Ketamine and esketamine in suicidal thoughts and behaviors: a systematic review
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ABSTRACT: Background:
More than 2% of the general population experience suicidal ideas each year
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PROVIDER: S-EPMC9912570 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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Project description:Passive sensing data from smartphones and wearables may help improve the prediction of suicidal thoughts and behaviors (STB). In this systematic review, we explored the feasibility and predictive validity of passive sensing for STB. On June 24, 2024, we systematically searched Medline, Embase, Web of Science, PubMed, and PsycINFO. Studies were eligible if they investigated the association between STB and passive sensing, or the feasibility of passive sensing in this context. From 2107 unique records, we identified eleven prediction studies, ten feasibility studies, and seven protocols. Studies indicated generally lower model performance for passive compared to active data, with three out of four studies finding no incremental value. PROBAST ratings revealed major shortcomings in methodology and reporting. Studies suggested that passive sensing is feasible in high-risk populations. In conclusion, there is limited evidence on the predictive value of passive sensing for STB. We highlight important quality characteristics for future research.
| S-EPMC11420362 | biostudies-literature
Project description:BackgroundSuicide is a severe public health problem, resulting in a high number of attempts and deaths each year. Early detection of suicidal thoughts and behaviors (STBs) is key to preventing attempts. We discuss passive sensing of digital and behavioral markers to enhance the detection and prediction of STBs.ObjectiveThe paper presents the protocol for a systematic review that aims to summarize existing research on passive sensing of STBs and evaluate whether the STB prediction can be improved using passive sensing compared to prior prediction models.MethodsA systematic search will be conducted in the scientific databases MEDLINE, PubMed, Embase, PsycINFO, and Web of Science. Eligible studies need to investigate any passive sensor data from smartphones or wearables to predict STBs. The predictive value of passive sensing will be the primary outcome. The practical implications and feasibility of the studies will be considered as secondary outcomes. Study quality will be assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). If studies are sufficiently homogenous, we will conduct a meta-analysis of the predictive value of passive sensing on STBs.ResultsThe review process started in July 2022 with data extraction in September 2022. Results are expected in December 2022.ConclusionsDespite intensive research efforts, the ability to predict STBs is little better than chance. This systematic review will contribute to our understanding of the potential of passive sensing to improve STB prediction. Future research will be stimulated since gaps in the current literature will be identified and promising next steps toward clinical implementation will be outlined.Trial registrationOSF Registries osf-registrations-hzxua-v1; https://osf.io/hzxua.International registered report identifier (irrid)DERR1-10.2196/42146.
| S-EPMC9748797 | biostudies-literature
Project description:Among the protective factors associated with reduced risk for suicide, scientific inquiries into school connectedness are especially important considering that schools are ideally situated to provide interventions reaching the vast majority of youth. Although there is a wealth of research that supports the association between school connectedness and reduced self-report of adolescents having a suicidal thought or making a suicide attempt, inconsistencies in the way studies have measured and operationalized school connectedness limit synthesis across findings. This meta-analytic study investigates the literature exploring associations between school connectedness and suicidal thoughts and behaviors across general and subpopulations (high risk and sexual minority youth) using a random effects model. Eligible studies examined a measure of school connectedness explicitly referred to as "school connectedness" or "connections at school" in relation to suicidal ideation or suicide attempts among youth enrolled in school (Grades 6-12). Multiple metaregression analyses were conducted to explore the influence of school connectedness measurement variation, as well as participant characteristics. Results, including 16 samples, support that higher school connectedness is associated with reduced reports of suicidal thoughts and behaviors across general (odds ratio [OR] = 0.536), high-risk (OR = 0.603), and sexual minority (OR = 0.608) adolescents. Findings are consistent when analyzed separately for suicidal ideation (OR = 0.529) and suicide attempts (OR = 0.589) and remain stable when accounting for measurement variability. Although limited by its cross-sectional nature, findings support recent calls to increase school connectedness and proffer important implications for screening and intervention efforts conducted in schools. (PsycINFO Database Record
| S-EPMC5359058 | biostudies-literature
Project description:Suicidal ideation, suicide attempt (SA) and suicide are significantly heritable phenotypes. However, the extent to which these phenotypes share genetic architecture is unclear. This question is of great relevance to determining key risk factors for suicide, and to alleviate the societal burden of suicidal thoughts and behaviors (STBs). To help address the question of heterogeneity, consortia efforts have recently shifted from a focus on suicide within the context of major psychopathology (e.g. major depressive disorder, schizophrenia) to suicide as an independent entity. Recent molecular studies of suicide risk by members of the Psychiatric Genomics Consortium and the International Suicide Genetics Consortium have identified genome-wide significant loci associated with SA and with suicide death, and have examined these phenotypes within and outside of the context of major psychopathology. This review summarizes important insights from epidemiological and biometrical research on suicide, and discusses key empirical findings from molecular genetic examinations of STBs. Polygenic risk scores for these phenotypes have been observed to be associated with case-control status and other risk phenotypes. In addition, estimated shared genetic covariance with other phenotypes suggests specific medical and psychiatric risks beyond major depressive disorder. Broadly, molecular studies suggest a complexity of suicide etiology that cannot simply be accounted for by depression. Discussion of the state of suicide genetics, a growing field, also includes important ethical and clinical implications of studying the genetic risk of suicide.
