical interviews, self-report questionnaires, and neuropsychological tests. Questionnaire intercorrelations indicated strong convergent validity (i.e., all rs > .50); however, evidence for discriminant validity was more variable. In examining relations to interviewer-assessed psychosis symptoms, all questionnaires demonstrated evidence of criterion validity, though the PQB showed the strongest convergent correlations (e.g., r = .48 with total symptoms). In terms of discriminant validity for specific positive symptoms, results were again more variable. PQB subscales demonstrated limited specificity with positive symptoms, whereas CAPE-P subscales showed some specificity and the LSHS-R showed high specificity. In addition, when correlations with internalizing and externalizing symptoms were examined, only the PQB showed consistent significant correlations. These results are interpreted in terms of the strengths and limitations of each measure, their value for screening, and their potential utility for clarifying differences between specific positive symptoms.

Attenuated niacin-induced skin flush response in individuals with clinical high risk for psychosis

Background

Impaired sensitivity of the skin flush response to niacin is one of the most replicated findings in patients with schizophrenia. However, prior studies have usually focused on postonset psychosis, and little is known about the clinical high-risk (CHR) phase of niacin sensitivity in psychosis.

Aims

To profile and compare the niacin flush response among CHR individuals (converters and non-converters), patients with first-episode schizophrenia (FES) and healthy controls (HCs).

Methods

Sensitivity to four concentrations (0.1-0.0001 M) of aqueous methylnicotinate was tested in 105 CHR individuals, 57 patients with FES and 52 HCs. CHR individuals were further grouped as converters and non-converters according to the 2-year follow-up outcomes. Skin flush response scores were rated on a 4-point scale.

Results

Of the 105 CHR individuals, 21 individuals were lost during the study, leaving 84 CHR individuals; 16 (19.0%) converted to full psychosis at 2 years of follow-up. Flush response scores identified in the CHR samples were characterised as modest degree levels, intermediate between those of HC individuals and patients with FES. The flush responses in the CHR group mimicked the responses observed in the FES group at higher concentrations (0.01 M, 0.1 M) and longer time points (15 min, 20 min); however, these became comparable with the responses in the HC group at the shorter time points and at lower concentrations. The converters exhibited lower mean flush response scores than the non-converters.

Conclusions

Attenuated niacin-induced flushing emerged during the early phase of psychosis. New devices should be developed and verified for objective quantification of skin responses in the CHR population.

Antipsychotics are related to psychometric conversion to psychosis in ultra-high-risk youth

BACKGROUND

The prescription of antipsychotics outside overt psychotic conditions remains controversial, especially in youth where it is relatively widespread. Furthermore, some studies seem to indicate that antipsychotic exposure in individuals at ultra-high-risk (UHR) for psychosis is associated with higher conversion rates. This study was set up to test whether the inter-current prescription of antipsychotics in UHR patients was related to the psychometric threshold for a diagnosis of psychosis.

METHODS

The 24-item Brief Psychiatric Rating Scale (BPRS) was used to quantify treatment response up to 2 years in 125 UHR participants. Standard psychometric criteria were used to quantify conversion to psychosis. Kaplan-Mayer and Cox proportional hazard survival analysis were applied to determine the impact of having or not received the prescription of an antipsychotic drug.

RESULTS

Over the study period 30 (24%) subjects received the prescription of an antipsychotic. In the sample, there were 31 participants (25%) who had reached the psychometric threshold for conversion to psychosis after 2 years of treatment. UHR people who received a prescription of antipsychotics during the first 2 years of treatment were statistically more likely to reach the psychometric threshold for conversion to psychosis on the BPRS: Hazard ratio = 3.03 (95%CI: 1.49-6.16); p = .003.

CONCLUSION

This finding supports the hypothesis that the prescription of antipsychotics within UHR cohorts is to be considered a red flag for higher incipient risk of conversion to psychosis.

Postural sway and neurocognition in individuals meeting criteria for a clinical high-risk syndrome

Neurocognitive deficits are implicated in individuals that meet criteria for a clinical high-risk (CHR) syndrome. Evidence in patients with schizophrenia suggests that cerebellar dysfunction may underlie neurocognitive deficits. However, little research has examined if similar associations are present in those meeting CHR criteria. This study examined associations between the MATRICS cognitive battery, postural sway (an index of cerebellar functioning), and SIPS-RC psychosis risk scores in a CHR sample (N = 66). Poorer working memory and processing speed were associated with less postural control. Consistent with the cognitive dysmetria theory of schizophrenia, neurocognitive deficits are associated with cerebellar dysfunction in this critical population.

