Hasil untuk "Psychiatry"

T. Insel

Q. Huys, T. Maia, M. Frank

D. Bzdok, A. Meyer-Lindenberg

G. Bateson, J. Aronson

B. Sadock, V. A. Sadock

E. Holmes, A. Ghaderi, C. Harmer et al.

P. Fusar-Poli, M. Solmi, N. Brondino et al.

The usefulness of current psychiatric classification, which is based on ICD/DSM categorical diagnoses, remains questionable. A promising alternative has been put forward as the “transdiagnostic” approach. This is expected to cut across existing categorical diagnoses and go beyond them, to improve the way we classify and treat mental disorders. This systematic review explores whether self‐defining transdiagnostic research meets such high expectations. A multi‐step Web of Science literature search was performed according to an a priori protocol, to identify all studies that used the word “transdiagnostic” in their title, up to May 5, 2018. Empirical variables which indexed core characteristics were extracted, complemented by a bibliometric and conceptual analysis. A total of 111 studies were included. Most studies were investigating interventions, followed by cognition and psychological processes, and neuroscientific topics. Their samples ranged from 15 to 91,199 (median 148) participants, with a mean age from 10 to more than 60 (median 33) years. There were several methodological inconsistencies relating to the definition of the gold standard (DSM/ICD diagnoses), of the outcome measures and of the transdiagnostic approach. The quality of the studies was generally low and only a few findings were externally replicated. The majority of studies tested transdiagnostic features cutting across different diagnoses, and only a few tested new classification systems beyond the existing diagnoses. About one fifth of the studies were not transdiagnostic at all, because they investigated symptoms and not disorders, a single disorder, or because there was no diagnostic information. The bibliometric analysis revealed that transdiagnostic research largely restricted its focus to anxiety and depressive disorders. The conceptual analysis showed that transdiagnostic research is grounded more on rediscoveries than on true innovations, and that it is affected by some conceptual biases. To date, transdiagnostic approaches have not delivered a credible paradigm shift that can impact classification and clinical care. Practical “TRANSD”iagnostic recommendations are proposed here to guide future research in this field.

D. Nutt, D. Erritzoe, R. Carhart-Harris

After a legally mandated, decades-long global arrest of research on psychedelic drugs, investigation of psychedelics in the context of psychiatric disorders is yielding exciting results. Outcomes of neuroscience and clinical research into 5-Hydroxytryptamine 2A (5-HT2A) receptor agonists, such as psilocybin, show promise for addressing a range of serious disorders, including depression and addiction.

G. Murray, Tian Lin, J. Austin et al.

Importance Polygenic risk scores (PRS) are predictors of the genetic susceptibility to diseases, calculated for individuals as weighted counts of thousands of risk variants in which the risk variants and their weights have been identified in genome-wide association studies. Polygenic risk scores show promise in aiding clinical decision-making in many areas of medical practice. This review evaluates the potential use of PRS in psychiatry. Observations On their own, PRS will never be able to establish or definitively predict a diagnosis of common complex conditions (eg, mental health disorders), because genetic factors only contribute part of the risk and PRS will only ever capture part of the genetic contribution. Combining PRS with other risk factors has potential to improve outcome prediction and aid clinical decision-making (eg, determining follow-up options for individuals seeking help who are at clinical risk of future illness). Prognostication of adverse physical health outcomes or response to treatment in clinical populations are of great interest for psychiatric practice, but data from larger samples are needed to develop and evaluate PRS. Conclusions and Relevance Polygenic risk scores will contribute to risk assessment in clinical psychiatry as it evolves to combine information from molecular, clinical, and lifestyle metrics. The genome-wide genotype data needed to calculate PRS are inexpensive to generate and could become available to psychiatrists as a by-product of practices in other medical specialties. The utility of PRS in clinical psychiatry, as well as ethical issues associated with their use, should be evaluated in the context of realistic expectations of what PRS can and cannot deliver. Clinical psychiatry has lagged behind other fields of health care in its use of new technologies and routine clinical data for research. Now is the time to catch up.