| S-EPMC8477225 | biostudies-literature
Project description:BackgroundMachine learning is a promising tool in the area of suicide prevention due to its ability to combine the effects of multiple risk factors and complex interactions. The power of machine learning has led to an influx of studies on suicide prediction, as well as a few recent reviews. Our study distinguished between data sources and reported the most important predictors of suicide outcomes identified in the literature.ObjectiveOur study aimed to identify studies that applied machine learning techniques to administrative and survey data, summarize performance metrics reported in those studies, and enumerate the important risk factors of suicidal thoughts and behaviors identified.MethodsA systematic literature search of PubMed, Medline, Embase, PsycINFO, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Allied and Complementary Medicine Database (AMED) to identify all studies that have used machine learning to predict suicidal thoughts and behaviors using administrative and survey data was performed. The search was conducted for articles published between January 1, 2019 and May 11, 2022. In addition, all articles identified in three recently published systematic reviews (the last of which included studies up until January 1, 2019) were retained if they met our inclusion criteria. The predictive power of machine learning methods in predicting suicidal thoughts and behaviors was explored using box plots to summarize the distribution of the area under the receiver operating characteristic curve (AUC) values by machine learning method and suicide outcome (i.e., suicidal thoughts, suicide attempt, and death by suicide). Mean AUCs with 95% confidence intervals (CIs) were computed for each suicide outcome by study design, data source, total sample size, sample size of cases, and machine learning methods employed. The most important risk factors were listed.ResultsThe search strategy identified 2,200 unique records, of which 104 articles met the inclusion criteria. Machine learning algorithms achieved good prediction of suicidal thoughts and behaviors (i.e., an AUC between 0.80 and 0.89); however, their predictive power appears to differ across suicide outcomes. The boosting algorithms achieved good prediction of suicidal thoughts, death by suicide, and all suicide outcomes combined, while neural network algorithms achieved good prediction of suicide attempts. The risk factors for suicidal thoughts and behaviors differed depending on the data source and the population under study.ConclusionThe predictive utility of machine learning for suicidal thoughts and behaviors largely depends on the approach used. The findings of the current review should prove helpful in preparing future machine learning models using administrative and survey data.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022333454 identifier CRD42022333454.
| S-EPMC10944962 | biostudies-literature
Project description:Identifying brain alterations that contribute to suicidal thoughts and behaviors (STBs) are important to develop more targeted and effective strategies to prevent suicide. In the last decade, and especially in the last 5 years, there has been exponential growth in the number of neuroimaging studies reporting structural and functional brain circuitry correlates of STBs. Within this narrative review, we conducted a comprehensive review of neuroimaging studies of STBs published to date and summarize the progress achieved on elucidating neurobiological substrates of STBs, with a focus on converging findings across studies. We review neuroimaging evidence across differing mental disorders for structural, functional, and molecular alterations in association with STBs, which converges particularly in regions of brain systems that subserve emotion and impulse regulation including the ventral prefrontal cortex (VPFC) and dorsal PFC (DPFC), insula and their mesial temporal, striatal and posterior connection sites, as well as in the connections between these brain areas. The reviewed literature suggests that impairments in medial and lateral VPFC regions and their connections may be important in the excessive negative and blunted positive internal states that can stimulate suicidal ideation, and that impairments in a DPFC and inferior frontal gyrus (IFG) system may be important in suicide attempt behaviors. A combination of VPFC and DPFC system disturbances may lead to very high risk circumstances in which suicidal ideation is converted to lethal actions via decreased top-down inhibition of behavior and/or maladaptive, inflexible decision-making and planning. The dorsal anterior cingulate cortex and insula may play important roles in switching between these VPFC and DPFC systems, which may contribute to the transition from suicide thoughts to behaviors. Future neuroimaging research of larger sample sizes, including global efforts, longitudinal designs, and careful consideration of developmental stages, and sex and gender, will facilitate more effectively targeted preventions and interventions to reduce loss of life to suicide.
