Schizophrenia Research最新文献

Background: Relapse following a first episode of schizophrenia (FES) is common and often results in serious adverse psychosocial consequences. Treatment non-adherence is a key risk factor for relapse, but why relapse occurs despite antipsychotic treatment adherence remains unclear. This study examined the differences in FES psychopathology trajectories over 24-months with assured long-acting injectable antipsychotic (LAIA) treatment, to control for treatment adherence between those who relapsed and those who did not and what moderates these group differences.

Methodology: We collected clinical and socio-demographic data from 107 participants with FES treated with LAIA medication over a 24-month period. Relapse was defined using the modified Csernansky criteria. Substance use was assessed through participant and family interviews and urine toxicology. Linear mixed model repeated measures models were constructed to (1) compare psychopathology trajectories over 24 months between relapse versus non-relapse groups (2) to examine factors moderating differential trajectories between the groups.

Results: Positive symptom trajectories were significantly worse in the relapse compared to non-relapse group over 24 months (F(8, 649 = 3.29), p = 0.001). More severe childhood trauma (CT), in particular physical abuse (PA) (F(39, 298 = 1.78), p = 0.004), was associated with worse positive symptom trajectories over 24 months in those who experienced a relapse event.

Conclusion: Our findings suggest that the examination of a history of CT and, in particular childhood PA measures for relapse in individuals with FES, is important.

Learning Health Systems (LHSs) strive to continuously integrate innovations and evidence-based practices in healthcare settings, thereby enhancing programmatic and patient outcomes. Duration of untreated psychosis (DUP) is a variable worthy of empirical attention, as the construct has been identified as a leading predictor of psychotic spectrum disorder prognosis and, despite the proliferation of early intervention for psychosis (EIP) teams across the U.S., remains longer than the recommended maximum established by the World Health Organization. Pathways to care are causally implicated as a DUP reduction rate-limiting factor. This paper illustrates a balanced care model, wherein resource-intensive community and clinical services are centralized to support a more efficient, standardized, and direct pathway to EIP care; identification of psychosis and psychotic risk states is made by highly-trained diagnosticians; and measurement-based care across the Learning Health System (LHS) is supported by a central assessment team. The Central Assessment of Psychosis Service (CAPS) streamlines core front-end EIP functions across the LHS, thereby alleviating the burden on EIP teams while enhancing access, equity, efficiency, and quality of the initial psychodiagnostic assessment. CAPS represents an innovative application of the balanced care model that preserves the core functions of the EIP team while task sharing or task shifting resource-intensive activities to an academic medical center partner. We review the five core functions of a centralized referral, screening, and assessment service. Given the potential for centralization to reduce DUP and enhance equity and access across the LHS, this paper will include concrete recommendations for policymakers considering centralizing core functions.

Background: Metacognitive training for psychosis (MCT) offers benefits for addressing hallmark deficits/symptoms in schizophrenia spectrum disorders including reductions in cognitive biases and positive/negative symptoms as well as improvements in social cognition and functioning. However, differing results exist regarding the relationship between MCT and neurocognition. A comprehensive understanding of the nature of this relationship would significantly contribute to the existing literature and our understanding of the potential added value of MCT as a cognitive intervention for psychosis.

Methods: Across eleven electronic databases, 1312 sources were identified, and 14 studies examining MCT and neurocognition in psychosis were included in this review. Measures of estimated effect sizes were calculated with Hedge's g, moderator analyses used Cochrane's Q statistic and significance tests to measure group differences according to control conditions.

Results: Twelve studies, 11 randomized controlled trials (RCTs) and 1 non-RCT, were included in the main meta-analyses, consisting of 673 participants (n_MCT = 345, n_control = 328). When comparing MCT against control interventions, non-significant differences in estimated effect sizes were observed across all neurocognitive domains when evaluating pre-post changes (g ≤ 0.1, p > .05). Two additional studies corroborated these results in a narrative review.

Conclusion: These findings suggest that when compared against control conditions, MCT does not pose a statistically meaningful benefit to neurocognitive performance. General practice/learning effects are likely the main contributor that explains improvement in neurocognitive performance, and not a difference of intervention allocation when considering MCT against the included control comparators. These findings help establish the specificity of the effects of MCT.

