General Psychiatry最新文献

Background: Tele-cognitive behavioural therapy (t-CBT) is the most studied remote therapy, and evidence supports its efficacy in treating depression and anxiety symptoms.

Aims: To compare the effectiveness of tele-interpersonal psychotherapy (t-IPT) to that of t-CBT. We hypothesise that t-IPT is as effective as t-CBT.

Methods: We conducted a randomised clinical trial with two parallel arms and equal randomisation. The allocation was on a 1:1 ratio based on a computerised randomisation sequence of permuted blocks of 50. Interventions and assessments were done via a website designed specifically for the trial. Participants were community-based adults with symptoms of anxiety, depression or irritability who received four sessions of t-CBT or t-IPT. The main outcome measures were the Patient Health Questionnaire-9 for depressive symptoms, Generalised Anxiety Disorder-7 for anxiety symptoms and Affective Reactivity Index for irritability.

Results: 149 individuals with a mean (standard deviation) age of 32.51 (10.73) years were randomised to receive t-CBT (n=73) or t-IPT (n=76). Seven participants withdrew from the interventions (t-CBT, n=4; t-IPT, n=3), and 20 participants completed the interventions but did not complete the follow-up questionnaires (t-CBT, n=9; t-IPT, n=11). Analysis was conducted by intention-to-treat. There was a significant overall reduction in symptoms of depression, anxiety and irritability (p<0.001) in both treatment arms; neither modality was superior to the other. Effectiveness analysis showed that the two interventions were equivalent.

Conclusions: In community adults, t-IPT is as effective as t-CBT in treating symptoms of anxiety, depression or irritability.

Background: Electrocardiogram (ECG) analysis has emerged as a promising tool for detecting physiological changes linked to non-cardiac disorders. Given the close connection between cardiovascular and neurocognitive health, ECG abnormalities may be present in individuals with co-occurring neurocognitive conditions. This highlights the potential of ECG as a biomarker to improve detection, therapy monitoring and risk stratification in patients with neurocognitive disorders, an area that remains underexplored.

Aims: We aimed to demonstrate the feasibility of predicting neurocognitive disorders from ECG features across diverse patient populations.

Methods: ECG features and demographic data were used to predict neurocognitive disorders, as defined by the International Classification of Diseases 10th revision, focusing on dementia, delirium and Parkinson's disease. Internal and external validations were performed using the Medical Information Mart for Intensive Care IV and ECG-View datasets. Predictive performance was assessed by the area under the receiver operating characteristic curve (AUROC) scores, and Shapley values were used to interpret feature contributions.

Results: Significant predictive performance was observed for several neurocognitive disorders. The highest predictive performance was observed for F03: dementia, with an internal AUROC of 0.848 (95% confidence interval (CI) 0.848 to 0.848) and an external AUROC of 0.865 (95% CI 0.864 to 0.965), followed by G30: Alzheimer's disease, with an internal AUROC of 0.809 (95% CI 0.808 to 0.810) and an external AUROC of 0.863 (95% CI 0.863 to 0.864). Feature importance analysis revealed both established and novel ECG correlates.

Conclusions: These findings suggest that ECG holds promise as a non-invasive, explainable biomarker for selected neurocognitive disorders. This study demonstrates robust performance across cohorts and lays the groundwork for future clinical applications, including early detection and personalised monitoring.

Background: Yueju Pill, a classic traditional Chinese medicine, shows antidepressant effects rapidly. However, biomarkers that can predict its treatment outcomes in major depressive disorder (MDD) are still lacking. Multimodal magnetic resonance imaging (MRI) offers a promising avenue to identify such biomarkers.

Aims: This pilot study aimed to explore whether therapeutic responses to Yueju Pill could be predicted by MRI-derived brain networks and to identify drug-specific biomarkers in comparison to escitalopram, a mainstream antidepressant.

Methods: We collected multimodal MRI data and blood samples from 28 outpatients with MDD from the Fourth People's Hospital of Taizhou, who were randomly divided into two groups to receive either Yueju Pill (23 g/time/day) or escitalopram (10 mg, two times a day) for 4 days. Morphological and functional brain networks were constructed and used to predict individual changes in symptoms quantified by the 24-item Hamilton Depression Scale (HAMD-24) scores and serum brain-derived neurotrophic factor (BDNF) levels.

