Hasil untuk "Psychiatry"

J. Firth, N. Siddiqi, A. Koyanagi et al.

The poor physical health of people with mental illness is a multifaceted, transdiagnostic, and global problem. People with mental illness have an increased risk of physical disease, as well as reduced access to adequate health care. As a result, physical health disparities are observed across the entire spectrum of mental illnesses in low-income, middle-income, and high-income countries. The high rate of physical comorbidity, which often has poor clinical management, drastically reduces life expectancy for people with mental illness, and also increases the personal, social, and economic burden of mental illness across the lifespan. This Commission summarises advances in understanding on the topic of physical health in people with mental illness, and presents clear directions for health promotion, clinical care, and future research. The wide range and multifactorial nature of physical health disparities across the range of mental health diagnoses generate a vast number of potential considerations. Therefore, rather than attempting to discuss all possible combinations of physical and mental comorbidities individually, the aims of this Commission are to: (1) establish highly pertinent aspects of physical health-related morbidity and mortality that have transdiagnostic applications; (2) highlight the common modifiable factors that drive disparities in physical health; (3) present actions and initiatives for health policy and clinical services to address these issues; and (4) identify promising areas for future research that could identify novel solutions. These aims are addressed across the five parts of the Commission: in Parts 1 and 2 we describe the scope, priorities, and key targets for physical health improvement across multiple mental illnesses; in Parts 3, 4, and 5, we highlight emerging strategies and present recommendations for improving physical health outcomes in people with mental illness.

C. Maslach, M. Leiter

J. Torous, S. Bucci, I. Bell et al.

As the COVID‐19 pandemic has largely increased the utilization of telehealth, mobile mental health technologies – such as smartphone apps, virtual reality, chatbots, and social media – have also gained attention. These digital health technologies offer the potential of accessible and scalable interventions that can augment traditional care. In this paper, we provide a comprehensive update on the overall field of digital psychiatry, covering three areas. First, we outline the relevance of recent technological advances to mental health research and care, by detailing how smartphones, social media, artificial intelligence and virtual reality present new opportunities for “digital phenotyping” and remote intervention. Second, we review the current evidence for the use of these new technological approaches across different mental health contexts, covering their emerging efficacy in self‐management of psychological well‐being and early intervention, along with more nascent research supporting their use in clinical management of long‐term psychiatric conditions – including major depression; anxiety, bipolar and psychotic disorders; and eating and substance use disorders – as well as in child and adolescent mental health care. Third, we discuss the most pressing challenges and opportunities towards real‐world implementation, using the Integrated Promoting Action on Research Implementation in Health Services (i‐PARIHS) framework to explain how the innovations themselves, the recipients of these innovations, and the context surrounding innovations all must be considered to facilitate their adoption and use in mental health care systems. We conclude that the new technological capabilities of smartphones, artificial intelligence, social media and virtual reality are already changing mental health care in unforeseen and exciting ways, each accompanied by an early but promising evidence base. We point out that further efforts towards strengthening implementation are needed, and detail the key issues at the patient, provider and policy levels which must now be addressed for digital health technologies to truly improve mental health research and treatment in the future.

J. Firth, M. Solmi, R. Wootton et al.

There is increasing academic and clinical interest in how “lifestyle factors” traditionally associated with physical health may also relate to mental health and psychological well‐being. In response, international and national health bodies are producing guidelines to address health behaviors in the prevention and treatment of mental illness. However, the current evidence for the causal role of lifestyle factors in the onset and prognosis of mental disorders is unclear. We performed a systematic meta‐review of the top‐tier evidence examining how physical activity, sleep, dietary patterns and tobacco smoking impact on the risk and treatment outcomes across a range of mental disorders. Results from 29 meta‐analyses of prospective/cohort studies, 12 Mendelian randomization studies, two meta‐reviews, and two meta‐analyses of randomized controlled trials were synthesized to generate overviews of the evidence for targeting each of the specific lifestyle factors in the prevention and treatment of depression, anxiety and stress‐related disorders, schizophrenia, bipolar disorder, and attention‐deficit/hyperactivity disorder. Standout findings include: a) convergent evidence indicating the use of physical activity in primary prevention and clinical treatment across a spectrum of mental disorders; b) emerging evidence implicating tobacco smoking as a causal factor in onset of both common and severe mental illness; c) the need to clearly establish causal relations between dietary patterns and risk of mental illness, and how diet should be best addressed within mental health care; and d) poor sleep as a risk factor for mental illness, although with further research required to understand the complex, bidirectional relations and the benefits of non‐pharmacological sleep‐focused interventions. The potentially shared neurobiological pathways between multiple lifestyle factors and mental health are discussed, along with directions for future research, and recommendations for the implementation of these findings at public health and clinical service levels.

