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

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

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

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

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.

Few of the previous studies of clinical high risk of psychosis (CHR) have explored whether outcomes other than conversion, such as poor functioning or treatment responses, are better predicted when using risk calculators. To answer this question, we compared the predictive accuracy between the outcome of conversion and poor functioning by using the NAPLS-2 risk calculator.

Three hundred CHR individuals were identified using the Chinese version of the Structured Interview for Prodromal Symptoms. Of these, 228 (76.0%) completed neurocognitive assessments at baseline and 199 (66.3%) had at least a 1-year follow-up assessment. The latter group was used in the NAPLS-2 risk calculator.

We divided the sample into two broad categories based on different outcome definitions, conversion (n = 46) v. non-conversion (n = 153) or recovery (n = 138) v. poor functioning (n = 61). Interestingly, the NAPLS-2 risk calculator showed moderate discrimination of subsequent conversion to psychosis in this sample with an area under the receiver operating characteristic curve (AUC) of 0.631 (p = 0.007). However, for discriminating poor functioning, the AUC of the model increased to 0.754 (p < 0.001).

Our results suggest that the current risk calculator was a better fit for predicting a poor functional outcome and treatment response than it was in the prediction of conversion to psychosis.

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.

The duration of untreated psychosis (DUP) has been widely studied. However, for individuals with attenuated psychosis syndrome (APS), it is unclear whether the duration of untreated prodromal symptoms (DUPrS) also has a negative effect on the progression of psychosis. Our aim was to identify demographic and clinical factors contributing to the DUPrS in a large sample of individuals with APS, and to evaluate the association between DUPrS and the conversion to psychosis.

A sample of 391 individuals with APS, who were identified through a structured interview for prodromal syndromes, were included in this study, of whom a total of 334 patients had completed at least a 1-year clinical follow-up. A total of 57 individuals had converted to psychosis.

The average DUPrS was 4.8 months for the whole sample. Individuals with a longer DUPrS were likely to be men, non-local residents, with abnormal thought symptoms, a higher severity level of negative symptoms, the lower severity level of general symptoms, and lower level of general function before the onset of attenuated positive symptoms. A DUPrS of less than 2 months, or more than 6 months, lowered the risk for conversion to psychosis.

Our data suggested that the association between the DUPrS and outcome in individuals with APS were likely to be different, which is either long or short DUPrS was not related to future psychosis onset. Individuals with APS were more likely to have a group of features associated with a longer DUPrS.

This study aims to explore moderation and mediation roles of caregiver self-efficacy between subjective caregiver burden and (a) behavioral and psychological symptoms (BPSD) of dementia; and (b) social support.

A cross-sectional study with 137 spouse caregivers of dementia patients was conducted in Shanghai. We collected demographic information for the caregiver–patient dyads, as well as information associated with dementia-related impairments, caregiver social support, caregiver self-efficacy, and SF-36.

Multiple regression analysis showed that caregiver self-efficacy was a moderator both between BPSD and subjective caregiver burden, and social support and subjective caregiver burden. Results also showed a partial mediation effect of caregiver self-efficacy on the impact of BPSD on subjective caregiver burden, and a mediation effect of social support on subjective caregiver burden. Caregiver self-efficacy and subjective burden significantly influenced BPSD and social support.

Caregiver self-efficacy played an important role in the paths by which the two factors influenced subjective burden. Enhancing caregiver self-efficacy for symptom management (particularly BPSD) can be an essential strategy for determining interventions to support dementia caregivers in China, and possibly in other countries.