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Abstract Background Suicide attempts (SA) constitute a serious clinical problem. People who attempt suicide are at high risk of further repetition. However, no interventions have been shown to be effective in reducing repetition in this group of patients. Methods/Design Multicentre randomized controlled trial. We examine the effectiveness of «ALGOS algorithm»: an intervention based in a decisional tree of contact type which aims at reducing the incidence of repeated suicide attempt during 6 months. This algorithm of case management comprises the two strategies of intervention that showed a significant reduction in the number of SA repeaters: systematic telephone contact (ineffective in first-attempters) and «Crisis card» (effective only in first-attempters). Participants who are lost from contact and those refusing healthcare, can then benefit from «short letters» or «postcards». Discussion ALGOS algorithm is easily reproducible and inexpensive intervention that will supply the guidelines for assessment and management of a population sometimes in difficulties with healthcare compliance. Furthermore, it will target some of these subgroups of patients by providing specific interventions for optimizing the benefits of case management strategy. Trial Registration The study was registered with the ClinicalTrials.gov Registry; number: NCT01123174.

Introduction: Early‐onset psychotic disorders include the prodromal phase and the first‐episode psychosis (FEP). They constitute a high‐risk period for suicidal behavior. Early intervention for psychosis (EIP) consists of intervening as early as possible. The effectiveness of early intervention on overall prognosis has been reported in numerous studies, and EIP services are emerging worldwide. Several authors report an improvement in suicidal behavior, but no study has looked at all the data. Aims of the Study: The aim of work is to study whether early intervention for psychosis has an impact on deaths by suicide and suicide attempts, and study which intervention methods have an impact on suicidal behavior. Methodology: By respecting the PRISMA criteria, previously declared on PROSPERO, by exploring 5 medical databases (PubMed, Cochrane, PsycINFO, Scopus, Embase), from their creation dates, published until 20/02/2023, in English, we carried out a meta‐analysis. The articles selected had to deal with the EIP and deaths by suicide or suicide attempts. Our primary outcome is the deaths by suicide and the secondary outcome the suicide attempt. Results: The exhaustive search identified a total of 2310 references. Nine articles were included. Their intervention modalities were pharmacotherapy, psychotherapy, case‐management, or related services, and psycho‐social therapies. Our meta‐analysis shows that early intervention for early‐onset psychotic disorders is associated with a statistically significant reduction by a third in deaths by suicide (ORa = 0.66 (0.49–0.88), p = 0.005) and by a third in suicide attempts (ORa = 0.66 (0.50–0.86), p = 0.002), with non‐significant heterogeneity. Sensitivity analyses excluding the study with statistical difficulties due to the absence of an event and studies with a high risk of bias point in the same direction, that is a statistically significant reduction and non‐significant heterogeneity. Conclusion: The literature shows that early intervention programs are associated with positive impact on deaths by suicide and suicide attempt. This is the first meta‐analysis of early intervention in early psychotic disorders and its impact on suicidal risk. The deployment of EIP should be supported worldwide in order to intervene as early as possible and prevent the risk of suicide. Trial Registration: PROSPERO CRD42022366976 [ABSTRACT FROM AUTHOR]

