A predictive model for readmission within 1-year post-discharge in patients with schizophrenia.

BACKGROUND: Schizophrenia is a pervasive and severe mental disorder characterized by significant disability and high rates of recurrence. The persistently high rates of readmission after discharge present a serious challenge and source of stress in treating this population. Early identification of this risk is critical for implementing targeted interventions. The present study aimed to develop an easy-to-use predictive instrument for identifying the risk of readmission within 1-year post-discharge among schizophrenia patients in China. METHODS: A prediction model, based on static factors, was developed using data from 247 schizophrenia inpatients admitted to the Mental Health Center in Wuxi, China, from July 1 to December 31, 2020. For internal validation, an additional 106 patients were included. Multivariate Cox regression was applied to identify independent predictors and to create a nomogram for predicting the likelihood of readmission within 1-year post-discharge. The model's performance in terms of discrimination and calibration was evaluated using bootstrapping with 1000 resamples. RESULTS: Multivariate cox regression demonstrated that involuntary admission (adjusted hazard ratio [aHR] 4.35, 95% confidence interval [CI] 2.13-8.86), repeat admissions (aHR 3.49, 95% CI 2.08-5.85), the prescription of antipsychotic polypharmacy (aHR 2.16, 95% CI 1.34-3.48), and a course of disease ≥ 20 years (aHR 1.80, 95% CI 1.04-3.12) were independent predictors for the readmission of schizophrenia patients within 1-year post-discharge. The area under the curve (AUC) and concordance index (C-index) of the nomogram constructed from these four factors were 0.820 and 0.780 in the training set, and 0.846 and 0.796 for the validation set, respectively. Furthermore, the calibration curves of the nomogram for both the training and validation sets closely approximated the ideal diagonal line. Additionally, decision curve analyses (DCAs) demonstrated a significantly better net benefit with this model. CONCLUSIONS: A nomogram, developed using pre-discharge static factors, was designed to predict the likelihood of readmission within 1-year post-discharge for patients with schizophrenia. This tool may offer clinicians an accurate and effective way for the timely prediction and early management of psychiatric readmissions.

Unveiling the interoception impairment in various major depressive disorder stages.

BACKGROUND: The intricate pathophysiological mechanisms of major depressive disorder (MDD) necessitate the development of comprehensive early indicators that reflect the complex interplay of emotional, physical, and cognitive factors. Despite its potential to fulfill these criteria, interoception remains underexplored in MDD. This study aimed to evaluate the potential of interoception in transforming MDD's clinical practices by examining interoception deficits across various MDD stages and analyzing their complex associations with the spectrum of depressive symptoms. METHODS: This study included 431 healthy individuals, 206 subclinical depression individuals, and 483 MDD patients. Depressive symptoms and interoception function were assessed using the PHQ-9 and MAIA-2, respectively. RESULTS: Interoception dysfunction occurred in the preclinical phase of MDD and further impaired in the clinical stage. Antidepressant therapies showed limited efficacy in improving interoception and might damage some dimensions. Interoceptive dimensions might predict depressive symptoms, primarily enhancing negative thinking patterns. The predictive model based on interoception was built with random split verification and demonstrated good discrimination and predictive performance in identifying MDD. CONCLUSIONS: Early alterations in the preclinical stage, multivariate associations with depressive symptoms, and good discrimination and predictive performance highlight the importance of interoception in MDD management, pointing to a paradigm shift in diagnostic and therapeutic approaches.

Study Protocol: Global Research Initiative on the Neurophysiology of Schizophrenia (GRINS) project.

BACKGROUND: Objective and quantifiable markers are crucial for developing novel therapeutics for mental disorders by 1) stratifying clinically similar patients with different underlying neurobiological deficits and 2) objectively tracking disease trajectory and treatment response. Schizophrenia is often confounded with other psychiatric disorders, especially bipolar disorder, if based on cross-sectional symptoms. Awake and sleep EEG have shown promise in identifying neurophysiological differences as biomarkers for schizophrenia. However, most previous studies, while useful, were conducted in European and American populations, had small sample sizes, and utilized varying analytic methods, limiting comprehensive analyses or generalizability to diverse human populations. Furthermore, the extent to which wake and sleep neurophysiology metrics correlate with each other and with symptom severity or cognitive impairment remains unresolved. Moreover, how these neurophysiological markers compare across psychiatric conditions is not well characterized. The utility of biomarkers in clinical trials and practice would be significantly advanced by well-powered transdiagnostic studies. The Global Research Initiative on the Neurophysiology of Schizophrenia (GRINS) project aims to address these questions through a large, multi-center cohort study involving East Asian populations. To promote transparency and reproducibility, we describe the protocol for the GRINS project. METHODS: The research procedure consists of an initial screening interview followed by three subsequent sessions: an introductory interview, an evaluation visit, and an overnight neurophysiological recording session. Data from multiple domains, including demographic and clinical characteristics, behavioral performance (cognitive tasks, motor sequence tasks), and neurophysiological metrics (both awake and sleep electroencephalography), are collected by research groups specialized in each domain. CONCLUSION: Pilot results from the GRINS project demonstrate the feasibility of this study protocol and highlight the importance of such research, as well as its potential to study a broader range of patients with psychiatric conditions. Through GRINS, we are generating a valuable dataset across multiple domains to identify neurophysiological markers of schizophrenia individually and in combination. By applying this protocol to related mental disorders often confounded with each other, we can gather information that offers insight into the neurophysiological characteristics and underlying mechanisms of these severe conditions, informing objective diagnosis, stratification for clinical research, and ultimately, the development of better-targeted treatment matching in the clinic.