Dominic Dwyer, P. Falkai, N. Koutsouleris

H. Sullivan

I. Gottesman, T. Gould

Adam M. Chekroud, J. Bondar, J. Delgadillo et al.

For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory power of a predictive model when tested on new, unseen datasets. In this paper, we review key studies using machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments. Next, we focus on some new sources of data that are being used for the development of predictive models based on machine learning, such as electronic health records, smartphone and social media data, and on the potential utility of data from genetics, electrophysiology, neuroimaging and cognitive testing. Finally, we discuss how far the field has come towards implementing prediction tools in real‐world clinical practice. Relatively few retrospective studies to‐date include appropriate external validation procedures, and there are even fewer prospective studies testing the clinical feasibility and effectiveness of predictive models. Applications of machine learning in psychiatry face some of the same ethical challenges posed by these techniques in other areas of medicine or computer science, which we discuss here. In short, machine learning is a nascent but important approach to improve the effectiveness of mental health care, and several prospective clinical studies suggest that it may be working already.

P. Fusar-Poli, C. Correll, C. Arango et al.

Preventive approaches have latterly gained traction for improving mental health in young people. In this paper, we first appraise the conceptual foundations of preventive psychiatry, encompassing the public health, Gordon's, US Institute of Medicine, World Health Organization, and good mental health frameworks, and neurodevelopmentally‐sensitive clinical staging models. We then review the evidence supporting primary prevention of psychotic, bipolar and common mental disorders and promotion of good mental health as potential transformative strategies to reduce the incidence of these disorders in young people. Within indicated approaches, the clinical high‐risk for psychosis paradigm has received the most empirical validation, while clinical high‐risk states for bipolar and common mental disorders are increasingly becoming a focus of attention. Selective approaches have mostly targeted familial vulnerability and non‐genetic risk exposures. Selective screening and psychological/psychoeducational interventions in vulnerable subgroups may improve anxiety/depressive symptoms, but their efficacy in reducing the incidence of psychotic/bipolar/common mental disorders is unproven. Selective physical exercise may reduce the incidence of anxiety disorders. Universal psychological/psychoeducational interventions may improve anxiety symptoms but not prevent depressive/anxiety disorders, while universal physical exercise may reduce the incidence of anxiety disorders. Universal public health approaches targeting school climate or social determinants (demographic, economic, neighbourhood, environmental, social/cultural) of mental disorders hold the greatest potential for reducing the risk profile of the population as a whole. The approach to promotion of good mental health is currently fragmented. We leverage the knowledge gained from the review to develop a blueprint for future research and practice of preventive psychiatry in young people: integrating universal and targeted frameworks; advancing multivariable, transdiagnostic, multi‐endpoint epidemiological knowledge; synergically preventing common and infrequent mental disorders; preventing physical and mental health burden together; implementing stratified/personalized prognosis; establishing evidence‐based preventive interventions; developing an ethical framework, improving prevention through education/training; consolidating the cost‐effectiveness of preventive psychiatry; and decreasing inequalities. These goals can only be achieved through an urgent individual, societal, and global level response, which promotes a vigorous collaboration across scientific, health care, societal and governmental sectors for implementing preventive psychiatry, as much is at stake for young people with or at risk for emerging mental disorders.

