The impact of machine learning in predicting risk of violence: a systematic review

Background: Inpatient violence in clinical and forensic settings is still an ongoing challenge to organizations and practitioners. Existing risk assessment instruments show only moderate benefits in clinical practice, are time consuming, and seem to scarcely generalize across different populations. In the last years, machine learning (ML) models have been applied in the study of risk factors for aggressive episodes. The objective of this systematic review is to investigate the potential of ML for identifying risk of violence in clinical and forensic populations.Methods: Following Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, a systematic review on the use of ML techniques in predicting risk of violence of psychiatric patients in clinical and forensic settings was performed. A systematic search was conducted on Medline/Pubmed, CINAHL, PsycINFO, Web of Science, and Scopus. Risk of bias and applicability assessment was performed using Prediction model Risk Of Bias ASsessment Tool (PROBAST).Results: We identified 182 potentially eligible studies from 2,259 records, and 8 papers were included in this systematic review. A wide variability in the experimental settings and characteristics of the enrolled samples emerged across studies, which probably represented the major cause for the absence of shared common predictors of violence found by the models learned. Nonetheless, a general trend toward a better performance of ML methods compared to structured violence risk assessment instruments in predicting risk of violent episodes emerged, with three out of eight studies with an AUC above 0.80. However, because of the varied experimental protocols, and heterogeneity in study populations, caution is needed when trying to quantitatively compare (e.g., in terms of AUC) and derive general conclusions from these approaches. Another limitation is represented by the overall quality of the included studies that suffer from objective limitations, difficult to overcome, such as the common use of retrospective data.Conclusion: Despite these limitations, ML models represent a promising approach in shedding light on predictive factors of violent episodes in clinical and forensic settings. Further research and more investments are required, preferably in large and prospective groups, to boost the application of ML models in clinical practice

Valutazioni psichiatriche-forensi e intelligenza artificiale: nuovi scenari possibili

Cognitive biases are defined as mental processes which can lead to the elaboration of misjudgements. Biases can influence thoughts, opinions, behaviours, and they are inevitably involved in psychiatricforensic evaluations. The delicate role that the mental health expert plays in the psy-chiatric examinations makes these mistakes highly relevant. Make sure that such a complex decision-making process is set up with care and attention is fundamental to guarantee the best protection of the individual rights, but also to avoid a mismanagement of government money. In order to avoid the possible use of cognitive biases in psychiatric-forensic assessments, it has been suggested to increase the standardization of evaluation procedures, but scientific literature shows that experts working in this area are often reluctant to question their own work. Recently, the application of Artificial Intelligence (AI) to the forensic field has opened new possibilities, but, on the other hand, it has generated new questions. AI seems to be beneficial for those decision-ma-king processes where greater standardization is required, but attempts to use AI tools in the field of forensic psychiatry have highlighted some critical issues. In this article, after discussing the problems that characterize both human and computational decision-making, we will propose possible solutions.Si definiscono “bias cognitivi” quei processi mentali che possono condurre a elaborare giudizi imprecisi o errati. I bias possono influenzare pensieri, opinioni, condotte e sono inevitabilmente implicati anche nelle valutazioni psichiatriche-forensi. Il delicato ruolo che l’esperto in salute mentale riveste all’interno del processo peritale rende questi errori particolarmente rilevanti. Assicurarsi che un pro-cesso decisionale così complesso sia messo in atto con la dovuta attenzione e cura risulta di fondamentale importanza per garantire la maggiore tutela possibile del singolo, ma anche per evitare una mala gestione dei soldi dello Stato. Per sfuggire all’eventuale utilizzo di bias cognitivi nelle valutazioni psichiatriche-forensi, si è suggerito di standardizzare maggiormente le procedure di valutazione, ma dalla letteratura emerge che gli esperti che operano in quest’ambito sono spesso restii a mettere in di-scussione il proprio stesso operato. Di recente, l’applicazione dell’Intelligenza Artificiale (IA) al campo forense ha aperto nuove possibilità, ma, d’altro canto, ha generato nuovi interrogativi. L’IA sembra essere vantaggiosa per quei processi decisionali in cui maggiore standardizzazione è richiesta, ma i ten-tativi di utilizzo di strumenti dotati di IA in campo forense hanno messo in evidenza alcune criticità. In questo articolo, dopo aver discusso i problemi che caratterizzano sia il processo decisionale umano che quello computazionale, proporremo possibili soluzioni