Translating RDoC to Real-World Impact in Developmental Psychopathology: A Neurodevelopmental Framework for Application of Mental Health Risk Calculators

The National Institute of Mental Health Research Domain Criteria's (RDoC) has prompted a paradigm shift from categorical psychiatric disorders to considering multiple levels of vulnerability for probabilistic risk of disorder. However, the lack of neurodevelopmentally-based tools for clinical decision-making has limited RDoC's real-world impact. Integration with developmental psychopathology principles and statistical methods actualize the clinical implementation of RDoC to inform neurodevelopmental risk. In this conceptual paper, we introduce the probabilistic mental health risk calculator as an innovation for such translation and lay out a research agenda for generating an RDoC- and developmentally-informed paradigm that could be applied to predict a range of developmental psychopathologies from early childhood to young adulthood. We discuss methods that weigh the incremental utility for prediction based on intensity and burden of assessment, the addition of developmental change patterns, considerations for assessing outcomes, and integrative data approaches. Throughout, we illustrate the risk calculator approach with different neurodevelopmental pathways and phenotypes. Finally, we discuss real-world implementation of these methods for improving early identification and prevention of developmental psychopathology. We propose that mental health risk calculators can build a needed bridge between RDoC's multiple units of analysis and developmental science.

Toward Generalizable and Transdiagnostic Tools for Psychosis Prediction: An Independent Validation and Improvement of the NAPLS-2 Risk Calculator in the Multisite PRONIA Cohort

BACKGROUND

Transition to psychosis is among the most adverse outcomes of clinical high-risk (CHR) syndromes encompassing ultra-high risk (UHR) and basic symptom states. Clinical risk calculators may facilitate an early and individualized interception of psychosis, but their real-world implementation requires thorough validation across diverse risk populations, including young patients with depressive syndromes.

METHODS

We validated the previously described NAPLS-2 (North American Prodrome Longitudinal Study 2) calculator in 334 patients (26 with transition to psychosis) with CHR or recent-onset depression (ROD) drawn from the multisite European PRONIA (Personalised Prognostic Tools for Early Psychosis Management) study. Patients were categorized into three risk enrichment levels, ranging from UHR, over CHR, to a broad-risk population comprising patients with CHR or ROD (CHR|ROD). We assessed how risk enrichment and different predictive algorithms influenced prognostic performance using reciprocal external validation.

RESULTS

After calibration, the NAPLS-2 model predicted psychosis with a balanced accuracy (BAC) (sensitivity, specificity) of 68% (73%, 63%) in the PRONIA-UHR cohort, 67% (74%, 60%) in the CHR cohort, and 70% (73%, 66%) in patients with CHR|ROD. Multiple model derivation in PRONIA-CHR|ROD and validation in NAPLS-2-UHR patients confirmed that broader risk definitions produced more accurate risk calculators (CHR|ROD-based vs. UHR-based performance: 67% [68%, 66%] vs. 58% [61%, 56%]). Support vector machines were superior in CHR|ROD (BAC = 71%), while ridge logistic regression and support vector machines performed similarly in CHR (BAC = 67%) and UHR cohorts (BAC = 65%). Attenuated psychotic symptoms predicted psychosis across risk levels, while younger age and reduced processing speed became increasingly relevant for broader risk cohorts.

CONCLUSIONS

Clinical-neurocognitive machine learning models operating in young patients with affective and CHR syndromes facilitate a more precise and generalizable prediction of psychosis. Future studies should investigate their therapeutic utility in large-scale clinical trials.

Conversion to psychosis in adolescents and adults: similar proportions, different predictors

BACKGROUND

Age effects may be important for improving models for the prediction of conversion to psychosis for individuals in the clinical high risk (CHR) state. This study aimed to explore whether adolescent CHR individuals (ages 9-17 years) differ significantly from adult CHR individuals (ages 18-45 years) in terms of conversion rates and predictors.

