The prediction of suicide in severe mental illness: development and validation of a clinical prediction rule (OxMIS)

Using Electronic Health Records to Facilitate Precision Psychiatry

The use of clinical prediction models to produce individualized risk estimates can facilitate the implementation of precision psychiatry. As a source of data from large, clinically representative patient samples, electronic health records (EHRs) provide a platform to develop and validate clinical prediction models, as well as potentially implement them in routine clinical care. The current review describes promising use cases for the application of precision psychiatry to EHR data and considers their performance in terms of discrimination (ability to separate individuals with and without the outcome) and calibration (extent to which predicted risk estimates correspond to observed outcomes), as well as their potential clinical utility (weighing benefits and costs associated with the model compared to different approaches across different assumptions of the number needed to test). We review 4 externally validated clinical prediction models designed to predict psychosis onset, psychotic relapse, cardiometabolic morbidity, and suicide risk. We then discuss the prospects for clinically implementing these models and the potential added value of integrating data from evidence syntheses, standardized psychometric assessments, and biological data into EHRs. Clinical prediction models can utilize routinely collected EHR data in an innovative way, representing a unique opportunity to inform real-world clinical decision making. Combining data from other sources (e.g., meta-analyses) or enhancing EHR data with information from research studies (clinical and biomarker data) may enhance our abilities to improve the performance of clinical prediction models.

Suicide among lymphoma patients

BACKGROUND

Higher suicide rates were observed in patients diagnosed with lymphoma. In this study, we accurately identified patients with high-risk lymphoma for suicide by constructing a nomogram with a view to effective interventions and reducing the risk of suicide.

METHODS

235,806 patients diagnosed with lymphoma between 2000 and 2020 were picked from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training (N = 165,064) and validation set (N = 70,742). A combination of the Least absolute shrinkage and selection operator (LASSO) and Cox proportional hazards regression identified the predictors that constructed the nomogram. To assess the discrimination, calibration, clinical applicability, and generalization of this nomogram, we implemented receiver operating characteristic curves (ROC), calibration curves, decision curve analysis (DCA), and internal validation. The robustness of the results was assessed by the competing risks regression model.

RESULTS

Age at diagnosis, gender, ethnicity, marital status, stage, surgery, radiotherapy, and annual household income were key predictors of suicide in lymphoma patients. A nomogram was created to visualize the risk of suicide after a lymphoma diagnosis. The c-index for the training set was 0.773, and the validation set was 0.777. The calibration curve for the nomogram fitted well with the diagonal and the clinical decision curve indicated its clinical benefit.

LIMITATION

The effects of unmeasured and unnoticed biases and confounders were difficult to eliminate due to retrospective studies.

CONCLUSION

A convenient and reliable model has been constructed that will help to individualize and accurately quantify the risk of suicide in patients diagnosed with lymphoma.

Building and Validation of an Acute Event Prediction Model for Severe Mental Disorders

Background

The global incidence of acute events in psychiatric patients is intensifying, and models to successfully predict acute events have attracted much attention.

Objective

To explore the influence factors of acute incident severe mental disorders (SMDs) and the application of Rstudio statistical software, and build and verify a nomogram prediction model.

Methods

SMDs were taken as research objects. The questionnaire survey method was adopted to collect data. Patients with acute event independent factors were screened. R software multivariable Logistic regression model was constructed and a nomogram was drawn.

Results

A total of 342 patients with SMDs were hospitalized, and the number of patients who encountered acute events was 64, which accounted for 18.70% of all patients. Statistical significances were found in many aspects (all P ˂ 0.05). Such aspects included Medication adherence, disease diagnosis, marital status, caregivers, social support and the hospitalization environment (odds ratio (OR) = 4.08, 11.62, 12.06, 10.52, 0.04 and 0.61, respectively) were independent risk factors for the acute events of patients with SMDs. The prediction model was modeled, and the AUC was 0.77 and 0.80. The calibration curve shows that the model has good calibration. The clinical decision curve shows that the model has a good clinical effect.

Conclusion

The constructed risk prediction model shows good prediction effectiveness in the acute events of patients with SMDs, which is helpful for the early detection of clinical mental health staff at high risk of acute events.

Predicting Lifetime Suicide Attempts in a Community Sample of Adolescents Using Machine Learning Algorithms

Suicide is a major global health concern and a prominent cause of death in adolescents. Previous research on suicide prediction has mainly focused on clinical or adult samples. To prevent suicides at an early stage, however, it is important to screen for risk factors in a community sample of adolescents. We compared the accuracy of logistic regressions, elastic net regressions, and gradient boosting machines in predicting suicide attempts by 17-year-olds in the Millennium Cohort Study (N = 7,347), combining a large set of self- and other-reported variables from different categories. Both machine learning algorithms outperformed logistic regressions and achieved similar balanced accuracies (.76 when using data 3 years before the self-reported lifetime suicide attempts and .85 when using data from the same measurement wave). We identified essential variables that should be considered when screening for suicidal behavior. Finally, we discuss the usefulness of complex machine learning models in suicide prediction.

Neurological, Metabolic, and Psychopathological Correlates of Lifetime Suicidal Behaviour in Major Depressive Disorder without Current Suicide Ideation

INTRODUCTION

Suicidal behaviour (SB) has a complex aetiology. Although suicidal ideation (SI) is considered the most important risk factor for future attempts, many people who engage in SB do not report it.

METHODS

We investigated neurological, metabolic, and psychopathological correlates of lifetime SB in two independent groups of patients with major depression (sample 1: n = 230; age: 18-65 years; sample 2: n = 258; age >60 years) who did not report SI during an index episode.

RESULTS

Among adults (sample 1), SB was reported by 141 subjects (58.7%) and severe SB by 33 (15%). After controlling for interactions, four risk factors for SB emerged: male gender (OR 2.55; 95% CI: 1.06-6.12), negative self-perception (OR 1.76; 95% CI: 1.08-2.87), subthreshold hypomania (OR 4.50; 95% CI: 1.57-12.85), and sexual abuse (OR 3.09; 95% CI: 1.28-7.48). The presence of at least two of these factors had the best accuracy in predicting SB: sensitivity = 57.6% (39.2-74.5); specificity = 75.1% (68.5-82.0); PPV = 27.9% (20.9-37.2); NPV = 91.4% (87.6-94.1). In older patients (sample 2), 23 subjects (9%) reported previous suicide attempts, which were characterized by earlier onset (25 years: OR 0.95: 0.92-0.98), impaired verbal performance (verbal fluency: OR 0.95: 0.89-0.99), higher HDL cholesterol levels (OR 1.04: 1.00-1.07) and more dyskinesias (OR 2.86: 1.22-6.70).

CONCLUSION

Our findings suggest that SB is common in major depressive disorder, even when SI is not reported. In these individuals it is feasible and recommended to investigate both psychiatric and organic risk factors. The predictive power of models excluding SI is comparable to that of models including SI.

Suicide risk assessment tools and prediction models: new evidence, methodological innovations, outdated criticisms

The number of prediction models for suicide-related outcomes has grown substantially in recent years. These models aim to assist in stratifying risk, improve clinical decision-making, and facilitate a personalised medicine approach to the prevention of suicidal behaviour. However, there are contrasting views as to whether prediction models have potential to inform and improve assessment of suicide risk. In this perspective, we discuss common misconceptions that characterise criticisms of suicide risk prediction research. First, we discuss the limitations of a classification approach to risk assessment (eg, categorising individuals as low-risk vs high-risk), and highlight the benefits of probability estimation. Second, we argue that the preoccupation with classification measures (such as positive predictive value) when assessing a model's predictive performance is inappropriate, and discuss the importance of clinical context in determining the most appropriate risk threshold for a given model. Third, we highlight that adequate discriminative ability for a prediction model depends on the clinical area, and emphasise the importance of calibration, which is almost entirely overlooked in the suicide risk prediction literature. Finally, we point out that conclusions about the clinical utility and health-economic value of suicide prediction models should be based on appropriate measures (such as net benefit and decision-analytic modelling), and highlight the role of impact assessment studies. We conclude that the discussion around using suicide prediction models and risk assessment tools requires more nuance and statistical expertise, and that guidelines and suicide prevention strategies should be informed by the new and higher quality evidence in the field.

The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review

Background

Machine 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.

Objective

Our 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.

Methods

A 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.

Results

The 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.

Conclusion

The 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 registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022333454 identifier CRD42022333454.

Identifying features of risk periods for suicide attempts using document frequency and language use in electronic health records

Background

Individualising mental healthcare at times when a patient is most at risk of suicide involves shifting research emphasis from static risk factors to those that may be modifiable with interventions. Currently, risk assessment is based on a range of extensively reported stable risk factors, but critical to dynamic suicide risk assessment is an understanding of each individual patient's health trajectory over time. The use of electronic health records (EHRs) and analysis using machine learning has the potential to accelerate progress in developing early warning indicators.