M. García-Gutiérrez, F. Navarrete, F. Sala et al.

During the last years, an extraordinary effort has been made to identify biomarkers as potential tools for improving prevention, diagnosis, drug response and drug development in psychiatric disorders. Contrary to other diseases, mental illnesses are classified by diagnostic categories with a broad variety list of symptoms. Consequently, patients diagnosed from the same psychiatric illness present a great heterogeneity in their clinical presentation. This fact together with the incomplete knowledge of the neurochemical alterations underlying mental disorders, contribute to the limited efficacy of current pharmacological options. In this respect, the identification of biomarkers in psychiatry is becoming essential to facilitate diagnosis through the developing of markers that allow to stratify groups within the syndrome, which in turn may lead to more focused treatment options. In order to shed light on this issue, this review summarizes the concept and types of biomarkers including an operational definition for therapeutic development. Besides, the advances in this field were summarized and sorted into five categories, which include genetics, transcriptomics, proteomics, metabolomics, and epigenetics. While promising results were achieved, there is a lack of biomarker investigations especially related to treatment response to psychiatric conditions. This review includes a final conclusion remarking the future challenges required to reach the goal of developing valid, reliable and broadly-usable biomarkers for psychiatric disorders and their treatment. The identification of factors predicting treatment response will reduce trial-and-error switches of medications facilitating the discovery of new effective treatments, being a crucial step towards the establishment of greater personalized medicine.

M. Chieze, S. Hurst, S. Kaiser et al.

Background: Determining the clinical effects of coercion is a difficult challenge, raising ethical, legal, and methodological questions. Despite limited scientific evidence on effectiveness, coercive measures are frequently used, especially in psychiatry. This systematic review aims to search for effects of seclusion and restraint on psychiatric inpatients with wider inclusion of outcomes and study designs than former reviews. Methods: A systematic search was conducted following PRISMA guidelines, primarily through Pubmed, Embase, and CENTRAL. Interventional and prospective observational studies on effects of seclusion and restraint on psychiatric inpatients were included. Main search keywords were restraint, seclusion, psychiatry, effect, harm, efficiency, efficacy, effectiveness, and quality of life. Results: Thirty-five articles were included, out of 6,854 records. Studies on the effects of seclusion and restraint in adult psychiatry comprise a wide range of outcomes and designs. The identified literature provides some evidence that seclusion and restraint have deleterious physical or psychological consequences. Estimation of post-traumatic stress disorder incidence after intervention varies from 25% to 47% and, thus, is not negligible, especially for patients with past traumatic experiences. Subjective perception has high interindividual variability, mostly associated with negative emotions. Effectiveness and adverse effects of seclusion and restraint seem to be similar. Compared to other coercive measures (notably forced medication), seclusion seems to be better accepted, while restraint seems to be less tolerated, possibly because of the perception of seclusion as “non-invasive.” Therapeutic interaction appears to have a positive influence on coercion perception. Conclusion: Heterogeneity of the included studies limited drawing clear conclusions, but the main results identified show negative effects of seclusion and restraint. These interventions should be used with caution and as a last resort. Patients’ preferences should be taken into account when deciding to apply these measures. The therapeutic relationship could be a focus for improvement of effects and subjective perception of coercion. In terms of methodology, studying coercive measures remains difficult but, in the context of current research on coercion reduction, is needed to provide workable baseline data and potential targets for interventions. Well-conducted prospective cohort studies could be more feasible than randomized controlled trials for interventional studies.

Alan J. Meehan, Stephanie J. Lewis, S. Fazel et al.