Predicting early treatment response in schizophrenia is pivotal for selecting the best therapeutic approach. Utilizing machine learning (ML) technique, we aimed to formulate a model predicting antipsychotic treatment outcomes. Data were obtained from 299 patients with schizophrenia from three multicenter, open-label, non-comparative clinical trials. For prediction of treatment response at weeks 4, 8, and 24, psychopathology (both objective and subjective symptoms), sociodemographic and clinical factors, functional outcomes, attitude toward medication, and metabolic characteristics were evaluated. Various ML techniques were applied. The highest area under the curve (AUC) at weeks 4, 8 and 24 was 0.711, 0.664 and 0.678 with extreme gradient boosting, respectively. Notably, our findings indicate that BMI and attitude toward medication play a pivotal role in predicting treatment responses at all-time points. Other salient features for weeks 4 and 8 included psychosocial functioning, negative symptoms, subjective symptoms like psychoticism and hostility, and the level of prolactin. For week 24, positive symptoms, depression, education level and duration of illness were also important. This study introduced a precise clinical model for predicting schizophrenia treatment outcomes using multiple readily accessible predictors. The findings underscore the significance of metabolic parameters and subjective traits.

Olfaction plays a key role in our daily life, influencing food enjoyment, threat detection, mood and social relationships. Numerous studies have provided evidence of abnormal olfactory function in schizophrenia and other psychotic disorders. This pre-registered meta-analysis was conducted to (a) provide an updated overview of olfactory function in schizophrenia-spectrum disorders, and (b) examine the modulatory effects of demographic and clinical variables on distinct olfactory abilities. We complied with the PRISMA guidelines, searching throughout PubMed, MEDLINE, and PsycInfo, until the 12th of August 2023. A total of 73 publications were included, comprising data from 3282 patients and 3321 healthy controls. Results revealed that (a) patients performed significantly worse in higher-order olfactory tests (identification and discrimination) compared to healthy controls, while no differences were observed in odor sensitivity; (b) patients' performance in odor identification was moderated by education, as well as disease duration and negative symptoms. Our findings support the presence of olfactory impairments in schizophrenia-spectrum disorders, leading to significantly poorer performance in both odor identification and discrimination, but not sensitivity, when compared to healthy controls.

The response rate to clozapine in patients with treatment-resistant schizophrenia spectrum disorders (TRSS) is around 40 %. But, in general, a better prognosis is noted for schizophrenia in developing countries, including India. Given the scarcity of related literature from India, this study aimed to evaluate the response rates to clozapine in TRSS and explore predictors of response. Sociodemographic and clinical information from randomly selected 250 patients on clozapine for TRSS was collected through a retrospective chart review. Clozapine response was determined using the Clinical Global Impression-Schizophrenia scale at 6, 12, 24 weeks, and one year of initiating clozapine. Elastic net logistic regression analysis was performed to identify predictors of clozapine response. A total of 54 % responded to clozapine, with much or very much improvement in positive and overall symptoms of schizophrenia by the end of 12 weeks of clozapine initiation. Among all the responders at 12 weeks, 94 % continued to maintain response at one-year follow-up, and among non-responders, 34.2 % showed clinical improvement by 1-year follow-up. Lower symptom severity at baseline, good response to clozapine at six weeks, history of more suicidal attempts, and few other clinical symptoms like delusions and sociodemographic factors predicted a response to clozapine. A higher response rate (54 %) to clozapine is noted in 3rd month of clozapine, contrasting with the existing literature. Persistence of treatment could elicit further response over a year in early non-responders. Our study findings revealed that the demographic profile and clinical determinants may have an effect on clozapine response.

Objective: The capacity of machine-learning algorithms to predict medication adherence was assessed using data from AiCure, a computer vision-assisted smartphone application, which records the medication ingestion event.

Methods: Patients treated with BI 409306 were recruited from two Phase II randomized, placebo-controlled trials in schizophrenia (NCT03351244) and attenuated psychotic disorders (NCT03230097). A machine-learning model was optimized to predict overall trial adherence using AiCure data collected over three monitoring periods (7/10/14 days), adherence cut-offs (0.6/0.7/0.8) and timepoints (Start/Mid/End). Area under the curve (AUC), false negative rate, and false omission rate averaged across 10 model cross-validations were analyzed. In NCT03351244, post hoc analyses compared time to first relapse in patients observed as adherent versus those predicted adherent by the model.