Ontology Mapping and Data Discovery for the Translational Investigator

An integrated data repository (IDR) containing aggregations of clinical, biomedical, economic, administrative, and public health data is a key component of an overall translational research infrastructure. But most available data repositories are designed using standard data warehouse architecture that employs arbitrary data encoding standards, making queries across disparate repositories difficult. In response to these shortcomings we have designed a Health Ontology Mapper (HOM) that translates terminologies into formal data encoding standards without altering the underlying source data. We believe the HOM system promotes inter-institutional data sharing and research collaboration, and will ultimately lower the barrier to developing and using an IDR

Role of interferon lambda 4 and ALT levels in optimising treatment of HCV for patients with low-stage fibrosis

The use of new anti-HCV drugs is currently limited by high costs and dual therapy; pegylated interferon and ribavirin (peg-IFN+RBV) still represents the only affordable treatment in patients with low-stage fibrosis. We evaluated the role of Interferon lambda4 (IFNL4) polymorphisms and its combination with on-treatment alanine transaminase (ALT) modification in predicting sustained virological response (SVR) in HCV genotype 1 and 4 patients with low-stage fibrosis. We retrospectively analysed 124 patients with Metavir ≤F2, who received dual therapy at our centre. Genotyping for IFNL4 polymorphisms was assessed at baseline, as well as ALT levels (baseline and week 2, 4, 12 and 24 of therapy). Thirty patients (24%) were TT/TT, 74 (60%) TT/DG and 20 (16%) DG/DG. The SVR rate was significantly higher in TT/TT genotype compare to TT/DG and DG/DG (97% vs. 53% and 50%, respectively, p=0.001). Patients that achieved a 60% reduction of ALT baseline value after 4 weeks of therapy had a significantly higher SVR rate (94% vs. 52%, p<0.001). Factors significantly associated with SVR were TT/TT genotype (p=0.029), RVR (p=0.019) and 60% ALT reduction at 4 week of therapy (p=0.005). The absence of both TT/TT genotype and 60% ALT reduction were negative predictors of SVR (p<0.001). In conclusion, the combined use of IFNL4 polymorphisms and ALT reduction at 4 week of treatment is able to optimize candidates’ selection for peg-IFN+RBV, discriminating those that could still benefit from dual therapy from the ones that need the new regimen

Safety and efficacy of direct-acting antivirals in transfusion-dependent thalassemic patients with chronic hepatitis C

Background: Hepatitis C virus (HCV) infection is a major cause of liver-related morbidity and mortality among thalassemic patients. New treatments based on direct-acting antivirals (DAAs) are highly effective and well-tolerated by patients; nonetheless, they have not been studied in thalassemic populations. In this study, we evaluated the safety and efficacy of these treatments in a cohort of Sardinian thalassemic patients with chronic HCV infection. Methods: We consecutively recruited thalassemic patients with HCV infection, who were eligible for DAA therapy at 3 liver units. Different drug combinations, depending on HCV genotype and hepatic disease severity, were used according to the current guidelines. Sustained virological response was assessed at 12 weeks posttreatment. Data regarding the side effects and transfusion requirements were also collected. Results: We recruited 49 patients, including 29 males (59.2%), with the mean age of 43 years (genotype 1, 55.1%). Twenty-one (42.9%) patients had a history of interferon-based treatment. Cirrhosis was detected in 28 (57.1%) patients; only 1 patient had ascites and hypoalbuminemia (Child-Pugh B7). On the other hand, 35 (71.4%) patients received a sofosbuvir-based regimen. Ribavirin treatment was reported in 26 (53.1%) cases. All the patients were followed-up for at least 12 weeks after therapy, and sustained virological response was observed in 98% of the patients. No treatment discontinuation was required due to adverse events. The most common side effects included fatigue (24.5%), headache (10.2%), and anaemia (77%), requiring further blood transfusion in patients receiving ribavirin. Conclusions: This prospective study showed that DAAs are safe and effective agents in thalassemic patients with advanced liver fibrosis, regardless of previous antiviral treatment responses

