Assessing the generalisability of the psychosis metabolic risk calculator (PsyMetRiC) for young people with first-episode psychosis with validation in a Hong Kong Chinese Han population: a 4-year follow-up study.

Background: Metabolic syndrome (MetS) is common following first-episode psychosis (FEP), contributing to substantial morbidity and mortality. The Psychosis Metabolic Risk Calculator (PsyMetRiC), a risk prediction algorithm for MetS following a FEP diagnosis, was developed in the United Kingdom and has been validated in other European populations. However, the predictive accuracy of PsyMetRiC in Chinese populations is unknown. Methods: FEP patients aged 15-35 y, first presented to the Early Assessment Service for Young People with Early Psychosis (EASY) Programme in Hong Kong (HK) between 2012 and 2021 were included. A binary MetS outcome was determined based on the latest available follow-up clinical information between 1 and 12 years after baseline assessment. The PsyMetRiC Full and Partial algorithms were assessed for discrimination, calibration and clinical utility in the HK sample, and logistic calibration was conducted to account for population differences. Sensitivity analysis was performed in patients aged >35 years and using Chinese MetS criteria. Findings: The main analysis included 416 FEP patients (mean age = 23.8 y, male sex = 40.4%, 22.4% MetS prevalence at follow-up). PsyMetRiC showed adequate discriminative performance (full-model C = 0.76, 95% C.I. = 0.69-0.81; partial-model: C = 0.73, 95% C.I. = 0.65-0.8). Systematic risk underestimation in both models was corrected using logistic calibration to refine PsyMetRiC for HK Chinese FEP population (PsyMetRiC-HK). PsyMetRiC-HK provided a greater net benefit than competing strategies. Results remained robust with a Chinese MetS definition, but worse for the older age group. Interpretation: With good predictive performance for incident MetS, PsyMetRiC-HK presents a step forward for personalized preventative strategies of cardiometabolic morbidity and mortality in young Hong Kong Chinese FEP patients. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Development of an individualized risk calculator of treatment resistance in patients with first-episode psychosis (TRipCal) using automated machine learning: a 12-year follow-up study with clozapine prescription as a proxy indicator.

About 15-40% of patients with schizophrenia are treatment resistance (TR) and require clozapine. Identifying individuals who have higher risk of development of TR early in the course of illness is important to provide personalized intervention. A total of 1400 patients with FEP enrolled in the early intervention for psychosis service or receiving the standard psychiatric service between July 1, 1998, and June 30, 2003, for the first time were included. Clozapine prescriptions until June 2015, as a proxy of TR, were obtained. Premorbid information, baseline characteristics, and monthly clinical information were retrieved systematically from the electronic clinical management system (CMS). Training and testing samples were established with random subsampling. An automated machine learning (autoML) approach was used to optimize the ML algorithm and hyperparameters selection to establish four probabilistic classification models (baseline, 12-month, 24-month, and 36-month information) of TR development. This study found 191 FEP patients (13.7%) who had ever been prescribed clozapine over the follow-up periods. The ML pipelines identified with autoML had an area under the receiver operating characteristic curve ranging from 0.676 (baseline information) to 0.774 (36-month information) in predicting future TR. Features of baseline information, including schizophrenia diagnosis and age of onset, and longitudinal clinical information including symptoms variability, relapse, and use of antipsychotics and anticholinergic medications were important predictors and were included in the risk calculator. The risk calculator for future TR development in FEP patients (TRipCal) developed in this study could support the continuous development of data-driven clinical tools to assist personalized interventions to prevent or postpone TR development in the early course of illness and reduce delay in clozapine initiation.

Clozapine dosing patterns and clinical outcomes in patients with treatment resistant schizophrenia.