P. McGorry, Cristina Mei, Naeem Dalal et al.

O. Howes, M. Thase, T. Pillinger

Treatment resistance affects 20–60% of patients with psychiatric disorders; and is associated with increased healthcare burden and costs up to ten-fold higher relative to patients in general. Whilst there has been a recent increase in the proportion of psychiatric research focussing on treatment resistance ( R 2  = 0.71, p  < 0.0001), in absolute terms this is less than 1% of the total output and grossly out of proportion to its prevalence and impact. Here, we provide an overview of treatment resistance, considering its conceptualisation, assessment, epidemiology, impact, and common neurobiological models. We also review new treatments in development and future directions. We identify 23 consensus guidelines on its definition, covering schizophrenia, major depressive disorder, bipolar affective disorder, and obsessive compulsive disorder (OCD). This shows three core components to its definition, but also identifies heterogeneity and lack of criteria for a number of disorders, including panic disorder, post-traumatic stress disorder, and substance dependence. We provide a reporting check-list to aid comparisons across studies. We consider the concept of pseudo-resistance, linked to poor adherence or other factors, and provide an algorithm for the clinical assessment of treatment resistance. We identify nine drugs and a number of non-pharmacological approaches being developed for treatment resistance across schizophrenia, major depressive disorder, bipolar affective disorder, and OCD. Key outstanding issues for treatment resistance include heterogeneity and absence of consensus criteria, poor understanding of neurobiology, under-investment, and lack of treatments. We make recommendations to address these issues, including harmonisation of definitions, and research into the mechanisms and novel interventions to enable targeted and personalised therapeutic approaches.

Szu-Wei Cheng, Chung-Wen Chang, W. Chang et al.

ChatGPT has sparked extensive discussions within the healthcare community since its November 2022 release. However, potential applications in the field of psychiatry have received limited attention. Deep learning has proven beneficial to psychiatry, and GPT is a powerful deep learning‐based language model with immense potential for this field. Despite the convenience of ChatGPT, this advanced chatbot currently has limited practical applications in psychiatry. It may be used to support psychiatrists in routine tasks such as completing medical records, facilitating communications between clinicians and with patients, polishing academic writings and presentations, and programming and performing analyses for research. The current training and application of ChatGPT require using appropriate prompts to maximize appropriate outputs and minimize deleterious inaccuracies and phantom errors. Moreover, future GPT advances that incorporate empathy, emotion recognition, personality assessment, and detection of mental health warning signs are essential for its effective integration into psychiatric care. In the near future, developing a fully‐automated psychotherapy system trained for expert communication (such as psychotherapy verbatim) is conceivable by building on foundational GPT technology. This dream system should integrate practical ‘real world’ inputs and friendly AI user and patient interfaces via clinically validated algorithms, voice comprehension/generation modules, and emotion discrimination algorithms based on facial expressions and physiological inputs from wearable devices. In addition to the technology challenges, we believe it is critical to establish generally accepted ethical standards for applying ChatGPT‐related tools in all mental healthcare environments, including telemedicine and academic/training settings.

K. T. Pham, Amir Nabizadeh, S. Selek

The utilization of artificial intelligence (AI) in psychiatry has risen over the past several years to meet the growing need for improved access to mental health solutions. Additionally, shortages of mental health providers during the COVID-19 pandemic have continued to exacerbate the burden of mental illness worldwide. AI applications already in existence include those enabled to assist with psychiatric diagnoses, symptom tracking, disease course prediction, and psychoeducation. Modalities of AI mental health care delivery include availability through the internet, smartphone applications, and digital gaming. Here we review emerging AI-based interventions in the form of chat and therapy bots, specifically conversational applications that teach the user emotional coping mechanisms and provide support for people with communication difficulties, computer generated images of faces that form the basis of avatar therapy, and intelligent animal-like robots with new advances in digital psychiatry. We discuss the implications of incorporating AI chatbots into clinical practice and offer perspectives on how these AI-based interventions will further impact the field of psychiatry.