METHOD

Consecutive CHR individuals (N = 517) were assessed for demographic and clinical characteristics and followed up for 3 years. Individuals with CHR were classified as adolescent (n = 244) or adult (n = 273) groups. Age-specific prediction models of psychosis were generated separately using Cox regression.

RESULTS

Similar conversion rates were found between age groups; 52 out of 216 (24.1%) adolescent CHR individuals and 55 out of 219 (25.1%) CHR adults converted to psychosis. The conversion outcome was best predicted by negative symptoms compared to other clinical variables in CHR adolescents (χ² = 7.410, p = 0.006). In contrast, positive symptoms better predicted conversion in CHR adults (χ² = 6.585, p = 0.01).

CONCLUSIONS

Adolescent and adult CHR individuals may require a different approach to early identification and prediction. These results can inform the development of more precise prediction models based on age-specific approaches.

Secondary Sources of Negative Symptoms in Those Meeting Criteria for a Clinical High-Risk Syndrome

BACKGROUND

Negative symptoms are diagnostic characteristics of schizophrenia. They can result from primary (i.e., idiopathic) or secondary (i.e., due to other factors such as depression, anxiety, psychosis, disorganization, medication effects) features of the illness. Although secondary sources of negative symptoms are prevalent among individuals meeting criteria for clinical high-risk syndromes that are due to high rates of comorbidity, the extent to which secondary sources account for variance in negative symptom domains is unknown. Addressing this gap is an important step in informing vulnerability models and treatments for negative symptoms. This study aimed to investigate secondary sources of negative symptoms in those meeting criteria for a clinical high-risk syndrome (N = 192).

METHODS

Simultaneous regression and hierarchical partitioning methods were used to determine the proportion of variance explained by selective serotonin reuptake inhibitor use, anxiety, depression, unusual thought content, and disorganized communication in predicting severity of five negative symptom domains (avolition, anhedonia, asociality, blunted affect, and alogia).

RESULTS

Findings revealed that depression explained the largest proportion of variance in avolition, asociality, and anhedonia. Anxiety was the most predictive of blunted affect, and selective serotonin reuptake inhibitor use explained the most variance in alogia. Analyses within male and female samples revealed that in males, depression explained a large proportion of variance in several negative symptom domains, while in females, selective serotonin reuptake inhibitor use explained variance in alogia.

CONCLUSIONS

Results highlight heterogeneity in variance explained by secondary sources of negative symptoms. These findings guide treatment development for secondary sources of negative symptoms. Furthermore, results inform etiologic models of psychosis and negative symptom conceptualizations.

Defeatist performance beliefs in individuals at clinical high-risk for psychosis and outpatients with chronic schizophrenia

AIM

Prior studies indicate that defeatist performance beliefs (DPBs) are elevated in those in the chronic phase of schizophrenia (SZ) and associated with negative symptoms, functional outcome and neurocognitive impairment. However, it is unclear whether these same patterns of results hold in participants at clinical high-risk (CHR) for psychosis.

METHODS

Two studies were conducted to determine whether prior results in SZ could be replicated and extended to CHR. Participants included 184 healthy controls (CN) and 186 outpatients with chronic SZ for Study 1, and 30 CN and 35 CHR in Study 2. In both studies, participants completed the DPB scale and measures of negative symptoms, psychosocial functioning and neurocognition.

RESULTS

Both chronic SZ and CHR participants had elevated DPBs compared to CN (p's < .01). In SZ, higher DPBs were associated with greater negative symptoms (r's = .31-.37, p's < .01), poorer social functioning and impaired social cognition (r = -.40, P < .001). In CHR, greater DPBs were associated with poorer social functioning (r = -.52, P < .05) and impairments in the neurocognitive domains of reasoning (r = -.48, P < .05) and processing speed (r = -.41, P < .05). Models testing whether DPBs mediated links between negative symptoms and functioning, negative symptoms and cognition and cognition and functioning were nonsignificant in SZ and CHR samples.

CONCLUSIONS

Findings generally provide support for the cognitive model of negative symptoms and functioning and suggest that DPBs are an important clinical target across phases of psychotic illness.

