Development and Validation of a Clinically Based Risk Calculator for the Transdiagnostic Prediction of Psychosis

Using Electronic Health Records to Facilitate Precision Psychiatry

The use of clinical prediction models to produce individualized risk estimates can facilitate the implementation of precision psychiatry. As a source of data from large, clinically representative patient samples, electronic health records (EHRs) provide a platform to develop and validate clinical prediction models, as well as potentially implement them in routine clinical care. The current review describes promising use cases for the application of precision psychiatry to EHR data and considers their performance in terms of discrimination (ability to separate individuals with and without the outcome) and calibration (extent to which predicted risk estimates correspond to observed outcomes), as well as their potential clinical utility (weighing benefits and costs associated with the model compared to different approaches across different assumptions of the number needed to test). We review 4 externally validated clinical prediction models designed to predict psychosis onset, psychotic relapse, cardiometabolic morbidity, and suicide risk. We then discuss the prospects for clinically implementing these models and the potential added value of integrating data from evidence syntheses, standardized psychometric assessments, and biological data into EHRs. Clinical prediction models can utilize routinely collected EHR data in an innovative way, representing a unique opportunity to inform real-world clinical decision making. Combining data from other sources (e.g., meta-analyses) or enhancing EHR data with information from research studies (clinical and biomarker data) may enhance our abilities to improve the performance of clinical prediction models.

Dynamic and Transdiagnostic Risk Calculator Based on Natural Language Processing for the Prediction of Psychosis in Secondary Mental Health Care: Development and Internal-External Validation Cohort Study

BACKGROUND

Automatic transdiagnostic risk calculators can improve the detection of individuals at risk of psychosis. However, they rely on assessment at a single point in time and can be refined with dynamic modeling techniques that account for changes in risk over time.

METHODS

We included 158,139 patients (5007 events) who received a first index diagnosis of a nonorganic and nonpsychotic mental disorder within electronic health records from the South London and Maudsley National Health Service Foundation Trust between January 1, 2008, and October 8, 2021. A dynamic Cox landmark model was developed to estimate the 2-year risk of developing psychosis according to the TRIPOD (Transparent Reporting of a multivariate prediction model for Individual Prognosis or Diagnosis) statement. The dynamic model included 24 predictors extracted at 9 landmark points (baseline, 0, 6, 12, 24, 30, 36, 42, and 48 months): 3 demographic, 1 clinical, and 20 natural language processing-based symptom and substance use predictors. Performance was compared with a static Cox regression model with all predictors assessed at baseline only and indexed via discrimination (C-index), calibration (calibration plots), and potential clinical utility (decision curves) in internal-external validation.

RESULTS

The dynamic model improved discrimination performance from baseline compared with the static model (dynamic: C-index = 0.9; static: C-index = 0.87) and the final landmark point (dynamic: C-index = 0.79; static: C-index = 0.76). The dynamic model was also significantly better calibrated (calibration slope = 0.97-1.1) than the static model at later landmark points (≥24 months). Net benefit was higher for the dynamic than for the static model at later landmark points (≥24 months).

CONCLUSIONS

These findings suggest that dynamic prediction models can improve the detection of individuals at risk for psychosis in secondary mental health care settings.

Development and temporal validation of a clinical prediction model of transition to psychosis in individuals at ultra-high risk in the UHR 1000+ cohort

The concept of ultra-high risk for psychosis (UHR) has been at the forefront of psychiatric research for several decades, with the ultimate goal of preventing the onset of psychotic disorder in high-risk individuals. Orygen (Melbourne, Australia) has led a range of observational and intervention studies in this clinical population. These datasets have now been integrated into the UHR 1000+ cohort, consisting of a sample of 1,245 UHR individuals with a follow-up period ranging from 1 to 16.7 years. This paper describes the cohort, presents a clinical prediction model of transition to psychosis in this cohort, and examines how predictive performance is affected by changes in UHR samples over time. We analyzed transition to psychosis using a Cox proportional hazards model. Clinical predictors for transition to psychosis were investigated in the entire cohort using multiple imputation and Rubin's rule. To assess performance drift over time, data from 1995-2016 were used for initial model fitting, and models were subsequently validated on data from 2017-2020. Over the follow-up period, 220 cases (17.7%) developed a psychotic disorder. Pooled hazard ratio (HR) estimates showed that the Comprehensive Assessment of At-Risk Mental States (CAARMS) Disorganized Speech subscale severity score (HR=1.12, 95% CI: 1.02-1.24, p=0.024), the CAARMS Unusual Thought Content subscale severity score (HR=1.13, 95% CI: 1.03-1.24, p=0.009), the Scale for the Assessment of Negative Symptoms (SANS) total score (HR=1.02, 95% CI: 1.00-1.03, p=0.022), the Social and Occupational Functioning Assessment Scale (SOFAS) score (HR=0.98, 95% CI: 0.97-1.00, p=0.036), and time between onset of symptoms and entry to UHR service (log transformed) (HR=1.10, 95% CI: 1.02-1.19, p=0.013) were predictive of transition to psychosis. UHR individuals who met the brief limited intermittent psychotic symptoms (BLIPS) criteria had a higher probability of transitioning to psychosis than those who met the attenuated psychotic symptoms (APS) criteria (HR=0.48, 95% CI: 0.32-0.73, p=0.001) and those who met the Trait risk criteria (a first-degree relative with a psychotic disorder or a schizotypal personality disorder plus a significant decrease in functioning during the previous year) (HR=0.43, 95% CI: 0.22-0.83, p=0.013). Models based on data from 1995-2016 displayed good calibration at initial model fitting, but showed a drift of 20.2-35.4% in calibration when validated on data from 2017-2020. Large-scale longitudinal data such as those from the UHR 1000+ cohort are required to develop accurate psychosis prediction models. It is critical to assess existing and future risk calculators for temporal drift, that may reduce their utility in clinical practice over time.

Pseudoneurotic Symptoms in the Schizophrenia Spectrum: A Longitudinal Study of Their Relation to Psychopathology and Clinical Outcomes

BACKGROUND AND HYPOTHESIS

Nonpsychotic symptoms (depression, anxiety, obsessions, etc.) are frequent in schizophrenia-spectrum disorders and are usually conceptualized as comorbidity or transdiagnostic symptoms. However, in twentieth century foundational psychopathological literature, many nonpsychotic symptoms with specific phenomenology (here termed pseudoneurotic symptoms) were considered relatively typical of schizophrenia. In this prospective study, we investigated potential associations of pseudoneurotic symptoms with diagnostic status, functional outcome as well as psychopathological dimensions of schizophrenia.

STUDY DESIGN

First-admitted patients (N = 121) diagnosed with non-affective psychosis, schizotypal disorder, or other mental illness were examined at initial hospitalization and 5 years later with a comprehensive assessment of psychopathology. Informed by the literature, we constructed scales targeting pseudoneurotic symptoms and other, more general, nonpsychotic symptoms.

STUDY RESULTS

Pseudoneurotic symptoms aggregated in schizophrenia-spectrum groups compared to other mental illnesses and occurred at similar levels at baseline and follow-up. They longitudinally predicted poorer social and occupational functioning in schizophrenia-spectrum patients over a 5-year-period but not transition to schizophrenia-spectrum disorders from other mental illnesses. Finally, the level of pseudoneurotic symptoms correlated with disorder of basic self at both assessments and with positive and negative symptoms at follow-up. The scale targeting general nonpsychotic symptoms did not show this pattern of associations.

CONCLUSIONS

The study supports that a group of nonpsychotic symptoms, ie, pseudoneurotic symptoms, are associated with schizophrenia-spectrum disorders and linked with temporally stable psychopathology, particularly disorder of the basic self. Their prospective association with social and occupational functioning needs replication.