Results: Of 235 patients, 60.4 % demonstrated ≥80 % adherence. At an adherence cut-off of 0.8, the 14-day model performed best (AUC: 0.81 versus 0.79 [10-day], 0.77 [7-day]). Within the 14-day model, 0.6 cut-off was optimal (AUC: 0.87 versus 0.85 [0.7 cut-off], 0.81 [0.8 cut-off]). The Trial-End timepoint yielded the most accurate prediction (AUC: 0.92 versus 0.87 [Start], 0.85 [Mid]). Despite NCT03351244 not meeting the primary endpoint, a reduction in risk of first relapse with BI 409306 versus placebo was observed when analyzed with adherent completers (≥80 % across trial; HR = 0.485) and patients with predicted adherence ≥60 % (HR = 0.510).

Conclusions: Adherence data with longer monitoring durations (14 days), lower adherence cut-offs (0.6), and later timepoints (Trial-End) produced most accurate adherence predictions. Accurate adherence prediction provides insights about medication adherence patterns that may help clinicians improve individual adherence.

Background: Social dysfunctions can affect the quality of life (QOL) of patients with schizophrenia. The autism-spectrum quotient (AQ) is a widely used measure of innate autistic traits. However, in patients with schizophrenia, the score may represent the severity of autism-like social dysfunctions as a consequence of symptoms. We tested the hypothesis that AQ would mediate the relationship between clinical symptoms and QOL in patients with schizophrenia, based on the assumption that the AQ measures autism-like social dysfunctions rather than autistic traits in this population.

Methods: We analyzed data from 108 outpatients with schizophrenia. The relationships among the scores on the Positive and Negative Syndrome Scale (PANSS), the Schizophrenia Quality of Life Scale (SQLS), and the AQ were examined using structural equation modeling (SEM).

Results: Path analyses of the total scale scores revealed partial mediation, but not full mediation or independent effects. However, both the AQ and PANSS scores could be mediators. SEM including the three domain scores of PANSS, the two factors of the AQ, and the three subscale scores of the SQLS showed a good fit of the AQ mediation model, but not the symptom mediation model, supporting our hypothesis. In this final model, the relationship between negative symptoms and QOL was mediated by autism-like social dysfunctions, whereas positive symptoms directly affected QOL.

Conclusions: Our findings advance our understanding of what the AQ measures when applied to patients with schizophrenia and suggest that autism-like social dysfunctions are important treatment targets for improving QOL in this population.

Background: Cognitive impairment is a cardinal feature in patients with schizophrenia and leads to poor social functioning. Recently, the treatment of schizophrenia has evolved to include the goal of improving quality of life (QoL). However, most of the factors influencing subjective QoL are unknown. Autistic traits have been shown to co-occur with various psychiatric conditions including schizophrenia. Hence, the present study aimed to investigate whether cognitive function and autistic trait severity are associated with social functioning and subjective QoL in patients with early schizophrenia.

Methods: Data were analyzed from 183 outpatients diagnosed with early schizophrenia in Tokyo, Japan. Information was obtained on neurocognition with the Japanese version of the Brief Assessment of Cognition in Schizophrenia. Autistic trait severity was assessed using the Autism Spectrum Quotient (AQ), while social functioning was measured with the Specific Levels of Functioning Scale Japanese version. Information was obtained on subjective QoL with the Subjective Well-being under Neuroleptic drug treatment Short form, Japanese version. Multiple regression analysis was used to examined associations.

Results: In an analysis adjusted for demographic characteristics (age, sex and education), both autistic trait severity (β = -0.56, p < 0.01) and neurocognitive function (β = 4.37, p < 0.01) were significantly associated with social function. On the other hand, only autistic trait severity made a significant contribution to the prediction of subjective QoL (β = -1.79, p < 0.01).

Conclusions: The results of this study suggest that efforts to detect and treat cognitive impairment and comorbid autistic trait in early schizophrenia may be important for improving social functioning and subjective QoL in this population. In particular intervention that targets autistic trait severity seems to be key to achieving personal recovery in patients with schizophrenia.