Schizophrenia (Heidelberg, Germany)最新文献

Schizophrenia spectrum disorders and schizotypy share traits across positive, negative, and disorganised domains. Instruments like the Multidimensional Schizotypy Scale (MSS) and the Oxford-Liverpool Inventory (O-LIFE) offer nuanced perspectives on schizotypy, including its expression as a personality trait and its potential clinical outcomes. This study investigates the structure of schizotypy using exploratory graph analysis (EGA) to analyse how the MSS and O-LIFE operationalise the construct in the same sample of German speakers. This approach allows comparison of their factorial structures from both statistical and clinical perspectives, while also assessing cross-cultural validity. 1059 German-speaking, healthy participants completed the MSS and O-LIFE questionnaires. EGA was conducted to identify dimensional structures within each scale, utilising the graphical lasso algorithm for network estimation and the Walktrap community detection method for clustering. Confirmatory factor analysis was used to assess model fit and reliability, using indices such as the comparative fit index and root mean square error of approximation. EGA of the MSS revealed three primary dimensions and an additional 'dissociated' domain. This four-dimensional model showed improved fit and reliability compared to the original model. For the O-LIFE, EGA identified a refined four-factor structure comprising: 'cognitive and behavioural disorganisation,' 'introversion', 'unusual experiences', and 'environmental valence', all validated by confirmatory analyses. This study supports the multidimensional view of schizotypy, introducing new factor structures in the MSS and O-LIFE that emphasise the previously unexplored domains 'dissociated' and 'environmental valence.' These findings highlight the value of culturally adaptive tools in enhancing schizotypy assessment and intervention strategies.

This study evaluated the potential of artificial intelligence (AI) in the diagnosis, treatment, and prognostic assessment of schizophrenia (SZ) and explored collaborative directions for AI applications in future medical innovations. SZ is a severe mental disorder that causes significant suffering and imposes challenges on patients. With the rapid advancement of machine learning and deep learning technologies, AI has demonstrated notable advantages in the early diagnosis of high-risk populations. By integrating multidimensional biomarkers and linguistic behavior data of patients, AI can provide further objective and precise diagnostic criteria. Moreover, it aids in formulating personalized treatment plans, enhancing therapeutic outcomes, and offering new therapeutic strategies for patients with treatment-resistant SZ. Furthermore, AI excels in developing individualized prognostic plans, which enables the rapid identification of disease progression, accurate prediction of disease trajectory, and timely adjustment of treatment strategies, thereby improving prognosis and facilitating recovery. Despite the immense potential of AI in SZ management, its role as an auxiliary tool must be emphasized, with clinical judgment and compassionate care from healthcare professionals remaining crucial. Future research should focus on optimizing human-machine interactions to achieve efficient AI application in SZ management. The in-depth integration of AI technology into clinical practice will advance the field of SZ, ultimately improving the quality of life and treatment outcomes of patients.

As populations age worldwide, it is essential to explore the changing landscape of schizophrenia across different age groups. We analyzed cases of schizophrenia reported over a 12-year period (2010-2021) using the Health Insurance Review and Assessment (HIRA) database of South Korea. We explored changes in the prevalence of schizophrenia, physical comorbidity burden, and healthcare utilization by age. The annual disease prevalence and hospitalization patterns were analyzed using linear regression and linear mixed models. Of 420,203 patients diagnosed with schizophrenia, the number and proportion of older adults (aged ≥ 50 years) significantly increased from 82,556 (0.51%) in 2010 to 188,359 (0.89%) in 2021 (p < 0.001), more rapidly than did those of younger adults. The proportion of older adults increased from 37.0% to 54.7% during this period (p < 0.001); by 2021, 44.9% were medical aid beneficiaries. Lengths of psychiatric hospitalization for older adults increased from 230.2 days to 251.8 days (p = 0.023), significantly greater than for younger adults (p < 0.001), and the use of tertiary/general hospitals decreased over time from 15.2% to 9.5% (p < 0.001) while that of hospitals/nursing homes increased from 76.9% to 88.5% (p < 0.001). Older adults consistently showed significantly higher Charlson Comorbidity Index scores (p < 0.001) and longer lengths of non-psychiatric hospitalization (p < 0.001) than younger adults. These findings highlight the distinct healthcare needs and increasing physical health burden of older adults with schizophrenia.