Preventive psychiatry: a blueprint for improving the mental health of young people

Preventive approaches have latterly gained traction for improving mental health in young people. In this paper, we first appraise the conceptual foundations of preventive psychiatry, encompassing the public health, Gordon's, US Institute of Medicine, World Health Organization, and good mental health frameworks, and neurodevelopmentally-sensitive clinical staging models. We then review the evidence supporting primary prevention of psychotic, bipolar and common mental disorders and promotion of good mental health as potential transformative strategies to reduce the incidence of these disorders in young people. Within indicated approaches, the clinical high-risk for psychosis paradigm has received the most empirical validation, while clinical high-risk states for bipolar and common mental disorders are increasingly becoming a focus of attention. Selective approaches have mostly targeted familial vulnerability and non-genetic risk exposures. Selective screening and psychological/psychoeducational interventions in vulnerable subgroups may improve anxiety/depressive symptoms, but their efficacy in reducing the incidence of psychotic/bipolar/common mental disorders is unproven. Selective physical exercise may reduce the incidence of anxiety disorders. Universal psychological/psychoeducational interventions may improve anxiety symptoms but not prevent depressive/anxiety disorders, while universal physical exercise may reduce the incidence of anxiety disorders. Universal public health approaches targeting school climate or social determinants (demographic, economic, neighbourhood, environmental, social/cultural) of mental disorders hold the greatest potential for reducing the risk profile of the population as a whole. The approach to promotion of good mental health is currently fragmented. We leverage the knowledge gained from the review to develop a blueprint for future research and practice of preventive psychiatry in young people: integrating universal and targeted frameworks; advancing multivariable, transdiagnostic, multi-endpoint epidemiological knowledge; synergically preventing common and infrequent mental disorders; preventing physical and mental health burden together; implementing stratified/personalized prognosis; establishing evidence-based preventive interventions; developing an ethical framework, improving prevention through education/training; consolidating the cost-effectiveness of preventive psychiatry; and decreasing inequalities. These goals can only be achieved through an urgent individual, societal, and global level response, which promotes a vigorous collaboration across scientific, health care, societal and governmental sectors for implementing preventive psychiatry, as much is at stake for young people with or at risk for emerging mental disorders.

Screening of the college students at clinical high risk for psychosis in China: a multicenter epidemiological study

BACKGROUND

To investigate a 3-stage screening procedure and explore the clinical features of subjects at Clinical High Risk (CHR) for psychosis in a representative sample of Chinese college students.

METHODS

An epidemiological survey of the prevalence of the CHR syndrome in Chinese college students that was selected by stratified random sampling from Shanghai, Nanjing and Nanchang cities was done following a 3-stage procedure. Participants were initially screened with the Prodromal Questionnaire-brief version (PQ-B), and whose distress score of PQ-B exceeded 24 would be invited to a telephone assessment using the subscale for positive symptoms of the Scale of Prodromal Symptoms (SOPS)/Structured Interview for Prodromal Syndromes (SIPS). Lastly, participants who scored 3 or higher in any item of the subscale would be administered with the SIPS interview conducted by trained researchers to confirm the diagnosis of CHR syndrome.

RESULTS

Twenty-three thousand sixty-three college students completed the survey during September 2017 to October 2018. Seventy-two students were diagnosed as CHR subjects, and the detection rate in the total sample was 0.3%. The peak age range for the first diagnosis of CHR was 17 to 20 years. Thirteen and forty-six were set as the cutoff points of PQ-B total score and distress score to balance the greatest sensitivity and specificity. Binary logistic regression revealed that 8 items in PQ-B showed significant distinction for detecting CHR subjects.

CONCLUSIONS

The 3-stage screening method can be utilized in the detection of CHR subjects for psychosis in the general population, during which delusional ideas, perceptual abnormalities and suspiciousness deserve great attention.

Perceived stress influences anhedonia and social functioning in a community sample enriched for psychosis-risk

Existing animal and human research support the causal role of stress in the emergence of anhedonia, and in turn, the influence of anhedonia in social functioning. However, this model has not been tested in relation to psychosis-risk; this literature gap is notable given that both anhedonia and declining social functioning represent key markers of risk of developing a psychotic disorder such as schizophrenia. The current research tested the evidence for this model using structural equation modeling in 240 individuals selected based on a range of psychosis-risk symptomatology from the general community. Results supported this model in comparison with alternative models, and additionally emphasized the direct role of perceived stress in social functioning outcomes. Findings suggest the clinical relevance of targeting early perceptions of stress in individuals meeting psychosis-risk self-report criteria in an effort to prevent subsequent anhedonia and declines in social functioning.

