Suicide and suicide attempts in adults: exploring suicide risk 24 months after a psychiatric emergency room visit

Transition to suicide ideation and attempt among emergency room patients

BACKGROUND

Suicidal thoughts and behaviours (STB) represent a persistent and serious public health problem, and suicide is among the leading causes of death worldwide. We focus on predictors of transition rates and time courses through the STB spectrum among psychiatric emergency room (PER) patients.

AIMS

We aimed to investigate (a) whether currently suicidal patients had prior referrals to the PER, (b) for which reason they were previously referred to the PER and (c) the timing of this referral.

METHOD

We performed a retrospective study spanning 20 years with 24 815 PER referrals. Descriptive statistics of patients' sociodemographic and clinical characteristics are provided and expressed as weighted proportions and means. Logistic regression was used to identify risk profiles of patients who had a higher chance of being referred for reasons of STB given their PER history. Multiple imputation and data weighting techniques were implemented.

RESULTS

STB among PER patients was persistent and led to repeated referrals (up to five times more likely), often within a short period (18% <1 month). Those previously referred for ideation/plan had 66% higher risk of making the transition to suicide attempt, with 25% making this transition within a month after previous referral. This is similar to the transition from depressed mood to suicide ideation/plan.

CONCLUSIONS

STBs in PER patients are persistent and lead to repeated referrals, often within a short period, including transitions to more severe forms of STB.

Prediction of Repeated Self-Harm in Six Months: Comparison of Traditional Psychometrics With Random Forest Algorithm

Suicidal risk has been a significant mental health problem. However, the predictive ability for repeated self-harm (SH) has not improved over the past decades. This study thus aimed to explore a potential tool with theoretical accommodation and clinical application by employing traditional logistic regression (LR) and newly developed machine learning, random forest algorithm (RF). Starting with 89 items from six commonly used scales (i.e., proximal suicide risk factors) as preliminary predictors, both LR and RF resulted in a better solution with much fewer items in two phases of item selections and analyses, with prediction accuracy 88.6% and 79.8%, respectively. A combination with 12 selected items, named LR-12, well predicted repeated self-harm in 6-month follow-up with satisfactory performance (AUC = 0.84, 95% CI: 0.76-0.92; cut-off point by 1/2 with sensitivity 81.1% and specificity 74.0%). The psychometrically appealing LR-12 could be used as a screening scale for suicide risk assessment.

The Impact of the COVID-19 Pandemic on Self-Harm Attempts Observed in a Hospital Emergency Department

Suicide is a significant public health concern, with one million lives lost to it every year. Suicidal ideation and attempts are markers of high risk. The COVID-19 pandemic has had a negative psychological impact on the population. This study aims to describe and analyze the clinical and sociodemographic characteristics of patients who have received medical attention for self-harm attempts in a hospital emergency department, comparing the period before and after the COVID-19 pandemic. This is a descriptive, retrospective study that collected data from medical records of patients who received care for self-harm attempts in the emergency department. The data included cases from 1 January 2018 to 31 December 2022. In total, 529 cases of self-harm attempts were identified, of which 62.8% were female. The number of post-pandemic self-harm attempts significantly increased compared to the period before the pandemic. The most used method for self-harm was medication ingestion. This study revealed that over one-third of the participants had previously attempted suicide. Most self-harm attempts were made by women in the 10-20 or 41-50 age groups, with a history of psychiatric illness and multiple medications. The study results also highlighted an increase in self-harm attempts during the COVID-19 pandemic.

Meta-analysis of clinical risk factors for suicide among people presenting to emergency departments and general hospitals with suicidal thoughts and behaviours

BACKGROUND

Suicidal thoughts and behaviours (STB) are a common reason for presentation to emergency departments and general hospitals. A meta-analysis of the strength of clinical risk factors for subsequent suicide might aid understanding of suicidal behaviour and help suicide prevention.

METHODS

We conducted a meta-analysis of cohort and controlled studies on clinical risk factors and later suicide among people presenting to emergency departments and general hospitals with STB. Data were extracted from papers meeting inclusion criteria, published in Medline, PsycINFO, and Embase between 1 January 1960 and 10 October 2022 using papers located with the search terms ((suicide*).m_titl AND (emergency* OR accident and emergency OR casualty OR general hospital OR toxicology service).mp) or were indexed in PubMed and had titles located with the search terms (suicide* OR self-harm OR self-harm OR self-injury OR self-injury OR self-poisoning OR self-poisoning OR overdose OR para-suicide OR parasuicide [title/abstract]) AND (Emergency department OR emergency room OR Casualty OR general hospital OR toxicology OR accident and emergency [all fields]). Data about the association between clinical risk factors and suicide extracted from three or more studies were included in a random-effects meta-analysis of the odds of later death by suicide. The study was registered in PROSPERO and conducted according to MOOSE and PRISMA guidelines.

RESULTS

Seventy-five studies reported on 741,624 people, of which 19,649 died by suicide (2.65%). Male sex (odds ratio (OR) = 1.99) and age (OR = 2.01) were the most consistently reported risk factors. The strongest associations with subsequent death by suicide related to violent self-harm methods at the hospital presentation, including: unspecified violent method (OR = 4.97), any violent method (OR = 4.57) and the specific violent methods of drowning (OR = 4.32), hanging (OR = 4.26), and use of firearms (OR = 10.08). Patients categorised as higher risk using suicide prediction scales or any other method that combined risk factors had moderately increased odds of suicide (OR = 2.58). Younger age, Black and Hispanic ethnicity, overdose, a diagnosis of adjustment disorder, and the absence of any psychiatric diagnosis were protective against suicide.

CONCLUSIONS

Most risk factors for suicide among people who have presented with STB are not strongly associated with later suicide. The strongest risk factors relate to self-harm methods. In the absence of clear indicators of future suicide, all people presenting with suicidality warrant a thorough assessment of their needs, and further research is needed before we can meaningfully categorise people with STB according to suicide risk.

Comparisons of deep learning and machine learning while using text mining methods to identify suicide attempts of patients with mood disorders

BACKGROUND

Suicide attempt is one of the most severe consequences for patients with mood disorders. This study aimed to perform deep learning and machine learning while using text mining to identify patients with suicide attempts and to compare their effectiveness.

METHODS

A total of 13,100 patients with mood disorders were selected. Two traditional text mining methods, logistic regression and Support vector machine (SVM), and one deep learning model (Convolutional neural network, CNN) were adopted to perform overall analysis and gender-specific subgroup analysis of patients to identify suicide attempts. The classification effectiveness of these models was evaluated by accuracy, F1-value, precision, recall, and the area under Receiver operator characteristic curve (ROC).

RESULTS

CNN's results were greater than the other two for all indicators except recall which was slightly smaller than SVM in male subgroup analysis. The accuracy values of the CNN were 98.4 %, 98.2 %, and 98.5 % in the overall analysis and the subgroup analysis for males and females, respectively. The results of McNemar's test showed that CNN and SVM models' predictions were statistically different from the logistic regression model's predictions in the overall analysis and the subgroup analysis for females (P < 0.050).

LIMITATIONS

A fixed number of features were selected based on document frequency to train models; this was a single-site study.

CONCLUSIONS

CNN model was a better way to detect suicide attempts in patients with mood disorders prior to hospital admission, saving time and resources in recognizing high-risk patients and preventing suicide.