Validation of an ICD-Code-Based Case Definition for Psychotic Illness Across Three Health Systems

BACKGROUND AND HYPOTHESIS

Psychosis-associated diagnostic codes are increasingly being utilized as case definitions for electronic health record (EHR)-based algorithms to predict and detect psychosis. However, data on the validity of psychosis-related diagnostic codes is limited. We evaluated the positive predictive value (PPV) of International Classification of Diseases (ICD) codes for psychosis.

STUDY DESIGN

Using EHRs at 3 health systems, ICD codes comprising primary psychotic disorders and mood disorders with psychosis were grouped into 5 higher-order groups. 1133 records were sampled for chart review using the full EHR. PPVs (the probability of chart-confirmed psychosis given ICD psychosis codes) were calculated across multiple treatment settings.

STUDY RESULTS

PPVs across all diagnostic groups and hospital systems exceeded 70%: Mass General Brigham 0.72 [95% CI 0.68-0.77], Boston Children's Hospital 0.80 [0.75-0.84], and Boston Medical Center 0.83 [0.79-0.86]. Schizoaffective disorder PPVs were consistently the highest across sites (0.80-0.92) and major depressive disorder with psychosis were the most variable (0.57-0.79). To determine if the first documented code captured first-episode psychosis (FEP), we excluded cases with prior chart evidence of a diagnosis of or treatment for a psychotic illness, yielding substantially lower PPVs (0.08-0.62).

CONCLUSIONS

We found that the first documented psychosis diagnostic code accurately captured true episodes of psychosis but was a poor index of FEP. These data have important implications for the case definitions used in the development of risk prediction models designed to predict or detect undiagnosed psychosis.

A transdiagnostic prodrome for severe mental disorders: an electronic health record study

Effective prevention of severe mental disorders (SMD), including non-psychotic unipolar mood disorders (UMD), non-psychotic bipolar mood disorders (BMD), and psychotic disorders (PSY), rely on accurate knowledge of the duration, first presentation, time course and transdiagnosticity of their prodromal stages. Here we present a retrospective, real-world, cohort study using electronic health records, adhering to RECORD guidelines. Natural language processing algorithms were used to extract monthly occurrences of 65 prodromal features (symptoms and substance use), grouped into eight prodromal clusters. The duration, first presentation, and transdiagnosticity of the prodrome were compared between SMD groups with one-way ANOVA, Cohen's f and d. The time course (mean occurrences) of prodromal clusters was compared between SMD groups with linear mixed-effects models. 26,975 individuals diagnosed with ICD-10 SMD were followed up for up to 12 years (UMD = 13,422; BMD = 2506; PSY = 11,047; median[IQR] age 39.8[23.7] years; 55% female; 52% white). The duration of the UMD prodrome (18[36] months) was shorter than BMD (26[35], d = 0.21) and PSY (24[38], d = 0.18). Most individuals presented with multiple first prodromal clusters, with the most common being non-specific ('other'; 88% UMD, 85% BMD, 78% PSY). The only first prodromal cluster that showed a medium-sized difference between the three SMD groups was positive symptoms (f = 0.30). Time course analysis showed an increase in prodromal cluster occurrences approaching SMD onset. Feature occurrence across the prodromal period showed small/negligible differences between SMD groups, suggesting that most features are transdiagnostic, except for positive symptoms (e.g. paranoia, f = 0.40). Taken together, our findings show minimal differences in the duration and first presentation of the SMD prodromes as recorded in secondary mental health care. All the prodromal clusters intensified as individuals approached SMD onset, and all the prodromal features other than positive symptoms are transdiagnostic. These results support proposals to develop transdiagnostic preventive services for affective and psychotic disorders detected in secondary mental healthcare.

Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk

Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.

Building and Validation of an Acute Event Prediction Model for Severe Mental Disorders

Background

The global incidence of acute events in psychiatric patients is intensifying, and models to successfully predict acute events have attracted much attention.

Objective

To explore the influence factors of acute incident severe mental disorders (SMDs) and the application of Rstudio statistical software, and build and verify a nomogram prediction model.

Methods

SMDs were taken as research objects. The questionnaire survey method was adopted to collect data. Patients with acute event independent factors were screened. R software multivariable Logistic regression model was constructed and a nomogram was drawn.

Results

A total of 342 patients with SMDs were hospitalized, and the number of patients who encountered acute events was 64, which accounted for 18.70% of all patients. Statistical significances were found in many aspects (all P ˂ 0.05). Such aspects included Medication adherence, disease diagnosis, marital status, caregivers, social support and the hospitalization environment (odds ratio (OR) = 4.08, 11.62, 12.06, 10.52, 0.04 and 0.61, respectively) were independent risk factors for the acute events of patients with SMDs. The prediction model was modeled, and the AUC was 0.77 and 0.80. The calibration curve shows that the model has good calibration. The clinical decision curve shows that the model has a good clinical effect.

Conclusion

The constructed risk prediction model shows good prediction effectiveness in the acute events of patients with SMDs, which is helpful for the early detection of clinical mental health staff at high risk of acute events.

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.

What is the duration of untreated psychosis worldwide? - A meta-analysis of pooled mean and median time and regional trends and other correlates across 369 studies

Duration of untreated psychosis (DUP) has been associated with poor mental health outcomes. We aimed to meta-analytically estimate the mean and median DUP worldwide, evaluating also the influence of several moderating factors. This PRISMA/MOOSE-compliant meta-analysis searched for non-overlapping individual studies from inception until 9/12/2022, reporting mean ± s.d. or median DUP in patients with first episode psychosis (FEP), without language restrictions. We conducted random-effect meta-analyses, stratified analyses, heterogeneity analyses, meta-regression analyses, and quality assessment (PROSPERO:CRD42020163640). From 12 461 citations, 369 studies were included. The mean DUP was 42.6 weeks (95% confidence interval (CI) 40.6-44.6, k = 283, n = 41 320), varying significantly across continents (p < 0.001). DUP was (in descending order) 70.0 weeks (95% CI 51.6-88.4, k = 11, n = 1508) in Africa; 48.8 weeks (95% CI 43.8-53.9, k = 73, n = 12 223) in Asia; 48.7 weeks (95% CI 43.0-54.4, k = 36, n = 5838) in North America; 38.6 weeks (95% CI 36.0-41.3, k = 145, n = 19 389) in Europe; 34.9 weeks (95% CI 23.0-46.9, k = 11, n = 1159) in South America and 28.0 weeks (95% CI 20.9-35.0, k = 6, n = 1203) in Australasia. There were differences depending on the income of countries: DUP was 48.4 weeks (95% CI 43.0-48.4, k = 58, n = 5635) in middle-low income countries and 41.2 weeks (95% CI 39.0-43.4, k = 222, n = 35 685) in high income countries. Longer DUP was significantly associated with older age (β = 0.836, p < 0.001), older publication year (β = 0.404, p = 0.038) and higher proportion of non-White FEP patients (β = 0.232, p < 0.001). Median DUP was 14 weeks (Interquartile range = 8.8-28.0, k = 206, n = 37 215). In conclusion, DUP is high throughout the world, with marked variation. Efforts to identify and intervene sooner in patients with FEP, and to promote global mental health and access to early intervention services (EIS) are critical, especially in developing countries.

Validation of an ICD-code-based case definition for psychotic illness across three health systems

Background and Hypothesis

Early detection of psychosis is critical for improving outcomes. Algorithms to predict or detect psychosis using electronic health record (EHR) data depend on the validity of the case definitions used, typically based on diagnostic codes. Data on the validity of psychosis-related diagnostic codes is limited. We evaluated the positive predictive value (PPV) of International Classification of Diseases (ICD) codes for psychosis.