Setting

EHR data from the South London and Maudsley NHS Foundation Trust (SLaM) which provides secondary mental healthcare for 1.8 million people living in four South London boroughs.

Objectives

To determine whether the time window proximal to a hospitalised suicide attempt can be discriminated from a distal period of lower risk by analysing the documentation and mental health clinical free text data from EHRs and (i) investigate whether the rate at which EHR documents are recorded per patient is associated with a suicide attempt; (ii) compare document-level word usage between documents proximal and distal to a suicide attempt; and (iii) compare n-gram frequency related to third-person pronoun use proximal and distal to a suicide attempt using machine learning.

Methods

The Clinical Record Interactive Search (CRIS) system allowed access to de-identified information from the EHRs. CRIS has been linked with Hospital Episode Statistics (HES) data for Admitted Patient Care. We analysed document and event data for patients who had at some point between 1 April 2006 and 31 March 2013 been hospitalised with a HES ICD-10 code related to attempted suicide (X60-X84; Y10-Y34; Y87.0/Y87.2).

Findings

n = 8,247 patients were identified to have made a hospitalised suicide attempt. Of these, n = 3,167 (39.8%) of patients had at least one document available in their EHR prior to their first suicide attempt. N = 1,424 (45.0%) of these patients had been "monitored" by mental healthcare services in the past 30 days. From 60 days prior to a first suicide attempt, there was a rapid increase in the monitoring level (document recording of the past 30 days) increasing from 35.1 to 45.0%. Documents containing words related to prescribed medications/drugs/overdose/poisoning/addiction had the highest odds of being a risk indicator used proximal to a suicide attempt (OR 1.88; precision 0.91 and recall 0.93), and documents with words citing a care plan were associated with the lowest risk for a suicide attempt (OR 0.22; precision 1.00 and recall 1.00). Function words, word sequence, and pronouns were most common in all three representations (uni-, bi-, and tri-gram).

Conclusion

EHR documentation frequency and language use can be used to distinguish periods distal from and proximal to a suicide attempt. However, in our study 55.0% of patients with documentation, prior to their first suicide attempt, did not have a record in the preceding 30 days, meaning that there are a high number who are not seen by services at their most vulnerable point.

Life expectancy and years of potential life lost in people with mental disorders: a systematic review and meta-analysis

Background

Mental disorders are associated with premature mortality. There is increasing research examining life expectancy and years-of-potential-life-lost (YPLL) to quantify the disease impact on survival in people with mental disorders. We aimed to systematically synthesize studies to estimate life expectancy and YPLL in people with any and specific mental disorders across a broad spectrum of diagnoses.

Methods

In this systematic review and meta-analysis, we searched Embase, MEDLINE, PsychINFO, WOS from inception to July 31, 2023, for published studies reporting life expectancy and/or YPLL for mental disorders. Criteria for study inclusion were: patients of all ages with any mental disorders; reported data on life expectancy and/or YPLL of a mental-disorder cohort relative to the general population or a comparison group without mental disorders; and cohort studies. We excluded non-cohort studies, publications containing non-peer-reviewed data or those restricted to population subgroups. Survival estimates, i.e., life expectancy and YPLL, were pooled (based on summary data extracted from the included studies) using random-effects models. Subgroup analyses and random-effects meta-regression analyses were performed to explore sources of heterogeneity. Risk-of-bias assessment was evaluated using the Newcastle-Ottawa Scale. This study is registered with PROSPERO (CRD42022321190).

Findings

Of 35,865 studies identified in our research, 109 studies from 24 countries or regions including 12,171,909 patients with mental disorders were eligible for analysis (54 for life expectancy and 109 for YPLL). Pooled life expectancy for mental disorders was 63.85 years (95% CI 62.63-65.06; I2 = 100.0%), and pooled YPLL was 14.66 years (95% CI 13.88-15.98; I2 = 100.0%). Disorder-stratified analyses revealed that substance-use disorders had the shortest life expectancy (57.07 years [95% CI 54.47-59.67]), while neurotic disorders had the longest lifespan (69.51 years [95% CI 67.26-71.76]). Substance-use disorders exhibited the greatest YPLL (20.38 years [95% CI 18.65-22.11]), followed by eating disorders (16.64 years [95% CI 7.45-25.82]), schizophrenia-spectrum disorders (15.37 years [95% CI 14.18-16.55]), and personality disorders (15.35 years [95% CI 12.80-17.89]). YPLLs attributable to natural and unnatural deaths in mental disorders were 4.38 years (95% CI 3.15-5.61) and 8.11 years (95% CI 6.10-10.13; suicide: 8.31 years [95% CI 6.43-10.19]), respectively. Stratified analyses by study period suggested that the longevity gap persisted over time. Significant cross-study heterogeneity was observed.

Interpretation

Mental disorders are associated with substantially reduced life expectancy, which is transdiagnostic in nature, encompassing a wide range of diagnoses. Implementation of comprehensive and multilevel intervention approaches is urgently needed to rectify lifespan inequalities for people with mental disorders.

Funding

None.

A Risk Calculator to Predict Suicide Attempts Among Individuals With Early-Onset Bipolar Disorder

Objectives

To build a one-year risk calculator (RC) to predict individualized risk for suicide attempt in early-onset bipolar disorder.

Methods

Youth numbering 394 with bipolar disorder who completed ≥2 follow-up assessments (median follow-up length = 13.1 years) in the longitudinal Course and Outcome of Bipolar Youth (COBY) study were included. Suicide attempt over follow-up was assessed via the A-LIFE Self-Injurious/Suicidal Behavior scale. Predictors from the literature on suicidal behavior in bipolar disorder that are readily assessed in clinical practice were selected and trichotomized as appropriate (presence past 6 months/lifetime history only/no lifetime history). The RC was trained via boosted multinomial classification trees; predictions were calibrated via Platt scaling. Half of the sample was used to train, and the other half to independently test the RC.

Results

There were 249 suicide attempts among 106 individuals. Ten predictors accounted for >90% of the cross-validated relative influence in the model (AUC = 0.82; in order of relative influence): (1) age of mood disorder onset; (2) non-suicidal self-injurious behavior (trichotomized); (3) current age; (4) psychosis (trichotomized); (5) socioeconomic status; (6) most severe depressive symptoms in past 6 months (trichotomized none/subthreshold/threshold); (7) history of suicide attempt (trichotomized); (8) family history of suicidal behavior; (9) substance use disorder (trichotomized); (10) lifetime history of physical/sexual abuse. For all trichotomized variables, presence in the past 6 months reliably predicted higher risk than lifetime history.

Conclusions

This RC holds promise as a clinical and research tool for prospective identification of individualized high-risk periods for suicide attempt in early-onset bipolar disorder.Reprinted from Bipolar Disord 2022; 24:749-757, with permission from John Wiley and Sons. Copyright © 2022.

Development and validation of a prediction model for rehospitalization among people with schizophrenia discharged from acute inpatient care

Objective

Relapses and rehospitalization prevent the recovery of individuals with schizophrenia or related psychoses. We aimed to build a model to predict the risk of rehospitalization among people with schizophrenia or related psychoses, including those with multiple episodes.

Methods

This retrospective cohort study included individuals aged 18 years or older, with schizophrenia or related psychoses, and discharged between January 2014 and December 2018 from one of three Japanese psychiatric hospital acute inpatient care ward. We collected nine predictors at the time of recruitment, followed up with the participants for 12 months, and observed whether psychotic relapse had occurred. Next, we applied the Cox regression model and used an elastic net to avoid overfitting. Then, we examined discrimination using bootstrapping, Steyerberg's method, and "leave-one-hospital-out" cross-validation. We also constructed a bias-corrected calibration plot.

Results

Data from a total of 805 individuals were analyzed. The significant predictors were the number of previous hospitalizations (HR 1.42, 95% CI 1.22-1.64) and the current length of stay in days (HR 1.31, 95% CI 1.04-1.64). In model development for relapse, Harrell's c-index was 0.59 (95% CI 0.55-0.63). The internal and internal-external validation for rehospitalization showed Harrell's c-index to be 0.64 (95% CI 0.59-0.69) and 0.66 (95% CI 0.57-0.74), respectively. The calibration plot was found to be adequate.

Conclusion

The model showed moderate discrimination of readmission after discharge. Carefully defining a research question by seeking needs among the population with chronic schizophrenia with multiple episodes may be key to building a useful model.

Predicting risk of suicidal behaviour after initiation of selective serotonin reuptake inhibitors in children, adolescents and young adults: protocol for development and validation of clinical prediction models

INTRODUCTION

There is concern regarding suicidal behaviour risk during selective serotonin reuptake inhibitor (SSRI) treatment among the young. A clinically useful model for predicting suicidal behaviour risk should have high predictive performance in terms of discrimination and calibration; transparency and ease of implementation are desirable.

