Kwai Chung Hospital

Depression has been reported to be associated with premature mortality. However, no meta-analysis has comprehensively examined all-cause and cause-specific mortality risk in people with this condition, focusing also on possible aggravating and attenuating factors, including antidepressant treatment. We conducted a systematic review and meta-analysis of cohort studies to synthesize mortality risk estimates associated with depression (major depressive disorder and dysthymia) due to any and specific causes, and when depression is accompanied by comorbid conditions. Effects of antidepressant medication and electroconvulsive therapy (ECT), and other potential moderators of mortality risk, were evaluated. We searched EMBASE, Medline and PsycINFO databases up to January 26, 2025, pooling mortality estimates using random-effect models. Publication bias, subgroup and meta-regression analyses, and quality assessment (Newcastle-Ottawa Scale) were performed. Across 268 studies, 10,842,094 individuals with depression and 2,837,933,536 control subjects were included. All-cause mortality was doubled in people with depression versus no depression/general population controls (relative risk, RR=2.10, 95% CI: 1.87-2.35, I2=99.9%), being especially high for suicide (RR=9.89, 95% CI: 7.59-12.88, I2=99.6%), but also elevated for natural causes (RR=1.63, 95% CI: 1.51-1.75, I2=99.6%). Among individuals with versus without depression matched for comorbid conditions, the depression-associated mortality risk was also significantly elevated (RR=1.29, 95% CI: 1.21-1.37, I2=99.9%). Depression with versus without psychotic symptoms (RR=1.61, 95% CI: 1.45-1.78, I2=6.3%), and treatment-resistant versus non-treatment-resistant depression (RR=1.27, 95% CI: 1.16-1.39, I2=85.3%), conferred an incremental mortality risk. Antidepressant use (versus no antidepressant use) was associated with significantly lower all-cause mortality in people with depression (RR=0.79, 95% CI: 0.68-0.93, I2=99.2%). ECT use (versus no ECT use) was associated with reduced all-cause mortality (RR=0.73, 95% CI: 0.66-0.82, I2=0%), natural-cause mortality (RR=0.76, 95% CI: 0.59-0.97, I2=12.0%), and suicide (RR=0.67, 95% CI: 0.53-0.85, I2=32.3%). Our results affirm heightened mortality risk in depression, identify clinically relevant patient subgroups with increased mortality risk, and highlight mortality-reducing effects of antidepressant treatment and ECT. Multipronged intervention approaches targeting physical health improvement and suicide risk alleviation, optimizing antidepressant treatment, and pursuing early identification and effective interventions for psychotic and treatment-resistant depression, could help reduce this mortality gap, which is still growing.

Background:The pivotal trial on venetoclax and hypomethylating agents in unfit elderly acute myeloid leukaemia (AML) has got FDA approval. However, Asian patients were under-represented and showed no survival advantage.Objective:We aimed to compare overall survival and healthcare resource utilisation in elderly patients newly diagnosed with AML, receiving venetoclax with hypomethylating agents versus hypomethylating agents alone.Design:Target trial emulation.Methods:Propensity score matching balanced the baseline characteristics. Kaplan-Meier curve and Cox regression compared overall survival. Negative binomial regression assessed healthcare resource utilisation with time offset.Results:Venetoclax plus hypomethylating agents treatment conferred superior overall survival in patients with AML ⩾60 years old and significantly reduced transfusion requirement compared to those receiving hypomethylating agents alone. The difference was particularly prominent among patients ⩾75 years old.Conclusion:Compared to hypomethylating agent alone, venetoclax plus hypomethylating agent benefits elderly patients with AML on overall survival and healthcare resource utilisation.