Study Design

Using EHRs at three health systems, ICD codes comprising primary psychotic disorders and mood disorders with psychosis were grouped into five higher-order groups. 1,133 records were sampled for chart review using the full EHR. PPVs (the probability of chart-confirmed psychosis given ICD psychosis codes) were calculated across multiple treatment settings.

Study Results

PPVs across all diagnostic groups and hospital systems exceeded 70%: Massachusetts General Brigham 0.72 [95% CI 0.68-0.77], Boston Children's Hospital 0.80 [0.75-0.84], and Boston Medical Center 0.83 [0.79-0.86]. Schizoaffective disorder PPVs were consistently the highest across sites (0.80-0.92) and major depressive disorder with psychosis were the most variable (0.57-0.79). To determine if the first documented code captured first-episode psychosis (FEP), we excluded cases with prior chart evidence of a diagnosis of or treatment for a psychotic illness, yielding substantially lower PPVs (0.08-0.62).

Conclusions

We found that the first documented psychosis diagnostic code accurately captured true episodes of psychosis but was a poor index of FEP. These data have important implications for the development of risk prediction models designed to predict or detect undiagnosed psychosis.

We need to talk: a qualitative inquiry into pathways to care for young men at ultra-high risk for psychosis

Introduction

It is known from the literature that men are slower to seek help and staying engaged in mental health care compared to women. Seeing that in psychosis, men more often than women have insidious onsets but also a more malign illness course, it is important to find ways to improve timely help-seeking. The aim of this study was to explore barriers and facilitators for help-seeking in young male persons struggling with early signs of psychosis.

Methods

Qualitative interviews with nine young men who suffer from a first episode of psychosis or psychosis risk symptoms.

Results

Male stereotypical ideals, significant others, and knowledge of symptoms and where to get help as well characteristics of symptom trajectories appeared to be important determinants of help-seeking behavior.

Discussion

Interviews indicated that help-seeking in the participants was delayed first, because of reluctancy to disclose distress and second, because significant others were unable to accurately recognize symptoms. Information, awareness, and easy access to care remain important in early detection and intervention in psychosis and psychosis risk. However, more emphasis should be placed on de-stigmatizing mental health problems in men and aiming information specifically at them.

Improving prediction of psychosis in youth at clinical high-risk: pre-baseline symptom duration and cortical thinning as moderators of the NAPLS2 risk calculator

BACKGROUND

Clinical implementation of risk calculator models in the clinical high-risk for psychosis (CHR-P) population has been hindered by heterogeneous risk distributions across study cohorts which could be attributed to pre-ascertainment illness progression. To examine this, we tested whether the duration of attenuated psychotic symptom (APS) worsening prior to baseline moderated performance of the North American prodrome longitudinal study 2 (NAPLS2) risk calculator. We also examined whether rates of cortical thinning, another marker of illness progression, bolstered clinical prediction models.

METHODS

Participants from both the NAPLS2 and NAPLS3 samples were classified as either 'long' or 'short' symptom duration based on time since APS increase prior to baseline. The NAPLS2 risk calculator model was applied to each of these groups. In a subset of NAPLS3 participants who completed follow-up magnetic resonance imaging scans, change in cortical thickness was combined with the individual risk score to predict conversion to psychosis.

RESULTS

The risk calculator models achieved similar performance across the combined NAPLS2/NAPLS3 sample [area under the curve (AUC) = 0.69], the long duration group (AUC = 0.71), and the short duration group (AUC = 0.71). The shorter duration group was younger and had higher baseline APS than the longer duration group. The addition of cortical thinning improved the prediction of conversion significantly for the short duration group (AUC = 0.84), with a moderate improvement in prediction for the longer duration group (AUC = 0.78).

CONCLUSIONS

These results suggest that early illness progression differs among CHR-P patients, is detectable with both clinical and neuroimaging measures, and could play an essential role in the prediction of clinical outcomes.

Prediction of estimated risk for bipolar disorder using machine learning and structural MRI features

BACKGROUND

Individuals with bipolar disorder are commonly correctly diagnosed a decade after symptom onset. Machine learning techniques may aid in early recognition and reduce the disease burden. As both individuals at risk and those with a manifest disease display structural brain markers, structural magnetic resonance imaging may provide relevant classification features.

METHODS

Following a pre-registered protocol, we trained linear support vector machine (SVM) to classify individuals according to their estimated risk for bipolar disorder using regional cortical thickness of help-seeking individuals from seven study sites (N = 276). We estimated the risk using three state-of-the-art assessment instruments (BPSS-P, BARS, EPIbipolar).

RESULTS

For BPSS-P, SVM achieved a fair performance of Cohen's κ of 0.235 (95% CI 0.11-0.361) and a balanced accuracy of 63.1% (95% CI 55.9-70.3) in the 10-fold cross-validation. In the leave-one-site-out cross-validation, the model performed with a Cohen's κ of 0.128 (95% CI -0.069 to 0.325) and a balanced accuracy of 56.2% (95% CI 44.6-67.8). BARS and EPIbipolar could not be predicted. In post hoc analyses, regional surface area, subcortical volumes as well as hyperparameter optimization did not improve the performance.

CONCLUSIONS

Individuals at risk for bipolar disorder, as assessed by BPSS-P, display brain structural alterations that can be detected using machine learning. The achieved performance is comparable to previous studies which attempted to classify patients with manifest disease and healthy controls. Unlike previous studies of bipolar risk, our multicenter design permitted a leave-one-site-out cross-validation. Whole-brain cortical thickness seems to be superior to other structural brain features.

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.

Value of P300 amplitude in the diagnosis of untreated first-episode schizophrenia and psychosis risk syndrome in children and adolescents

BACKGROUND

Identifying the characteristic neurobiological changes of early psychosis is helpful for early clinical diagnosis. However, previous studies on the brain electrophysiology of children and adolescents with psychosis are rare.

METHODS

This study compared P300 amplitude at multiple electrodes between children and adolescents with first-episode schizophrenia (FES, n = 48), children and adolescents with psychosis risk syndrome (PRS, n = 24), and healthy controls (HC, n = 30). Receiver operating characteristic (ROC) analysis was used to test the ability of P300 amplitude to distinguish FES, PRS and HC individuals.

RESULTS

The P300 amplitude in the FES group were significantly lower than those in the HC at the Cz, Pz, and Oz electrodes. The P300 amplitude was also significantly lower in the prodromal group than in the HC at the Pz and Oz electrodes. ROC curve analysis showed that at the Pz electrode, the P300 amplitude evoked by the target and standard stimulus showed high sensitivity, specificity, accuracy, and area under the curve value for distinguishing FES from HC individuals.

CONCLUSIONS

This study found early visual P300 deficits in children and adolescents with FES and PRS, with the exclusion of possible influence of medication and chronic medical conditions, suggesting the value of P300 amplitude for the identification of early psychosis.

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.

Machine learning techniques in diagnostics and prediction of the clinical features of schizophrenia: a narrative review

BACKGROUND

Schizophrenia is a severe psychiatric disorder associated with a significant negative impact. Early diagnosis and treatment of schizophrenia has a favorable effect on the clinical outcome and patients quality of life. In this context, machine learning techniques open up new opportunities for a more accurate diagnosis and prediction of the clinical features of this illness.

AIM

This literature review is aimed to search for information on the use of machine learning techniques in the prediction and diagnosis of schizophrenia and the determination of its clinical features.

METHODS

The Google Scholar, PubMed, and eLIBRARY.ru databases were used to search for relevant data. The review included articles that had been published not earlier than January 1, 2010, and not later than March 31, 2023. Combinations of the following keywords were applied for search queries: machine learning, deep learning, schizophrenia, neural network, predictors, artificial intelligence, diagnostics, suicide, depressive, insomnia, and cognitive. Original articles regardless of their design were included in the review. Descriptive analysis was used to summarize the retrieved data.