METHODS AND ANALYSIS

Using Swedish national registers, we will identify individuals initiating an SSRI aged 8-24 years 2007-2020. We will develop: (A) a model based on a broad set of predictors, and (B) a model based on a restricted set of predictors. For the broad predictor model, we will consider an ensemble of four base models: XGBoost (XG), neural net (NN), elastic net logistic regression (EN) and support vector machine (SVM). The predictors with the greatest contribution to predictive performance in the base models will be determined. For the restricted predictor model, clinical input will be used to select predictors based on the top predictors in the broad model, and inputted in each of the XG, NN, EN and SVM models. If any show superiority in predictive performance as defined by the area under the receiver-operator curve, this model will be selected as the final model; otherwise, the EN model will be selected. The training and testing samples will consist of data from 2007 to 2017 and from 2018 to 2020, respectively. We will additionally assess the final model performance in individuals receiving a depression diagnosis within 90 days before SSRI initiation.The aims are to (A) develop a model predicting suicidal behaviour risk after SSRI initiation among children and youths, using machine learning methods, and (B) develop a model with a restricted set of predictors, favouring transparency and scalability.

ETHICS AND DISSEMINATION

The research is approved by the Swedish Ethical Review Authority (2020-06540). We will disseminate findings by publishing in peer-reviewed open-access journals, and presenting at international conferences.

Predictive value of suicidal risk assessment using data from China's largest suicide prevention hotline

BACKGROUND

Suicide hotlines are widely used, with potential for identification of callers at especially high risk.

METHODS

This prospective study was conducted at the largest psychological support hotline in China. From 2015 to 2017, all distressed callers were consecutively included and assessed, using a standardized scale consisting of 12 elements, yielding scores of high risk (8-16), moderate risk (4-7), and low risk (0-3) for suicidal act. All high-risk and half of moderate- and low-risk callers were scheduled for a 12-month follow-up. Main outcomes were suicidal acts (nonlethal attempt, death) over follow-up.

RESULTS

Of 21,346 fully assessed callers, 5822, 11,791, and 3733 were classified as high-, moderate-, or low-risk for suicidal acts, with 8869 callers (4076 high-, 3258 moderate-, and 1535 low-risk) followed up over 12 months. Over follow-up, 802 (9.0 %) callers attempted suicide or died by suicide. The high-risk callers (15.1 %) had 3-fold higher risk for subsequent suicidal acts than moderate- (5.1 %) and 12-fold higher risk than low-risk callers (1.3 %). The weighted sensitivity, specificity, and positive predictive value of high risk scores were 56.4 %, 74.9 %, and 14.4 %.

LIMITATIONS

Assessed callers with different risk levels were followed disproportionally.

CONCLUSIONS

Suicidal risk assessment during a hotline call is both feasible and predictive of risk, guiding resource allocation to higher risk callers.

Cause-specific excess mortality after first diagnosis of bipolar disorder: population-based cohort study

BACKGROUND

Bipolar disorder (BD) is associated with increased mortality, but evidence on cause-specific mortality is limited.

OBJECTIVE

To investigate cause-specific premature excess mortality in BD.

METHODS

Finnish nationwide cohort study of individuals with and without a diagnosis of BD who were aged 15-64 years during 2004-2018. Standardised mortality ratios (SMRs) with 95% CIs were calculated for BD using the mortality rates in the Finnish general population without BD as weights. Causes of death were defined by the International Classification of Diseases, 10th revision codes.

FINDINGS

Of the included 47 018 individuals with BD, 3300 (7%) died during follow-up. Individuals with BD had sixfold higher mortality due to external causes (SMR: 6.01, 95% CI: 5.68, 6.34) and twofold higher mortality due to somatic causes (SMR: 2.06, 95% CI: 1.97, 2.15). Of the deaths due to external causes, 83% (1061/1273) were excess deaths, whereas 51% (1043/2027) of the deaths due to somatic causes were excess. About twice the number of potential years of life were lost in excess due to external causes than due to somatic causes. Alcohol-related causes contributed more to excess mortality than deaths due to cardiovascular disease.

CONCLUSION

External causes of death contributed more to the mortality gap than somatic causes after controlling for age-specific background general population mortality.

CLINICAL IMPLICATION

A balanced consideration between therapeutic response, different treatment options and risk of cause-specific mortality is needed to prevent premature mortality in BD and to reduce the mortality gap.

Risk of death by suicide following self-harm presentations to healthcare: development and validation of a multivariable clinical prediction rule (OxSATS)

BACKGROUND

Assessment of suicide risk in individuals who have self-harmed is common in emergency departments, but is often based on tools developed for other purposes.

OBJECTIVE

We developed and validated a predictive model for suicide following self-harm.

METHODS

We used data from Swedish population-based registers. A cohort of 53 172 individuals aged 10+ years, with healthcare episodes of self-harm, was split into development (37 523 individuals, of whom 391 died from suicide within 12 months) and validation (15 649 individuals, 178 suicides within 12 months) samples. We fitted a multivariable accelerated failure time model for the association between risk factors and time to suicide. The final model contains 11 factors: age, sex, and variables related to substance misuse, mental health and treatment, and history of self-harm. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis guidelines were followed for the design and reporting of this work.

FINDINGS

An 11-item risk model to predict suicide was developed using sociodemographic and clinical risk factors, and showed good discrimination (c-index 0.77, 95% CI 0.75 to 0.78) and calibration in external validation. For risk of suicide within 12 months, using a 1% cut-off, sensitivity was 82% (75% to 87%) and specificity was 54% (53% to 55%). A web-based risk calculator is available (Oxford Suicide Assessment Tool for Self-harm or OxSATS).

CONCLUSIONS

OxSATS accurately predicts 12-month risk of suicide. Further validations and linkage to effective interventions are required to examine clinical utility.

CLINICAL IMPLICATIONS

Using a clinical prediction score may assist clinical decision-making and resource allocation.

Predicting suicide risk in 137,112 people with severe mental illness in Finland: external validation of the Oxford Mental Illness and Suicide tool (OxMIS)

Oxford Mental Illness and Suicide tool (OxMIS) is a standardised, scalable, and transparent instrument for suicide risk assessment in people with severe mental illness (SMI) based on 17 sociodemographic, criminal history, familial, and clinical risk factors. However, alongside most prediction models in psychiatry, external validations are currently lacking. We utilised a Finnish population sample of all persons diagnosed by mental health services with SMI (schizophrenia-spectrum and bipolar disorders) between 1996 and 2017 (n = 137,112). To evaluate the performance of OxMIS, we initially calculated the predicted 12-month suicide risk for each individual by weighting risk factors by effect sizes reported in the original OxMIS prediction model and converted to a probability. This probability was then used to assess the discrimination and calibration of the OxMIS model in this external sample. Within a year of assessment, 1.1% of people with SMI (n = 1475) had died by suicide. The overall discrimination of the tool was good, with an area under the curve of 0.70 (95% confidence interval: 0.69-0.71). The model initially overestimated suicide risks in those with elevated predicted risks of >5% over 12 months (Harrell's E_max = 0.114), which applied to 1.3% (n = 1780) of the cohort. However, when we used a 5% maximum predicted suicide risk threshold as is recommended clinically, the calibration was excellent (ICI = 0.002; E_max = 0.005). Validating clinical prediction tools using routinely collected data can address research gaps in prediction psychiatry and is a necessary step to translating such models into clinical practice.

A health impact assessment of gender inequities associated with psychological distress during COVID19 in Australia's most locked down state-Victoria

BACKGROUND

Since March 2020, when the COVID19 pandemic hit Australia, Victoria has been in lockdown six times for 264 days, making it the world's longest cumulative locked-down city. This Health Impact Assessment evaluated gender disparities, especially women's mental health, represented by increased levels of psychological distress during the lockdowns.

METHODS

A desk-based, retrospective Health Impact Assessment was undertaken to explore the health impacts of the lockdown public health directive with an equity focus, on the Victorian population, through reviewing available qualitative and quantitative published studies and grey literature.

RESULTS

Findings from the assessment suggest the lockdown policies generated and perpetuated avoidable inequities harming mental health demonstrated through increased psychological distress, particularly for women, through psychosocial determinants.

CONCLUSION

Ongoing research is needed to elucidate these inequities further. Governments implementing policies to suppress and mitigate COVID19 need to consider how to reduce harmful consequences of these strategies to avoid further generating inequities towards vulnerable groups within the population and increasing inequalities in the broader society.