Recent years have seen the rapid proliferation of clinical prediction models aiming to support risk stratification and individualized care within psychiatry. Despite growing interest, attempts to synthesize current evidence in the nascent field of precision psychiatry have remained scarce. This systematic review therefore sought to summarize progress towards clinical implementation of prediction modeling for psychiatric outcomes. We searched MEDLINE, PubMed, Embase, and PsychINFO databases from inception to September 30, 2020, for English-language articles that developed and/or validated multivariable models to predict (at an individual level) onset, course, or treatment response for non-organic psychiatric disorders (PROSPERO: CRD42020216530). Individual prediction models were evaluated based on three key criteria: (i) mitigation of bias and overfitting; (ii) generalizability, and (iii) clinical utility. The Prediction model Risk Of Bias ASsessment Tool (PROBAST) was used to formally appraise each study’s risk of bias. 228 studies detailing 308 prediction models were ultimately eligible for inclusion. 94.5% of developed prediction models were deemed to be at high risk of bias, largely due to inadequate or inappropriate analytic decisions. Insufficient internal validation efforts (within the development sample) were also observed, while only one-fifth of models underwent external validation in an independent sample. Finally, our search identified just one published model whose potential utility in clinical practice was formally assessed. Our findings illustrated significant growth in precision psychiatry with promising progress towards real-world application. Nevertheless, these efforts have been inhibited by a preponderance of bias and overfitting, while the generalizability and clinical utility of many published models has yet to be formally established. Through improved methodological rigor during initial development, robust evaluations of reproducibility via independent validation, and evidence-based implementation frameworks, future research has the potential to generate risk prediction tools capable of enhancing clinical decision-making in psychiatric care.

D. Durstewitz, G. Koppe, A. Meyer-Lindenberg

Machine and deep learning methods, today’s core of artificial intelligence, have been applied with increasing success and impact in many commercial and research settings. They are powerful tools for large scale data analysis, prediction and classification, especially in very data-rich environments (“big data”), and have started to find their way into medical applications. Here we will first give an overview of machine learning methods, with a focus on deep and recurrent neural networks, their relation to statistics, and the core principles behind them. We will then discuss and review directions along which (deep) neural networks can be, or already have been, applied in the context of psychiatry, and will try to delineate their future potential in this area. We will also comment on an emerging area that so far has been much less well explored: by embedding semantically interpretable computational models of brain dynamics or behavior into a statistical machine learning context, insights into dysfunction beyond mere prediction and classification may be gained. Especially this marriage of computational models with statistical inference may offer insights into neural and behavioral mechanisms that could open completely novel avenues for psychiatric treatment.

C. Bousman, S. Bengesser, K. Aitchison et al.

Abstract The implementation of pharmacogenomic (PGx) testing in psychiatry remains modest, in part due to divergent perceptions of the quality and completeness of the evidence base and diverse perspectives on the clinical utility of PGx testing among psychiatrists and other healthcare providers. Recognizing the current lack of consensus within the field, the International Society of Psychiatric Genetics assembled a group of experts to conduct a narrative synthesis of the PGx literature, prescribing guidelines, and product labels related to psychotropic medications as well as the key considerations and limitations related to the use of PGx testing in psychiatry. The group concluded that to inform medication selection and dosing of several commonly-used antidepressant and antipsychotic medications, current published evidence, prescribing guidelines, and product labels support the use of PGx testing for 2 cytochrome P450 genes (CYP2D6, CYP2C19). In addition, the evidence supports testing for human leukocyte antigen genes when using the mood stabilizers carbamazepine (HLA-A and HLA-B), oxcarbazepine (HLA-B), and phenytoin (CYP2C9, HLA-B). For valproate, screening for variants in certain genes (POLG, OTC, CSP1) is recommended when a mitochondrial disorder or a urea cycle disorder is suspected. Although barriers to implementing PGx testing remain to be fully resolved, the current trajectory of discovery and innovation in the field suggests these barriers will be overcome and testing will become an important tool in psychiatry.