RESULTS

Machine learning techniques are widely used in the functional assessment of patients with schizophrenia. They are used for interpretation of MRI, EEG, and actigraphy findings. Also, models created using machine learning algorithms can analyze speech, behavior, and the creativity of people and these data can be used for the diagnosis of psychiatric disorders. It has been found that different machine learning-based models can help specialists predict and diagnose schizophrenia based on medical history and genetic data, as well as epigenetic information. Machine learning techniques can also be used to build effective models that can help specialists diagnose and predict clinical manifestations and complications of schizophrenia, such as insomnia, depressive symptoms, suicide risk, aggressive behavior, and changes in cognitive functions over time.

CONCLUSION

Machine learning techniques play an important role in psychiatry, as they have been used in models that help specialists in the diagnosis of schizophrenia and determination of its clinical features. The use of machine learning algorithms is one of the most promising direction in psychiatry, and it can significantly improve the effectiveness of the diagnosis and treatment of schizophrenia.

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.

Isolated high altitude psychosis, delirium at high altitude, and high altitude cerebral edema: are these diagnoses valid?

Psychosis is a psychopathological syndrome that can be triggered or caused by exposure to high altitude (HA). Psychosis can occur alone as isolated HA psychosis or can be associated with other mental and often also somatic symptoms as a feature of delirium. Psychosis can also occur as a symptom of high altitude cerebral edema (HACE), a life-threatening condition. It is unclear how psychotic symptoms at HA should be classified into existing diagnostic categories of the most widely used classification systems of mental disorders, including the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) and the International Statistical Classification of Diseases and Related Health Problems (ICD-11). We provide a diagnostic framework for classifying symptoms using the existing diagnostic categories: psychotic condition due to a general medical condition, brief psychotic disorder, delirium, and HACE. We also discuss the potential classification of isolated HA psychosis into those categories. A valid and reproducible classification of symptoms is essential for communication among professionals, ensuring that patients receive optimal treatment, planning further trips to HA for individuals who have experienced psychosis at HA, and advancing research in the field.

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.

Psychotic disorders as a framework for precision psychiatry

People with psychotic disorders can show marked interindividual variations in the onset of illness, responses to treatment and relapse, but they receive broadly similar clinical care. Precision psychiatry is an approach that aims to stratify people with a given disorder according to different clinical outcomes and tailor treatment to their individual needs. At present, interindividual differences in outcomes of psychotic disorders are difficult to predict on the basis of clinical assessment alone. Therefore, current research in psychosis seeks to build models that predict outcomes by integrating clinical information with a range of biological measures. Here, we review recent progress in the application of precision psychiatry to psychotic disorders and consider the challenges associated with implementing this approach in clinical practice.

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

INTRODUCTION

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

METHODS AND ANALYSIS

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

ETHICS AND DISSEMINATION

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

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.

Pseudoneurotic symptoms in the schizophrenia spectrum: An empirical study

BACKGROUND

Nonpsychotic symptoms (depression, anxiety, obsessions etc.) are frequent in schizophrenia-spectrum disorders. Twentieth century foundational psychopathological literature claimed that certain nonpsychotic symptoms (here termed pseudoneurotic symptoms) are relatively closely linked with the schizophrenia-spectrum, despite descriptive overlap with symptoms of other diagnoses. In this study, we investigated the association of pseudoneurotic and other nonpsychotic symptoms with the schizophrenia-spectrum as well as a hypothesis about an association of pseudoneurotic symptoms with disorder of basic self.

METHODS

The sample (N = 226) comprised patients with non-affective psychosis (N = 119), schizotypal personality disorder (N = 51) and other mental illness (N = 56), who were examined with a comprehensive assessment of lifetime psychopathology. Informed by the literature, we constructed scales targeting pseudoneurotic symptoms and other, more general, nonpsychotic symptoms.

RESULTS

Pseudoneurotic symptoms aggregated significantly in schizophrenia-spectrum disorders with an Area under the receiver operating characteristic curve of 0.84 (SE 0.03) for classifying patients with schizophrenia-spectrum disorders versus other mental illness. Patients with non-affective psychosis scored slightly, but significantly, higher on the scale targeting general nonpsychotic symptomatology than the other groups. In multiple regression analysis, pseudoneurotic symptoms were predicted by general nonpsychotic symptoms, disorders of basic self, and negative symptoms but not positive symptoms.

CONCLUSION

The study supports that certain neurotic-like symptoms with specific descriptive features (pseudoneurotic symptoms) are associated with schizophrenia-spectrum disorders. It suggests that pseudoneurotic symptoms are linked with temporally stable schizophrenia psychopathology (disorder of basic self and negative symptoms).

Conceptualizing eating disorder recovery research: Current perspectives and future research directions

BACKGROUND

How we research eating disorder (ED) recovery impacts what we know (perceive as fact) about it. Traditionally, research has focused more on the "what" of recovery (e.g., establishing criteria for recovery, reaching consensus definitions) than the "how" of recovery research (e.g., type of methodologies, triangulation of perspectives). In this paper we aim to provide an overview of the ED field's current perspectives on recovery, discuss how our methodologies shape what is known about recovery, and suggest a broadening of our methodological "toolkits" in order to form a more complete picture of recovery. BODY: This paper examines commonly used methodologies in research, and explores how incorporating different perspectives can add to our understanding of the recovery process. To do this, we (1) provide an overview of commonly used methodologies (quantitative, qualitative), (2) consider their benefits and limitations, (3) explore newer approaches, including mixed-methods, creative methods (e.g., Photovoice, digital storytelling), and multi-methods (e.g., quantitative, qualitative, creative methods, psycho/physiological, behavioral, laboratory, online observations), and (4) suggest that broadening our methodological "toolkits" could spur more nuanced and specific insights about ED recoveries. We propose a potential future research model that would ideally have a multi-methods design, incorporate different perspectives (e.g., expanding recruitment of diverse participants, including supportive others, in study co-creation), and a longitudinal course (e.g., capturing cognitive and emotional recovery, which often comes after physical). In this way, we hope to move the field towards different, more comprehensive, perspectives on ED recovery.

CONCLUSION

Our current perspectives on studying ED recovery leave critical gaps in our knowledge about the process. The traditional research methodologies impact our conceptualization of recovery definitions, and in turn limit our understanding of the phenomenon. We suggest that we expand our range of methodologies, perspectives, and timeframes in research, in order to form a more complete picture of what is possible in recovery; the multiple aspects of an individual's life that can improve, the greater number of people who can recover than previously believed, and the reaffirmation of hope that, even after decades, individuals can begin, and successfully continue, their ED recovery process.

A risk calculator to predict suicide attempts among individuals with early-onset bipolar disorder

OBJECTIVES

To build a one-year risk calculator (RC) to predict individualized risk for suicide attempt in early-onset bipolar disorder.

METHODS

Youth numbering 394 with bipolar disorder who completed ≥2 follow-up assessments (median follow-up length = 13.1 years) in the longitudinal Course and Outcome of Bipolar Youth (COBY) study were included. Suicide attempt over follow-up was assessed via the A-LIFE Self-Injurious/Suicidal Behavior scale. Predictors from the literature on suicidal behavior in bipolar disorder that are readily assessed in clinical practice were selected and trichotomized as appropriate (presence past 6 months/lifetime history only/no lifetime history). The RC was trained via boosted multinomial classification trees; predictions were calibrated via Platt scaling. Half of the sample was used to train, and the other half to independently test the RC.

RESULTS

There were 249 suicide attempts among 106 individuals. Ten predictors accounted for >90% of the cross-validated relative influence in the model (AUC = 0.82; in order of relative influence): (1) age of mood disorder onset; (2) non-suicidal self-injurious behavior (trichotomized); (3) current age; (4) psychosis (trichotomized); (5) socioeconomic status; (6) most severe depressive symptoms in past 6 months (trichotomized none/subthreshold/threshold); (7) history of suicide attempt (trichotomized); (8) family history of suicidal behavior; (9) substance use disorder (trichotomized); (10) lifetime history of physical/sexual abuse. For all trichotomized variables, presence in the past 6 months reliably predicted higher risk than lifetime history.