Cost-effectiveness of implementing a suicide prediction tool (OxMIS) in severe mental illness: Economic modeling study

BACKGROUND

Cost-effectiveness analysis needs to be considered when introducing new tools and treatments to clinical services. The number of new assessment tools in mental health has rapidly expanded, including suicide risk assessment. Such suicide-based assessments, when linked to preventative interventions, are integral to high-quality mental health care for people with severe mental illness (SMI). We examined the cost implications of implementing Oxford Mental Illness and Suicide (OxMIS), an evidence-based, scalable suicide risk assessment tool that provides probabilistic estimates of suicide risk over 12 months for people with SMI in England.

METHODS

We developed a decision analytic model using secondary data to estimate the potential cost-effectiveness of incorporating OxMIS into clinical decision-making in secondary care as compared to usual care. Cost-effectiveness was measured in terms of costs per quality-adjusted life years (QALYs) gained. Uncertainty was addressed with deterministic and probabilistic sensitivity analysis.

RESULTS

Conducting suicide risk assessment with OxMIS was potentially cheaper than clinical risk assessment alone by £250 (95% confidence interval, -786;31) to £599 (-1,321;-156) (in 2020-2021 prices) per person with SMI and associated with a small increase in quality of life (0.01 [-0.03;0.05] to 0.01 QALY, [-0.04;0.07]). The estimated incremental cost-effectiveness ratio of implementing OxMIS was cost saving. Using probabilistic sensitivity analysis, 99.96% of 10,000 simulations remained cost saving.

CONCLUSION

Cost-effectiveness analysis can be conducted on risk prediction models. Implementing one such model that focuses on suicide risk in a high-risk population can lead to cost savings and improved health outcomes, especially if explicitly linked to preventative treatments.

A risk calculator to predict suicide attempts among individuals with early-onset bipolar disorder

OBJECTIVES

To build a one-year risk calculator (RC) to predict individualized risk for suicide attempt in early-onset bipolar disorder.

METHODS

Youth numbering 394 with bipolar disorder who completed ≥2 follow-up assessments (median follow-up length = 13.1 years) in the longitudinal Course and Outcome of Bipolar Youth (COBY) study were included. Suicide attempt over follow-up was assessed via the A-LIFE Self-Injurious/Suicidal Behavior scale. Predictors from the literature on suicidal behavior in bipolar disorder that are readily assessed in clinical practice were selected and trichotomized as appropriate (presence past 6 months/lifetime history only/no lifetime history). The RC was trained via boosted multinomial classification trees; predictions were calibrated via Platt scaling. Half of the sample was used to train, and the other half to independently test the RC.

RESULTS

There were 249 suicide attempts among 106 individuals. Ten predictors accounted for >90% of the cross-validated relative influence in the model (AUC = 0.82; in order of relative influence): (1) age of mood disorder onset; (2) non-suicidal self-injurious behavior (trichotomized); (3) current age; (4) psychosis (trichotomized); (5) socioeconomic status; (6) most severe depressive symptoms in past 6 months (trichotomized none/subthreshold/threshold); (7) history of suicide attempt (trichotomized); (8) family history of suicidal behavior; (9) substance use disorder (trichotomized); (10) lifetime history of physical/sexual abuse. For all trichotomized variables, presence in the past 6 months reliably predicted higher risk than lifetime history.

CONCLUSIONS

This RC holds promise as a clinical and research tool for prospective identification of individualized high-risk periods for suicide attempt in early-onset bipolar disorder.

Risk factors for suicide in adults: systematic review and meta-analysis of psychological autopsy studies

QUESTION

Effective prevention of suicide requires a comprehensive understanding of risk factors.

STUDY SELECTION AND ANALYSIS

Five databases were systematically searched to identify psychological autopsy studies (published up to February 2022) that reported on risk factors for suicide mortality among adults in the general population. Effect sizes were pooled as odds ratios (ORs) using random-effects models for each risk factor examined in at least three independent samples.

FINDINGS

A total of 37 case-control studies from 23 countries were included, providing data on 40 risk factors in 5633 cases and 7101 controls. The magnitude of effect sizes varied substantially both between and within risk factor domains. Clinical factors had the strongest associations with suicide, including any mental disorder (OR=13.1, 95% CI 9.9 to 17.4) and a history of self-harm (OR=10.1, 95% CI 6.6 to 15.6). By comparison, effect sizes were smaller for other domains relating to sociodemographic status, family history, and adverse life events (OR range 2-5).

CONCLUSIONS

A wide range of predisposing and precipitating factors are associated with suicide among adults in the general population, but with clear differences in their relative strength.

PROSPERO REGISTRATION NUMBER

CRD42021232878.

Selective serotonin reuptake inhibitors and suicidal behaviour: a population-based cohort study

There is concern that selective serotonin reuptake inhibitor (SSRI) treatment may increase the risk of suicide attempts or deaths, particularly among children and adolescents. However, debate remains regarding the nature of the relationship. Using nationwide Swedish registers, we identified all individuals aged 6-59 years with an incident SSRI dispensation (N = 538,577) from 2006 to 2013. To account for selection into treatment, we used a within-individual design to compare the risk of suicide attempts or deaths (suicidal behaviour) in time periods before and after SSRI-treatment initiation. Within-individual incidence rate ratios (IRRs) of suicidal behaviour were estimated. The 30 days before SSRI-treatment initiation was associated with the highest risk of suicidal behaviour compared with the 30 days 1 year before SSRI initiation (IRR = 7.35, 95% CI 6.60-8.18). Compared with the 30 days before SSRI initiation, treatment periods after initiation had a reduced risk-the IRR in the 30 days after initiation was 0.62 (95% CI 0.58-0.65). The risk then declined over treatment time. These patterns were similar across age strata, and when stratifying on history of suicide attempts. Initiation with escitalopram was associated with the greatest risk reduction, though CIs for the IRRs of the different SSRI types were overlapping. The results do not suggest that SSRI-treatment increases the risk for suicidal behaviour in either youths or adults; rather, it may reduce the risk. Further research with different study designs and in different populations is warranted.

A review and meta-analysis of gene expression profiles in suicide

Suicide claims over 800,000 deaths worldwide, making it a serious public health problem. The etiopathophysiology of suicide remains unclear and is highly complex, and postmortem gene expression studies can offer insights into the molecular biological mechanism underlying suicide. In the current study, we conducted a meta-analysis of postmortem brain gene expression in relation to suicide. We identified five gene expression datasets for postmortem orbitofrontal, prefrontal, or dorsolateral prefrontal cortical brain regions from the Gene Expression Omnibus repository. After quality control, the total sample size was 380 (141 suicide deaths and 239 deaths from other causes). We performed the analyses using two meta-analytic approaches. We further performed pathway and cell-set enrichment analyses. We found reduced expression of the KCNJ2 (Potassium Inwardly Rectifying Channel Subfamily J Member 2), A2M (Alpha-2-Macroglobulin), AGT (Angiotensinogen), PMP2 (Peripheral Myelin Protein 2), and VEZF1 (Vascular Endothelial Zinc Finger 1) genes (FDR p<0.05). Our findings support the involvement of astrocytes, stress response, immune system, and microglia in suicide. These findings will require further validation in additional large datasets.

Evaluating the Risk of Suicide and Violence in Severe Mental Illness: A Feasibility Study of Two Risk Assessment Tools (OxMIS and OxMIV) in General Psychiatric Settings

BACKGROUND

Two OxRisk risk assessment tools, the Oxford Mental Illness and Suicide (OxMIS) and the Oxford Mental Illness and Violence (OxMIV), were developed and validated using national linked registries in Sweden, to assess suicide and violence risk in individuals with severe mental illness (schizophrenia-spectrum disorders and bipolar disorders). In this study, we aim to examine the feasibility and acceptability of the tools in three different clinical services.

METHOD

We employed a two-step mixed-methods approach, by combining quantitative analyses of risk scores of 147 individual patients, and thematic analyses of qualitative data. First, 38 clinicians were asked to use OxMIS and OxMIV when conducting their routine risk assessments in patients with severe mental illness. The risk scores for each patient (which provide a probability of the outcome over 12 months) were then compared to the unstructured clinical risk assessment made by the treating clinician. Second, we carried out semi-structured interviews with the clinicians on the acceptability and utility of the tools. Thematic analysis was conducted on the qualitative data to identify common themes, in terms of the utility, accuracy, and acceptability of the tools. The investigations were undertaken in three general adult psychiatric clinics located in the cities of Barcelona and Sevilla (Spain), and Changsha (China).

RESULTS

Median risk probabilities over 12 months for OxMIS were 1.0% in the Spanish patient sample and 1.9% in the Chinese sample. For OxMIV, they were 0.7% (Spanish) and 0.8% (Chinese). In the thematic analysis, clinicians described the tools as easy to use, and thought that the risk score improved risk management. Potential additions to predictors were suggested, including family history and the patient's support network. Concordance rates of risk estimates between the tools and clinicians was high for violence (94.4%; 68/72) and moderate for suicide (50.0%; 36/72).