| S-EPMC6974434 | biostudies-literature
Project description:BackgroundIdentifying clinical correlates associated with reduced suicidal ideation may highlight new avenues for the treatment of suicidal thoughts. Anhedonia occurs across psychiatric diagnoses and has been associated with specific neural circuits in response to rapid-acting treatments, such as ketamine. This analysis sought to evaluate whether reductions in suicidal ideation after ketamine administration were related to reduced levels of anhedonia, independent of depressive symptoms.MethodsThis post-hoc analysis included treatment-resistant patients with either major depressive disorder (MDD) or bipolar disorder (BD) from several clinical trials of ketamine. Anhedonia was assessed using a subscale of the Beck Depression Inventory (BDI) and the Snaith-Hamilton Pleasure Scale (SHAPS). The outcome of interest was suicidal ideation, as measured by a subscale of the Scale for Suicide Ideation (SSI5), one day post-ketamine administration.ResultsAnhedonia, as measured by the SHAPS, was associated with suicidal thoughts independent of depressive symptoms both before and after ketamine administration. One day post-ketamine administration, improvements on the SHAPS accounted for an additional 13% of the variance in suicidal thought reduction, beyond the influence of depressive symptoms. The BDI anhedonia subscale was not significantly associated with suicidal thoughts after adjusting for depressive symptoms.LimitationsData were limited to patients experiencing a major depressive episode and may not be generalizable to patients experiencing an active suicidal crisis.ConclusionsSuicidal thoughts may be related to symptoms of anhedonia independent of other depressive symptoms. These results have implications for the potential mechanisms of action of ketamine on suicidal thoughts.
| S-EPMC5515296 | biostudies-literature
Project description:BackgroundElectroretinogram (ERG) is one of the tools used to investigate the electrophysiological underpinnings of mental health illnesses and major clinical phenomena (e.g., suicide) to improve their diagnosis and care. While multiple studies have reported specific ERG changes among individuals with suicidal behaviors, we know of no review that has been done to characterize their findings to inform future research.MethodsThis review included available literature concerning ERG and suicidal behaviors. The paper's first section briefly overviews the theoretical basis of ERG and neurotransmitters involved in suicidal behaviors. The second section describes the findings of a review of studies reporting ERG findings among individuals with suicidal behaviors.ResultsMost reviewed studies reported normal amplitude and implicit time of the a-waves, but the latency in individuals with suicidal behaviors was lower than normal. Additionally, the b-waves amplitude was reduced, but the implicit time and latency were increased. The b-a amplitude ratio and oscillatory potential were decreased.ConclusionDespite identifying certain ERG correlates with suicidal behaviors in the existing studies, there is a need for adequately powered and methodologically robust studies to advance clinical translation.
| S-EPMC10724949 | biostudies-literature
Project description:Non-suicidal self-injury (NSSI) is a serious public health concern that typically onsets during early adolescence. Adolescents (N = 980, ages 12-19 years) admitted for acute, residential psychiatric treatment completed baseline clinical interviews assessing mental disorders and questionnaires measuring demographics, early life adversity, and symptom severity. Prevalence rates of NSSI for lifetime (thoughts: 78%; behaviors: 72%), past year (thoughts: 74%; behaviors: 65%), and past month (thoughts: 68%; behaviors: 51%) were high. Although effect sizes were modest, the presence of a lifetime depressive disorder, sexual abuse, and comorbidity (i.e., three or more current disorders) were significant correlates of experiencing NSSI thoughts and behaviors. Furthermore, lifetime depressive disorder, current anxiety disorder, and comorbidity were associated with a greater odds of persistent NSSI thoughts and/or behaviors. Longitudinal studies are needed to determine whether targeting these factors reduces the persistence of NSSI thoughts and behaviors.
| S-EPMC9782727 | biostudies-literature
Project description:BackgroundIn the present study, we examined the relationship between cannabis involvement and suicidal ideation (SI), plan and attempt, differentiating the latter into planned and unplanned attempt, taking into account other substance involvement and psychopathology.MethodsWe used two community-based twin samples from the Australian Twin Registry, including 9583 individuals (58.5% female, aged between 27 and 40). The Semi-Structured Assessment of the Genetics of Alcoholism (SSAGA) was used to assess cannabis involvement which was categorized into: (0) no cannabis use (reference category); (1) cannabis use only; (2) 1-2 cannabis use disorder symptoms; (3) 3 or more symptoms. Separate multinomial logistic regression analyses were conducted for SI and suicide attempt with or without a plan. Twin analyses examined the genetic overlap between cannabis involvement and SI.ResultsAll levels of cannabis involvement were related to SI, regardless of duration (odds ratios [ORs]=1.28-2.00, p<0.01). Cannabis use and endorsing ≥3 symptoms were associated with unplanned (SANP; ORs=1.95 and 2.51 respectively, p<0.05), but not planned suicide attempts (p>0.10). Associations persisted even after controlling for other psychiatric disorders and substance involvement. Overlapping genetic (rG=0.45) and environmental (rE=0.21) factors were responsible for the covariance between cannabis involvement and SI.ConclusionsCannabis involvement is associated, albeit modestly, with SI and unplanned suicide attempts. Such attempts are difficult to prevent and their association with cannabis use and cannabis use disorder symptoms requires further study, including in different samples and with additional attention to confounders.
| S-EPMC4460828 | biostudies-literature