CONCLUSIONS

This RC holds promise as a clinical and research tool for prospective identification of individualized high-risk periods for suicide attempt in early-onset bipolar disorder.

DNA methylation levels of RELN promoter region in ultra-high risk, first episode and chronic schizophrenia cohorts of schizophrenia

The essential role of the Reelin gene (RELN) during brain development makes it a prominent candidate in human epigenetic studies of Schizophrenia. Previous literature has reported differing levels of DNA methylation (DNAm) in patients with psychosis. Therefore, this study aimed to (1) examine and compare RELN DNAm levels in subjects at different stages of psychosis cross-sectionally, (2) analyse the effect of antipsychotics (AP) on DNAm, and (3) evaluate the effectiveness and applicability of RELN promoter DNAm as a possible biological-based marker for symptom severity in psychosis.. The study cohort consisted of 56 healthy controls, 87 ultra-high risk (UHR) individuals, 26 first-episode (FE) psychosis individuals and 30 chronic schizophrenia (CS) individuals. The Positive and Negative Syndrome Scale (PANSS) was used to assess Schizophrenia severity. After pyrosequencing selected CpG sites of peripheral blood, the Average mean DNAm levels were compared amongst the 4 subgroups. Our results showed differing levels of DNAm, with UHR having the lowest (7.72 ± 0.19) while the CS had the highest levels (HC: 8.78 ± 0.35; FE: 7.75 ± 0.37; CS: 8.82 ± 0.48). Significantly higher Average mean DNAm levels were found in CS subjects on AP (9.12 ± 0.61) compared to UHR without medication (UHR(-)) (7.39 ± 0.18). A significant association was also observed between the Average mean DNAm of FE and PANSS Negative symptom factor (R² = 0.237, ß = -0.401, *p = 0.033). In conclusion, our findings suggested different levels of DNAm for subjects at different stages of psychosis. Those subjects that took AP have different DNAm levels. There were significant associations between FE DNAm and Negative PANSS scores. With more future experiments and on larger cohorts, there may be potential use of DNAm of the RELN gene as one of the genes for the biological-based marker for symptom severity in psychosis.

Evaluating the tendencies of community practitioners who actively practice in child and adolescent psychiatry to diagnose and treat DSM-5 attenuated psychotic syndrome

The detection of individuals at clinical ultra-high risk for psychosis (CHR-P) may be a key limiting step for early interventions, and there is some uncertainty regarding the true clinical reliability of the CHR-P states. The aim of this study was to explore how practitioners who were in the direct treatment of children with psychiatric disorders [child psychiatry specialists/trainees (n = 227, n = 131), adult psychiatrists (n = 27), and child neurologists (n = 2)] perceive the DSM-5-Attenuated Psychosis Syndrome (DSM-5-APS), and their clinical routine practice in the treatment of it. Three vignettes describing fictional cases presented with symptoms of either DSM-5-Schizophrenia, DSM-5-APS, and no psychotic symptoms were created. We asked these practitioners to apply a DSM-5 diagnosis and to choose appropriate treatment(s) for these vignettes. Of the responders, 43% correctly diagnosed the APS vignette, whereas 37.4% mentioned that it had a full-blown psychotic episode. Regarding the therapeutic approach for the APS vignette, 72.1% of all practitioners chose a psychopharmacological intervention and 32% individual psychotherapy. This study showed that the diagnostic inter-rater reliability of the DSM-5-APS among child/adolescent mental health practitioners was consistent with the results from the DSM-5 field trials (Kappa = 0.46). Moreover, almost three in four practitioners endorsed psychopharmacological intervention as a treatment option for the DSM-5-APS case. The lack of evidence of psychopharmacological interventions in CHR-P situations emphasizes that the least harmful interventions should be recommended. Thus, our findings indicated a need for raising awareness regarding the CHR-P paradigm and its treatment as well as the development of solid guidelines that can be implemented in clinical practice.

Clinical high at-risk mental state in young subjects accessing a mental disorder prevention service in Italy

We aim to assess how functioning, depressive symptoms, and psychotic symptoms are associated with different numbers of Clinical High At-Risk Mental State (CHARMS) categories. In this cross-sectional study, we assessed 62 help-seeking subjects aged 15-24 with a drop in functioning, with Structured Clinical Interview for DSM-5, Social and Occupational Functioning Assessment Scale (SOFAS), Comprehensive Assessment of At-Risk Mental State to define risk for psychosis, Hamilton Depression Rating scale (HAM-D), Positive and Negative Symptoms Scale, 6 items (PANSS-6). CHARMS criteria were assessed via retrospective chart review. Overall, 30.6% did not meet any CHARMS component criteria at baseline (CHARMS-), 27.4%, 33.9% and 8.1% met one, two, and three or more CHARMS groups. Overall, 48.8% met criteria for ultra-high risk for psychosis (17.7% without other CHARMS categories), 25.8% risk of borderline personality disorder (3.2% alone), 35.5% mild depression (8.1% alone), 11.3% risk of bipolar disorder (1.6% alone). SOFAS score and HAM-D score worsened from CHARMS- to three or more CHARMS categories, whilst PANSS-6 score did not. In a multivariate regression only PANSS-6 (beta=-1.105, p<0.001) was associated with SOFAS (R²=0.385). Help-seeking youth with poor functioning present symptoms meeting CHARMS criteria. Meeting criteria for multiple CHARMS categories is associated with increased depressive, but not psychotic symptoms, while psychotic symptoms play a prominent role in determining functional impairment. Results should be interpreted within the limitations of the study including the small sample size and the cross-sectional design, and need further replications.

Ethical considerations for precision psychiatry: A roadmap for research and clinical practice

Precision psychiatry is an emerging field with transformative opportunities for mental health. However, the use of clinical prediction models carries unprecedented ethical challenges, which must be addressed before accessing the potential benefits of precision psychiatry. This critical review covers multidisciplinary areas, including psychiatry, ethics, statistics and machine-learning, healthcare and academia, as well as input from people with lived experience of mental disorders, their family, and carers. We aimed to identify core ethical considerations for precision psychiatry and mitigate concerns by designing a roadmap for research and clinical practice. We identified priorities: learning from somatic medicine; identifying precision psychiatry use cases; enhancing transparency and generalizability; fostering implementation; promoting mental health literacy; communicating risk estimates; data protection and privacy; and fostering the equitable distribution of mental health care. We hope this blueprint will advance research and practice and enable people with mental health problems to benefit from precision psychiatry.

Development of a probability calculator for psychosis risk in children, adolescents, and young adults

BACKGROUND

Assessment of risks of illnesses has been an important part of medicine for decades. We now have hundreds of 'risk calculators' for illnesses, including brain disorders, and these calculators are continually improving as more diverse measures are collected on larger samples.

METHODS

We first replicated an existing psychosis risk calculator and then used our own sample to develop a similar calculator for use in recruiting 'psychosis risk' enriched community samples. We assessed 632 participants age 8-21 (52% female; 48% Black) from a community sample with longitudinal data on neurocognitive, clinical, medical, and environmental variables. We used this information to predict psychosis spectrum (PS) status in the future. We selected variables based on lasso, random forest, and statistical inference relief; and predicted future PS using ridge regression, random forest, and support vector machines.

RESULTS

Cross-validated prediction diagnostics were obtained by building and testing models in randomly selected sub-samples of the data, resulting in a distribution of the diagnostics; we report the mean. The strongest predictors of later PS status were the Children's Global Assessment Scale; delusions of predicting the future or having one's thoughts/actions controlled; and the percent married in one's neighborhood. Random forest followed by ridge regression was most accurate, with a cross-validated area under the curve (AUC) of 0.67. Adjustment of the model including only six variables reached an AUC of 0.70.