S. Rutherford, S. M. Kia, T. Wolfers et al.

Normative modeling is an emerging and innovative framework for mapping individual differences at the level of a single subject or observation in relation to a reference model. It involves charting centiles of variation across a population in terms of mappings between biology and behavior, which can then be used to make statistical inferences at the level of the individual. The fields of computational psychiatry and clinical neuroscience have been slow to transition away from patient versus ‘healthy’ control analytic approaches, probably owing to a lack of tools designed to properly model biological heterogeneity of mental disorders. Normative modeling provides a solution to address this issue and moves analysis away from case–control comparisons that rely on potentially noisy clinical labels. Here we define a standardized protocol to guide users through, from start to finish, normative modeling analysis using the Predictive Clinical Neuroscience toolkit (PCNtoolkit). We describe the input data selection process, provide intuition behind the various modeling choices and conclude by demonstrating several examples of downstream analyses that the normative model may facilitate, such as stratification of high-risk individuals, subtyping and behavioral predictive modeling. The protocol takes ~1–3 h to complete. This protocol guides the user through normative modeling analysis using the Predictive Clinical Neuroscience toolkit (PCNtoolkit), enabling individual differences to be mapped at the level of a single subject or observation in relation to a reference model.

Z. Chen, Prathamesh Kulkarni, I. Galatzer-Levy et al.

Summary In light of the National Institute of Mental Health (NIMH)’s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.

Jie Sun, Qun Dong, San-Wang Wang et al.

Psychiatric disorders are now responsible for the largest proportion of the global burden of disease, and even more challenges have been seen during the COVID-19 pandemic. Artificial intelligence (AI) is commonly used to facilitate the early detection of disease, understand disease progression, and discover new treatments in the fields of both physical and mental health. The present review provides a broad overview of AI methodology and its applications in data acquisition and processing, feature extraction and characterization, psychiatric disorder classification, potential biomarker detection, real-time monitoring, and interventions in psychiatric disorders. We also comprehensively summarize AI applications with regard to the early warning, diagnosis, prognosis, and treatment of specific psychiatric disorders, including depression, schizophrenia, autism spectrum disorder, attention-deficit/hyperactivity disorder, addiction, sleep disorders, and Alzheimer's disease. The advantages and disadvantages of AI in psychiatry are clarified. We foresee a new wave of research opportunities to facilitate and improve AI technology and its long-term implications in psychiatry during and after the COVID-19 era.

Zuo Fei, Kezhi Wang, Xiaomin Chen et al.

Computational psychiatry faces a fundamental trade-off: traditional reinforcement learning (RL) models offer interpretability but lack behavioral realism, while large language model (LLM) agents generate realistic behaviors but lack structural interpretability. We introduce BioLLMAgent, a novel hybrid framework that combines validated cognitive models with the generative capabilities of LLMs. The framework comprises three core components: (i) an Internal RL Engine for experience-driven value learning; (ii) an External LLM Shell for high-level cognitive strategies and therapeutic interventions; and (iii) a Decision Fusion Mechanism for integrating components via weighted utility. Comprehensive experiments on the Iowa Gambling Task (IGT) across six clinical and healthy datasets demonstrate that BioLLMAgent accurately reproduces human behavioral patterns while maintaining excellent parameter identifiability (correlations $>0.67$). Furthermore, the framework successfully simulates cognitive behavioral therapy (CBT) principles and reveals, through multi-agent dynamics, that community-wide educational interventions may outperform individual treatments. Validated across reward-punishment learning and temporal discounting tasks, BioLLMAgent provides a structurally interpretable "computational sandbox" for testing mechanistic hypotheses and intervention strategies in psychiatric research.