Background: The At Risk Mental State (ARMS) (also known as the Ultra or Clinical High Risk) criteria identify individuals at high risk for psychotic disorder. However, there is a need to improve prediction as only about 18% of individuals meeting these criteria develop a psychosis with 12-months. We have developed and internally validated a prediction model using characteristics that could be used in routine practice.

Methods: We conducted a systematic review and individual participant data meta-analysis, followed by focus groups with clinicians and service users to ensure that identified factors were suitable for routine practice. The model was developed using logistic regression with backwards selection and an individual participant dataset. Model performance was evaluated via discrimination and calibration. Bootstrap resampling was used for internal validation.

Results: We received data from 26 studies contributing 3739 individuals; 2909 from 20 of these studies, of whom 359 developed psychosis, were available for model building. Age, functioning, disorders of thought content, perceptual abnormalities, disorganised speech, antipsychotic medication, cognitive behavioural therapy, depression and negative symptoms were associated with transition to psychosis. The final prediction model included disorders of thought content, disorganised speech and functioning. Discrimination of 0.68 (0.5-1 scale; 1=perfect discrimination) and calibration of 0.91 (0-1 scale; 1=perfect calibration) showed the model had fairly good predictive ability.

Discussion: The statistically robust prediction model, built using the largest dataset in the field to date, could be used to guide frequency of monitoring and enable rational use of health resources following assessment of external validity and clinical utility.

Clozapine is highly effective for treatment-resistant schizophrenia but is underutilized due to patient and clinician-related concerns. Little is known about the general level of patient satisfaction with clozapine and determinants thereof. We therefore explored determinants of patient satisfaction with clozapine in individuals diagnosed with schizophrenia spectrum disorders (SSDs). Cross-sectional data from 480 clozapine users were used to examine demographic and clinical factors, including symptom severity, treatment response, and adverse drug reactions (ADRs). Patient satisfaction was self-rated on a scale of 1 to 10. Results showed a mean satisfaction score of 7.4 (SD = 1.9), with significant associations between satisfaction and treatment response (B = 0.42, R² = 0.19, p = 3.9 × 10⁻¹⁸), symptom severity (B = 0.10, R² = 0.05, p = 2.06 × 10^-9), occurrence of ADRs (B = -0.16, R² = 0.06, p = 3.2 × 10^-5), and recreational drug use (B = -1.32, R² = 0.05, p = 2.09 × 10^-4). Hypersalivation and prolonged sleep duration were the only ADRs linked to lower satisfaction (B = -0.72, R² = 0.06, p = 3.5 × 10^-5 and B = -0.57, R² = 0.04, p = 1.4 × 10^-3, respectively). Despite concerns about ADRs, treatment effectiveness showed a stronger association with patient satisfaction among clozapine users than the occurrence of ADRs. In conclusion, our findings suggest that strategies aimed at bolstering clozapine's effectiveness may help counter worldwide underprescription rates of clozapine in patients with SSDs.

Machine learning has been proposed to utilize D-amino acid oxidase (DAO) and DAO activator (DAOA [or pLG72]) protein levels to ascertain disease status in schizophrenia. However, it remains unclear whether machine learning can effectively evaluate clinical features in relation to DAO and DAOA in schizophrenia patients. We employed an interpretable machine learning (IML) framework including linear regression, least absolute shrinkage and selection operator (Lasso) models, and generalized additive models (GAMs) to analyze DAO/DAOA levels using 380 Taiwanese schizophrenia patients. Additionally, we incorporated 27 parameters encompassing demographic variables, clinical assessments, functional outcomes, and cognitive function as features. The IML framework facilitated linear and non-linear relationships between features and DAO/DAOA. DAO levels demonstrated significant associations with the 17-item Hamilton Depression Rating Scale (HAMD17) based on linear regression. The Lasso model identified four features-HAMD17, age, working memory, and overall cognitive function (OCF)-and highlighted HAMD17 as the most significant feature, using DAO from chronically stable patients. Utilizing DAOA from acutely exacerbated patients, the Lasso model also identified four features-OCF, Scale for the Assessment of Negative Symptoms 20-item, quality of life scale (QLS), and category fluency-and emphasized OCF as the most significant feature. Furthermore, GAMs revealed a non-linear relationship between category fluency and DAO in chronically stable patients, as well as between QLS and DAOA in acutely exacerbated patients. The study suggests that an IML framework holds promise for assessing linear and non-linear relationships between DAO/DAOA and various features in clinical assessments, functional outcomes, and cognitive function in patients with schizophrenia.