When to initiate antipsychotic treatment for psychotic symptoms: At the premorbid phase or first episode of psychosis?

OBJECTIVE

Antipsychotic drugs are widely used for treating patients with first episode of psychosis, targeting threshold psychotic symptoms. The clinical high risk of psychosis is characterized as subthreshold psychotic symptoms and it is unclear whether they can also benefit from antipsychotic drugs treatment. This study attempted to determine whether initiating antipsychotic drugs treatment in the clinical high risk of psychosis phase was superior to initiating antipsychotic drugs treatment in the first episode of psychosis phase, after the 2-year symptomatic and functional outcomes.

METHOD

Drawing on 517 individuals with clinical high risk of psychosis from the ShangHai At Risk for Psychosis program, we identified 105 patients who converted to first episode of psychosis within the following 2 years. Patients who initiated antipsychotic drugs while at clinical high risk of psychosis (CHR_AP; n = 70) were compared with those who initiated antipsychotic drugs during a first episode of psychosis (FEP_AP; n = 35). Summary scores on positive symptoms and the global function scores at baseline and at 2 months, 1 year and 2 years of follow-up were analyzed to evaluate outcomes.

RESULTS

The CHR_AP and FEP_AP groups were not different in the severity of positive symptoms and functioning at baseline. However, the CHR_AP group exhibited significantly more serious negative symptoms and total symptoms than the FEP_AP group. Both groups exhibited a significant reduction in positive symptoms and function (p < 0.001). Repeated-measures analysis of variance revealed group by time interaction for symptomatic (F = 3.196, df = 3, p = 0.024) and functional scores (F = 7.306, df = 3, p < 0.001). The FEP_AP group showed higher remission rates than the CHR_AP group (χ² = 22.270, p < 0.001). Compared to initiating antipsychotic drug treatments in the clinical high risk of psychosis state, initiating antipsychotic drugs treatments in the first episode of psychosis state predicted remission in a regression model for FEP_AP (odds ratio = 5.567, 95% confidence interval = [1.783, 17.383], p = 0.003).

CONCLUSION

For clinical high risk of psychosis, antipsychotic drugs might be not the first choice in terms of long-term remission, which is more reasonable to use at the first episode of psychosis phase.

Towards clinical application of prediction models for transition to psychosis: A systematic review and external validation study in the PRONIA sample

A multitude of prediction models for a first psychotic episode in individuals at clinical high-risk (CHR) for psychosis have been proposed, but only rarely validated. We identified transition models based on clinical and neuropsychological data through a registered systematic literature search and evaluated their external validity in 173 CHRs from the Personalised Prognostic Tools for Early Psychosis Management (PRONIA) study. Discrimination performance was assessed with the area under the receiver operating characteristic curve (AUC), and compared to the prediction of clinical raters. External discrimination performance varied considerably across the 22 identified models (AUC 0.40-0.76), with two models showing good discrimination performance. None of the tested models significantly outperformed clinical raters (AUC = 0.75). Combining predictions of clinical raters and the best model descriptively improved discrimination performance (AUC = 0.84). Results show that personalized prediction of transition in CHR is potentially feasible on a global scale. For implementation in clinical practice, further rounds of external validation, impact studies, and development of an ethical framework is necessary.

Individualized risk components guiding antipsychotic delivery in patients with a clinical high risk of psychosis: application of a risk calculator

BACKGROUND

Antipsychotics are widely used for treating patients with psychosis, and target threshold psychotic symptoms. Individuals at clinical high risk (CHR) for psychosis are characterized by subthreshold psychotic symptoms. It is currently unclear who might benefit from antipsychotic treatment. Our objective was to apply a risk calculator (RC) to identify people that would benefit from antipsychotics.

