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.
Positive and negative symptoms are prominent but heterogeneous characteristics of schizophrenia spectrum disorder (SSD). Within the framework of the Genetic Risk and Outcome of Psychosis (GROUP) longitudinal cohort study, we aimed to distinguish and identify the genetic and non-genetics predictors of homogenous subgroups of the long-term course of positive and negative symptoms in SSD patients (n = 1119) and their unaffected siblings (n = 1059) in comparison to controls (n = 586). Data were collected at baseline, and after 3- and 6-year follow-ups. Group-based trajectory modeling was applied to identify latent subgroups using positive and negative symptoms or schizotypy scores. A multinomial random-effects logistic regression model was used to identify predictors of latent subgroups. Patients had decreasing, increasing, and relapsing symptoms course. Unaffected siblings and healthy controls had three to four subgroups characterized by stable, decreasing, or increasing schizotypy. PRSSCZ did not predict the latent subgroups. Baseline symptoms severity in patients, premorbid adjustment, depressive symptoms, and quality of life in siblings predicted long-term trajectories while were nonsignificant in controls. In conclusion, up to four homogenous latent subgroups of symptom course can be distinguished within patients, siblings, and controls, while non-genetic factors are the main factors associated with the latent subgroups.
Social inclusion is poor among patients with chronic disorders such as schizophrenia spectrum disorder (SSD). It significantly impacts patient life, healthcare and society. We aimed to study multidimensional social inclusion (mSI) among patients diagnosed with SSD, and to test the prediction of mSI. We used the baseline and 3-year follow-up data of 1,119 patients from the Genetic Risk and Outcome in Psychosis (GROUP) cohort. The mSI was conceptualized by all subscales from social functioning (measured by Social Functioning Scale [SFS]) and quality of life (measured by the brief version of World Health Organization Quality of Life [WHOQOL-BREF]) questionnaires. K-means clustering was applied to identify mSI subgroups. Prediction models were built and internally validated via multinomial logistic regression (MLR) and random forest (RF) methods. Model fittings were compared by common factors, accuracy and the discriminability of mSI subgroups. We identified five mSI groups: “very low (social functioning)/very low (quality of life)”, “low/low”, “high/low”, “medium/high”, and “high/high”. The mSI was robustly predicted by genetic predisposition, premorbid social functioning, symptoms (i.e., positive, negative and depressive), number of met needs and baseline satisfaction with the environment and social life. The RF model was cautiously regarded to outperform the MLR model. We distinguished meaningful subgroups of mSI by combining rather than using two measurements standalone. The mSI subgroups were modestly predictable. The mSI has the potentials for personalized interventions to improve social recovery in patients. Different from conventional outcomes, we introduced mSI which has implications beyond clinics and could be applied to other disorders.
IntroductionPatients with inflammatory bowel diseases (IBD) often report psychological problems, unemployment, disability, sick leave and compromised quality of life. The effect of psychological interventions on health-related outcomes in IBD is controversial as previous reviews faced the obstacle of high heterogeneity among provided multimodular interventions. The heterogeneity can be addressed with network meta-analysis (NMA) and (multi)component NMA (CNMA). We aim to investigate whether psychological interventions can improve quality of life, clinical and social outcomes in IBD using NMA and CNMA. This is the study protocol.Methods and analysisWe will consider randomised, quasi-randomised and non-randomised controlled trials, including cluster randomised and cross-over trials with 2 months of minimum follow-up. The conditions to be studied comprise Crohn’s disease and ulcerative colitis in children, adolescents and adults. We will include any psychological intervention aiming to change the health status of the study participant.We will search Medline, Embase, Web of Science, CENTRAL, LILACS, Psyndex, PsycINFO, Google Scholar and trial registries from inception (the search will be updated before the review completion). Two authors will independently screen all references based on titles and abstracts. For data extraction, standard forms are developed and tested before extraction. All information will be assessed independently by at least two reviewers, and disagreements solved by consensus discussion or a third rater if necessary.The data synthesis will include a pairwise meta-analysis supported by meta-regression. We will conduct NMA (all treatments will constitute single nodes of the network) and CNMA (we will define all treatments as sums of core components, eg, cognitive +behaviour, or cognitive +behaviour + relaxation, and additionally consider interactions) using the R Package netmeta.Ethics and disseminationNo ethical approval is required. Reports will include the final report to the funder, conference presentation, peer-reviewed publication and a patient report.PROSPERO registration numberCRD42021250446.
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