CONCLUSIONS

Results support the potential application of risk calculators for screening and identification of at-risk community youth in prospective investigations of developmental trajectories of the PS.

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.

Prognostic accuracy and clinical utility of psychometric instruments for individuals at clinical high-risk of psychosis: a systematic review and meta-analysis

Accurate prognostication of individuals at clinical high-risk for psychosis (CHR-P) is an essential initial step for effective primary indicated prevention. We aimed to summarise the prognostic accuracy and clinical utility of CHR-P assessments for primary indicated psychosis prevention. Web of Knowledge databases were searched until 1st January 2022 for longitudinal studies following-up individuals undergoing a psychometric or diagnostic CHR-P assessment, reporting transition to psychotic disorders in both those who meet CHR-P criteria (CHR-P + ) or not (CHR-P-). Prognostic accuracy meta-analysis was conducted following relevant guidelines. Primary outcome was prognostic accuracy, indexed by area-under-the-curve (AUC), sensitivity and specificity, estimated by the number of true positives, false positives, false negatives and true negatives at the longest available follow-up time. Clinical utility analyses included: likelihood ratios, Fagan's nomogram, and population-level preventive capacity (Population Attributable Fraction, PAF). A total of 22 studies (n = 4 966, 47.5% female, age range 12-40) were included. There were not enough meta-analysable studies on CHR-P diagnostic criteria (DSM-5 Attenuated Psychosis Syndrome) or non-clinical samples. Prognostic accuracy of CHR-P psychometric instruments in clinical samples (individuals referred to CHR-P services or diagnosed with 22q.11.2 deletion syndrome) was excellent: AUC = 0.85 (95% CI: 0.81-0.88) at a mean follow-up time of 34 months. This result was driven by outstanding sensitivity (0.93, 95% CI: 0.87-0.96) and poor specificity (0.58, 95% CI: 0.50-0.66). Being CHR-P + was associated with a small likelihood ratio LR + (2.17, 95% CI: 1.81-2.60) for developing psychosis. Being CHR-P- was associated with a large LR- (0.11, 95%CI: 0.06-0.21) for developing psychosis. Fagan's nomogram indicated a low positive (0.0017%) and negative (0.0001%) post-test risk in non-clinical general population samples. The PAF of the CHR-P state is 10.9% (95% CI: 4.1-25.5%). These findings consolidate the use of psychometric instruments for CHR-P in clinical samples for primary indicated prevention of psychosis. Future research should improve the ability to rule in psychosis risk.

Cognitive Dysfunction as a Risk Factor for Psychosis

The current chapter summarizes recent evidence for cognition as a risk factor for the development of psychosis, including the range of cognitive impairments that exist across the spectrum of psychosis risk symptoms. The chapter examines several possible theories linking cognitive deficits with the development of psychotic symptoms, including evidence that cognitive deficits may be an intermediate risk factor linking genetic and/or neural metrics to psychosis spectrum symptoms. Although there is not strong evidence for unique cognitive markers associated specifically with psychosis compared to other forms of psychopathology, psychotic disorders are generally associated with the greatest severity of cognitive deficits. Cognitive deficits precede the development of psychotic symptoms and may be detectable as early as childhood. Across the psychosis spectrum, both the presence and severity of psychotic symptoms are associated with mild to moderate impairments across cognitive domains, perhaps most consistently for language, cognitive control, and working memory domains. Research generally indicates the size of these cognitive impairments worsens as psychosis symptom severity increases. The chapter points out areas of unclarity and unanswered questions in each of these areas, including regarding the mechanisms contributing to the association between cognition and psychosis, the timing of deficits, and whether any cognitive systems can be identified that function as specific predictors of psychosis risk symptoms.

Cannabis Use in Autism: Reasons for Concern about Risk for Psychosis

Being particularly vulnerable to the pro-psychotic effects of cannabinoid exposure, autism spectrum individuals present with an increased risk of psychosis, which may be passed on to their own children. More specifically, cannabis exposure among autism spectrum individuals seems to exert disruptive epigenetic effects that can be intergenerationally inherited in brain areas which play a critical role in schizophrenia pathophysiology. Additionally, because of such cannabinoid-induced epigenetic effects, autism candidate genes present with bivalent chromatin markings which make them more vulnerable to subsequent disruption, possibly leading to psychosis onset later in life. Thus, findings support a developmental trajectory between autism and psychosis, as per endocannabinoid system modulation. However, such evidence has not received the attention it deserves.

Persistent Childhood and Adolescent Anxiety and Risk for Psychosis: A Longitudinal Birth Cohort Study

BACKGROUND

Persistent anxiety in childhood and adolescence could represent a novel treatment target for psychosis, potentially targeting activation of stress pathways and secondary nonresolving inflammatory response. Here, we examined the association between persistent anxiety through childhood and adolescence with individuals with psychotic experiences (PEs) or who met criteria for psychotic disorder (PD) at age 24 years. We also investigated whether C-reactive protein mediated any association.

METHODS

Data from the Avon Longitudinal Study of Parents and Children (ALSPAC) were available in 8242 children at age 8 years, 7658 at age 10 years, 6906 at age 13 years, and 3889 at age 24 years. The Development and Well-Being Assessment was administered to capture child and adolescent anxiety. We created a composite score of generalized anxiety at ages 8, 10, and 13. PEs and PD were assessed at age 24, derived from the Psychosis-like Symptoms Interview. The mean of C-reactive protein at ages 9 and 15 years was used as a mediator.

RESULTS

Individuals with persistent high levels of anxiety were more likely to develop PEs (odds ratio 2.02, 95% CI 1.26-3.23, p = .003) and PD at age 24 (odds ratio 4.23, 95% CI 2.27-7.88, p < .001). The mean of C-reactive protein at ages 9 and 15 mediated the associations of persistent anxiety with PEs (bias-corrected estimate -0.001, p = .013) and PD (bias-corrected estimate 0.001, p = .003).

CONCLUSIONS

Persistent high levels of anxiety through childhood and adolescence could be a risk factor for psychosis. Persistent anxiety is potentially related to subsequent psychosis via activation of stress hormones and nonresolving inflammation. These results contribute to the potential for preventive interventions in psychosis, with the novel target of early anxiety.

Mismatch Negativity in Response to Auditory Deviance and Risk for Future Psychosis in Youth at Clinical High Risk for Psychosis

Importance

Although clinical criteria for identifying youth at risk for psychosis have been validated, they are not sufficiently accurate for predicting outcomes to inform major treatment decisions. The identification of biomarkers may improve outcome prediction among individuals at clinical high risk for psychosis (CHR-P).

Objective

To examine whether mismatch negativity (MMN) event-related potential amplitude, which is deficient in schizophrenia, is reduced in young people with the CHR-P syndrome and associated with outcomes, accounting for effects of antipsychotic medication use.

Design, Setting, and Participants

MMN data were collected as part of the multisite case-control North American Prodrome Longitudinal Study (NAPLS-2) from 8 university-based outpatient research programs. Baseline MMN data were collected from June 2009 through April 2013. Clinical outcomes were assessed throughout 24 months. Participants were individuals with the CHR-P syndrome and healthy controls with MMN data. Participants with the CHR-P syndrome who developed psychosis (ie, converters) were compared with those who did not develop psychosis (ie, nonconverters) who were followed up for 24 months. Analysis took place between December 2019 and December 2021.

Main Outcomes and Measures

Electroencephalography was recorded during a passive auditory oddball paradigm. MMN elicited by duration-, pitch-, and duration + pitch double-deviant tones was measured.

