Asian journal of psychiatry最新文献

Suicide is a global public health concern, with increasing rates observed in various regions, including Iran. This study focuses on the province of Hamadan, Iran, where suicide rates have been on the rise. The research aims to predict factors influencing suicide outcomes by leveraging machine learning techniques on the Hamadan Suicide Registry Program data collected from 2016 to 2017. The study employs Naïve Bayes and Random Forest algorithms, comparing their performance to logistic regression. Results highlight the superiority of the Random Forest model. Based on the variable importance and multiple logistic regression analyses, the most important determinants of suicide outcomes were identified as suicide method, age, and timing of attempts, income, and motivation. The findings emphasize the cultural context's impact on suicide methods and underscore the importance of tailoring prevention programs to address specific risk factors, especially for older individuals. This study contributes valuable insights for suicide prevention efforts in the region, advocating for context-specific interventions and further research to refine predictive models and develop targeted prevention strategies.

Objectives

Ketamine can induce persisting psychosis in a subset of individuals who use it chronically and heavily. Previously, we found that the psychopathology and cognitive impairments in patients with ketamine dependence (KD) exhibiting persistent psychosis (KPP) bear resemblances with schizophrenia, albeit with less severity in those with no persistent psychosis (KNP). Furthermore, we also showed that patients with KD had higher blood levels of neurofilament light chain (NFL), a biomarker for neuroaxonal injury, compared to healthy controls. In this study, we aimed to investigate the differences in NFL levels between patients with KPP and KNP while comparing the levels of individuals with schizophrenia and healthy controls.

Methods

We enrolled 64 treatment-seeking ketamine-dependent patients (53 with KNP and 11 with KPP), 37 medication-free patients with schizophrenia, and 80 healthy controls. Blood NFL levels were measured by single molecule array immunoassay.

Results

NFL levels were highest in the KPP subgroup, followed by the KNP subgroup, and then the schizophrenia and control groups (mean ± SD: 24.5 ± 24.7, 12.9 ± 10.9, 9.2 ± 12.2, and 6.2 ± 2.2 pg/mL, respectively), with no significant difference observed between the schizophrenia and control groups.

Conclusions

We found that KD is associated with higher NFL levels compared to schizophrenia, with the KPP subgroup showing the most consistent alterations. The observation of accentuated neuroaxonal pathology in individuals with KPP implies that this clinical manifestation is associated with a specific neurobiological phenotype, despite prior evidence suggesting syndromal similarity between schizophrenia and KPP.

This study aimed to assess the diagnostic accuracy of the Patient Health Questionnaire (PHQ) for identifying common mental disorders in an outpatient clinical psychologist office setting in Indonesia. A total of 661 outpatients from a clinical psychology office in Jakarta, Indonesia, participated in the study. The complete PHQ was administered, and its results were compared with diagnoses made by clinical psychologists based on ICD-11 criteria, including somatoform disorder (n = 6), depression (n = 117), Generalized Anxiety Disorder (GAD, n = 50), panic disorder (n = 42), bulimia nervosa (n = 2), binge eating disorder (n = 2), and other diagnoses such as OCD and BPD (n = 442). Receiver operating characteristics were computed to examine cut-off points, and optimal cut-off points based on the Youden Index were identified for somatoform disorder (PHQ-15 ≥ 13), depression (PHQ-9 ≥ 13), GAD (GAD- 7 ≥ 10), and panic disorder (PHQ-PD ≥ 7). Cut-off points for the alcohol abuse and eating disorder modules of the PHQ could not be determined due to a lack of sample, and AUC was suboptimal for PHQ-9, GAD-7, and PHQ-ED. The Indonesian PHQ demonstrated good sensitivity but low specificity in identifying somatoform disorder, depression, GAD, and panic disorders based on ICD-11 criteria among Indonesian clinical psychologist office outpatients. In the Indonesian outpatient psychiatric context, the utility of the Indonesian PHQ appeared to be most effective in ruling out diagnoses.