METHODS

Drawing on 400 CHR individuals recruited between 2011 and 2016, 208 individuals who received antipsychotic treatment were included. Clinical and cognitive variables were entered into an individualized RC for psychosis; personal risk was estimated and 4 risk components (negative symptoms-RC-NS, general function-RC-GF, cognitive performance-RC-CP, and positive symptoms-RC-PS) were constructed. The sample was further stratified according to the risk level. Higher risk was defined based on the estimated risk score (20% or higher).

RESULTS

In total, 208 CHR individuals received daily antipsychotic treatment of an olanzapine-equivalent dose of 8.7 mg with a mean administration duration of 58.4 weeks. Of these, 39 (18.8%) developed psychosis within 2 years. A new index of factors ratio (FR), which was derived from the ratio of RC-PS plus RC-GF to RC-NS plus RC-CP, was generated. In the higher-risk group, as FR increased, the conversion rate decreased. A small group (15%) of CHR individuals at higher-risk and an FR >1 benefitted from the antipsychotic treatment.

CONCLUSIONS

Through applying a personal risk assessment, the administration of antipsychotics should be limited to CHR individuals with predominantly positive symptoms and related function decline. A strict antipsychotic prescription strategy should be introduced to reduce inappropriate use.

Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression

IMPORTANCE

Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and biological models and by broadening the risk spectrum to young patients with depressive syndromes remains unclear.

OBJECTIVES

To evaluate whether psychosis transition can be predicted in patients with CHR or recent-onset depression (ROD) using multimodal machine learning that optimally integrates clinical and neurocognitive data, structural magnetic resonance imaging (sMRI), and polygenic risk scores (PRS) for schizophrenia; to assess models' geographic generalizability; to test and integrate clinicians' predictions; and to maximize clinical utility by building a sequential prognostic system.

DESIGN, SETTING, AND PARTICIPANTS

This multisite, longitudinal prognostic study performed in 7 academic early recognition services in 5 European countries followed up patients with CHR syndromes or ROD and healthy volunteers. The referred sample of 167 patients with CHR syndromes and 167 with ROD was recruited from February 1, 2014, to May 31, 2017, of whom 26 (23 with CHR syndromes and 3 with ROD) developed psychosis. Patients with 18-month follow-up (n = 246) were used for model training and leave-one-site-out cross-validation. The remaining 88 patients with nontransition served as the validation of model specificity. Three hundred thirty-four healthy volunteers provided a normative sample for prognostic signature evaluation. Three independent Swiss projects contributed a further 45 cases with psychosis transition and 600 with nontransition for the external validation of clinical-neurocognitive, sMRI-based, and combined models. Data were analyzed from January 1, 2019, to March 31, 2020.

MAIN OUTCOMES AND MEASURES

Accuracy and generalizability of prognostic systems.

RESULTS

A total of 668 individuals (334 patients and 334 controls) were included in the analysis (mean [SD] age, 25.1 [5.8] years; 354 [53.0%] female and 314 [47.0%] male). Clinicians attained a balanced accuracy of 73.2% by effectively ruling out (specificity, 84.9%) but ineffectively ruling in (sensitivity, 61.5%) psychosis transition. In contrast, algorithms showed high sensitivity (76.0%-88.0%) but low specificity (53.5%-66.8%). A cybernetic risk calculator combining all algorithmic and human components predicted psychosis with a balanced accuracy of 85.5% (sensitivity, 84.6%; specificity, 86.4%). In comparison, an optimal prognostic workflow produced a balanced accuracy of 85.9% (sensitivity, 84.6%; specificity, 87.3%) at a much lower diagnostic burden by sequentially integrating clinical-neurocognitive, expert-based, PRS-based, and sMRI-based risk estimates as needed for the given patient. Findings were supported by good external validation results.

CONCLUSIONS AND RELEVANCE

These findings suggest that psychosis transition can be predicted in a broader risk spectrum by sequentially integrating algorithms' and clinicians' risk estimates. For clinical translation, the proposed workflow should undergo large-scale international validation.