Exploring the Role of Killer Cell Immunoglobulin-Like Receptors and Their HLA Class I Ligands in Autoimmune Hepatitis

Background Natural killer cells are involved in the complex mechanisms underlying autoimmune diseases but few studies have investigated their role in autoimmune hepatitis. Killer immunoglobulin-like receptors are key regulators of natural killer cell-mediated immune responses. Methods and Findings KIR gene frequencies, KIR haplotypes, KIR ligands and combinations of KIRs and their HLA Class I ligands were investigated in 114 patients diagnosed with type 1 autoimmune hepatitis and compared with a group of 221 healthy controls. HLA Class I and Class II antigen frequencies were compared to those of 551 healthy unrelated families representative of the Sardinian population. In our cohort, type 1 autoimmune hepatitis was strongly associated with the HLA-B18, Cw5, DR3 haplotype. The KIR2DS1 activating KIR gene and the high affinity HLA-C2 ligands were significantly higher in patients compared to controls. Patients also had a reduced frequency of HLA-Bw4 ligands for KIR3DL1 and HLA-C1 ligands for KIR2DL3. Age at onset was significantly associated with the KIR2DS1 activating gene but not with HLA-C1 or HLA-C2 ligand groups. Conclusions The activating KIR gene KIR2DS1 resulted to have an important predictive potential for early onset of type 1 autoimmune hepatitis. Additionally, the low frequency of the KIR-ligand combinations KIR3DL1/HLA-Bw4 and KIR2DL3/HLA-C1 coupled to the high frequency of the HLA-C2 high affinity ligands for KIR2DS1 could contribute to unwanted NK cell autoreactivity in AIH-1

Rationale and design of an independent randomised controlled trial evaluating the effectiveness of aripiprazole or haloperidol in combination with clozapine for treatment-resistant schizophrenia

<p>Abstract</p> <p>Background</p> <p>One third to two thirds of people with schizophrenia have persistent psychotic symptoms despite clozapine treatment. Under real-world circumstances, the need to provide effective therapeutic interventions to patients who do not have an optimal response to clozapine has been cited as the most common reason for simultaneously prescribing a second antipsychotic drug in combination treatment strategies. In a clinical area where the pressing need of providing therapeutic answers has progressively increased the occurrence of antipsychotic polypharmacy, despite the lack of robust evidence of its efficacy, we sought to implement a pre-planned protocol where two alternative therapeutic answers are systematically provided and evaluated within the context of a pragmatic, multicentre, independent randomised study.</p> <p>Methods/Design</p> <p>The principal clinical question to be answered by the present project is the relative efficacy and tolerability of combination treatment with clozapine plus aripiprazole compared with combination treatment with clozapine plus haloperidol in patients with an incomplete response to treatment with clozapine over an appropriate period of time. This project is a prospective, multicentre, randomized, parallel-group, superiority trial that follow patients over a period of 12 months. Withdrawal from allocated treatment within 3 months is the primary outcome.</p> <p>Discussion</p> <p>The implementation of the protocol presented here shows that it is possible to create a network of community psychiatric services that accept the idea of using their everyday clinical practice to produce randomised knowledge. The employed pragmatic attitude allowed to randomly allocate more than 100 individuals, which means that this study is the largest antipsychotic combination trial conducted so far in Western countries. We expect that the current project, by generating evidence on whether it is clinically useful to combine clozapine with aripiprazole rather than with haloperidol, provides physicians with a solid evidence base to be directly applied in the routine care of patients with schizophrenia.</p> <p>Trial Registration</p> <p><b>Clincaltrials.gov Identifier</b>: NCT00395915</p