D. Nutt, Robin Carhart-Harris

In the 1950s, the Swiss pharmaceutical company Sandoz, which employed the chemist Albert Hofmann, who discovered lysergic acid diethylamide (LSD) and the similar serotonergic psychedelic psilocybin, made these drugs available to the psychiatric research community as the products Delysid and Indocybin, respectively. By the 1960s, these drugs had caused a revolution in brain science and psychiatry because of their widespread use by researchers and clinicians in many Western countries, especially the US. Before LSD was banned, the US National Institutes of Health funded more than 130 studies exploring its clinical utility, with positive results in a range of disorders but particularly anxiety, depression, and alcoholism. However, the displacement of LSD into recreational use and eventual association with the antiVietnam war movement led to all psychedelics being banned in the US. This ban became ratified globally under the 1971 UN Convention on narcotics. Since then, research funding, drug production, and the study of psychedelics as clinical agents has been virtually stopped. Until very recently, no companies would manufacture medical-grade psychedelics, which made getting regulatory approval for clinical research—especially clinical trials—very difficult and in some countries (eg, Germany) impossible. The past decade has seen a resurrection in human psychedelic drug research, especially involving psilocybin. There were 2 drivers to this. The first was the discovery by Griffiths et al1 that a single high dose (25 mg) of psilocybin, given in a psychotherapeutic setting, produced enduring positive changes in mood and wellbeing in people who do not have depression. The second was our series2 of neuroimaging studies in healthy volunteers, which revealed that psilocybin produced profound and meaningful alterations in brain function, especially of the default mode network, consistent with an antidepressant effect. These findings suggested the possible utility of psilocybin for treating depression and initiated the launch of studies in the UK and US that further supported an antidepressant outcome from a single, 25-mg psilocybin dose in people with resistant depression3 and those with anxiety and depression symptoms provoked by life-threatening cancer diagnoses.4,5 There have also been open studies showing efficacy in both alcohol and tobacco dependence.6 Based on these positive findings, at least 2 companies have been set up to take psilocybin to the clinic by funding multicenter, dose-finding studies of psilocybin in depression, and a search of ClinicalTrials.gov (in April 2020) revealed that more than 30 psychedelic drug trials are registered (mostly with psilocybin, although a few are with LSD). These include studies in anorexia, obsessive-compulsive disorder, and addictions, as well as depression. At least 2 of the depression trials7,8 (those of COMPASS Pathways and Usona Institute) are randomized clinical trials compatible with the US Food and Drug Administration and European Medicines Agency registration processes and have been given fast-track status in this field. Many of the trials in other disorders are openlabel designs to gather feasibility and safety data to underpin subsequent double-blind randomized clinical trials. Once these regulatory-standard trials have been conducted, if the outcomes are positive, then it seems plausible that psilocybin will become a licensed medicine for some forms of mental illness when used in an approved treatment model. In the depression trials, the treatment model is becoming standardized as a 4-stage process: assessment, preparation, experience, and integration. Assessment determines if the patient is suitable for psychedelic therapy, both from a mental and physical perspective. Currently, people with a personal or family history of psychosis and bipolar disorder are excluded, as are those with significant health issues (eg, hypertension) because psychedelics transiently increase blood pressure. Certain medications need to be stopped or at least reduced before the treatment, because they can block or attenuate the effect of the psychedelic. Specifically, medicines that block 5-HT2A receptors (eg, amitriptyline, olanzapine, quetiapine, risperidone, trazodone) need to be withdrawn, and serotonin reuptake inhibitors ideally stopped or, if that is not feasible, tapered down, because they produce subsensitivity of the 5-HT2A receptor. In modern studies,3-5 preparation sessions typically take place the day before the drug administration, the participant is prepared for the experience by at least 1 trained therapist, who are often referred to as guides, based on the analogy of the psychedelic experience being a psychological journey. An overview of the dynamics and nature of psychedelic experiences is explained, including how it can be challenging for many people, how any such challenges can be best confronted, and how the participant can get the most out of the experience. During the psychedelic experience, the individual is offered eyeshades and earphones to listen to a music compilation that has been prepared in advance (which they can specify) because music seems to enhance the therapeutic process. For oral psilocybin, the sessions last 4 to 5 hours. Verbal engagement with the therapists is not expected, and most patients go deep into their own visions, thoughts, and memories and do not want to be disturbed. But the guide or guides are present, and with permission, they can hold the patient’s hand to reassure the person that he or she is being looked after. The next day is the integration session—during which the same guide or guides talk through the experience and help the patient make sense of it. Ideally, a small number of standard, talk-based psychotherapeutic sessions are further available for issues that emerged during the psychedelic experience to be processed, VIEWPOINT

