Dataset Information

The characteristics of elderly suicidal attempters in the emergency department in Korea: a retrospective study.

ABSTRACT:

Background

Although Korea ranks first in the suicide rate of elderly individuals, there is limited research on those who attempt suicide, with preventive measures largely based on population-based studies. We compared the demographic and clinical characteristics of elderly individuals who attempted suicide with those of younger adults who visited the emergency department after suicide attempts and identified the factors associated with lethality in the former group.

Methods

Individuals who visited the emergency department after a suicide attempt from April 1, 2017, to January 31, 2020, were included. Participants were classified into two groups according to age (elderly, ≥65 years; adult, 18-64 years). Among the 779 adult patients, 123 were elderly. We conducted a chi-square test to compare the demographic and clinical features between these groups and a logistic regression analysis to identify the risk factors for lethality in the elderly group.

Results

Most elderly participants were men, with no prior psychiatric history or suicide attempts, and had a higher prevalence of underlying medical conditions and attributed their attempts to physical illnesses. Being sober and planning suicide occurred more frequently in this group. In the elderly group, factors that increased the mortality rate were biological male sex (p<0.05), being accompanied by family members (p<0.05), and poisoning as a suicide method (p<0.01).

Conclusion

Suicide attempts in elderly individuals have different characteristics from those in younger adults and are associated with physical illness. Suicides in the former group are unpredictable, deliberate, and fatal. Therefore, tailored prevention and intervention strategies addressing the characteristics of those who are elderly and attempt suicide are required.

SUBMITTER: Jang JS

PROVIDER: S-EPMC10834272 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The characteristics of elderly suicidal attempters in the emergency department in Korea: a retrospective study.

Jang Ji-Seon JS Seo Wan-Seok WS Koo Bon-Hoon BH Kim Hey-Geum HG Yun Seok-Ho SH Jo So-Hey SH Bai Dae-Seok DS Kim Young-Gyo YG Cheon Eun-Jin EJ

Journal of Yeungnam medical science 20231229 1

<h4>Background</h4>Although Korea ranks first in the suicide rate of elderly individuals, there is limited research on those who attempt suicide, with preventive measures largely based on population-based studies. We compared the demographic and clinical characteristics of elderly individuals who attempted suicide with those of younger adults who visited the emergency department after suicide attempts and identified the factors associated with lethality in the former group.<h4>Methods</h4>Indivi ...[more]

PMID: 38155553

Similar Datasets

Project description:BackgroundIndividuals presenting to hospital due to suicide attempt are at high risk for repeated suicidal act, yet there are meager data on the extent to which repeated suicidal acts result in death.MethodsThis retrospective cohort study was based on a general hospital self-harm register system in a rural county in China. Identified individuals who attempted suicide were contacted and followed up for up to 8 years. Main outcomes over follow-up were: 1) suicide death, 2) nonlethal suicide attempt, and 3) suicidal acts including suicide death and nonlethal attempt. Incidence densities, correlates, and case fatality of repeated suicidal acts were estimated.FindingsA total of 1086 individuals (two-thirds females, mean age 40.6 years) with a suicide attempt presenting to hospital (index attempt) were identified and followed up, with most of the index attempts by pesticide ingestion (79%). Over follow-up, there were 116 suicidal acts carried out by 108 individuals (69 females, 39 males), including 34 suicide deaths (21 females, 13 males), yielding a high case-fatality of 29.3%. During follow-up, suicide death rates were also high overall and in the first year of follow-up (846.7 and 1787.2 per 100,000 person years). Over follow-up, pesticide was the most common method (47/116) of repeated suicidal act and yielded a higher case-fatality than other methods (46.8% vs 17.4%, χ2 = 11.68, P < 0.001). The incidence densities of repeated suicidal acts and nonlethal attempts were low compared to rates reported in previous literature.InterpretationIncidence densities of repeated suicidal acts in a rural China cohort were low compared to previous studies. However, rates of suicide deaths over follow-up were high, a result driven by the high case-fatality of suicidal acts and attributable to the common use of pesticides. Reducing suicidal acts with pesticides is a key target for suicide prevention in rural China.FundingBeijing Municipal High Rank Public Health Researcher Training Program, Beijing Hospitals Authority Clinical Medicine Development of Special Funding Support, and Beijing Hospitals Authority's Ascent Plan.