Results

The CHR-P group (n = 580; mean [SD] age, 19.24 [4.39] years) included 247 female individuals (42.6%) and the healthy control group (n = 241; mean age, 20.33 [4.74] years) included 114 female individuals (47.3%). In the CHR-P group, 450 (77.6%) were not taking antipsychotic medication at baseline. Baseline MMN amplitudes, irrespective of deviant type, were deficient in future CHR-P converters to psychosis (n = 77, unmedicated n = 54) compared with nonconverters (n = 238, unmedicated n = 190) in both the full sample (d = 0.27) and the unmedicated subsample (d = 0.33). In the full sample, baseline medication status interacted with group and deviant type indicating that double-deviant MMN, compared with single deviants, was reduced in unmedicated converters compared with nonconverters (d = 0.43). Further, within the unmedicated subsample, deficits in double-deviant MMN were most strongly associated with earlier conversion to psychosis (hazard ratio, 1.40 [95% CI, 1.03-1.90]; P = .03], which persisted over and above positive symptom severity.

Conclusions and Relevance

This study found that MMN amplitude deficits were sensitive to future psychosis conversion among individuals at risk of CHR-P, particularly those not taking antipsychotic medication at baseline, although associations were modest. While MMN shows limited promise as a biomarker of psychosis onset on its own, it may contribute novel risk information to multivariate prediction algorithms and serve as a translational neurophysiological target for novel treatment development in a subgroup of at-risk individuals.

Developing and internally validating a prognostic model (P Risk) to improve the prediction of psychosis in a primary care population using electronic health records: The MAPPED study

BACKGROUND

An accurate risk prediction algorithm could improve psychosis outcomes by reducing duration of untreated psychosis.

OBJECTIVE

To develop and validate a risk prediction model for psychosis, for use by family doctors, using linked electronic health records.

METHODS

A prospective prediction study. Records from family practices were used between 1/1/2010 to 31/12/2017 of 300,000 patients who had consulted their family doctor for any nonpsychotic mental health problem. Records were selected from Clinical Practice Research Datalink Gold, a routine database of UK family doctor records linked to Hospital Episode Statistics, a routine database of UK secondary care records. Each patient had 5-8 years of follow up data. Study predictors were consultations, diagnoses and/or prescribed medications, during the study period or historically, for 13 nonpsychotic mental health problems and behaviours, age, gender, number of mental health consultations, social deprivation, geographical location, and ethnicity. The outcome was time to an ICD10 psychosis diagnosis.

FINDINGS

830 diagnoses of psychosis were made. Patients were from 216 family practices; mean age was 45.3 years and 43.5 % were male. Median follow-up was 6.5 years (IQR 5.6, 7.8). Overall 8-year psychosis incidence was 45.8 (95 % CI 42.8, 49.0)/100,000 person years at risk. A risk prediction model including age, sex, ethnicity, social deprivation, consultations for suicidal behaviour, depression/anxiety, substance abuse, history of consultations for suicidal behaviour, smoking history and prescribed medications for depression/anxiety/PTSD/OCD and total number of consultations had good discrimination (Harrell's C = 0.774). Identifying patients aged 17-100 years with predicted risk exceeding 1.0 % over 6 years had sensitivity of 71 % and specificity of 84 %.

FUNDING

NIHR, School for Primary Care Research, Biomedical Research Centre.

Training a machine learning classifier to identify ADHD based on real-world clinical data from medical records

The diagnostic process of attention deficit hyperactivity disorder (ADHD) is complex and relies on criteria sensitive to subjective biases. This may cause significant delays in appropriate treatment initiation. An automated analysis relying on subjective and objective measures might not only simplify the diagnostic process and reduce the time to diagnosis, but also improve reproducibility. While recent machine learning studies have succeeded at distinguishing ADHD from healthy controls, the clinical process requires differentiating among other or multiple psychiatric conditions. We trained a linear support vector machine (SVM) classifier to detect participants with ADHD in a population showing a broad spectrum of psychiatric conditions using anonymized data from clinical records (N = 299 participants). We differentiated children and adolescents with ADHD from those not having the condition with an accuracy of 66.1%. SVM using single features showed slight differences between features and overlapping standard deviations of the achieved accuracies. An automated feature selection achieved the best performance using a combination 19 features. Real-world clinical data from medical records can be used to automatically identify individuals with ADHD among help-seeking individuals using machine learning. The relevant diagnostic information can be reduced using an automated feature selection without loss of performance. A broad combination of symptoms across different domains, rather than specific domains, seems to indicate an ADHD diagnosis.

Predicting the emergence of full-threshold bipolar I, bipolar II and psychotic disorders in young people presenting to early intervention mental health services

BACKGROUND

Predictors of new-onset bipolar disorder (BD) or psychotic disorder (PD) have been proposed on the basis of retrospective or prospective studies of 'at-risk' cohorts. Few studies have compared concurrently or longitudinally factors associated with the onset of BD or PDs in youth presenting to early intervention services. We aimed to identify clinical predictors of the onset of full-threshold (FT) BD or PD in this population.

METHOD

Multi-state Markov modelling was used to assess the relationships between baseline characteristics and the likelihood of the onset of FT BD or PD in youth (aged 12-30) presenting to mental health services.

RESULTS

Of 2330 individuals assessed longitudinally, 4.3% (n = 100) met criteria for new-onset FT BD and 2.2% (n = 51) met criteria for a new-onset FT PD. The emergence of FT BD was associated with older age, lower social and occupational functioning, mania-like experiences (MLE), suicide attempts, reduced incidence of physical illness, childhood-onset depression, and childhood-onset anxiety. The emergence of a PD was associated with older age, male sex, psychosis-like experiences (PLE), suicide attempts, stimulant use, and childhood-onset depression.

CONCLUSIONS

Identifying risk factors for the onset of either BD or PDs in young people presenting to early intervention services is assisted not only by the increased focus on MLE and PLE, but also by recognising the predictive significance of poorer social function, childhood-onset anxiety and mood disorders, and suicide attempts prior to the time of entry to services. Secondary prevention may be enhanced by greater attention to those risk factors that are modifiable or shared by both illness trajectories.

Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges

Recent years have seen the rapid proliferation of clinical prediction models aiming to support risk stratification and individualized care within psychiatry. Despite growing interest, attempts to synthesize current evidence in the nascent field of precision psychiatry have remained scarce. This systematic review therefore sought to summarize progress towards clinical implementation of prediction modeling for psychiatric outcomes. We searched MEDLINE, PubMed, Embase, and PsychINFO databases from inception to September 30, 2020, for English-language articles that developed and/or validated multivariable models to predict (at an individual level) onset, course, or treatment response for non-organic psychiatric disorders (PROSPERO: CRD42020216530). Individual prediction models were evaluated based on three key criteria: (i) mitigation of bias and overfitting; (ii) generalizability, and (iii) clinical utility. The Prediction model Risk Of Bias ASsessment Tool (PROBAST) was used to formally appraise each study's risk of bias. 228 studies detailing 308 prediction models were ultimately eligible for inclusion. 94.5% of developed prediction models were deemed to be at high risk of bias, largely due to inadequate or inappropriate analytic decisions. Insufficient internal validation efforts (within the development sample) were also observed, while only one-fifth of models underwent external validation in an independent sample. Finally, our search identified just one published model whose potential utility in clinical practice was formally assessed. Our findings illustrated significant growth in precision psychiatry with promising progress towards real-world application. Nevertheless, these efforts have been inhibited by a preponderance of bias and overfitting, while the generalizability and clinical utility of many published models has yet to be formally established. Through improved methodological rigor during initial development, robust evaluations of reproducibility via independent validation, and evidence-based implementation frameworks, future research has the potential to generate risk prediction tools capable of enhancing clinical decision-making in psychiatric care.