This study analyzes 21 years of suicide trend among Asian American Pacific Islander (AAPI) young adults aged 18–25. Analyzing NCHS data (1999–2020) through the Joinpoint Regression, we examined temporal trends of suicide death across sexes and identified trend shifts. Findings indicate a significant and steady increasing trend in AAPI female suicides from 1999 to 2020. Alarmingly, AAPI males displayed a significant and sharp increasing trend of suicide from 2008 onwards. It is important to consider other risk factors of suicide, including cultural factors and gendered racial experiences, to understand the context of AAPI young adults’ suicide patterns.

Introduction

Medical decision-making is crucial for effective treatment, especially in psychiatry where diagnosis often relies on subjective patient reports and a lack of high-specificity symptoms. Artificial intelligence (AI), particularly Large Language Models (LLMs) like GPT, has emerged as a promising tool to enhance diagnostic accuracy in psychiatry. This comparative study explores the diagnostic capabilities of several AI models, including Aya, GPT-3.5, GPT-4, GPT-3.5 clinical assistant (CA), Nemotron, and Nemotron CA, using clinical cases from the DSM-5.

Methods

We curated 20 clinical cases from the DSM-5 Clinical Cases book, covering a wide range of psychiatric diagnoses. Four advanced AI models (GPT-3.5 Turbo, GPT-4, Aya, Nemotron) were tested using prompts to elicit detailed diagnoses and reasoning. The models' performances were evaluated based on accuracy and quality of reasoning, with additional analysis using the Retrieval Augmented Generation (RAG) methodology for models accessing the DSM-5 text.

Results

The AI models showed varied diagnostic accuracy, with GPT-3.5 and GPT-4 performing notably better than Aya and Nemotron in terms of both accuracy and reasoning quality. While models struggled with specific disorders such as cyclothymic and disruptive mood dysregulation disorders, others excelled, particularly in diagnosing psychotic and bipolar disorders. Statistical analysis highlighted significant differences in accuracy and reasoning, emphasizing the superiority of the GPT models.

Discussion

The application of AI in psychiatry offers potential improvements in diagnostic accuracy. The superior performance of the GPT models can be attributed to their advanced natural language processing capabilities and extensive training on diverse text data, enabling more effective interpretation of psychiatric language. However, models like Aya and Nemotron showed limitations in reasoning, indicating a need for further refinement in their training and application.

Conclusion

AI holds significant promise for enhancing psychiatric diagnostics, with certain models demonstrating high potential in interpreting complex clinical descriptions accurately. Future research should focus on expanding the dataset and integrating multimodal data to further enhance the diagnostic capabilities of AI in psychiatry.

Background

For depression, ketamine is more conveniently administered by oral than by intravenous (iv) routes. The relative antidepressant efficacy of oral vs iv ketamine is unknown.

Objectives

To assess the acute efficacy and the persistence of improvement with open-label oral versus iv ketamine in outpatients with treatment-resistant depression (TRD).

Methods

Adults with TRD were randomized to oral (N=30) or IV (N=31) ketamine. Oral ketamine was dosed at 150 mg in 50 mL of water, sipped across 15 min. IV ketamine was dosed at 0.5 mg/kg, infused across 40 min. Ketamine sessions (total, 7) were administered on alternate days for 2 weeks. Ongoing antidepressant drugs were continued unchanged. Patients were assessed at baseline, day 14, and day 30. The primary outcome was the endpoint Hamilton Rating Scale for Depression score on day 14. Secondary outcomes were endpoint scores on the Montgomery-Asberg Depression Rating Scale, Beck Depression Inventory, and Clinical Global Impression-Severity of Illness and Improvement.

Results

Overall dropout was lower with oral than with iv ketamine (26.7 % vs 54.8 %; P=0.03). The 2 groups did not differ in depression ratings and in response and remission rates on all instruments on both days 14 and 30. Adverse events such as headache (56.7 % vs 74.2 %) and drowsiness (0.0 % vs 22.6 %) were less common with oral ketamine.

Conclusion

In TRD outpatients treated in general hospitals, oral ketamine maybe better accepted and tolerated than iv ketamine. Conclusions about relative efficacy cannot be drawn because of the high dropout rate with iv ketamine.