Project description:Study objectiveTo assess the association of patient and organisational factors with emergency department length of stay (ED-LOS) in elderly ED patients (226565 years old) and in younger patients (<65 years old).MethodsA retrospective cohort study of internal medicine patients visiting the emergency department between September 1st 2010 and August 31st 2011 was performed. All emergency department visits by internal medicine patients 226565 years old and a random sample of internal medicine patients <65 years old were included. Organisational factors were defined as non-medical factors. ED-LOS is defined as the time between ED arrival and ED discharge or admission. Prolonged ED-LOS is defined as ≥75th percentile of ED-LOS in the study population, which was 208 minutes.ResultsData on 1782 emergency department visits by elderly patients and 597 emergency department visits by younger patients were analysed. Prolonged ED-LOS in elderly patients was associated with three organisational factors: >1 consultation during the emergency department visit (odds ratio (OR) 3.2, 95% confidence interval (CI) 2.3-4.3), a higher number of diagnostic tests (OR 1.2, 95% CI 1.16-1.33) and evaluation by a medical student or non-trainee resident compared with a medical specialist (OR 4.2, 95% CI 2.0-8.8 and OR 2.3, 95% CI 1.4-3.9). In younger patients, prolonged ED-LOS was associated with >1 consultation (OR 2.6, 95% CI 1.4-4.6). Factors associated with shorter ED-LOS were arrival during nights or weekends as well as a high urgency level in elderly patients and self-referral in younger patients.ConclusionOrganisational factors, such as a higher number of consultations and tests in the emergency department and a lower seniority of the physician, were the main aspects associated with prolonged ED-LOS in elderly patients. Optimisation of the organisation and coordination of emergency care is important to accommodate the needs of the continuously growing number of elderly patients in a better way.

Project description:BackgroundWithin the trauma system, the emergency department (ED) is the hospital's first contact and is vital for allocating medical resources. However, there is generally limited information about patients that die in the ED.ObjectiveThe aim of this study was to develop an artificial intelligence (AI) model to predict trauma mortality and analyze pertinent mortality factors for all patients visiting the ED.MethodsWe used the Korean National Emergency Department Information System (NEDIS) data set (N=6,536,306), incorporating over 400 hospitals between 2016 and 2019. We included the International Classification of Disease 10th Revision (ICD-10) codes and chose the following input features to predict ED patient mortality: age, sex, intentionality, injury, emergent symptom, Alert/Verbal/Painful/Unresponsive (AVPU) scale, Korean Triage and Acuity Scale (KTAS), and vital signs. We compared three different feature set performances for AI input: all features (n=921), ICD-10 features (n=878), and features excluding ICD-10 codes (n=43). We devised various machine learning models with an ensemble approach via 5-fold cross-validation and compared the performance of each model with that of traditional prediction models. Lastly, we investigated explainable AI feature effects and deployed our final AI model on a public website, providing access to our mortality prediction results among patients visiting the ED.ResultsOur proposed AI model with the all-feature set achieved the highest area under the receiver operating characteristic curve (AUROC) of 0.9974 (adaptive boosting [AdaBoost], AdaBoost + light gradient boosting machine [LightGBM]: Ensemble), outperforming other state-of-the-art machine learning and traditional prediction models, including extreme gradient boosting (AUROC=0.9972), LightGBM (AUROC=0.9973), ICD-based injury severity scores (AUC=0.9328 for the inclusive model and AUROC=0.9567 for the exclusive model), and KTAS (AUROC=0.9405). In addition, our proposed AI model outperformed a cutting-edge AI model designed for in-hospital mortality prediction (AUROC=0.7675) for all ED visitors. From the AI model, we also discovered that age and unresponsiveness (coma) were the top two mortality predictors among patients visiting the ED, followed by oxygen saturation, multiple rib fractures (ICD-10 code S224), painful response (stupor, semicoma), and lumbar vertebra fracture (ICD-10 code S320).ConclusionsOur proposed AI model exhibits remarkable accuracy in predicting ED mortality. Including the necessity for external validation, a large nationwide data set would provide a more accurate model and minimize overfitting. We anticipate that our AI-based risk calculator tool will substantially aid health care providers, particularly regarding triage and early diagnosis for trauma patients.

Dataset Information

The characteristics of elderly suicidal attempters in the emergency department in Korea: a retrospective study.

Background

Methods

Results

Conclusion

Publications

The characteristics of elderly suicidal attempters in the emergency department in Korea: a retrospective study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets