Understanding Lifelong Factors and Prediction Models of Social Functioning After Psychosis Onset Using the Large-Scale GROUP Cohort Study

Abstract

Background and hypothesis Current rates of poor social functioning (SF) in people with psychosis history reach 80% worldwide. We aimed to identify a core set of lifelong predictors and build prediction models of SF after psychosis onset. Study design We utilized data of 1119 patients from the Genetic Risk and Outcome in Psychosis (GROUP) longitudinal Dutch cohort. First, we applied group-based trajectory modeling to identify premorbid adjustment trajectories. We further investigated the association between the premorbid adjustment trajectories, six-year-long cognitive deficits, positive, and negative symptoms trajectories, and SF at 3-year and 6-year follow-ups. Next, we checked associations between demographics, clinical, and environmental factors measured at the baseline and SF at follow-up. Finally, we built and internally validated 2 predictive models of SF. Study results We found all trajectories were significantly associated with SF (P < .01), explaining up to 16% of SF variation (R2 0.15 for 3- and 0.16 for 6-year follow-up). Demographics (sex, ethnicity, age, education), clinical parameters (genetic predisposition, illness duration, psychotic episodes, cannabis use), and environment (childhood trauma, number of moves, marriage, employment, urbanicity, unmet needs of social support) were also significantly associated with SF. After validation, final prediction models explained a variance up to 27% (95% CI: 0.23, 0.30) at 3-year and 26% (95% CI: 0.22, 0.31) at 6-year follow-up. Conclusions We found a core set of lifelong predictors of SF. Yet, the performance of our prediction models was moderate.

Deep Clinical Phenotyping of Schizophrenia Spectrum Disorders Using Data-Driven Methods: Marching towards Precision Psychiatry

“…Psychosocial functions : In patients, we identified five latent subtypes of multidimensional social inclusion [ 24 ]. In addition, we revealed six latent subtypes of premorbid adjustment in patients [ 25 , 26 ].…”

“…A multidimensional social inclusion composite score was created from three years measurement of the SFS and WHOQOL-BREF using the Z-score method [ 24 ]. Premorbid adjustment was retrospectively measured using the premorbid adjustment sale (PAS) in three life periods: childhood (<12 years), early adolescence (12–16 years) and late adolescence (16–19 years) [ 25 , 26 ]. All information was gathered from parents or siblings and a mean score was created to quantify overall premorbid functioning [ 25 , 26 ].…”

“…Premorbid adjustment was retrospectively measured using the premorbid adjustment sale (PAS) in three life periods: childhood (<12 years), early adolescence (12–16 years) and late adolescence (16–19 years) [ 25 , 26 ]. All information was gathered from parents or siblings and a mean score was created to quantify overall premorbid functioning [ 25 , 26 ]. Sociodemographic, genetic and clinical data were also collected.…”

“…Identification of latent subtypes : Person-centred latent class models were used to identify subgroups of individuals with similar phenotypes, which we call latent subtypes throughout this article. In five studies [ 19 , 20 , 21 , 22 , 26 ], we applied group-based trajectory modelling (GBTM) to identify latent subtypes and evaluate their longitudinal course throughout the six-year follow-up, whereas we applied K-means clustering in three studies [ 23 , 24 , 25 ] to identify latent subtypes at one time point (e.g., baseline data, or three-year follow-up). The analyses were carried out using SAS and R software.…”

“…The in-depth analysis of phenotype plays a key role in clinical practice and medical research [ 18 ]. This article provides an overview of findings from eight Genetic Risk and Outcome of Psychosis (GROUP) cohort studies on the deep clinical phenotyping of schizophrenia spectrum disorders targeting positive and negative symptoms [ 19 , 20 ], cognitive impairments [ 21 , 22 , 23 ] and psychosocial functioning [ 24 , 25 , 26 ]. Briefly, we present identified latent subtypes (i.e., subphenotypes), their longitudinal course or profiles of subtypes and important predictors.…”