Systematic Review and Meta-analysis: Psychosis Risk in Children and Adolescents With an At-Risk Mental State

OBJECTIVE

The "At Risk Mental State" (ARMS) approach to psychosis, also called "Clinical/Ultra High Risk," has had a major impact on psychosis services internationally. Despite well-established developmental differences in the prevalence and expression of psychotic symptoms from childhood into adulthood, there has been no systematic review of psychosis transitions specifically in children and adolescents up to age of 18 years. Evidence for this age group is crucial for developmentally appropriate clinical decisions by child and adolescent psychiatrists.

METHOD

Systematic review and meta-analysis of psychosis risk among children diagnosed with ARMS up to age 18 years, with pooled transition rates after 1-year, 2-year and ≥5-year follow-up.

RESULTS

We retrieved 1,107 records and identified 16 articles from 9 studies reporting transition rates on 436 individuals with ARMS aged 9 to 18 years. The pooled transition rate to psychosis at 1 year was 9.5% (95% CI = 5.5%-14.2%, 7 studies included), at 2-years 12.1% (95% CI = 6.7%-18.6%, 4 studies included), and at ≥5 years 16.1% (95% CI = 5.6%-30.0%, 4 studies included). We did not find evidence that the diagnosis of ARMS was associated with increased risk of psychosis once risk-enriching recruitment strategies were taken into account.

CONCLUSION

At 5-year follow-up, 1 in 6 youths diagnosed with an ARMS had transitioned to psychosis, but we did not find evidence that this risk was related to ARMS diagnosis as opposed to sampling/recruitment strategies. Our findings indicate a need for caution in applying ARMS methodology to children and adolescents. and highlight the need for developmentally sensitive approaches when considering psychosis risk.

Study protocol for the development and internal validation of Schizophrenia Prediction of Resistance to Treatment (SPIRIT): a clinical tool for predicting risk of treatment resistance to antipsychotics in first-episode schizophrenia

INTRODUCTION

Treatment-resistant schizophrenia (TRS) is associated with significant impairment of functioning and high treatment costs. Identification of patients at high risk of TRS at the time of their initial diagnosis may significantly improve clinical outcomes and minimise social and functional disability. We aim to develop a prognostic model for predicting the risk of developing TRS in patients with first-episode schizophrenia and to examine its potential utility and acceptability as a clinical decision tool.

METHODS AND ANALYSIS

We will use two well-characterised longitudinal UK-based first-episode psychosis cohorts: Aetiology and Ethnicity in Schizophrenia and Other Psychoses and Genetics and Psychosis for which data have been collected on sociodemographic and clinical characteristics. We will identify candidate predictors for the model based on current literature and stakeholder consultation. Model development will use all data, with the number of candidate predictors restricted according to available sample size and event rate. A model for predicting risk of TRS will be developed based on penalised regression, with missing data handled using multiple imputation. Internal validation will be undertaken via bootstrapping, obtaining optimism-adjusted estimates of the model's performance. The clinical utility of the model in terms of clinically relevant risk thresholds will be evaluated using net benefit and decision curves (comparative to competing strategies). Consultation with patients and clinical stakeholders will determine potential thresholds of risk for treatment decision-making. The acceptability of embedding the model as a clinical tool will be explored using qualitative focus groups with up to 20 clinicians in total from early intervention services. Clinicians will be recruited from services in Stafford and London with the focus groups being held via an online platform.

ETHICS AND DISSEMINATION

The development of the prognostic model will be based on anonymised data from existing cohorts, for which ethical approval is in place. Ethical approval has been obtained from Keele University for the qualitative focus groups within early intervention in psychosis services (ref: MH-210174). Suitable processes are in place to obtain informed consent for National Health Service staff taking part in interviews or focus groups. A study information sheet with cover letter and consent form have been prepared and approved by the local Research Ethics Committee. Findings will be shared through peer-reviewed publications, conference presentations and social media. A lay summary will be published on collaborator websites.

A multimodal study of a first episode psychosis cohort: potential markers of antipsychotic treatment resistance

Treatment resistant (TR) psychosis is considered to be a significant cause of disability and functional impairment. Numerous efforts have been made to identify the clinical predictors of TR. However, the exploration of molecular and biological markers is still at an early stage. To understand the TR condition and identify potential molecular and biological markers, we analyzed demographic information, clinical data, structural brain imaging data, and molecular brain imaging data in 7 Tesla magnetic resonance spectroscopy from a first episode psychosis cohort that includes 136 patients. Age, gender, race, smoking status, duration of illness, and antipsychotic dosages were controlled in the analyses. We found that TR patients had a younger age at onset, more hospitalizations, more severe negative symptoms, a reduction in the volumes of the hippocampus (HP) and superior frontal gyrus (SFG), and a reduction in glutathione (GSH) levels in the anterior cingulate cortex (ACC), when compared to non-TR patients. The combination of multiple markers provided a better classification between TR and non-TR patients compared to any individual marker. Our study shows that ACC-GSH, HP and SFG volumes, and age at onset, could potentially be biomarkers for TR diagnosis, while hospitalization and negative symptoms could be used to evaluate the progression of the disease. Multimodal cohorts are essential in obtaining a comprehensive understanding of brain disorders.

Research Trends in Individuals at High Risk for Psychosis: A Bibliometric Analysis

The study of clinical high risk for psychosis (CHR-P) has progressed rapidly over the last decades and has developed into a significant branch of schizophrenia research. Organizing the information about this rapidly growing subject through bibliometric analysis enables us to gain a better understanding of current research trends and future directions to be pursued. Electronic searches from January 1991 to December 2020 yielded 5,601 studies, and included 1,637 original articles. After processing the data, we were able to determine that this field has grown significantly in a short period of time. It has been confirmed that researchers, institutions, and countries are collaborating closely to conduct research; moreover, these networks are becoming increasingly complex over time. Additionally, there was a shift over time in the focus of the research subject from the prodrome, recognition, prevention, diagnosis to cognition, neuroimaging, neurotransmitters, cannabis, and stigma. We should aim for collaborative studies in which various countries participate, thus covering a wider range of races and cultures than would be covered by only a few countries.

Mapping the implementation and challenges of clinical services for psychosis prevention in England

INTRODUCTION

Indicated primary prevention of psychosis is recommended by NICE clinical guidelines, but implementation research on Clinical High Risk for Psychosis (CHR-P) services is limited.

METHODS

Electronic audit of CHR-P services in England, conducted between June and September 2021, addressing core implementation domains: service configuration, detection of at-risk individuals, prognostic assessment, clinical care, clinical research, and implementation challenges, complemented by comparative analyses across service model. Descriptive statistics, Fisher's exact test and Mann-Whitney U-tests were employed.

RESULTS

Twenty-four CHR-P clinical services (19 cities) were included. Most (83.3%) services were integrated within other mental health services; only 16.7% were standalone. Across 21 services, total yearly caseload of CHR-P individuals was 693 (average: 33; range: 4-115). Most services (56.5%) accepted individuals aged 14-35; the majority (95.7%) utilized the Comprehensive Assessment of At Risk Mental States (CAARMS). About 65% of services reported some provision of NICE-compliant interventions encompassing monitoring of mental state, cognitive-behavioral therapy (CBT), and family interventions. However, only 66.5 and 4.9% of CHR-P individuals actually received CBT and family interventions, respectively. Core implementation challenges included: recruitment of specialized professionals, lack of dedicated budget, and unmet training needs. Standalone services reported fewer implementation challenges, had larger caseloads (p = 0.047) and were more likely to engage with clinical research (p = 0.037) than integrated services.

DISCUSSION

While implementation of CHR-P services is observed in several parts of England, only standalone teams appear successful at detection of at-risk individuals. Compliance with NICE-prescribed interventions is limited across CHR-P services and unmet needs emerge for national training and investments.