CONCLUSION

Both OxMIS and OxMIV are feasible and practical in different general adult psychiatric settings. Clinicians interviewed found that both tools provide a useful structured approach to estimate the risk of suicide and violence. Risk scores from OxMIS and OxMIV can also be used to assist clinical decision-making for future management.

Experiences of nurses amidst giving care to COVID-19 patients in clinical settings in Iraqi Kurdistan: A qualitative descriptive study

AIM AND OBJECTIVE

We explored the experiences of nurses who cared for coronavirus disease 2019 patients in Iraqi Kurdistan.

BACKGROUND

Nurses play a major role in response to pandemics and epidemics in delivering patient care. The experiences of nurses who provided care have significant short and long-term consequences for individuals, communities, and the nursing profession.

METHODS

Descriptive qualitative research approach was adopted in this study. We interviewed 12 nurses (22-50 years) who cared for the coronavirus disease 2019 patients in one of the clinical units of two coronavirus disease 2019 hospitals in Iraqi Kurdistan in 2020. Interviews were conducted via phone calls and were analysed using the thematic analysis method. The Consolidated criteria for reporting qualitative research checklist was applied when constructing this paper.

RESULTS

The nurses had to care for a number of situations during the outbreak of coronavirus disease 2019 in Kurdistan. As people in the public did not believe that there was such a virus, nurses often had to deal with this lack of knowledge and aggression from some patients and their family members. Most nurses changed their preventive behaviours since the coronavirus disease 2019 outbreak at hospital or in public. This was mainly to protect not only themselves but their patients, colleagues, family members and friends. They were cautious about the use of a mask at the hospital and in public. Most nurses experienced fear, stress, anxiety and isolation during this period.

CONCLUSIONS

The patients had some concerns about their health and staying at hospitals, and some of them had aggressive behaviours towards nurses at corona hospitals. The public, close friends and relatives of the nurses had a fear of getting the infection by the virus through the nurses. However, the nurses attempted to protect themselves, colleagues and family members, and provide the best care to coronavirus disease 2019 patients. The nurses had a high obligation towards care giving at hospitals.

RELEVANCE TO CLINICAL PRACTICE

The negative experiences of the nurses regarding the care of coronavirus disease 2019 patients must be considered in clinical settings. Sensitive policy programs must be established to protect nurses from the ostracization and stigmatization of the coronavirus disease 2019 pandemic and to allow them to be able to achieve their professional practices safely.

Validation of the youth mood recurrences risk calculator in an adult sample with bipolar disorder

BACKGROUND

The ability to predict an individual's risk of mood episode recurrence can facilitate personalized medicine in bipolar disorder (BD). We sought to externally validate, in an adult sample, a risk calculator of mood episode recurrence developed in youth/young adults with BD from the Course and Outcome of Bipolar Youth (COBY) study.

METHODS

Adult participants from the National Institute of Mental Health Collaborative Depression Study (CDS; N=258; mean(SD) age=35.5(12.0) years; mean follow-up=24.9 years) were utilized as a sample to validate the youth COBY risk calculator for onset of depressive, manic, or any mood episodes.

RESULTS

In this older validation sample, the risk calculator predicted recurrence of any episode over 1, 2, 3, or 5-year follow-up intervals, with Area Under the Curves (AUCs) approximating 0.77. The AUC for prediction of depressive episodes was about 0.81 for each of the time windows, which was higher than for manic or hypomanic episodes (AUC=0.72). While the risk calculator was well-calibrated across the range of risk scores, it systematically underestimated risk in the CDS sample by about 20%. The length of current remission was a highly significant predictor of recurrence risk in the CDS sample.

LIMITATIONS

Predominantly self-reported White samples may limit generalizability; the risk calculator does not assess more proximal risk (e.g., 1 month).

CONCLUSIONS

Risk of mood episode recurrence can be predicted with good accuracy in youth and adults with BD in remission. The risk calculators may help identify higher risk BD subgroups for treatment and research.

Problemi della previsione in psichiatria

Sono esaminati vari problemi relativi alla previsione in psichiatria. I dati disponibili mostrano, in modo simile alle scienze sociali, ampi limiti nella capacità previsionale, specie per quanto riguarda il suicidio, la violenza e altri aspetti comportamentali. Vengono esaminate le difficoltà che nascono dal cercare di derivare il futuro della persona dal suo passato, la mancata coerenza fra aspetti di personalità e possibili comportamenti e il privilegio dato a strumenti psicopatologici incentrati sul singolo caso, rispetto a quelli attuariali con valutazioni testistiche e statistiche. Vengono anche evidenziati i numerosi bias cognitivi che distorcono le previsioni, in particolare l'errore fondamentale di attribuzione, che privilegia aspetti personologici rispetto a quelli situazionali. Ma altri bias hanno una importante azione distorsiva, da quelli della rappresentatività a quelli della disponibilità, da quelli statistici, al framing o al priming. Emerge una psichiatria molto legata nelle pratiche ancora al senso comune e alla folk psychology, con la ricchezza ma anche i molti errori che la caratterizzano. Di fatto esiste una modesta capacità previsionale riconosciuta alla psicologia popolare e alla psichiatria, ma è legata più a vincoli situazionali che a modelli personologici e psicopatologici e in ogni caso scarsamente affidabile per la previsione clinica in psichiatria.

Childhood trauma and the severity of past suicide attempts in outpatients with cocaine use disorders

INTRODUCTION

Suicide attempts have been associated with both cocaine use disorder (CocUD) and childhood trauma. We investigated how childhood trauma is an independent risk factor for serious and recurrent suicide attempts in CocUD. Method: 298 outpatients (23% women) with CocUD underwent standardized assessments of substance dependence (Diagnostic and Statistical Manual-mental disorders, fourth edition, text revised), impulsiveness, resilience, and childhood trauma, using validated tools. Suicide attempts history was categorized as single vs. recurrent or non-serious vs. serious depending on the lifetime number of suicide attempts and the potential or actual lethality of the worst attempt reported, respectively. Bivariate and multinomial regression analyses were used to characterize which childhood trauma patterns were associated with the suicide attempts groups. Results: 58% of CocUD patients reported childhood trauma. Recurrent and serious suicide attempts clustered together and were thus combined into "severe SA." Severe suicide attempt risk increased proportionally to the number of childhood traumas (test for trend, p = 9 × 10^-7). Non-severe suicide attempt risk increased with impulsiveness and decreased with resilience. In multinomial regression models, a higher number of traumas and emotional abuse were independently and only associated with severe vs. non-severe suicide attempts (effect size = 0.82, AUC = 0.7). The study was limited by its cross-sectional design. Conclusion: These preferential associations between childhood trauma and severe suicide attempts warrant specific monitoring of suicide attempts risk in CocUD, regardless of the severity of addiction profiles.

Development of a Suicide Prediction Model for the Elderly Using Health Screening Data

Suicide poses a serious problem globally, especially among the elderly population. To tackle the issue, this study aimed to develop a model for predicting suicide by using machine learning based on the elderly population. To obtain a large sample, the study used the big data health screening cohort provided by the National Health Insurance Sharing Service. By applying a machine learning technique, a predictive model that comprehensively utilized various factors was developed to select the elderly aged > 65 years at risk of suicide. A total of 48,047 subjects were included in the analysis. Individuals who died by suicide were older, and the number of men was significantly greater. The suicide group had a more prominent history of depression, with the use of medicaments significantly higher. Specifically, the prescription of benzodiazepines alone was associated with a high suicide risk. Furthermore, body mass index, waist circumference, total cholesterol, and low-density lipoprotein level were lower in the suicide group. We developed a model for predicting suicide by using machine learning based on the elderly population. This suicide prediction model can satisfy the performance to some extent by employing only the medical service usage behavior without subjective reports.

Psychiatry in the Digital Age: A Blessing or a Curse?

Social distancing and the shortage of healthcare professionals during the COVID-19 pandemic, the impact of population aging on the healthcare system, as well as the rapid pace of digital innovation are catalyzing the development and implementation of new technologies and digital services in psychiatry. Is this transformation a blessing or a curse for psychiatry? To answer this question, we conducted a literature review covering a broad range of new technologies and eHealth services, including telepsychiatry; computer-, internet-, and app-based cognitive behavioral therapy; virtual reality; digital applied games; a digital medicine system; omics; neuroimaging; machine learning; precision psychiatry; clinical decision support; electronic health records; physician charting; digital language translators; and online mental health resources for patients. We found that eHealth services provide effective, scalable, and cost-efficient options for the treatment of people with limited or no access to mental health care. This review highlights innovative technologies spearheading the way to more effective and safer treatments. We identified artificially intelligent tools that relieve physicians from routine tasks, allowing them to focus on collaborative doctor-patient relationships. The transformation of traditional clinics into digital ones is outlined, and the challenges associated with the successful deployment of digitalization in psychiatry are highlighted.