Results: After the treatment, both groups exhibited significant reductions in the HAMD-24 scores, while only the Yueju Pill group showed significant increases in the BDNF levels. Gyrification Index-based morphological networks predicted change rates of the HAMD-24 scores in both groups, but sulcus depth-based and cortical thickness-based morphological networks predicted change rates of the HAMD-24 scores and BDNF levels, respectively, only in the Yueju Pill group. Subnetwork analyses revealed that the visual network independently predicted the changes in both the HAMD-24 scores (sulcus depth-based networks) and BDNF levels (cortical thickness-based networks) following Yueju Pill treatment.

Conclusions: Morphological but not functional brain networks can predict symptom improvement and BDNF changes of patients with MDD after Yueju Pill treatment. Sulcus depth-based and cortical thickness-based morphological brain networks, particularly their visual subnetworks, might serve as Yueju Pill-specific biomarkers for predicting the therapeutic responses. These findings have the potential to guide personalised therapy for patients with MDD early in the therapeutic process.

Trial registration number: ChiCTR1900021114.

Background: International students contribute to the academic and economic vitality of US higher education while facing exacerbated mental health challenges. Little is known about national trends in anxiety, depression, suicidal ideation and mental health service utilisation in this population.

Aims: This study examined national trends in the prevalence of clinically significant anxiety, depression, suicidal ideation and service utilisation among international students in US higher education from 2015 to 2024.

Methods: This repeated cross-sectional study analysed annual data from the Healthy Minds Study, a national survey of collegiate mental health, including 44 560 international students. Weighted prevalence estimates were calculated, and multivariable logistic regression models were used to examine temporal trends, controlling for demographic characteristics.

Results: The weighted annual prevalence of anxiety increased by 78.25% (from 20.46% in 2015-2016 to 36.47% in 2023-2024), depression increased by 73.04% (from 20.44% to 35.37%), suicidal ideation increased by 92.52% (from 5.35% to 10.30%) and service utilisation increased by 45.82% (from 5.26% to 7.67%). In logistic models controlling for demographic characteristics, the increasing trends in anxiety (adjusted odds ratio (aOR) 2.21; 95% CI 2.07 to 2.36; p<0.001), depression (aOR 1.93; 95% CI 1.80 to 2.06; p<0.001), suicidal ideation (aOR 1.57; 95% CI 1.41 to 1.74; p<0.001) and service utilisation (aOR 2.01; 95% CI 1.79 to 2.26; p<0.001) remained statistically significant over time.

Conclusions: The prevalence of anxiety, depression and suicidal ideation nearly doubled among international students from 2015 to 2024, while counselling service utilisation increased at a slower rate, indicating persistent gaps in mental healthcare. These findings suggest the need for proactive interventions, culturally competent services and expanded outreach efforts to bridge the mental health service gap for international students.

[This corrects the article DOI: 10.1136/gpsych-2023-101166.].

Background: Biomarkers for predicting suicide risk in hospitalised patients with mental disorders have been understudied. Currently, suicide risk assessment tools based on objective indicators are limited in China.

Aims: To examine the value of various biomarkers in suicide risk prediction and develop a risk assessment model with clinical utility using machine learning.

Methods: This cohort study analysed patients with major depressive disorder (MDD) who were hospitalised for the first time between January 2016 and March 2023 from four specialised mental health institutions. A total of 139 features, including biomarker measurements, medical orders and psychological scales, were assessed for analysis. Their suicide risk was evaluated by qualified nurses using Nurse's Global Assessment of Suicide Risk within 1 week after admission. Five machine learning models were trained with 10-fold cross-validation across three hospitals and were externally validated in an independent cohort. The primary performance was assessed using the area under the receiver operating characteristic curve (AUROC). The model was interpreted using the SHapley Additive exPlanations (SHAP) analysis. Biomarker importance was evaluated by comparing model performance with and without these biomarkers.

Results: Of 3143 patients with MDD included in this study, the incidence of high suicide risk within 1 week after first admission was 660 (21.0%). Among all models, the Extreme Gradient Boosting can more effectively predict future risks, with an AUROC higher than 0.8 (p<0.001). The SHAP values identified the 10 most important features, including five biomarkers. After clustering analysis, electroconvulsive therapy, physical restraint, β2-microglobulin and triiodothyronine were found to have heterogeneous effects on suicide risk. Combining biomarkers with other data from electronic health records significantly improved the performance and clinical utility of machine learning models based on demographics, diagnosis, laboratory tests, medical orders and psychological scales.

Conclusions: This study demonstrates the potential for a biomarker-based suicide risk assessment for patients with MDD, emphasising the interaction between biomarkers and therapeutic interventions.