This study aimed to assess the independent and joint associations of genomic and exposomic liabilities for schizophrenia with distressing psychotic experiences (PEs) and their persistence in early adolescence. The Adolescent Brain and Cognitive Development Study data from children with European ancestry were used (N = 5122). The primary outcome was past-month distressing PEs at the 3-year follow-up. Secondary outcomes were distressing PEs at varying cutoffs of persistence. Multilevel logistic regression models were used to test the associations of binary modes (>75th percentile) of polygenic risk score for schizophrenia (PRS-SCZ₇₅) and exposome score for schizophrenia (ES-SCZ₇₅) on the outcomes. Relative excess risk due to interaction (RERI) calculation indicated additive interaction. When analyzed independently, PRS-SCZ₇₅ was not significantly associated with past-month distressing PEs but with lifetime (OR 1.29 [95% CI 1.08, 1.53]) and repeating distressing PEs ≥2 waves (OR 1.34 [95% CI 1.08, 1.65]); whereas, ES-SCZ₇₅ was consistently associated with all outcomes, with increasing strength of association as a function of PEs persistence (one wave: OR 2.77 [95% CI 2.31, 3.31]; two waves: OR 3.16 [95% CI 2.54, 3.93]; three waves: OR 3.93 [95% CI 2.86, 5.40]; four waves: OR 3.65 [95% CI 2.34, 5.70]). When considered jointly, ES-SCZ₇₅ and PRS-SCZ₇₅ did not additively interact to predict past-month distressing PEs but showed significant additive interactions for lifetime (RERI = 1.26 [95%CI 0.14, 2.38]) and repeating distressing PEs ≥2 waves (RERI = 1.79 [95%CI 0.35, 3.23]). Genomic and exposomic liabilities for schizophrenia were independently and jointly associated with distressing PEs and their persistence in early adolescence.

Social cognition, the perception and processing of social information, is adversely affected in multiple psychiatric, neurological, and neurodevelopmental disorders, and these impairments negatively impact quality of life for individuals across the globe. Despite the clear importance of social cognition, efforts to advance research via harmonization of data across cultures and diagnoses has been stymied by the lack of uniformly used and suitable assessments. To address this issue, the current study conducted an expert survey and consensus process to identify social cognitive assessments that are best suited for cross-cultural and transdiagnostic use among adults. A large group of experts in social cognition were surveyed to gather nominations for cross-culturally and transdiagnostically appropriate measures. These measures were then critically evaluated by a smaller group of experts using a Delphi consensus process to identify the best existing tasks for each use. Ninety-eight experts, representing 25 countries, responded to the initial survey and nominated a total of 81 tasks. Initial rounds of the Delphi process identified 50 tasks with adequate psychometric properties that were then subdivided into social cognition domains. For each domain, members ranked the five best tasks, once for cross-cultural use and once for transdiagnostic use, and rated the suitability of those tasks for the intended use. No tasks were identified as ideally suited for either use; however, within each domain, 4-5 tasks emerged as the most consistently selected, and all were ranked as having "good" or better suitability for use. While there is still a critical need for social cognitive assessments that are specifically designed for cross-cultural and transdiagnostic use, there does appear to be a handful of existing tasks that are currently available and likely informative. Caution is warranted however, as these still require comprehensive evaluation in cross-cultural and transdiagnostic studies.