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The British Journal of Psychiatry : the... Sep 2023The COVID-19 pandemic has transformed healthcare significantly and telepsychiatry is now the primary means of treatment in some countries. (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
The COVID-19 pandemic has transformed healthcare significantly and telepsychiatry is now the primary means of treatment in some countries.
AIMS
To compare the efficacy of telepsychiatry and face-to-face treatment.
METHOD
A comprehensive meta-analysis comparing telepsychiatry with face-to-face treatment for psychiatric disorders. The primary outcome was the mean change in the standard symptom scale scores used for each psychiatric disorder. Secondary outcomes included all meta-analysable outcomes, such as all-cause discontinuation and safety/tolerability.
RESULTS
We identified 32 studies ( = 3592 participants) across 11 mental illnesses. Disease-specific analyses showed that telepsychiatry was superior to face-to-face treatment regarding symptom improvement for depressive disorders ( = 6 studies, = 561; standardised mean difference s.m.d. = -0.325, 95% CI -0.640 to -0.011, = 0.043), whereas face-to-face treatment was superior to telepsychiatry for eating disorder ( = 1, = 128; s.m.d. = 0.368, 95% CI 0.018-0.717, = 0.039). No significant difference was seen between telepsychiatry and face-to-face treatment when all the studies/diagnoses were combined ( = 26, = 2290; = 0.248). Telepsychiatry had significantly fewer all-cause discontinuations than face-to-face treatment for mild cognitive impairment ( = 1, = 61; risk ratio RR = 0.552, 95% CI 0.312-0.975, = 0.040), whereas the opposite was seen for substance misuse ( = 1, = 85; RR = 37.41, 95% CI 2.356-594.1, = 0.010). No significant difference regarding all-cause discontinuation was seen between telepsychiatry and face-to-face treatment when all the studies/diagnoses were combined ( = 27, = 3341; = 0.564).
CONCLUSIONS
Telepsychiatry achieved a symptom improvement effect for various psychiatric disorders similar to that of face-to-face treatment. However, some superiorities/inferiorities were seen across a few specific psychiatric disorders, suggesting that its efficacy may vary according to disease type.
Topics: Humans; COVID-19; Pandemics; Psychiatry; Telemedicine; Cognitive Dysfunction; Randomized Controlled Trials as Topic
PubMed: 37655816
DOI: 10.1192/bjp.2023.86 -
Journal of Medical Internet Research Mar 2022Mental health disorders are a leading cause of medical disabilities across an individual's lifespan. This burden is particularly substantial in children and adolescents... (Review)
Review
BACKGROUND
Mental health disorders are a leading cause of medical disabilities across an individual's lifespan. This burden is particularly substantial in children and adolescents because of challenges in diagnosis and the lack of precision medicine approaches. However, the widespread adoption of wearable devices (eg, smart watches) that are conducive for artificial intelligence applications to remotely diagnose and manage psychiatric disorders in children and adolescents is promising.
OBJECTIVE
This study aims to conduct a scoping review to study, characterize, and identify areas of innovations with wearable devices that can augment current in-person physician assessments to individualize diagnosis and management of psychiatric disorders in child and adolescent psychiatry.
METHODS
This scoping review used information from the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A comprehensive search of several databases from 2011 to June 25, 2021, limited to the English language and excluding animal studies, was conducted. The databases included Ovid MEDLINE and Epub ahead of print, in-process and other nonindexed citations, and daily; Ovid Embase; Ovid Cochrane Central Register of Controlled Trials; Ovid Cochrane Database of Systematic Reviews; Web of Science; and Scopus.
RESULTS
The initial search yielded 344 articles, from which 19 (5.5%) articles were left on the final source list for this scoping review. Articles were divided into three main groups as follows: studies with the main focus on autism spectrum disorder, attention-deficit/hyperactivity disorder, and internalizing disorders such as anxiety disorders. Most of the studies used either cardio-fitness chest straps with electrocardiogram sensors or wrist-worn biosensors, such as watches by Fitbit. Both allowed passive data collection of the physiological signals.
CONCLUSIONS
Our scoping review found a large heterogeneity of methods and findings in artificial intelligence studies in child psychiatry. Overall, the largest gap identified in this scoping review is the lack of randomized controlled trials, as most studies available were pilot studies and feasibility trials.
