Schizophrenia Bulletin最新文献

Background and hypotheses: Negative symptoms of schizophrenia (NSS) carry a substantial burden, and there are no treatments currently approved for NSS. The efficacy of pimavanserin, a selective 5-HT2A inverse agonist and antagonist, in treating NSS was assessed.

Study design: ADVANCE-2 was a phase 3, randomized, double-blind, placebo-controlled study of pimavanserin in patients with schizophrenia and predominantly negative symptoms. Patients were randomized (1:1) to receive pimavanserin (34 mg/day) or placebo alongside ongoing background antipsychotic medication. Eligible adults were aged 18-55 years and had access to a caregiver. The primary and key secondary endpoints were the change from baseline to week 26 in the Negative Symptom Assessment-16 (NSA-16) total score and Clinical Global Impression-Schizophrenia Scale-Severity (CGI-SCH-S) negative symptom score, respectively.

Study results: Of the 454 randomized patients, 71 (39 placebo; 32 pimavanserin) discontinued and 383 (188 placebo; 195 pimavanserin) completed the study. The safety and full analysis sets comprised 453 and 446 patients, respectively. The NSA-16 change from baseline to week 26 was not significantly different between groups (least squares mean difference: -0.67; SE, 0.95; [95% CI: -2.54, 1.20]; P = .48; Cohen's d effect size: 0.07). Treatment-emergent adverse events occurred in 30.4% with pimavanserin and 40.3% with placebo.

Conclusions: In this study, pimavanserin was well tolerated, and although it demonstrated a similar treatment effect as in the prior phase 2 study favoring pimavanserin, treatment with pimavanserin vs placebo did not result in significant differences for primary or other endpoints.

Background and hypothesis: Minor physical abnormalities (MPAs) are neurodevelopmental markers that can be traced to prenatal events and may be significant features of early-onset schizophrenia (EOS). Therefore, our study aimed to (1) find the primary and interaction effects of MPAs for EOS and (2) develop and validate the model for EOS based on explainable machine learning algorithms.

Study design: The study included 549 patients with schizophrenia (193 EOS and 356 AOS) and 420 healthy controls (HC) in southern Taiwan. For the feature selection, variable selection using random forests (varSelRF) and recursive feature elimination (RFE) were applied to identify the important variables of MPAs. We used different machine learning algorithms to build the prediction models based on the selected MPAs variables.

Study results: The results showed that the mouth anomalies are significant MPAs variables and have interaction effects with craniofacial MPAs variables for EOS. The prediction models using the selected MPAs variables performed better in discriminating EOS vs HC compared to AOS vs HC. The AUC values for distinguishing EOS vs HC were 0.85-0.93, AOS vs HC were 0.80-0.87, and EOS vs AOS were 0.67-0.77 in validation sets.

Conclusions: This risk prediction model provides a clinical decision support system for detecting patients at high risk of developing EOS and enables early intervention in clinical practice.

Background: Young people at clinical high risk for psychosis (CHR-p) commonly experience social impairment, which contributes to functional decline and predicts transition to psychotic illness. Although the use of smart phone technology and social media platforms for social interaction is widespread among today's youth, it is unclear whether aberrant digital social interactions contribute to risk for conversion and functional impairment in CHR-p. The current study sought to characterize the nature of social smartphone and social media use in a CHR-p sample and determine its association with clinical symptoms and risk for conversion to psychosis.

Study design: CHR-p (n = 132) and HC (n = 61) participants completed clinical interviews and 6 days of digital phenotyping that monitored total smartphone use, ratio of outgoing to incoming text messages and phone calls, social media use, and ecological momentary assessment surveys focused on in-person and electronic social interactions. Study Results: CHR-p did not differ from HC in total smartphone use for social communication or active social media use. However, CHR-p participants reported significantly less daily passive social media use compared to HC peers, and decreased text message reciprocity predicted 1- and 2-year conversion risk.

Conclusions: Results demonstrate a nuanced digital social landscape with divergent relationships from in-person social behavior and suggest online socialization has implications for high-precision identification and intervention strategies among the CHR-p population.