BackgroundCaregivers of children with attention-deficit/hyperactivity disorder (ADHD) are vulnerable to increased psychological distress during COVID-19. However, specific pandemic- and child-related factors contributing to their poor mental health have not been fully investigated. We aimed to comprehensively examine prevalence and correlates of depression and anxiety among caregivers of ADHD children in Hong-Kong during the peak of fifth pandemic wave with territory-wide school closure.MethodOne-hundred-ninety-nine caregivers of ADHD children were assessed with self-rated questionnaires between 29 March–14 April, 2022. Questionnaires encompassed socio-demographics, pre-existing physical/psychiatric morbidity, COVID-19 related factors, children’s ADHD and other psychiatric symptoms. Caregivers’ psychological distress was quantified by Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder Scale-7 (GAD-7). Caregivers who scored ≥ 10 in PHQ-9 and GAD-7 were classified as having probable depression and probable anxiety, respectively. Univariate and multivariate logistic regression analyses were conducted to identify correlates associated with caregivers’ probable depression and anxiety status.ResultsForty-four (22.4%) and 33 (16.9%) caregivers had probable depression and anxiety, respectively. Multivariate regression analyses revealed that caregivers’ pre-existing mental disorder, greater COVID-19 stressor burden, and more severe emotional symptoms and insomnia of ADHD children were independently associated with both caregivers’ depression and anxiety status.ConclusionA significant proportion of caregivers of ADHD children experienced clinically-significant depressive and anxiety symptoms during the peak of fifth pandemic wave and school closure. Findings suggest that optimizing management of caregivers’ pre-existing mental health conditions, addressing emotional and sleep symptoms in ADHD children, and implementing strategies to alleviate pandemic-related stressors may reduce caregivers’ psychological distress.

Abstract For practical deep neural network design on mobile devices, it is essential to consider the constraints incurred by the computational resources and the inference latency in various applications. Among deep network acceleration approaches, pruning is a widely adopted practice to balance the computational resource consumption and the accuracy, where unimportant connections can be removed either channel-wisely or randomly with a minimal impact on model accuracy. The coarse-grained channel pruning instantly results in a significant latency reduction, while the fine-grained weight pruning is more flexible to retain accuracy. In this paper, we present a unified framework for the Joint Channel pruning and Weight pruning, named JCW, which achieves a better pruning proportion between channel and weight pruning. To fully optimize the trade-off between latency and accuracy, we further develop a tailored multi-objective evolutionary algorithm in the JCW framework, which enables one single round search to obtain the accurate candidate architectures for various deployment requirements. Extensive experiments demonstrate that the JCW achieves a better trade-off between the latency and accuracy against previous state-of-the-art pruning methods on the ImageNet classification dataset.

In general, graph neural networks (GNNs) adopt the message-passing scheme to capture the information of a node (i.e., nodal attributes, and local graph structure) by iteratively transforming, aggregating the features of its neighbors. Nonetheless, recent studies show that the performance of GNNs can be easily hampered by the existence of abnormal or malicious nodes due to the vulnerability of neighborhood aggregation. Thus it is necessary to learn anomaly-resistant GNNs without the prior knowledge of ground-truth anomalies, given the fact that labeling anomalies is costly and requires intensive domain knowledge. Though removing anomalies through unsupervised anomaly detection methods could be a possible solution, it may render unreasonable GNN model performance on target tasks due to the non-differentiable gap between the two learning procedures. In order to keep the effectiveness of GNNs on anomaly-contaminated graphs, in this paper, we propose a new framework named RARE-GNN (Reinforced Anomaly-REsistant Graph Neural Networks) which can detect anomalies from the input graph and learn anomaly-resistant GNNs simultaneously. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed framework.

Background: Recent findings indicated a high comorbidity between attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD), as well as shared genetic influences on them. The latter might contribute at least partly to the former clinical scenario. This study aimed at investigating whether SHANK genes were potential pleiotropic genes to the two said disorders, underlying their genetic overlap.Methods: This study recruited 298 boys with ADHD (including 256 family trios of 1 ADHD boy and his 2 biological parents), 134 boys with ASD, 109 boys with both ADHD and ASD, and 232 typically developing boys as community controls. They were aged between 6 and 11 years old.Results: There was no significant difference in allele frequency of a number of single nucleotide polymorphisms (SNPs) in SHANK2/SHANK3 between the three clinical groups (ADHD, ASD, and ADHD + ASD) and between the two control groups (community controls and pseudo-controls), respectively. The three clinical groups and the two control groups were thus, respectively, combined. A comparison between the two aggregated samples identified significant evidence of disease association for three SHANK2 SNPs with both ADHD and ASD, even after multiple testing correction: rs11236616 (OR = 0.762, permuted p = 0.0376), rs7106631 (OR = 0.720, permuted p = 0.0034), and rs9888288 (OR = 0.770, permuted p = 0.0407). Comparisons among individual groups pointed to a similar trend of findings.Conclusion:SHANK2 could be considered a potential pleiotropic gene underlying the genetic overlap between ADHD and ASD. This might contribute partly to their high comorbidity in the afflicted children.

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows: To assess the effects of family‐based interventions for people with schizophrenia or schizophrenia‐like disorders, when compared to standard care.