Topics: Adolescent; Adolescent Psychiatry; Artificial Intelligence; Autism Spectrum Disorder; Child Psychiatry; Humans; Wearable Electronic Devices
PubMed: 35285812
DOI: 10.2196/33560 -
Journal of Personalized Medicine Jun 2023This systematic review evaluated the animal and human evidence for pharmacomicrobiomics (PMx) interactions of antidepressant medications. Studies of gut microbiota... (Review)
Review
This systematic review evaluated the animal and human evidence for pharmacomicrobiomics (PMx) interactions of antidepressant medications. Studies of gut microbiota effects on functional and behavioral effects of antidepressants in human and animal models were identified from PubMed up to December 2022. Risk of bias was assessed, and results are presented as a systematic review following PRISMA guidelines. A total of 28 (21 animal, 7 human) studies were included in the review. The reviewed papers converged on three themes: (1) Antidepressants can alter the composition and metabolites of gut microbiota, (2) gut microbiota can alter the bioavailability of certain antidepressants, and (3) gut microbiota may modulate the clinical or modeled mood modifying effects of antidepressants. The majority (n = 22) of studies had at least moderate levels of bias present. While strong evidence is still lacking to understand the clinical role of antidepressant PMx in human health, there is evidence for interactions among antidepressants, microbiota changes, microbiota metabolite changes, and behavior. Well-controlled studies of the mediating and moderating effects of baseline and treatment-emergent changes in microbiota on therapeutic and adverse responses to antidepressants are needed to better establish a potential role of PMx in personalizing antidepressant treatment selection and response prediction.
PubMed: 37511699
DOI: 10.3390/jpm13071086 -
Psychiatric Genetics Dec 2022Psychiatric diseases exact a heavy socioeconomic toll, and it is particularly difficult to identify their risk factors and causative mechanisms due to their... (Review)
Review
Psychiatric diseases exact a heavy socioeconomic toll, and it is particularly difficult to identify their risk factors and causative mechanisms due to their multifactorial nature, the limited physiopathological insight, the many confounding factors, and the potential reverse causality between the risk factors and psychiatric diseases. These characteristics make Mendelian randomization (MR) a precious tool for studying these disorders. MR is an analytical method that employs genetic variants linked to a certain risk factor, to assess if an observational association between that risk factor and a health outcome is compatible with a causal relationship. We report the first systematic review of all existing applications and findings of MR in psychiatric disorders, aiming at facilitating the identification of risk factors that may be common to different psychiatric diseases, and paving the way to transdiagnostic MR studies in psychiatry, which are currently lacking. We searched Web of Knowledge, Scopus, and Pubmed databases (until 3 May 2022) for articles on MR in psychiatry. The protocol was preregistered in PROSPERO (CRD42021285647). We included methodological details and results from 50 articles, mainly on schizophrenia, major depression, autism spectrum disorders, and bipolar disorder. While this review shows how MR can offer unique opportunities for unraveling causal links in risk factors and etiological elements of specific psychiatric diseases and transdiagnostically, some methodological flaws in the existing literature limit reliability of results and probably underlie their heterogeneity. We highlight perspectives and recommendations for future works on MR in psychiatry.
Topics: Humans; Mendelian Randomization Analysis; Reproducibility of Results; Causality; Psychiatry; Depressive Disorder, Major
PubMed: 36354137
DOI: 10.1097/YPG.0000000000000327 -
Neuroscience and Biobehavioral Reviews Jun 2023We aimed to identify promising novel medications for child and adolescent mental health problems. We systematically searched https://clinicaltrials.gov/ and... (Review)
Review
The future of child and adolescent clinical psychopharmacology: A systematic review of phase 2, 3, or 4 randomized controlled trials of pharmacologic agents without regulatory approval or for unapproved indications.
We aimed to identify promising novel medications for child and adolescent mental health problems. We systematically searched https://clinicaltrials.gov/ and https://www.clinicaltrialsregister.eu/ (from 01/01/2010-08/23/2022) for phase 2 or 3 randomized controlled trials (RCTs) of medications without regulatory approval in the US, Europe or Asia, including also RCTs of dietary interventions/probiotics. Additionally, we searched phase 4 RCTs of agents targeting unlicensed indications for children/adolescents with mental health disorders. We retrieved 234 ongoing or completed RCTs, including 26 (11%) with positive findings on ≥ 1 primary outcome, 43 (18%) with negative/unavailable results on every primary outcome, and 165 (70%) without publicly available statistical results. The only two compounds with evidence of significant effects that were replicated in ≥ 1 additional RCT without any negative RCTs were dasotraline for attention-deficit/hyperactivity disorder, and carbetocin for hyperphagia in Prader-Willi syndrome. Among other strategies, targeting specific symptom dimensions in samples stratified based on clinical characteristics or established biomarkers may increase chances of success in future development programmes.