Development and validation of the Durham Risk Score for estimating suicide attempt risk: A prospective cohort analysis

BACKGROUND

Worldwide, nearly 800,000 individuals die by suicide each year; however, longitudinal prediction of suicide attempts remains a major challenge within the field of psychiatry. The objective of the present research was to develop and evaluate an evidence-based suicide attempt risk checklist [i.e., the Durham Risk Score (DRS)] to aid clinicians in the identification of individuals at risk for attempting suicide in the future.

METHODS AND FINDINGS

Three prospective cohort studies, including a population-based study from the United States [i.e., the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) study] as well as 2 smaller US veteran cohorts [i.e., the Assessing and Reducing Post-Deployment Violence Risk (REHAB) and the Veterans After-Discharge Longitudinal Registry (VALOR) studies], were used to develop and validate the DRS. From a total sample size of 35,654 participants, 17,630 participants were selected to develop the checklist, whereas the remaining participants (N = 18,024) were used to validate it. The main outcome measure was future suicide attempts (i.e., actual suicide attempts that occurred after the baseline assessment during the 1- to 3-year follow-up period). Measure development began with a review of the extant literature to identify potential variables that had substantial empirical support as longitudinal predictors of suicide attempts and deaths. Next, receiver operating characteristic (ROC) curve analysis was utilized to identify variables from the literature review that uniquely contributed to the longitudinal prediction of suicide attempts in the development cohorts. We observed that the DRS was a robust prospective predictor of future suicide attempts in both the combined development (area under the curve [AUC] = 0.91) and validation (AUC = 0.92) cohorts. A concentration of risk analysis found that across all 35,654 participants, 82% of prospective suicide attempts occurred among individuals in the top 15% of DRS scores, whereas 27% occurred in the top 1%. The DRS also performed well among important subgroups, including women (AUC = 0.91), men (AUC = 0.93), Black (AUC = 0.92), White (AUC = 0.93), Hispanic (AUC = 0.89), veterans (AUC = 0.91), lower-income individuals (AUC = 0.90), younger adults (AUC = 0.88), and lesbian, gay, bisexual, transgender, and queer or questioning (LGBTQ) individuals (AUC = 0.88). The primary limitation of the present study was its its reliance on secondary data analyses to develop and validate the risk score.

CONCLUSIONS

In this study, we observed that the DRS was a strong predictor of future suicide attempts in both the combined development (AUC = 0.91) and validation (AUC = 0.92) cohorts. It also demonstrated good utility in many important subgroups, including women, men, Black, White, Hispanic, veterans, lower-income individuals, younger adults, and LGBTQ individuals. We further observed that 82% of prospective suicide attempts occurred among individuals in the top 15% of DRS scores, whereas 27% occurred in the top 1%. Taken together, these findings suggest that the DRS represents a significant advancement in suicide risk prediction over traditional clinical assessment approaches. While more work is needed to independently validate the DRS in prospective studies and to identify the optimal methods to assess the constructs used to calculate the score, our findings suggest that the DRS is a promising new tool that has the potential to significantly enhance clinicians' ability to identify individuals at risk for attempting suicide in the future.

Characterization of time-variant and time-invariant assessment of suicidality on Reddit using C-SSRS

Suicide is the 10th leading cause of death in the U.S (1999-2019). However, predicting when someone will attempt suicide has been nearly impossible. In the modern world, many individuals suffering from mental illness seek emotional support and advice on well-known and easily-accessible social media platforms such as Reddit. While prior artificial intelligence research has demonstrated the ability to extract valuable information from social media on suicidal thoughts and behaviors, these efforts have not considered both severity and temporality of risk. The insights made possible by access to such data have enormous clinical potential-most dramatically envisioned as a trigger to employ timely and targeted interventions (i.e., voluntary and involuntary psychiatric hospitalization) to save lives. In this work, we address this knowledge gap by developing deep learning algorithms to assess suicide risk in terms of severity and temporality from Reddit data based on the Columbia Suicide Severity Rating Scale (C-SSRS). In particular, we employ two deep learning approaches: time-variant and time-invariant modeling, for user-level suicide risk assessment, and evaluate their performance against a clinician-adjudicated gold standard Reddit corpus annotated based on the C-SSRS. Our results suggest that the time-variant approach outperforms the time-invariant method in the assessment of suicide-related ideations and supportive behaviors (AUC:0.78), while the time-invariant model performed better in predicting suicide-related behaviors and suicide attempt (AUC:0.64). The proposed approach can be integrated with clinical diagnostic interviews for improving suicide risk assessments.

Digital Technologies and Coercion in Psychiatry

Psychiatry has a contentious history of coercion in the care of patients with mental illness, and legal frameworks often govern use of coercive interventions, such as involuntary hospitalization, physical restraints, and medication over objection. Research also suggests that informal coercion, including subtle inducements, leverage, or threats, is prevalent and influential in psychiatric settings. Digital technologies bring promise for expanding access to psychiatric care and improving delivery of these services; however, use and misuse of digital technologies, such as electronic medical record flags, surveillance cameras, videoconferencing, and risk assessment tools, could lead to unexpected coercion of patients with mental illness. Using several composite case examples, the author proposes that the integration of digital technologies into psychiatric care can influence patients' experiences of coercion and provides recommendations for studying and addressing these effects.

Misdiagnosis, detection rate, and associated factors of severe psychiatric disorders in specialized psychiatry centers in Ethiopia

BACKGROUND

There are limited studies on the prevalence of misdiagnosis as well as detection rates of severe psychiatric disorders in specialized and non-specialized healthcare settings. To the best of our knowledge, this is the first study to determine the prevalence of misdiagnosis and detection rates of severe psychiatric disorders including schizophrenia, schizoaffective, bipolar, and depressive disorders in a specialized psychiatric setting.

METHOD

In this cross-sectional study, a random sample of 309 patients with severe psychiatric disorders was selected by systematic sampling technique. Severe psychiatric disorders were assessed using the Structured Clinical Interview for DSM-IV (SCID). The potential determinates of misdiagnosis were explored using univariable and multivariable logistic regression models, adjusting for the potential confounding factors.

RESULT

This study revealed that more than a third of patients with severe psychiatric disorders were misdiagnosed (39.16%). The commonly misdiagnosed disorder was found to be a schizoaffective disorder (75%) followed by major depressive disorder (54.72%), schizophrenia (23.71%), and bipolar disorder (17.78%). Among the patients detected with the interview by SCID criteria, the highest level of the correct diagnosis was recorded in the medical record for schizophrenia (76.29%) followed by bipolar (72.22%), depressive (42.40%), and schizoaffective (25%) disorders with detection rate (sensitivity) of 0.76 (95% CI 0.69-0.84), 0.42 (95% CI 0.32-0.53), 0.72 (95% CI 0.60-0.84), and 0.25 (95% CI 0.09-0.41), respectively for schizophrenia, depressive, bipolar, and schizoaffective disorders. Patients with bipolar disorder were more likely to be misdiagnosed as having schizophrenia (60%), whereas schizophrenic patients were more likely to be misdiagnosed as having bipolar disorder (56.25%) and patients with depressive disorders were more likely to be misdiagnosed as having schizophrenia (54.72%). Having a diagnosis of schizoaffective and depressive disorders, as well as suicidal ideation, was found to be significant predictors of misdiagnosis.

CONCLUSION

This study showed that roughly four out of ten patients with severe psychiatric disorders had been misdiagnosed in a specialized psychiatric setting in Ethiopia. The highest rate of misdiagnosis was observed for schizoaffective disorder (3 out of 4), followed by major depressive disorder (1 out of 2), schizophrenia (1 out of 4), and bipolar disorders (1 in 5). The detection rates were highest for schizophrenia, followed by bipolar, depressive, and schizoaffective disorders. Having a diagnosis of schizoaffective and depressive disorders as well as suicidal ideation was found to be significant predictors of misdiagnosis.

Comparisons between suicide in persons with serious mental illness, other mental disorders, or no known mental illness: Results from 37 U.S. states, 2003-2017

BACKGROUND

Suicide is a leading cause of death in persons with schizophrenia and other serious mental illnesses (SMI), however, little is known about the characteristics and circumstances of suicide decedents with SMI in the US compared to those with other or no known mental illness.

METHODS

This study was a retrospective analysis of suicide deaths in individuals aged ≥18 years from the National Violent Death Reporting System, 2003-2017. Odds ratios compared sociodemographic and clinical characteristics, cause of death, precipitating circumstances, and post-mortem toxicology results. All analyses were stratified by gender.

