Prevention of disorders has become a central element of psychiatric research and clinic. Currently, Ultra High Risk (UHR) criteria are internationally recognized for psychiatric risk assessment. Self Disorders (SD) aroused particular interest because they were found to be specific to schizophrenic spectrum disorders and a marker of vulnerability for psychotic onset.

To evaluate the correlation between psychotic risk and depressive symptoms in at-risk adolescent population.

We collected data from 80 patients, aged 14-18, with sufficient skills in the Italian language and an IQ ≥70, excluding patients with disorders related to direct effects of a general medical condition or substance. Psychodiagnostic evaluation included K-SADS-PL, SIPS/SOPS, EASE (for the assessment of SDs) and the CDSS (for the assessment of Depression).

35 subjects have UHR criteria, while 45 do not have a psychotic risk syndrome or psychotic features. Between the two groups there is a significant difference in the total SCORE of EASE, in domains 1, 2 and 5. In addition, a positive correlation between SDs and depressive symptoms emerged, in particular with pathological guilt and with reference ideas of guilt.

The results confirm the validity of SDs for early detection of psychosis. Depressive features appear to be associated with the presence of abnormalities of experience. This results suggest a close care and monitoring of depressive symptoms in adolescence, because they can mask disorders of different nature, particularly pathological guilt and guilty ideas of reference that are depressive “cognitive” symptoms more correlate with psychotic risk.

No significant relationships.

Neurocognitive abnormalities are prevalent in both first episode schizophrenia patients and in ultra high risk (UHR) patients.

To compare verbal fluency performance at baseline in UHR in patients that did and did not make the transition to psychosis.

Baseline verbal fluency performance in UHR-patients (n = 47) was compared to match first episode patients (n = 69) and normal controls (n = 42).

Verbal fluency (semantic category) scores in UHR-patients did not differ significantly from the score in first episode schizophrenia patients. Both the UHR group (p < 0.003) and the patient group (p < 0.0001) performed significantly worse than controls. Compared to the non-transition group, the transition group performed worse on verbal fluency, semantic category (p < 0.006) at baseline.

Verbal fluency (semantic category) is disturbed in UHR-patients that make the transition to psychosis and could contribute to an improved prediction of transition to psychosis in UHR-patients.

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

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

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

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

Only 30% or fewer of individuals at clinical high risk (CHR) convert to full psychosis within 2 years. Efforts are thus underway to refine risk identification strategies to increase their predictive power. Our objective was to develop and validate the predictive accuracy and individualized risk components of a mobile app-based psychosis risk calculator (RC) in a CHR sample from the SHARP (ShangHai At Risk for Psychosis) program.

In total, 400 CHR individuals were identified by the Chinese version of the Structured Interview for Prodromal Syndromes. In the first phase of 300 CHR individuals, 196 subjects (65.3%) who completed neurocognitive assessments and had at least a 2-year follow-up assessment were included in the construction of an RC for psychosis. In the second phase of the SHARP sample of 100 subjects, 93 with data integrity were included to validate the performance of the SHARP-RC.

The SHARP-RC showed good discrimination of subsequent transition to psychosis with an AUC of 0.78 (p < 0.001). The individualized risk generated by the SHARP-RC provided a solid estimation of conversion in the independent validation sample, with an AUC of 0.80 (p = 0.003). A risk estimate of 20% or higher had excellent sensitivity (84%) and moderate specificity (63%) for the prediction of psychosis. The relative contribution of individual risk components can be simultaneously generated. The mobile app-based SHARP-RC was developed as a convenient tool for individualized psychosis risk appraisal.

The SHARP-RC provides a practical tool not only for assessing the probability that an individual at CHR will develop full psychosis, but also personal risk components that might be targeted in early intervention.

In the 1990s criteria were developed to detect individuals at high and imminent risk of developing a psychotic disorder. These are known as the at risk mental state, ultra high risk or clinical high risk criteria. Individuals meeting these criteria are symptomatic and help-seeking. Services for such individuals are now found worldwide. Recently Psychological Medicine published two articles that criticise these services and suggest that they should be dismantled or restructured. One paper also provides recommendations on how ARMS services should be operate.

In this paper we draw on the existing literature in the field and present the perspective of some ARMS clinicians and researchers.

Many of the critics' arguments are refuted. Most of the recommendations included in the Moritz et al. paper are already occurring.

ARMS services provide management of current problems, treatment to reduce risk of onset of psychotic disorder and monitoring of mental state, including attenuated psychotic symptoms. These symptoms are associated with a range of poor outcomes. It is important to assess them and track their trajectory over time. A new approach to detection of ARMS individuals can be considered that harnesses broad youth mental health services, such as headspace in Australia, Jigsaw in Ireland and ACCESS Open Minds in Canada. Attention should also be paid to the physical health of ARMS individuals. Far from needing to be dismantled we feel that the ARMS approach has much to offer to improve the health of young people.

The predictive accuracy of the Clinical High Risk criteria for Psychosis (CHR-P) regarding the future development of the disorder remains suboptimal. It is therefore necessary to incorporate refined risk estimation tools which can be applied at the individual subject level. The aim of the study was to develop an easy-to use, short refined risk estimation tool to predict the development of psychosis in a new CHR-P cohort recruited in European country with less established early detection services.