Introduction: Bipolar disorders (BD) are characterized by mood symptoms that can worsen medication side effects. We aimed to study the association between residual mood signs and self‐reported side effects in the euthymic phase of BD. Methods: We assessed residual mood signs using the Montgomery–Asberg Depression Rating scale (MADRS) and Young Mania Rating scale (YMRS) and self‐reported side effects using the Patient‐Rated Inventory of Side Effects (PRISE‐M) for 880 males and 1369 females with BD. We conducted a network analysis to test the associations between 52 items of the three scales for males and females separately. We then identified clusters of nodes that fit the networks well. Results: We report only positive associations between residual mood signs and side effects. An elevated mood (YMRS) in females and increased energy (YMRS) in males were central nodes, strongly influencing the development of additional mood symptoms and side effects. Furthermore, we identified three clusters of nodes in both sexes: (1) a "mood cluster", including most YMRS and MADRS items and the PRISE‐M items evaluating sedation, sleep, and restlessness, (2) a cluster of nonsexual side effects (mostly PRISE‐M items), and (3) a cluster of sexual side effects. In both sexes, we identified bridge nodes that may favor the communication between mood and side effects, namely palpitations (PRISE‐M) and agitation (PRISE‐M). Conclusions: The results justify the particular attention of practitioners to monitor elevated moods or increased energy to try to reduce self‐reported side effects and other residual mood symptoms in the euthymic phase of BD. Our findings suggest that clinicians could consider patient‐reported loss of energy, difficulty in falling asleep, and restlessness as mood symptoms rather than medications' side effects. Palpitations and agitation may contribute to the development of additional mood symptoms or somatic complaints. [ABSTRACT FROM AUTHOR]

BackgroundMachine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining. ObjectiveThe primary aim of this systematic review was to summarize and characterize, in methodological and technical terms, studies that used machine learning and NLP techniques for mental health. The secondary aim was to consider the potential use of these methods in mental health clinical practice MethodsThis systematic review follows the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) guidelines and is registered with PROSPERO (Prospective Register of Systematic Reviews; number CRD42019107376). The search was conducted using 4 medical databases (PubMed, Scopus, ScienceDirect, and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, and mental disorder. The exclusion criteria were as follows: languages other than English, anonymization process, case studies, conference papers, and reviews. No limitations on publication dates were imposed. ResultsA total of 327 articles were identified, of which 269 (82.3%) were excluded and 58 (17.7%) were included in the review. The results were organized through a qualitative perspective. Although studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into 3 categories: patients included in medical databases, patients who came to the emergency room, and social media users. The main objectives were to extract symptoms, classify severity of illness, compare therapy effectiveness, provide psychopathological clues, and challenge the current nosography. Medical records and social media were the 2 major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than transparent functioning classifiers. Python was the most frequently used platform. ConclusionsMachine learning and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new information, and only one major category of the population (ie, social media users) is an imprecise cohort. Moreover, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, machine learning and NLP techniques provide useful information from unexplored data (ie, patients’ daily habits that are usually inaccessible to care providers). Before considering It as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner. Machine learning and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice.

Background. Physical pain is a common issue in people with bipolar disorder (BD). It worsens mental health and quality of life, negatively impacts treatment response, and increases the risk of suicide. Lithium, which is prescribed in BD as a mood stabilizer, has shown promising effects on pain. Methods. This naturalistic study included 760 subjects with BD (FACE-BD cohort) divided in two groups: with and without self-reported pain (evaluated with the EQ-5D-5L questionnaire). In this sample, 176 subjects were treated with lithium salts. The objectives of the study were to determine whether patients receiving lithium reported less pain, and whether this effect was associated with the recommended mood-stabilizing blood concentration of lithium. Results. Subjects with lithium intake were less likely to report pain (odds ratio [OR] = 0.59, 95% confidence interval [CI], 0.35-0.95; p = 0.036) after controlling for sociodemographic variables, BD type, lifetime history of psychiatric disorders, suicide attempt, personality traits, current depression and anxiety levels, sleep quality, and psychomotor activity. Subjects taking lithium were even less likely to report pain when lithium concentration in blood was =0.5 mmol/l (OR = 0.45, 95% CI, 0.24-0.79; p = 0.008). Conclusions. This is the first naturalistic study to show lithium's promising effect on pain in subjects suffering from BD after controlling for many confounding variables. This analgesic effect seems independent of BD severity and comorbid conditions. Randomized controlled trials are needed to confirm the analgesic effect of lithium salts and to determine whether lithium decreases pain in other vulnerable populations. [ABSTRACT FROM AUTHOR]