Minghui Lu, Jiajun Wei, Qiang Cai

BackgroundThe optimal timing for tracheostomy in patients with intracerebral hemorrhage extending into the ventricles who require mechanical ventilation remains controversial, and there is a paucity of evidence to guide clinical practice. This study aimed to elucidate the impact of early vs. late tracheostomy on clinical outcomes and complications in this population, utilizing multivariable models to identify risk factors and define the potential beneficiary population.MethodsThis single-center retrospective cohort study consecutively enrolled 157 patients with severe spontaneous intracerebral hemorrhage extending into the ventricles requiring mechanical ventilation (GCS score ≤8) between January 2020 and December 2023. Based on the timing of tracheostomy, patients were classified into an early group (ET, ≤7 days after mechanical ventilation, n = 81) and a late group (LT, &gt;7 days after mechanical ventilation, n = 76). Baseline characteristics, treatment measures, and outcome data were collected. Hematoma volumes in both the brain parenchyma and ventricles on admission CT scans were precisely quantified using 3D Slicer software. The primary outcome was the 6-month modified Rankin Scale (mRS) score. Secondary outcomes included the duration of mechanical ventilation, ICU length of stay (LOS), and the incidence of short-term complications [ventilator-associated pneumonia (VAP), new-onset arrhythmia, shock, and acute kidney injury (AKI)]. Multivariable logistic regression analysis was employed to identify independent risk factors for complications and to assess the protective effect of early tracheostomy.ResultsIn this cohort of 157 mechanically ventilated patients with severe intraventricular hemorrhage, baseline characteristics were well-balanced between Early (ET, n = 81) and Late Tracheostomy (LT, n = 76) groups. While 6-month functional outcomes (mRS) showed no significant difference (P = 0.360), the ET group demonstrated substantially shorter duration of mechanical ventilation (13 vs. 19 days, P &lt; 0.001) and ICU stay (17 vs. 25 days, P &lt; 0.001). ET was associated with significantly lower incidence of ventilator-associated pneumonia (28.40 vs. 48.68%, P = 0.009), new-onset arrhythmia (18.52 vs. 32.89%, P = 0.039), and shock requiring vasopressors (24.7 vs. 40.79%, P = 0.031). Multivariable analysis identified GCS score &lt;6 (OR 3.588, P = 0.008) and Graeb score ≥8 (OR 8.735, P = 0.037) as independent risk factors for complications, while confirming early tracheostomy as an independent protective factor (aOR 0.306, P = 0.019) after adjustment for confounders.ConclusionIn this single-center retrospective cohort study, early tracheostomy was associated with shorter durations of mechanical ventilation and ICU stay, as well as a lower incidence of major complications, and demonstrates a favorable safety profile. Although it does not improve long-term neurological function, early tracheostomy serves as an independent protective factor. When combined with the identification of risk factors such as GCS &lt;6 and Graeb score ≥8, it provides a basis for individualized treatment. These findings suggest an association that warrants further investigation in prospective studies.

Bosco Garcia, Eugene Y. S. Chua, Harman Singh Brah

Large language models (LLMs) are increasingly proposed as scalable solutions to the global mental health crisis. But their deployment in psychiatric contexts raises a distinctive ethical concern: the problem of atypicality. Because LLMs generate outputs based on population-level statistical regularities, their responses -- while typically appropriate for general users -- may be dangerously inappropriate when interpreted by psychiatric patients, who often exhibit atypical cognitive or interpretive patterns. We argue that standard mitigation strategies, such as prompt engineering or fine-tuning, are insufficient to resolve this structural risk. Instead, we propose dynamic contextual certification (DCC): a staged, reversible and context-sensitive framework for deploying LLMs in psychiatry, inspired by clinical translation and dynamic safety models from artificial intelligence governance. DCC reframes chatbot deployment as an ongoing epistemic and ethical process that prioritises interpretive safety over static performance benchmarks. Atypicality, we argue, cannot be eliminated -- but it can, and must, be proactively managed.

David Benrimoh, Ryan Smith, Andreea O. Diaconescu et al.

Studying psychiatric illness has often been limited by difficulties in connecting symptoms and behavior to neurobiology. Computational psychiatry approaches promise to bridge this gap by providing formal accounts of the latent information processing changes that underlie the development and maintenance of psychiatric phenomena. Models based on these theories generate individual-level parameter estimates which can then be tested for relationships to neurobiology. In this review, we explore computational modelling approaches to one key aspect of health and illness: affect. We discuss strengths and limitations of key approaches to modelling affect, with a focus on reinforcement learning, active inference, the hierarchical gaussian filter, and drift-diffusion models. We find that, in this literature, affect is an important source of modulation in decision making, and has a bidirectional influence on how individuals infer both internal and external states. Highlighting the potential role of affect in information processing changes underlying symptom development, we extend an existing model of psychosis, where affective changes are influenced by increasing cortical noise and consequent increases in either perceived environmental instability or expected noise in sensory input, becoming part of a self-reinforcing process generating negatively valenced, over-weighted priors underlying positive symptom development. We then provide testable predictions from this model at computational, neurobiological, and phenomenological levels of description.