Analysis of shared common genetic risk between amyotrophic lateral sclerosis and epilepsy

Because hyper-excitability has been shown to be a shared pathophysiological mechanism, we used the latest and largest genome-wide studies in amyotrophic lateral sclerosis (n = 36,052) and epilepsy (n = 38,349) to determine genetic overlap between these conditions. First, we showed no significant genetic correlation, also when binned on minor allele frequency. Second, we confirmed the absence of polygenic overlap using genomic risk score analysis. Finally, we did not identify pleiotropic variants in meta-analyses of the 2 diseases. Our findings indicate that amyotrophic lateral sclerosis and epilepsy do not share common genetic risk, showing that hyper-excitability in both disorders has distinct origins

Cabbage and fermented vegetables : From death rate heterogeneity in countries to candidates for mitigation strategies of severe COVID-19

Large differences in COVID-19 death rates exist between countries and between regions of the same country. Some very low death rate countries such as Eastern Asia, Central Europe, or the Balkans have a common feature of eating large quantities of fermented foods. Although biases exist when examining ecological studies, fermented vegetables or cabbage have been associated with low death rates in European countries. SARS-CoV-2 binds to its receptor, the angiotensin-converting enzyme 2 (ACE2). As a result of SARS-CoV-2 binding, ACE2 downregulation enhances the angiotensin II receptor type 1 (AT(1)R) axis associated with oxidative stress. This leads to insulin resistance as well as lung and endothelial damage, two severe outcomes of COVID-19. The nuclear factor (erythroid-derived 2)-like 2 (Nrf2) is the most potent antioxidant in humans and can block in particular the AT(1)R axis. Cabbage contains precursors of sulforaphane, the most active natural activator of Nrf2. Fermented vegetables contain many lactobacilli, which are also potent Nrf2 activators. Three examples are: kimchi in Korea, westernized foods, and the slum paradox. It is proposed that fermented cabbage is a proof-of-concept of dietary manipulations that may enhance Nrf2-associated antioxidant effects, helpful in mitigating COVID-19 severity.Peer reviewe

Nrf2-interacting nutrients and COVID-19 : time for research to develop adaptation strategies

There are large between- and within-country variations in COVID-19 death rates. Some very low death rate settings such as Eastern Asia, Central Europe, the Balkans and Africa have a common feature of eating large quantities of fermented foods whose intake is associated with the activation of the Nrf2 (Nuclear factor (erythroid-derived 2)-like 2) anti-oxidant transcription factor. There are many Nrf2-interacting nutrients (berberine, curcumin, epigallocatechin gallate, genistein, quercetin, resveratrol, sulforaphane) that all act similarly to reduce insulin resistance, endothelial damage, lung injury and cytokine storm. They also act on the same mechanisms (mTOR: Mammalian target of rapamycin, PPAR gamma:Peroxisome proliferator-activated receptor, NF kappa B: Nuclear factor kappa B, ERK: Extracellular signal-regulated kinases and eIF2 alpha:Elongation initiation factor 2 alpha). They may as a result be important in mitigating the severity of COVID-19, acting through the endoplasmic reticulum stress or ACE-Angiotensin-II-AT(1)R axis (AT(1)R) pathway. Many Nrf2-interacting nutrients are also interacting with TRPA1 and/or TRPV1. Interestingly, geographical areas with very low COVID-19 mortality are those with the lowest prevalence of obesity (Sub-Saharan Africa and Asia). It is tempting to propose that Nrf2-interacting foods and nutrients can re-balance insulin resistance and have a significant effect on COVID-19 severity. It is therefore possible that the intake of these foods may restore an optimal natural balance for the Nrf2 pathway and may be of interest in the mitigation of COVID-19 severity