Suicidality: risk factors and the effects of antidepressants. The example of parallel reduction of suicidality and other depressive symptoms during treatment with the SNRI, milnacipran

Suicidal behavior (SB) represents a major public health issue. Clinical and basic research suggests that SB is a specific entity in psychiatric nosology involving a combination of personality traits, genetic factors, childhood abuse and neuroanatomical abnormalities. The principal risk factor for suicide is depression. More than 60% of patients who complete suicide are depressed at the time of suicide, most of them untreated. There has been a controversy concerning a possible increased risk of SB in some depressed patients treated with antidepressants. Most recent evidence suggests, however, that treatment of depressed patients is associated with a favorable benefit-risk ratio. A recent study has determined the effects of 6 weeks of antidepressant treatment with the serotonin and norepinephrine reuptake inhibitor, milnacipran, on suicidality in a cohort of 30 patients with mild to moderate depression. At baseline, mild suicidal thoughts were present in 46.7% of patients. Suicidal thoughts decreased progressively throughout the study in parallel with other depressive symptoms and were essentially absent at the end of the study. At no time during treatment was there any indication of an increased suicidal risk. Retardation and psychic anxiety decreased in parallel possibly explaining the lack of any “activation syndrome” in this study

Association between fetal DES-exposure and psychiatric disorders in adolescence/adulthood: evidence from a French cohort of 1002 prenatally exposed children

International audienceIn utero diethylstilbestrol (DES) exposure has been demonstrated to be associated with somatic abnormalities in adult men and women. Conversely, the data are contradictory regarding the association with psychological or psychiatric disorders during adolescence and adulthood. This work was designed to determine whether prenatal exposure to DES affects brain development and whether it is associated with psychiatric disorders in male and female adolescents and young adults. HHORAGES Association, a national patient support group, has assembled a cohort of 1280 women who took DES during pregnancy. We obtained questionnaire responses from 529 families, corresponding to 1182 children divided into three groups: Group 1 (n = 180): firstborn children without DES treatment, Group 2 (n = 740): exposed children, and Group 3 (n = 262): children born after a previous pregnancy treated by DES. No psychiatric disorders were reported in Group 1. In Group 2, the incidence of disorders was drastically elevated (83.8%), and in Group 3, the incidence was still elevated (6.1%) compared with the general population. This work demonstrates that prenatal exposure to DES is associated with a high risk of psychiatric disorders in adolescence and adulthood

Effectiveness of Psychotherapy on Suicidal Risk: A Systematic Review of Observational Studies

Background: Suicidal behavior is a major public health concern worldwide, and the interest in the development of novel and more efficient treatment strategies and therapies to reduce suicidal risk is increasing. Some recent studies have summarized the results of randomized clinical trials (RCTs) assessing the efficacy of psychotherapeutic tools designed to treat patients at suicidal risk. However, observational studies, which reflect real-world effectiveness and may use original approaches, have not been reviewed.Method: The aim of this study is to systematically review the available scientific evidence issued from observational studies on the clinical effectiveness of psychotherapeutic tools designed to treat patients at suicide risk. We have thus performed a systematic search of PubMed and Web of Science databases.Results: Out of 1578 papers, 40 original observational studies fulfilled our selection criteria. The most used psychotherapeutic treatments were dialectical behavioral therapy (DBT, 27.5%) and cognitive behavioral therapy (CBT, 15.0%) in patients with a diagnosis of borderline personality disorder (32.5%) and depression (15.0%). Despite the between-study heterogeneity, interventions lead to a reduction in suicidal outcomes, i.e., suicidal ideation (55.0%) and suicide attempts (37.5%). The content and reporting quality varied considerably between the studies.Conclusion: DBT and CBT are the most widely used psychotherapeutic interventions and show promising results in existing observational studies. Some of the included studies provide innovative approaches. Group therapies and internet-based therapies, which are cost-effective methods, are promising treatments and would need further study

Horror Vacui: Emptiness Might Distinguish between Major Suicide Repeaters and Nonmajor Suicide Repeaters: A Pilot Study

This Letter to the Editor is brought to you for free and open access by the Psychiatry at UKnowledge. It has been accepted for inclusion in Psychiatr

Are we studying the right populations to understand suicide?

International audienc

An approach for data mining of electronic health record data for suicide risk management: Database analysis for clinical decision support

Background: In an electronic health context, combining traditional structured clinical assessment methods and routine electronic health-based data capture may be a reliable method to build a dynamic clinical decision-support system (CDSS) for suicide prevention. Objective: The aim of this study was to describe the data mining module of a Web-based CDSS and to identify suicide repetition risk in a sample of suicide attempters. Methods: We analyzed a database of 2802 suicide attempters. Clustering methods were used to identify groups of similar patients, and regression trees were applied to estimate the number of suicide attempts among these patients. Results: We identified 3 groups of patients using clustering methods. In addition, relevant risk factors explaining the number of suicide attempts were highlighted by regression trees. Conclusions: Data mining techniques can help to identify different groups of patients at risk of suicide reattempt. The findings of this study can be combined with Web-based and smartphone-based data to improve dynamic decision making for clinicians.This study received a Hospital Clinical Research Grant (PHRC 2009) from the French Health Ministry. None of the funding sources had any involvement in the study design; collection, analysis, or interpretation of data; writing of the report; or the decision to submit the paper for publication. This study was funded partially by Instituto de Salud Carlos III (ISCIII PI13/02200; PI16/01852), Delegación del Gobierno para el Plan Nacional de Drogas (20151073), and the American Foundation for Suicide Prevention (LSRG-1-005-16)

