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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.
Neurological Examination Abnormalities (NES) are quantified by measuring subtle, partially localizable (cerebello-thalamo-prefrontal cortical circuit) and heritable neurological signs comprising sensory integration, motor coordination and complex motor sequencing that are associated with first-episode psychosis (FEP). A few studies have evaluated NES longitudinally and as a predictor for diagnostic and response classification, but these studies have been confounded, underpowered and divergent. We examined (1) baseline and longitudinal NES differences between diagnostic and year 1 response groups; (2) if NES predicts diagnostic and response groups and (3) relationships between clinical variables and NES measures in antipsychotic-naïve FEP.
NES and clinical measures were obtained for FEP-schizophrenia (FEP-SZ, n = 232), FEP non-schizophrenia (FEP-NSZ, n = 117) and healthy controls (HC, n = 204). Response groups with >25% improvement in average year 1 positive and negative symptomatology scores were classified as responsive (n = 97) and <25% improvement as non-responsive (n = 95). Analysis of covariance, NES trajectory analysis and logistic regression models assessed diagnostic and response group differences. Baseline and longitudinal NES relationships with clinical variables were performed with Spearman correlations. Data were adjusted for age, sex, race, socioeconomic status and handedness.
Cognitive perceptual (COGPER) score was better than repetitive motor (REPMOT) at differentiating FEP-SZ from FEP-NSZ and distinguishing responders from non-responders. We identified significant group-specific associations between COGPER and worse GAF, positive and negative symptomatology and some of these findings persisted at 1-year assessment.
NES are an easy to administer, bedside-elicited, endophenotypic measure and could be a cost-effective clinical tool in antipsychotic-naïve FEP.
Autism Spectrum Disorder (ASD) and schizophrenia are neurodevelopmental disorders which share substantial overlap in cognitive deficits during adulthood. However, treatment evaluation in ASD and treatment comparisons across ASD and schizophrenia are limited by a dearth of empirical work establishing the validity of a standard cognitive battery across ASD and schizophrenia. Promisingly, the MATRICS Consensus Cognitive Battery (MCCB) has been validated in schizophrenia and encompasses cognitive domains that are impacted in ASD. Thus, this study aimed to establish MCCB's generalizability from schizophrenia to ASD.
Community-residing adults with schizophrenia (N = 100) and ASD (N = 113) underwent MCCB assessment. Using multigroup confirmatory factor analysis, MCCB's transdiagnostic validity was evaluated by examining whether schizophrenia and ASD demonstrate the same configuration, magnitude, and directionality of relationships within and among measures and their underlying cognitive domains.
Across schizophrenia and ASD, the same subsets of MCCB measures inform three cognitive domains: processing speed, attention/working memory, and learning. Except for group means in category fluency, continuous performance, and spatial span, both groups show vastly comparable factor structures and characteristics.
To our knowledge, this study is the first to establish the validity of a standard cognitive battery in adults with ASD and furthermore the first to establish a cognitive battery's comparability across ASD and schizophrenia. Cognitive domain scores can be compared across new samples using weighted sums of MCCB scores resulting from this study. These findings highlight MCCB's applicability to ASD and support its utility for standardizing treatment evaluation of cognitive outcomes across the autism-schizophrenia spectrum.