Topics: Humans; Child; Adolescent; Psychopharmacology; Randomized Controlled Trials as Topic; Attention Deficit Disorder with Hyperactivity; Prader-Willi Syndrome; Clinical Trials, Phase II as Topic
PubMed: 37001575
DOI: 10.1016/j.neubiorev.2023.105149 -
Current Neuropharmacology 2023Traditional medicine and biomedical sciences are reaching a turning point because of the constantly growing impact and volume of Big Data. Machine Learning (ML)...
Traditional medicine and biomedical sciences are reaching a turning point because of the constantly growing impact and volume of Big Data. Machine Learning (ML) techniques and related algorithms play a central role as diagnostic, prognostic, and decision-making tools in this field. Another promising area becoming part of everyday clinical practice is personalized therapy and pharmacogenomics. Applying ML to pharmacogenomics opens new frontiers to tailored therapeutical strategies to help clinicians choose drugs with the best response and fewer side effects, operating with genetic information and combining it with the clinical profile. This systematic review aims to draw up the state-of-the-art ML applied to pharmacogenomics in psychiatry. Our research yielded fourteen papers; most were published in the last three years. The sample comprises 9,180 patients diagnosed with mood disorders, psychoses, or autism spectrum disorders. Prediction of drug response and prediction of side effects are the most frequently considered domains with the supervised ML technique, which first requires training and then testing. The random forest is the most used algorithm; it comprises several decision trees, reduces the training set's overfitting, and makes precise predictions. ML proved effective and reliable, especially when genetic and biodemographic information were integrated into the algorithm. Even though ML and pharmacogenomics are not part of everyday clinical practice yet, they will gain a unique role in the next future in improving personalized treatments in psychiatry.
Topics: Humans; Pharmacogenetics; Precision Medicine; Machine Learning; Mental Disorders; Psychiatry
PubMed: 37559539
DOI: 10.2174/1570159X21666230808170123 -
Translational Psychiatry Jul 2020To tackle the phenotypic heterogeneity of schizophrenia, data-driven methods are often applied to identify subtypes of its symptoms and cognitive deficits. However, a... (Review)
Review
To tackle the phenotypic heterogeneity of schizophrenia, data-driven methods are often applied to identify subtypes of its symptoms and cognitive deficits. However, a systematic review on this topic is lacking. The objective of this review was to summarize the evidence obtained from longitudinal and cross-sectional data-driven studies in positive and negative symptoms and cognitive deficits in patients with schizophrenia spectrum disorders, their unaffected siblings and healthy controls or individuals from general population. Additionally, we aimed to highlight methodological gaps across studies and point out future directions to optimize the translatability of evidence from data-driven studies. A systematic review was performed through searching PsycINFO, PubMed, PsycTESTS, PsycARTICLES, SCOPUS, EMBASE and Web of Science electronic databases. Both longitudinal and cross-sectional studies published from 2008 to 2019, which reported at least two statistically derived clusters or trajectories were included. Two reviewers independently screened and extracted the data. In this review, 53 studies (19 longitudinal and 34 cross-sectional) that conducted among 17,822 patients, 8729 unaffected siblings and 5520 controls or general population were included. Most longitudinal studies found four trajectories that characterized by stability, progressive deterioration, relapsing and progressive amelioration of symptoms and cognitive function. Cross-sectional studies commonly identified three clusters with low, intermediate (mixed) and high psychotic symptoms and cognitive profiles. Moreover, identified subgroups were predicted by numerous genetic, sociodemographic and clinical factors. Our findings indicate that schizophrenia symptoms and cognitive deficits are heterogeneous, although methodological limitations across studies are observed. Identified clusters and trajectories along with their predictors may be used to base the implementation of personalized treatment and develop a risk prediction model for high-risk individuals with prodromal symptoms.
Topics: Cognition; Cognition Disorders; Cross-Sectional Studies; Humans; Psychotic Disorders; Schizophrenia
PubMed: 32694510
DOI: 10.1038/s41398-020-00919-x -
Frontiers in Psychiatry 2019Pharmacological treatment is of great importance in forensic psychiatry, and the vast majority of patients are treated with antipsychotic agents. There are several...