Relationship Between Cognitive and Clinical Insight at Different Durations of Untreated Attenuated Psychotic Symptoms in High-Risk Individuals

Background: This study examines whether cognitive insight is impaired in high-risk individuals with attenuated psychotic symptoms (APS) and explores the relationship between cognitive and clinical insight at different durations of untreated attenuated psychotic symptoms (DUAPS). Methods: The Structured Interview for Psychosis high-risk Syndrome (SIPS) was used to identify APS individuals. APS (n = 121) and healthy control (HC, n = 87) subjects were asked to complete the Beck Cognitive Insight Scale (BCIS). Clinical insight of APS individuals was evaluated using the Schedule for Assessment of Insight (SAI). APS individuals were classified into four subgroups based on DUAPS, including 0-3, 4-6, 7-12, and >12 months. Power analysis for significant correlation was conducted using the WebPower package in R. Results: Compared with HC subjects, APS individuals showed poorer cognitive insight, with lower scores on BCIS self-reflectiveness and composite index (BCIS self-reflectiveness minus BCIS self-certainty). Only when DUAPS was longer than 12 months did the significant positive correlation between cognitive and clinical insight obtain the power about 0.8, including the associations between self-reflectiveness and awareness of illness, self-reflectiveness and the total clinical insight, and composite index and awareness of illness. The positive associations of composite index with awareness of illness within 0-3 months DUAPS and with the total score of SAI when DUAPS > 12 months were significant but failed to obtain satisfactory power. Conclusions: APS individuals may have impaired cognitive insight, demonstrating lower self-reflectiveness. The correlation between cognitive and clinical insight is associated with the duration of untreated attenuated psychotic symptoms.

Prediction and Prevention in the Clinical High-Risk for Psychosis Paradigm: A Review of the Current Status and Recommendations for Future Directions of Inquiry

Prediction and prevention of negative clinical and functional outcomes represent the two primary objectives of research conducted within the clinical high-risk for psychosis (CHR-P) paradigm. Several multivariable "risk calculator" models have been developed to predict the likelihood of developing psychosis, although these models have not been translated to clinical use. Overall, less progress has been made in developing effective interventions. In this paper, we review the existing literature on both prediction and prevention in the CHR-P paradigm and, primarily, outline ways in which expanding and combining these paths of inquiry could lead to a greater improvement in individual outcomes for those most at risk.

Frequency of Self-reported Psychotic Symptoms among 2542 Outpatients at Their First Visit for Mental Health Services

Objective: Psychotic symptoms are prevalent in both clinical settings and the general population. The distribution of psychotic symptoms across patients with different types of psychotic and non-psychotic mental disorders is helpful for understanding symptom specificity. This study aimed to explore the distribution differences of psychotic symptoms in an outpatient population in terms of frequency, age, gender, and psychotic and non-psychotic disorders.Methods: Outpatients were recruited consecutively at their first visit to the Shanghai Mental Health Center. Psychotic symptoms over the preceding year were self-reported through the PRIME Screen-Revised (PS-R) questionnaire. Seven categories of psychotic symptoms were grouped: perplexity and delusional mood (Item-1,5); first rank symptoms (Item-3,6,11); overvalued beliefs (Item-2,4); suspiciousness/persecutory ideas (Item-7), grandiose ideas (Item 8), perceptual abnormalities (Item-9,10), and disorganized communication (Item-12). Comparisons were made with respect to age group, sex, and diagnostic category.Results: Of 2542 outpatients, 1448(57.0%) were screened as positive, which was defined as having two or more symptoms with at least "somewhat agree" scores, ranging from 0 to 6. The threshold of one or more "yes" items was an endorsement to categorize the participant as positive for psychotic symptoms. The frequency of psychotic symptoms declined with age. Younger patients tended to report more psychotic symptoms than older patients(p < .001). Suspiciousness(p = .038) and disorganized communication (p = .004) were more common in females than males. Age, first rank symptoms, suspiciousness/persecutory ideas, grandiose ideas, and perceptual abnormalities were found to significantly differ between psychotic and non-psychotic disorders.Conclusions: Psychotic symptoms appear to be common in the clinical population and represent nonspecific indicators of psychopathology. The difference between psychotic and non-psychotic psychopathologies is more a function of the presence, frequency, and severity of psychotic symptoms.