From eHealth to iHealth: Transition to participatory and personalized medicine in mental health

Clinical assessment in psychiatry is commonly based on findings from brief, regularly scheduled in-person appointments. Although critically important, this approach reduces assessment to cross-sectional observations that miss essential information about disease course. The mental health provider makes all medical decisions based on this limited information. Thanks to recent technological advances such as mobile phones and other personal devices, electronic health (eHealth) data collection strategies now can provide access to real-Time patient self-report data during the interval between visits. Since mobile phones are generally kept on at all times and carried everywhere, they are an ideal platform for the broad implementation of ecological momentary assessment technology. Integration of these tools into medical practice has heralded the eHealth era. Intelligent health (iHealth) further builds on and expands eHealth by adding novel built-in data analysis approaches based on (1) incorporation of new technologies into clinical practice to enhance real-Time self-monitoring, (2) extension of assessment to the patient's environment including caregivers, and (3) data processing using data mining to support medical decision making and personalized medicine. This will shift mental health care from a reactive to a proactive and personalized discipline.This research was partially support by Instituto de Salud Carlos III (PI16/01852 Grant), Plan Nacional de Drogas (20151073 Project), and American Foundation for Suicide Prevention (LSRG-1-005-16). SB’s work was supported by Fondation de l’Avenir, the French Embassy in Madrid; MMPR's work was supported by a National Alliance for Research on Schizophrenia and Depression (NARSAD) Young Investigator Award (YIA) grant and a KL2 Faculty Scholar (KL2TR001435) grant (PI: Perez-Rodriguez

Deep Sequential Models for Suicidal Ideation from Multiple Source Data

This paper presents a novel method for predicting suicidal ideation from electronic health records (EHR) and ecological momentary assessment (EMA) data using deep sequential models. Both EHR longitudinal data and EMA question forms are defined by asynchronous, variable length, randomly sampled data sequences. In our method, we model each of them with a recurrent neural network, and both sequences are aligned by concatenating the hidden state of each of them using temporal marks. Furthermore, we incorporate attention schemes to improve performance in long sequences and time-independent pre-trained schemes to cope with very short sequences. Using a database of 1023 patients, our experimental results show that the addition of EMA records boosts the system recall to predict the suicidal ideation diagnosis from 48.13% obtained exclusively from EHR-based state-of-the-art methods to 67.78%. Additionally, our method provides interpretability through the t-distributed stochastic neighbor embedding (t-SNE) representation of the latent space. Furthermore, the most relevant input features are identified and interpreted medically.This work was supported in part by the Spanish MINECO under Grants TEC2015-69868-C2-1-R, TEC2016-78434-C3-3-R, and TEC2017-92552-EXP, in part by Spanish MICINN under Grant RTI2018-099655-B-I00, in part by Comunidad de Madrid under Grants IND2017/TIC-7618, IND2018/TIC-9649, Y2018/TCS-4705, and B2017/BMD-3740 AGES-CM 2CM, in part by BBVA Foundation under Deep-DARWiN - FBBVA Grant for scientific research teams 2018, in part by ISCIII under Grant PI16/01852, and in part by AFSP under Grant LSRG-1-005-16

Development of a Web-Based Clinical Decision Support System for Drug Prescription: ...

Purpose The emergence of electronic prescribing devices with clinical decision support systems (CDSS) is able to significantly improve management pharmacological treatments. We developed a web application available on smartphones in order to help clinicians monitor prescription and further propose CDSS. Method A web application (www.MEmind.net) was developed to assess patients and collect data regarding gender, age, diagnosis and treatment. We analyzed antipsychotic prescriptions in 4345 patients attended in five Psychiatric Community Mental Health Centers from June 2014 to October 2014. The web-application reported average daily dose prescribed for antipsychotics, prescribed daily dose (PDD), and the PDD to defined daily dose (DDD) ratio. Results The MEmind web-application reported that antipsychotics were used in 1116 patients out of the total sample, mostly in 486 (44%) patients with schizophrenia related disorders but also in other diagnoses. Second generation antipsychotics (quetiapine, aripiprazole and long-acting paliperidone) were preferably employed. Low doses were more frequently used than high doses. Long acting paliperidone and ziprasidone however, were the only two antipsychotics used at excessive dosing. Antipsychotic polypharmacy was used in 287 (26%) patients with classic depot drugs, clotiapine, amisulpride and clozapine. Conclusions In this study we describe the first step of the development of a web application that is able to make polypharmacy, high dose usage and off label usage of antipsychotics visible to clinicians. Current development of the MEmind web application may help to improve prescription security via momentary feedback of prescription and clinical decision support system