Clozapine is the only medication found to be effective for patients with treatment resistant schizophrenia-spectrum disorders (TRS) and its prescription patterns may impact on their outcomes. The study aims to explore the impact of clozapine dosing frequency, dose level and presence of pharmacological augmentation on the clinical, social and cognitive outcomes in patients with TRS. Patients with TRS and on clozapine were interviewed. Daily defined dose (DDD) and anticholinergic burden were calculated. Patients were categorized in three ways: the single daily dose (SDD) and multiple daily dose (MDD), ≤300 mg/day (LD) and >300 mg/day (HD) of clozapine, and clozapine monotherapy (MT) and augmentation therapy (AT). The impact of these clozapine prescription patterns and their interaction on patient outcomes were examined with ANOVA. Of 124 patients on clozapine, 98 patients (79%) had SDD, 59 patients (47.6%) received LD, and 58 patients (46.8%) had MT. Patients in the LD group had significantly better cognitive functions. Though no significant effect of clozapine dosing frequency on outcomes, among patients on LD, those on MDD had better processing speed, short-term and visual memory. Patients with MT had better motivation. Among patients on HD, those with MT had better motivation and vocational functioning. These results provide guidance to the clozapine prescription in a naturalistic setting to achieve optimizing outcomes for patients with TRS in social and cognitive functions. Further longitudinal studies are needed to verify the results.

Sex differences in symptom severity, cognition and psychosocial functioning among individuals with at-risk mental state for psychosis.

AIM: Sex differences are well documented in schizophrenia, but have been much less studied in at-risk mental state (ARMS) for psychosis. We aimed to examine sex differences in symptomatology, cognition, social and role functioning in individuals with ARMS, with specific focus on clarifying relationships between sex, negative symptoms and functioning. METHODS: One hundred and seventy-seven Chinese participants aged 15-40 years with ARMS were recruited from a specialized early intervention service in Hong Kong. ARMS status was verified by Comprehensive Assessment of At-Risk Mental State. Assessments encompassing symptom profiles, a brief battery of cognitive tests and social and role functioning were conducted. Brief Negative Symptom Scale was adapted to measure negative symptoms at the level of five core domains. RESULTS: Males with ARMS exhibited significantly poorer social functioning and more severe asociality of negative symptoms than female counterparts. Mediation analysis revealed that sex difference in social functioning became statistically insignificant when asocality was included in the model, indicating that asociality mediated the relationship between sex and social functioning. No sex differences were observed in other core domains of negative symptoms, other symptom dimensions, cognitive measures and role functioning. CONCLUSIONS: This study suggests that sex differences in ARMS may be less pronounced that those observed in established psychotic disorders. Our findings of differential pattern of asociality between sexes and its mediating role on sex difference in social functioning underscore the importance in investigating negative symptoms at a separable domain-level. Further research is required to identify sex-specific predictors of longitudinal outcomes in at-risk populations.

Psychiatric comorbidity in individuals at-risk for psychosis: Relationships with symptoms, cognition and psychosocial functioning.

AIM: Psychiatric comorbidity frequently occurs with at-risk mental state (ARMS) for psychosis. Its relationships with psychopathology, cognition and functioning, however, remain to be further clarified. We aimed to examine prevalence and correlates of psychiatric comorbidity, and its associations with psychosocial functioning and subjective quality-of-life (QoL) in a representative sample of Chinese ARMS individuals. METHODS: One hundred ten help-seeking participants aged 15 to 40 years with ARMS were recruited from a specialized early psychosis service in Hong Kong. ARMS status was verified by comprehensive assessment of at-risk mental state (CAARMS). Comorbid Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition non-psychotic psychiatric disorders at baseline were ascertained using diagnostic interview and medical record review. Assessments encompassing symptom profiles, psychosocial functioning, subjective QoL and a brief cognitive battery were conducted. RESULTS: Forty-nine (44.5%) ARMS participants were diagnosed as having comorbid non-psychotic psychiatric disorders at baseline, primarily depressive and anxiety disorders. Binary multiple logistic regression analysis revealed that female gender, more severe depressive symptoms, higher suicidality and poorer global cognitive functioning were independently associated with comorbid diagnosis status. ARMS participants with psychiatric comorbidity displayed significantly more limited extended social networks and poorer subjective QoL than those without psychiatric comorbidity. CONCLUSION: Comorbid disorders were frequently observed in Chinese ARMS individuals, and were linked to poorer cognition and higher suicide risk. Our findings underscore a potential critical role of psychiatric comorbidity in determining social functioning and subjective QoL in at-risk individuals. Further longitudinal research is required to clarify trajectories of comorbid disorder status and its prospective impact on clinical and functional outcomes in ARMS populations.