Incorporating cortisol into the NAPLS2 individualized risk calculator for prediction of psychosis

BACKGROUND

Risk calculators are useful tools that can help clinicians and researchers better understand an individual's risk of conversion to psychosis. The North American Prodrome Longitudinal Study (NAPLS2) Individualized Risk Calculator has good predictive accuracy but could be potentially improved by the inclusion of a biomarker. Baseline cortisol, a measure of hypothalamic-pituitary-adrenal (HPA) axis functioning that is impacted by biological vulnerability to stress and exposure to environmental stressors, has been shown to be higher among individuals at clinical high-risk for psychosis (CHRP) who eventually convert to psychosis than those who do not. We sought to determine whether the addition of baseline cortisol to the NAPLS2 risk calculator improved the performance of the risk calculator.

METHODS

Participants were drawn from the NAPLS2 study. A subset of NAPLS2 participants provided salivary cortisol samples. A multivariate Cox proportional hazards regression evaluated the likelihood of an individual's eventual conversion to psychosis based on demographic and clinical variables in addition to baseline cortisol levels.

RESULTS

A total of 417 NAPLS2 participants provided salivary cortisol and were included in the analysis. Higher levels of cortisol were predictive of conversion to psychosis in a univariate model (C-index = 0.59, HR = 21.5, p-value = 0.004). The inclusion of cortisol in the risk calculator model resulted in a statistically significant improvement in performance from the original risk calculator model (C-index = 0.78, SE = 0.028).

CONCLUSIONS

Salivary cortisol is an inexpensive and non-invasive biomarker that could improve individual predictions about conversion to psychosis and treatment decisions for CHR-P individuals.

Transdiagnostic individualized clinically-based risk calculator for the automatic detection of individuals at-risk and the prediction of psychosis: external replication in 2,430,333 US patients

The real-world impact of psychosis prevention is reliant on effective strategies for identifying individuals at risk. A transdiagnostic, individualized, clinically-based risk calculator to improve this has been developed and externally validated twice in two different UK healthcare trusts with convincing results. The prognostic performance of this risk calculator outside the UK is unknown. All individuals who accessed primary or secondary health care services belonging to the IBM® MarketScan® Commercial Database between January 2015 and December 2017, and received a first ICD-10 index diagnosis of nonorganic/nonpsychotic mental disorder, were included. According to the risk calculator, age, gender, ethnicity, age-by-gender, and ICD-10 cluster diagnosis at index date were used to predict development of any ICD-10 nonorganic psychotic disorder. Because patient-level ethnicity data were not available city-level ethnicity proportions were used as proxy. The study included 2,430,333 patients with a mean follow-up of 15.36 months and cumulative incidence of psychosis at two years of 1.43%. There were profound differences compared to the original development UK database in terms of case-mix, psychosis incidence, distribution of baseline predictors (ICD-10 cluster diagnoses), availability of patient-level ethnicity data, follow-up time and availability of specialized clinical services for at-risk individuals. Despite these important differences, the model retained accuracy significantly above chance (Harrell's C = 0.676, 95% CI: 0.672-0.679). To date, this is the largest international external replication of an individualized prognostic model in the field of psychiatry. This risk calculator is transportable on an international scale to improve the automatic detection of individuals at risk of psychosis.

Enhancing Psychosis Risk Prediction Through Computational Cognitive Neuroscience

Research suggests that early identification and intervention with individuals at clinical high risk (CHR) for psychosis may be able to improve the course of illness. The first generation of studies suggested that the identification of CHR through the use of specialized interviews evaluating attenuated psychosis symptoms is a promising strategy for exploring mechanisms associated with illness progression, etiology, and identifying new treatment targets. The next generation of research on psychosis risk must address two major limitations: (1) interview methods have limited specificity, as recent estimates indicate that only 15%-30% of individuals identified as CHR convert to psychosis and (2) the expertise needed to make CHR diagnosis is only accessible in a handful of academic centers. Here, we introduce a new approach to CHR assessment that has the potential to increase accessibility and positive predictive value. Recent advances in clinical and computational cognitive neuroscience have generated new behavioral measures that assay the cognitive mechanisms and neural systems that underlie the positive, negative, and disorganization symptoms that are characteristic of psychotic disorders. We hypothesize that measures tied to symptom generation will lead to enhanced sensitivity and specificity relative to interview methods and the cognitive intermediate phenotype measures that have been studied to date that are typically indicators of trait vulnerability and, therefore, have a high false positive rate for conversion to psychosis. These new behavioral measures have the potential to be implemented on the internet and at minimal expense, thereby increasing accessibility of assessments.