RESULTS

Of the 174,001 suicide decedents, 8.7% had a known SMI, 33.0% had other mental disorders, and 58.2% had no known mental illness. Relative to persons with other mental disorders, SMI decedents were younger and more likely to have previous suicide attempts and co-occurring drug use. Problems with intimate partners, poor physical health, and recent institutional release were the most common precipitating circumstances for SMI decedents. Firearms were the most common suicide method for males with SMI. Although 67.0% male and 76.0% of female SMI decedents were currently in treatment, toxicology results suggest many were not taking antipsychotic or antidepressant medications at the time of death.

CONCLUSIONS

Persons with SMI are over-represented in suicide deaths. Efforts to improve treatment of co-occurring substance use disorders, continuity of care following hospitalization, medication adherence, and to reduce access to firearms are important suicide prevention strategies.

1324 prison suicides in 10 countries in South America: incidence, relative risks, and ecological factors

PURPOSE

Although suicide rates of prison populations and incidence factors have been reported for high-income countries, data from low- and middle-income regions are lacking. The purpose of the study was to estimate suicide rates among prison populations in South America, to examine prison-related factors, and to compare suicide rates between prison and general populations.

METHODS

In this observational study, we collected the numbers of suicides in prison, rates of prison occupancy, and incarceration rates from primary sources in South America between 2000 and 2017. We compared suicide rates among prisoners with incidence rates in the general populations by calculating incidence rate ratios. We assessed the effect of gender, year, incarceration rates and occupancy on suicide rates in the prison populations using regression analyses.

RESULTS

There were 1324 suicides reported during 4,437,591 person years of imprisonment between 2000 and 2017 in 10 South American countries. The mean suicide rate was 40 (95% CI 16-65) per 100,000 person years for male and female genders combined. The pooled incidence rate ratio of suicide between prison and general populations was 3.9 (95% CI 3.1-5.1) for both genders combined, 2.4 (95% CI 1.9-3.1) for men and a higher ratio in women (13.5, 95% CI 6.9-26.9). High occupancies of prisons were associated with lower incidence of suicide (β = - 58, 95% CI - 108.5 to - 7.1).

CONCLUSIONS

Suicides during imprisonment in South America are an important public health problem. Suicide prevention strategies need to target prison populations.

Meta-analysis of the strength of exploratory suicide prediction models; from clinicians to computers

BACKGROUND

Suicide prediction models have been formulated in a variety of ways and are heterogeneous in the strength of their predictions. Machine learning has been a proposed as a way of improving suicide predictions by incorporating more suicide risk factors.

AIMS

To determine whether machine learning and the number of suicide risk factors included in suicide prediction models are associated with the strength of the resulting predictions.

METHOD

Random-effect meta-analysis of exploratory suicide prediction models constructed by combining two or more suicide risk factors or using clinical judgement (Prospero Registration CRD42017059665). Studies were located by searching for papers indexed in PubMed before 15 August 2020 with the term suicid* in the title.

RESULTS

In total, 86 papers reported 102 suicide prediction models and included 20 210 411 people and 106 902 suicides. The pooled odds ratio was 7.7 (95% CI 6.7-8.8) with high between-study heterogeneity (I2 = 99.5). Machine learning was associated with a non-significantly higher odds ratio of 11.6 (95% CI 6.0-22.3) and clinical judgement with a non-significantly lower odds ratio of 4.7 (95% CI 2.1-10.9). Models including a larger number of suicide risk factors had a higher odds ratio when machine-learning studies were included (P = 0.02). Among non-machine-learning studies, suicide prediction models including fewer risk factors performed just as well as those including more risk factors.

CONCLUSIONS

Machine learning might have the potential to improve the performance of suicide prediction models by increasing the number of included suicide risk factors but its superiority over other methods is unproven.

Socio-Demographic and Clinical Characteristics of Psychiatric Patients Who Have Committed Suicide: Analysis of Bulgarian Regional Suicidal Registry for 10 Years

Introduction: Suicide is a major public health problem but factors determining suicide risk are still unclear. Studies in this field in Bulgaria are limited, especially on a regional level. Methods: By a cross-sectional design, we accessed the medical records of all psychiatric patients committed suicide over a 10-year period (2009-2018) in one major administrative region of Bulgaria. A statistical analysis was performed of the association between age of suicide as an indirect yet measurable expression of the underlying suicidal diathesis and a number of socio-demographic and clinical characteristics. Results: Seventy-seven of 281 suicides (28%) had psychiatric records. Most common diagnoses were mood disorders (44%), followed by schizophrenia (27%), anxiety disorders (10%), substance use disorders (9%) and organic conditions (8%). Male gender, single/divorced marital status, early illness onset, co-occurring substance misuse and lower educational attainment (for patients aged below 70) were significantly associated with earlier age of suicide whereas past suicide attempts and psychiatric hospitalizations, comorbid somatic conditions and unemployment showed insignificant association. Substantial proportion of patients (60%) had contacted psychiatric service in the year preceding suicide, with nearly half of these encounters being within 30 days of the accident. Conclusion: Severe mental disorders are major suicide risk factor with additional contribution of certain socio-demographic and illness-related characteristics. Monitoring for suicidality must be constant in chronic psychiatric patients. Registration of suicide cases in Bulgaria needs improvement in terms of information concerning mental health. More studies with larger samples and longitudinal design are needed to further elucidate distal and proximal suicide risk factors.

Mental illness and the risk of self- and other-directed aggression: Results from the National Survey on Drug Use and Health

Aggression and mental illness have been classically interlinked, often causing controversy and debate. Previous studies have shown that mental illness can be a risk factor to self- and other-directed aggression. However, these associations have rarely been simultaneously studied within the same population. Therefore, we aimed to study whether psychiatric disorders differentially increase the likelihood of one subtype of aggression over the other. We used the publicly available data of the National Survey on Drug Use and Health (NSDUH) from 2008 through 2014, for a total sample of 270,227 adult respondents. We designed our independent variable according to three categories: no mental illness (NMI), low or moderate (LMMI) and serious (SMI). We constructed regression models to estimate the odds ratios for those having a mental illness committing (a) a subtype of aggression over the past year compared with no aggression and (b) other-directed compared to self-directed aggression. We found that most respondents with mental illness reported no past-year aggression of any type. However, respondents with mental illness had higher odds of perpetrating all subtypes of aggression. Additionally, respondents with LMMI and SMI were respectively 1.7 and 3 times more likely to engage in self- rather than other-directed aggression. Future research should focus on identifying accurate and reliable predictors of self- and other-directed aggression among individuals with mental illness.

Prediction of suicide among 372,813 individuals under medical check-up

BACKGROUND

Suicide is a serious social and public health problem. Social stigma and prejudice reduce the accessibility of mental health care services for high-risk groups, resulting in them not receiving interventions and committing suicide. A suicide prediction model is necessary to identify high-risk groups in the general population.

METHODS

We used national medical check-up data from 2009 to 2015 in Korea. The latest medical check-up data for each subject was set as an index point. Analysis was undertaken for an overall follow-up period (index point to the final tracking period) as well as for a one-year follow-up period. The training set was cross-validated fivefold. The predictive model was trained using a random forest algorithm, and its performance was measured using a separate test set not included in the training.

RESULTS

The analysis covered 372,813 individuals, with an average (SD) overall follow-up duration of 1.52 (1.52) years. When we predicted suicide during the overall follow-up period, the area under the receiver operating characteristic curve (AUC) was 0.849, sensitivity was 0.817, and specificity was 0.754. The performance of the predicted suicide risk model for one year from the index point was AUC 0.818, sensitivity 0.788, and specificity 0.657.

CONCLUSIONS

This is probably the first suicide predictive model using machine learning based on medical check-up data from the general population. It could be used to screen high-risk suicidal groups from the population through routine medical check-ups. Future studies may test preventive interventions such as exercise and alcohol in these high-risk groups.

The development and validation of an algorithm to predict future depression onset in unselected youth

BACKGROUND

Universal depression screening in youth typically focuses on strategies for identifying current distress and impairment. However, these protocols also play a critical role in primary prevention initiatives that depend on correctly estimating future depression risk. Thus, the present study aimed to identify the best screening approach for predicting depression onset in youth.

METHODS

Two multi-wave longitudinal studies (N = 591, AgeM = 11.74; N = 348, AgeM = 12.56) were used as the 'test' and 'validation' datasets among youth who did not present with a history of clinical depression. Youth and caregivers completed inventories for depressive symptoms, adversity exposure (including maternal depression), social/academic impairment, cognitive vulnerabilities (rumination, dysfunctional attitudes, and negative cognitive style), and emotional predispositions (negative and positive affect) at baseline. Subsequently, multi-informant diagnostic interviews were completed every 6 months for 2 years.

RESULTS

Self-reported rumination, social/academic impairment, and negative affect best predicted first depression onsets in youth across both samples. Self- and parent-reported depressive symptoms did not consistently predict depression onset after controlling for other predictors. Youth with high scores on the three inventories were approximately twice as likely to experience a future first depressive episode compared to the sample average. Results suggested that one's likelihood of developing depression could be estimated based on subthreshold and threshold risk scores.

