Dyskinesia is most centrally situated in an estimated network of extrapyramidal syndrome in Asian patients with schizophrenia: findings from research on Asian psychotropic prescription patterns for antipsychotics

Network analysis of psychiatric symptoms in schizophrenia: Findings from the Research on Asian Psychotropic Prescription Patterns for Antipsychotics (REAP-AP)

AIMS

Schizophrenia is a major mental disorder with a wide range of psychiatric symptoms. This study explored the structure of psychiatric symptoms of schizophrenia using network analysis in a large representative Asian sample based on a survey of clinical features and treatment used in schizophrenia patients across 15 countries/territories in Asia.

METHODS

Data on the demographic characteristics and psychiatric symptoms in schizophrenia patients were extracted from the dataset of the fourth Research on Asia Psychotropic Prescription for Antipsychotics (REAP-AP) project. The presence of the following psychiatric symptoms including delusions, hallucinations, disorganized speech, grossly disorganized or catatonic behavior, negative symptoms, social/occupational dysfunction, verbal aggression, physical aggression, and affective symptoms were analyzed.

RESULTS

A total of 3681 patients were included. The network analysis revealed that verbal aggression, hallucinations, and social/occupational dysfunction were the most central symptoms, while the connections between social/occupational dysfunction and verbal aggression, and between hallucinations and disorganized speech were the two strongest edges. There were significant gender differences in the network structure based on the network structure invariance test (M=0.74, P = 0.03) and invariant edge strength test. The positive correlation between verbal aggression and hallucinations was significantly stronger in the female network than that in the male network (P = 0.03), while a negative correlation between affective symptoms and negative symptoms was found in the female, but not the male network (P < 0.01).

CONCLUSION

Central symptoms including verbal aggression, hallucinations, and socio-occupational dysfunction should be addressed in developing targeted treatment strategy for schizophrenia patients.

Tardive dyskinesia in Chinese patients with schizophrenia: Prevalence, clinical correlates and relationship with cognitive impairment

OBJECTIVE

Tardive dyskinesia (TD) has a high prevalence and is one of the distressing side effects of antipsychotic medications. Few studies have explored the relationship between TD, clinical correlates, and cognition. The aim of this study was to assess the prevalence, clinical correlates and cognitive impairment of co-occurring TD in Chinese patients with schizophrenia.

METHODS

We recruited 655 patients with chronic schizophrenia who met the DSM-IV diagnostic criteria for schizophrenia and collected clinical and demographic data. All patients were assessed using the Abnormal Involuntary Movement Scale (AIMS) for the severity of TD, Positive and Negative Syndrome Scale (PANSS) for psychopathological symptoms, and Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) for cognition.

RESULTS

The overall TD prevalence was 41.1%, 42.9% (246/574) in men and 28.4% (23/81) in women (χ2 = 6.1 df = 1, p < 0.05). There were significant differences in age, sex, duration of illness, number of hospitalizations, drug type, smoking and PANSS negative symptom subscore between TD and non-TD groups (all p < 0.05). Moreover, patients with TD scored lower for immediate memory, attention, delayed memory, and RBANS total scores (all p < 0.05). Logistic regression showed a significant correlation between TD and age, sex, drug type and attention subscore.

CONCLUSION

Our results suggest that multiple demographic and clinical variables may be associated with the development of TD. Moreover, TD patients may exhibit more cognitive impairment than non-TD patients.

Machine Learning Algorithm-Based Prediction Model for the Augmented Use of Clozapine with Electroconvulsive Therapy in Patients with Schizophrenia

The augmentation of clozapine with electroconvulsive therapy (ECT) has been an optimal treatment option for patients with treatment- or clozapine-resistant schizophrenia. Using data from the Research on Asian Psychotropic Prescription Patterns for Antipsychotics survey, which was the largest international psychiatry research collaboration in Asia, our study aimed to develop a machine learning algorithm-based substantial prediction model for the augmented use of clozapine with ECT in patients with schizophrenia in terms of precision medicine. A random forest model and least absolute shrinkage and selection operator (LASSO) model were used to develop a substantial prediction model for the augmented use of clozapine with ECT. Among the 3744 Asian patients with schizophrenia, those treated with a combination of clozapine and ECT were characterized by significantly greater proportions of females and inpatients, a longer duration of illness, and a greater prevalence of negative symptoms and social or occupational dysfunction than those not treated. In the random forest model, the area under the curve (AUC), which was the most preferred indicator of the prediction model, was 0.774. The overall accuracy was 0.817 (95% confidence interval, 0.793−0.839). Inpatient status was the most important variable in the substantial prediction model, followed by BMI, age, social or occupational dysfunction, persistent symptoms, illness duration > 20 years, and others. Furthermore, the AUC and overall accuracy of the LASSO model were 0.831 and 0.644 (95% CI, 0.615−0.672), respectively. Despite the subtle differences in both AUC and overall accuracy of the random forest model and LASSO model, the important variables were commonly shared by the two models. Using the machine learning algorithm, our findings allow the development of a substantial prediction model for the augmented use of clozapine with ECT in Asian patients with schizophrenia. This substantial prediction model can support further studies to develop a substantial prediction model for the augmented use of clozapine with ECT in patients with schizophrenia in a strict epidemiological context.