Priya Sharma, Talia Vasaturo-Kolodner, Valentina Mancini et al.

Adwitiya Ray, Akansha Bhardwaj, Y. Malik et al.

The burden of mental illness both in world and India is increasing at an alarming rate. Adding to it, there has been an increase in mental health challenges during covid-19 pandemic with a rise in suicide, loneliness and substance use. Artificial intelligence can act as a potential solution to address this shortage. The use of artificial intelligence is increasingly being employed in various fields of mental health like affective disorders, psychosis, and geriatric psychiatry. The benefits are various like lower costs, wider reach but at the same time it comes with its own disadvantages. This article reviews the current understanding of artificial intelligence, the types of Artificial intelligence, its current use in various mental health disorders, current status in India, advantages, disadvantages and future potentials. With the passage of time and digitalization of the modern age, there will be an increase in the use of artificial intelligence in psychiatry hence a detailed understanding will be thoughtful. For this, we searched PubMed, Google Scholar, and Science Direct, China national Knowledge Infrastructure (CNKI), Globus Index Medicus search engines by using keywords. Initial searches involved the use of each individual keyword while the later searches involved the use of more than one word in different permutation combinations.

K. Friston

This review considers computational psychiatry from a particular viewpoint: namely, a commitment to explaining psychopathology in terms of pathophysiology. It rests on the notion of a generative model as underwriting (i) sentient processing in the brain, and (ii) the scientific process in psychiatry. The story starts with a view of the brain—from cognitive and computational neuroscience—as an organ of inference and prediction. This offers a formal description of neuronal message passing, distributed processing and belief propagation in neuronal networks; and how certain kinds of dysconnection lead to aberrant belief updating and false inference. The dysconnections in question can be read as a pernicious synaptopathy that fits comfortably with formal notions of how we—or our brains—encode uncertainty or its complement, precision . It then considers how the ensuing process theories are tested empirically, with an emphasis on the computational modelling of neuronal circuits and synaptic gain control that mediates attentional set, active inference, learning and planning. The opportunities afforded by this sort of modelling are considered in light of in silico experiments; namely, computational neuropsychology, computational phenotyping and the promises of a computational nosology for psychiatry. The resulting survey of computational approaches is not scholarly or exhaustive. Rather, its aim is to review a theoretical narrative that is emerging across subdisciplines within psychiatry and empirical scales of investigation. These range from epilepsy research to neurodegenerative disorders; from post-traumatic stress disorder to the management of chronic pain, from schizophrenia to functional medical symptoms.

P. Fusar-Poli, M. Manchia, N. Koutsouleris et al.

Precision psychiatry is an emerging field with transformative opportunities for mental health. However, the use of clinical prediction models carries unprecedented ethical challenges, which must be addressed before accessing the potential benefits of precision psychiatry. This critical review covers multidisciplinary areas, including psychiatry, ethics, statistics and machine-learning, healthcare and academia, as well as input from people with lived experience of mental disorders, their family, and carers. We aimed to identify core ethical considerations for precision psychiatry and mitigate concerns by designing a roadmap for research and clinical practice. We identified priorities: learning from somatic medicine; identifying precision psychiatry use cases; enhancing transparency and generalizability; fostering implementation; promoting mental health literacy; communicating risk estimates; data protection and privacy; and fostering the equitable distribution of mental health care. We hope this blueprint will advance research and practice and enable people with mental health problems to benefit from precision psychiatry.