CONCLUSIONS

Most pediatric depression screening protocols assess current manifestations of depressive symptoms. Screening for prospective first onsets of depressive episodes can be better accomplished via an algorithm incorporating rumination, negative affect, and impairment.

Predicting suicide attempt or suicide death following a visit to psychiatric specialty care: A machine learning study using Swedish national registry data

BACKGROUND

Suicide is a major public health concern globally. Accurately predicting suicidal behavior remains challenging. This study aimed to use machine learning approaches to examine the potential of the Swedish national registry data for prediction of suicidal behavior.

METHODS AND FINDINGS

The study sample consisted of 541,300 inpatient and outpatient visits by 126,205 Sweden-born patients (54% female and 46% male) aged 18 to 39 (mean age at the visit: 27.3) years to psychiatric specialty care in Sweden between January 1, 2011 and December 31, 2012. The most common psychiatric diagnoses at the visit were anxiety disorders (20.0%), major depressive disorder (16.9%), and substance use disorders (13.6%). A total of 425 candidate predictors covering demographic characteristics, socioeconomic status (SES), electronic medical records, criminality, as well as family history of disease and crime were extracted from the Swedish registry data. The sample was randomly split into an 80% training set containing 433,024 visits and a 20% test set containing 108,276 visits. Models were trained separately for suicide attempt/death within 90 and 30 days following a visit using multiple machine learning algorithms. Model discrimination and calibration were both evaluated. Among all eligible visits, 3.5% (18,682) were followed by a suicide attempt/death within 90 days and 1.7% (9,099) within 30 days. The final models were based on ensemble learning that combined predictions from elastic net penalized logistic regression, random forest, gradient boosting, and a neural network. The area under the receiver operating characteristic (ROC) curves (AUCs) on the test set were 0.88 (95% confidence interval [CI] = 0.87-0.89) and 0.89 (95% CI = 0.88-0.90) for the outcome within 90 days and 30 days, respectively, both being significantly better than chance (i.e., AUC = 0.50) (p < 0.01). Sensitivity, specificity, and predictive values were reported at different risk thresholds. A limitation of our study is that our models have not yet been externally validated, and thus, the generalizability of the models to other populations remains unknown.

CONCLUSIONS

By combining the ensemble method of multiple machine learning algorithms and high-quality data solely from the Swedish registers, we developed prognostic models to predict short-term suicide attempt/death with good discrimination and calibration. Whether novel predictors can improve predictive performance requires further investigation.

Assessing high risk of suicide amongst physicians and nurses in treatment

Little is known about the suicidal behaviour of health professionals admitted to specialised programmes. This study aims to describe the factors associated with high risk of suicide (HRS) of physicians and nurses in treatment at the Galatea Care Programme. We conducted a retrospective naturalistic study with data from 1,214 electronic medical records of physicians and nurses working in Catalonia and in treatment at the Galatea Clinic during 2017 and 2018. HRS was registered in the medical record according to the screening criteria of the Catalonia Risk Suicide Code; 62.4% (n = 757) were physicians and 37.6% (n = 457) were nurses. HRS was identified in 5% physicians and 5.2% nurses. Patients who were in a relationship or were not on a sick leave were less likely to have HRS, whereas those with affective disorders were more likely to have HRS compared with those with anxiety disorders or substance use disorders. Patients with HRS were more likely to have concurrent mental disorders. Specialised treatment programmes for health professionals should regularly screen for suicide risk, especially amongst those having affective disorders, comorbid mental disorders or when their working and interpersonal life areas are impaired.

Identifying Predictors of Suicide in Severe Mental Illness: A Feasibility Study of a Clinical Prediction Rule (Oxford Mental Illness and Suicide Tool or OxMIS)

BACKGROUND

Oxford Mental Illness and Suicide tool (OxMIS) is a brief, scalable, freely available, structured risk assessment tool to assess suicide risk in patients with severe mental illness (schizophrenia-spectrum disorders or bipolar disorder). OxMIS requires further external validation, but a lack of large-scale cohorts with relevant variables makes this challenging. Electronic health records provide possible data sources for external validation of risk prediction tools. However, they contain large amounts of information within free-text that is not readily extractable. In this study, we examined the feasibility of identifying suicide predictors needed to validate OxMIS in routinely collected electronic health records.

METHODS

In study 1, we manually reviewed electronic health records of 57 patients with severe mental illness to calculate OxMIS risk scores. In study 2, we examined the feasibility of using natural language processing to scale up this process. We used anonymized free-text documents from the Clinical Record Interactive Search database to train a named entity recognition model, a machine learning technique which recognizes concepts in free-text. The model identified eight concepts relevant for suicide risk assessment: medication (antidepressant/antipsychotic treatment), violence, education, self-harm, benefits receipt, drug/alcohol use disorder, suicide, and psychiatric admission. We assessed model performance in terms of precision (similar to positive predictive value), recall (similar to sensitivity) and F1 statistic (an overall performance measure).

RESULTS

In study 1, we estimated suicide risk for all patients using the OxMIS calculator, giving a range of 12 month risk estimates from 0.1-3.4%. For 13 out of 17 predictors, there was no missing information in electronic health records. For the remaining 4 predictors missingness ranged from 7-26%; to account for these missing variables, it was possible for OxMIS to estimate suicide risk using a range of scores. In study 2, the named entity recognition model had an overall precision of 0.77, recall of 0.90 and F1 score of 0.83. The concept with the best precision and recall was medication (precision 0.84, recall 0.96) and the weakest were suicide (precision 0.37), and drug/alcohol use disorder (recall 0.61).

CONCLUSIONS

It is feasible to estimate suicide risk with the OxMIS tool using predictors identified in routine clinical records. Predictors could be extracted using natural language processing. However, electronic health records differ from other data sources, particularly for family history variables, which creates methodological challenges.

Effects of awareness material featuring individuals with experience of depression and suicidal thoughts on an audience with depressive symptoms: Randomized controlled trial

BACKGROUND AND OBJECTIVE

Suicide prevention plans support individuals with personal experience of mental disorders and suicidality to provide their narratives of coping in the media. The evidence how such portrayals impact on individuals with similar symptoms is limited and there are concerns about unwanted side effects.

METHODS

This was a double-blinded randomized controlled online trial conducted from August to November 2018. N = 158 young adults aged 18-24 with current depressive symptoms and suicidal thoughts were randomized to watch a short film featuring a young individual with personal experience of depression and suicidality (n = 81), or a thematically unrelated control film (n = 77) with similar stylistic elements. Questionnaire data were collected before and immediately after exposure and analysed with ANOVA. The primary outcome was suicidal ideation; secondary outcomes were depressed mood and help-seeking intentions. We also tested the moderating effects of the degree of depressive symptoms on the effects.

RESULTS

Depressed mood was significantly lower, with small-to medium effect size, in the intervention group compared to the control group (F(1,111) = 4.13, P < .05, η_p² = .036). There was no effect on suicidal ideation or help-seeking intentions in the total sample. Participants screening positive for moderately severe depression or higher experienced an increase in suicidal ideation in the control group.

LIMITATIONS

Self-reported variables in an online setting.

CONCLUSIONS

Videos featuring personal experience of coping with depression appear safe for young individuals with similar or higher symptoms of depression and suicidal ideation on the short run, and might have some benefits.

TRIAL REGISTRATION

German Clinical Trial Registry, DRKS00015095 (registration date: 2018-07-16).

Bipolar Disorder and Suicide: a Review

PURPOSE OF REVIEW

Bipolar disorder has the highest rate of suicide of all psychiatric conditions and is approximately 20-30 times that of the general population. The purpose of this review is to discuss findings relevant to bipolar disorder and suicide.

RECENT FINDINGS

Risk factors include male gender, living alone, divorced, no children, Caucasian, younger age (< 35 years), elderly age (> 75 years), unemployment, and a personal history of suicide attempt and family history of suicide attempt or suicide completion, as well as predominant depressive polarity. Suicide is associated with the depressed or mixed subtypes, not mania. Although there are emerging treatments for bipolar depression, such as ketamine and TMS, lithium remains the only medication associated with lowered suicide rates in bipolar disorder. Understanding clinical and demographic risk factors for suicide in bipolar disorder remains the best way to prevent suicidal behavior. Early intervention and treatment with anti-suicidal medications, such as lithium, along with close observation and follow-up is the best way to mitigate suicide in patients with bipolar disorder.

Suicide

Throughout the world, approximately 800,000 people die by suicide every year, accounting for 1.5% of all deaths. Suicide is the 10th leading cause of death in North America and the foremost cause of death worldwide among persons 15 to 24 years of age.