Smoking may increase the usage of antidepressant: evidence from genomic perspective analysis.

This study uses the two-sample Mendelian randomization (TSMR) method to explore the causal relationships between smoking initiation (SMKI), never smoking (NSMK), past tobacco smoking (PTSMK), and the usage of antidepressants (ATD). Single-nucleotide polymorphisms (SNPs) with genome-wide significance (P < 5E-08) related to SMKI, NSMK, and PTSMK were selected from the genome-wide association study (GWAS) database as instrumental variables (IVs). The main method, inverse variance weighted (IVW), was utilized to investigate the causal relationship. The results demonstrated a positive causal relationship between SMKI and ATD use, where SMKI leads to an increase in ATD use. Conversely, NSMK and PTSMK showed a negative causal relationship with ATD use, meaning that NSMK and PTSMK lead to a reduction in ATD use. Additionally, sensitivity analysis showed that the results of this study were robust and reliable. Using the TSMR method and from a genetic perspective, this study found that SMKI leads to an increase in ATD use, while NSMK and PTSMK reduce ATD use.

Two-Hour Nicotine Withdrawal Improves Inhibitory Control Dysfunction in Male Smokers: Evidence from a Smoking-Cued Go/No-Go Task ERP Study.

Purpose: Nicotine withdrawal is a multifaceted physiological and psychological process that can induce a spectrum of mood disturbances. Gaining a more nuanced understanding of how pure nicotine withdrawal influences cognitive control functions may provide valuable insights for the enhancement of smoking cessation programs. This study investigated changes in inhibitory control function in smokers after 2-hour nicotine withdrawal using the event-related potential (ERP) technique. Participants and Methods: 28 nicotine dependence (ND) patients and 28 health controls (HCs) completed a smoking-cued Go/No-go task containing two different types of picture stimuli, smoking-cued and neutral picture stimuli. We analyzed the behavioral and ERP data using a mixed model Repeated Measure Analysis of Variance (ANOVA). Results: No-go trials accuracy rate (ACC) at baseline (time 1) was lower in the ND group compared to HCs with smoking-cued stimuli, and No-go trials ACC after 2-hour nicotine withdrawal (time 2) was not lower in the ND group compared to HCs. When confronted with smoking-cued stimuli, the No-go trials ACC was higher in time 2 than in time 1 in the ND group. For the ERP component, the No-go N2 amplitudes in the ND group with smoking-cued stimuli were lower than that of HCs, whereas after 2-hour nicotine withdrawal, the ND group's No-go N2 amplitudes higher than that at time 1, and did not differ from that of HCs. No-go P3 amplitudes were not significantly different between the two groups. Conclusion: Evidenced from ERP data, ND patients have an inhibitory control dysfunction in the face of smoking cues, which is mainly manifested in the early stage of response inhibition rather than in the late stage. Two-hour nicotine withdrawal improves inhibitory control dysfunction in ND patients. The No-go N2 component is an important and sensitive neuroelectrophysiological indicator of inhibitory control function in ND patients.

Neural Correlates of Cognitive Dysfunction in Conditional Reasoning in Schizophrenia: An Event-related Potential Study.