A cohort of 105 CHR-P individuals was assessed with the Comprehensive Assessment of At Risk Mental States12/2006, and then followed for a median period of 36 months (25th-75th percentile:10–59 months) for transition to psychosis. A multivariate Cox regression model predicting transition was generated with preselected clinical predictors and was internally validated with 1000 bootstrap resamples.

Speech disorganization and unusual thought content were selected as potential predictors of conversion on the basis of published literature. The prediction model was significant (p < 0.0001) and confirmed that both speech disorganization (HR = 1.69; 95%CI: 1.39–2.05) and unusual thought content (HR = 1.51; 95%CI: 1.27–1.80) were significantly associated with transition. The prognostic accuracy of the model was adequate (Harrell’s c- index = 0.79), even after optimism correction through internal validation procedures (Harrell’s c-index = 0.78).

The clinical prediction model developed, and internally validated, herein to predict transition from a CHR-P to psychosis may be a promising tool for use in clinical settings. It has been incorporated into an online tool available at: https://link.konsta.com.pl/psychosis. Future external replication studies are needed.

This study aim to derive and validate a simple and well-performing risk calculator (RC) for predicting psychosis in individual patients at clinical high risk (CHR).

From the ongoing ShangHai-At-Risk-for-Psychosis (SHARP) program, 417 CHR cases were identified based on the Structured Interview for Prodromal Symptoms (SIPS), of whom 349 had at least 1-year follow-up assessment. Of these 349 cases, 83 converted to psychosis. Logistic regression was used to build a multivariate model to predict conversion. The area under the receiver operating characteristic (ROC) curve (AUC) was used to test the effectiveness of the SIPS-RC. Second, an independent sample of 100 CHR subjects was recruited based on an identical baseline and follow-up procedures to validate the performance of the SIPS-RC.

Four predictors (each based on a subset of SIPS-based items) were used to construct the SIPS-RC: (1) functional decline; (2) positive symptoms (unusual thoughts, suspiciousness); (3) negative symptoms (social anhedonia, expression of emotion, ideational richness); and (4) general symptoms (dysphoric mood). The SIPS-RC showed moderate discrimination of subsequent transition to psychosis with an AUC of 0.744 (p < 0.001). A risk estimate of 25% or higher had around 75% accuracy for predicting psychosis. The personalized risk generated by the SIPS-RC provided a solid estimate of conversion outcomes in the independent validation sample, with an AUC of 0.804 [95% confidence interval (CI) 0.662–0.951].

The SIPS-RC, which is simple and easy to use, can perform in the same manner as the NAPLS-2 RC in the Chinese clinical population. Such a tool may be used by clinicians to counsel appropriately their patients about clinical monitor v. potential treatment options.

Accuracy of risk algorithms for psychosis prediction in “at risk mental state” (ARMS) samples may differ according to the recruitment setting. Standardized criteria used to detect ARMS individuals may lack specificity if the recruitment setting is a secondary mental health service. The authors tested a modified strategy to predict psychosis conversion in this setting by using a systematic selection of trait-markers of the psychosis prodrome in a sample with a heterogeneous ARMS status.

138 non-psychotic outpatients (aged 17–31) were consecutively recruited in secondary mental health services and followed-up for up to 3 years (mean follow-up time, 2.2 years; SD = 0.9). Baseline ARMS status, clinical, demographic, cognitive, and neurological soft signs measures were collected. Cox regression was used to derive a risk index.

48% individuals met ARMS criteria (ARMS-Positive, ARMS+). Conversion rate to psychosis was 21% for the overall sample, 34% for ARMS+, and 9% for ARMS-Negative (ARMS−). The final predictor model with a positive predictive validity of 80% consisted of four variables: Disorder of Thought Content, visuospatial/constructional deficits, sensory-integration, and theory-of-mind abnormalities. Removing Disorder of Thought Content from the model only slightly modified the predictive accuracy (−6.2%), but increased the sensitivity (+9.5%).

These results suggest that in a secondary mental health setting the use of trait-markers of the psychosis prodrome may predict psychosis conversion with great accuracy despite the heterogeneity of the ARMS status. The use of the proposed predictive algorithm may enable a selective recruitment, potentially reducing duration of untreated psychosis and improving prognostic outcomes.

Social dysfunction is a hallmark symptom of schizophrenia which commonly precedes the onset of psychosis. It is unclear if social symptoms in clinical high-risk patients reflect depressive symptoms or are a manifestation of negative symptoms.

We compared social function scores on the Social Adjustment Scale-Self Report between 56 young people (aged 13–27 years) at clinical high risk for psychosis and 22 healthy controls. The cases were also assessed for depressive and ‘prodromal’ symptoms (subthreshold positive, negative, disorganized and general symptoms).

Poor social function was related to both depressive and negative symptoms, as well as to disorganized and general symptoms. The symptoms were highly intercorrelated but linear regression analysis demonstrated that poor social function was primarily explained by negative symptoms within this cohort, particularly in ethnic minority patients.

Although this study demonstrated a relationship between social dysfunction and depressive symptoms in clinical high-risk cases, this association was primarily explained by the relationship of each of these to negative symptoms. In individuals at heightened risk for psychosis, affective changes may be related to a progressive decrease in social interaction and loss of reinforcement of social behaviors. These findings have relevance for potential treatment strategies for social dysfunction in schizophrenia and its risk states and predict that antidepressant drugs, cognitive behavioral therapy and/or social skills training may be effective.