Pharmacological treatment is of great importance in forensic psychiatry, and the vast majority of patients are treated with antipsychotic agents. There are several systematic differences between general and forensic psychiatric patients, e.g. severe violent behavior, the amount of comorbidity, such as personality disorders and/or substance abuse. Based on that, it is reasonable to suspect that effects of pharmacological treatments also may differ. The objective of this systematic review was to investigate the effects of pharmacological interventions for patients within forensic psychiatry. The systematic review protocol was pre-registered in PROSPERO (CRD42017075308). Six databases were used for literature search on January 11, 2018. Controlled trials from forensic psychiatric care reporting on the effects of antipsychotic agents, mood stabilizers, benzodiazepines, antidepressants, as well as pharmacological agents used for the treatment of addiction or ADHD, were included. Two authors independently reviewed the studies, evaluated risk of bias and assessed certainty of evidence using Grading of Recommendations Assessment, Development and Evaluation (GRADE). The literature search resulted in 1783 records (titles and abstracts) out of which 10 studies were included. Most of the studies included were retrospective and non-randomized. Five of them focused on treatment with clozapine and the remaining five on other antipsychotics or mood stabilizers. Five studies with a high risk of bias indicated positive effects of clozapine on time from treatment start to discharge, crime-free time, time from discharge to readmission, improved clinical functioning, and reduction in aggressive behavior. Psychotic symptoms after treatment were more pronounced in the clozapine group. Mainly due to the high risk of bias the reliability of the evidence for all outcomes was assessed as very low. This systematic review highlights the shortage of knowledge on the effectiveness of pharmacological treatment within forensic psychiatry. Due to very few studies being available in this setting, as well as limitations in their execution and reporting, it is challenging to overview the outcomes of pharmacological interventions in this context. The frequent use of antipsychotics, sometimes in combination with other pharmacological agents, in this complex and heterogeneous patient group, calls for high-quality studies performed in this specific setting.
PubMed: 32009993
DOI: 10.3389/fpsyt.2019.00963 -
European Addiction Research 2021Although the recreational cannabis use is expressive worldwide, the literature about medical potential of cannabis extracts, including its anti-inflammatory properties,...
INTRODUCTION
Although the recreational cannabis use is expressive worldwide, the literature about medical potential of cannabis extracts, including its anti-inflammatory properties, remains inconclusive.
METHODS
We screened all articles, published on the PubMed database, on inflammatory mediators and any information about cannabis use from 1980 to March 2019.
RESULTS
Six studies were included, and the main findings were as follows: (i) among healthy volunteers and cannabis users, cannabinoids seemed to decrease the inflammatory response, thus decreasing the immune response, which led to a higher risk of infections; (ii) among patients with multiple sclerosis, cannabinoids seemed to have little impact on the inflammatory markers' levels.
DISCUSSION
Although cannabis use can produce immune inflammatory suppression in healthy people, this effect is not robust enough to change inflammatory mediators' levels in situations of highly dysfunctional inflammatory activation. Nevertheless, the impact of cannabinoids in clinical outcomes of these conditions remains to be determined.
Topics: Analgesics; Cannabis; Humans; Inflammation Mediators; Multiple Sclerosis
PubMed: 32726782
DOI: 10.1159/000508840 -
Journal of Psychiatric Research Aug 2023People with mental disorders, such as psychosis or autism spectrum disorder (ASD), often present impairments in social cognition (SC), which may cause significant... (Review)
Review
People with mental disorders, such as psychosis or autism spectrum disorder (ASD), often present impairments in social cognition (SC), which may cause significant difficulties in real-world functioning. SC deficits are seen also in unaffected relatives, indicating a genetic substratum. The present review evaluated the evidence on the association between SC and the polygenic risk score (PRS), a single metric of the molecular genetic risk to develop a specific disorder. In July 2022, we conducted systematic searches in Scopus and PubMed following the PRISMA-ScR guidelines. We selected original articles written in English reporting results on the association between PRSs for any mental disorder and domains of SC either in people with mental disorders or controls. The search yielded 244 papers, of which 13 were selected for inclusion. Studies tested mainly PRSs for schizophrenia, ASD, and attention-deficit hyperactivity disorder. Emotion recognition was the most investigated domain of SC. Overall, evidence revealed that currently available PRSs for mental disorders do not explain variation in SC performances. To enhance the understanding of mechanisms underlying SC in mental disorders, future research should focus on the development of transdiagnostic PRSs, study their interaction with environmental risk factors, and standardize outcome measurement.
Topics: Humans; Social Cognition; Autism Spectrum Disorder; Psychotic Disorders; Attention Deficit Disorder with Hyperactivity; Risk Factors
PubMed: 37418886
DOI: 10.1016/j.jpsychires.2023.06.029