Purpose: Schizophrenia patients show impaired conditional reasoning. This study was to investigate event-related potential (ERP) characteristics of the conditional reasoning in schizophrenia. Patients and methods: Participants included 24 schizophrenia patients and 30 normal controls (NCs), and the measurements of ERPs were conducted during the Wason selection task. Results: Results showed that NCs consistently outperformed schizophrenia patients in terms of accuracy. Among the different rule types of the task, the precautionary type experiment yielded the highest accuracy rates. In contrast, both the descriptive and abstract type experiments resulted in lower accuracy. The RTs of the abstract type experiment were the shortest among the four experiments. In the abstract type of the Wason selection task, the NCs exhibited higher amplitudes for both the N1 and P2 components compared to the schizophrenia patients. At the parietal lobe, the N2 amplitudes were higher for the social contract type of the task compared to the precautionary version. At the frontal lobe, the N2 amplitudes were highest for the abstract type of the task. In the abstract type, the N2 amplitude at the parietal lobe was higher than that at the central lobe. The NCs displayed lower amplitudes for both the P3 and slow wave compared to the schizophrenia patients. Differences were observed between the NC and schizophrenia groups in terms of the latencies for N1, P2, N2, P3 and slow wave components across different experiment types and regions of interest. Conclusion: In conclusion, the observed ERP patterns provide valuable insights into the neural mechanisms underlying the Wason selection task, highlighting the differences between NCs and patients with schizophrenia.

The Association Between Clozapine Plasma Concentration, CYP2D6 (*10, *2) Polymorphisms and Risk of Adverse Reactions.

Background: The aim of this article was to study the relationships between the risk of adverse reactions, plasma concentration, and cytochrome P450 2D6 rs1065852 (*10) and rs16947 (*2) polymorphisms for clozapine. Methods: The steady-state clozapine plasma concentration of 100 Chinese inpatients with schizophrenia was determined using 2-dimensional liquid chromatography. The polymorphisms of cytochrome P450 2D6 (*10 and *2) were determined using fluorescent in situ hybridization protocols. Results: The decreased percentages of white blood cells and neutrophils and the elevated percentages of creatine kinase, alanine aminotransferase, and aspartate transferase in patients treated with clozapine for 6 months were linearly associated with clozapine plasma concentration. Compared with the corresponding groups, the clozapine plasma concentrations of individuals with the *10TT genotype and individuals with the *2CC genotype were the highest (P < .05). The decreased percentages of white blood cells and neutrophils and elevated percentages of creatine kinase, alanine aminotransferase, and aspartate transferase for patients with the *10TT genotype were significantly higher than those for patients with the *10CC and *10CT genotypes (P < .05). The decreased percentages of white blood cells and neutrophils and increased percentages of creatine kinase, alanine aminotransferase, and aspartate transferase for patients with the *2CC genotype were significantly higher than those of the other groups (P < .05). The therapeutic reference range of clozapine for Chinese patients with schizophrenia was defined as 102.5-483.1 ng/mL. Conclusion: This study demonstrated that the determination of cytochrome P450 2D6 polymorphisms and therapeutic drug monitoring of clozapine might be beneficial for identifying patients with a higher risk of adverse reactions.

A spectrum of altered non-rapid eye movement sleep in schizophrenia.

Background: Multiple facets of sleep neurophysiology, including electroencephalography (EEG) metrics such as non-rapid eye movement (NREM) spindles and slow oscillations (SO), are altered in individuals with schizophrenia (SCZ). However, beyond group-level analyses which treat all patients as a unitary set, the extent to which NREM deficits vary among patients is unclear, as are their relationships to other sources of heterogeneity including clinical factors, illness duration and ageing, cognitive profiles and medication regimens. Using newly collected high density sleep EEG data on 103 individuals with SCZ and 68 controls, we first sought to replicate our previously reported (Kozhemiako et. al, 2022) group-level mean differences between patients and controls (original N=130). Then in the combined sample (N=301 including 175 patients), we characterized patient-to-patient variability in NREM neurophysiology. Results: We replicated all group-level mean differences and confirmed the high accuracy of our predictive model (Area Under the ROC Curve, AUC = 0.93 for diagnosis). Compared to controls, patients showed significantly increased between-individual variability across many (26%) sleep metrics, with patterns only partially recapitulating those for group-level mean differences. Although multiple clinical and cognitive factors were associated with NREM metrics including spindle density, collectively they did not account for much of the general increase in patient-to-patient variability. Medication regimen was a greater (albeit still partial) contributor to variability, although original group mean differences persisted after controlling for medications. Some sleep metrics including fast spindle density showed exaggerated age-related effects in SCZ, and patients exhibited older predicted biological ages based on an independent model of ageing and the sleep EEG. Conclusion: We demonstrated robust and replicable alterations in sleep neurophysiology in individuals with SCZ and highlighted distinct patterns of effects contrasting between-group means versus within-group variances. We further documented and controlled for a major effect of medication use, and pointed to greater age-related change in NREM sleep in patients. That increased NREM heterogeneity was not explained by standard clinical or cognitive patient assessments suggests the sleep EEG provides novel, nonredundant information to support the goals of personalized medicine. Collectively, our results point to a spectrum of NREM sleep deficits among SCZ patients that can be measured objectively and at scale, and that may offer a unique window on the etiological and genetic diversity that underlies SCZ risk, treatment response and prognosis.