Dataset Information

Suicide Methods and Specific Types of Accidental Death and Fatal Poisoning Among Discharged Psychiatric Patients: A National Cohort Study.

ABSTRACT: OBJECTIVE:Persons discharged from inpatient psychiatric units are at greatly elevated risk of dying unnaturally. We conducted a comprehensive examination of specific causes of unnatural death post-discharge in a national register-based cohort. METHOD:A cohort of 1,683,645 Danish residents born 1967-1996 was followed from their 15th birthday until death, emigration, or December 31, 2011, whichever came first. Survival analysis techniques were used to estimate incidence rate ratios (IRRs) comparing risk for persons with and without psychiatric admission history in relation to (a) suicide method, (b) accidental death type, (c) fatal poisoning type, and (d) homicide. RESULTS:More than half (52.5%, n = 711) of all unnatural deaths post-discharge were fatal poisonings, compared with less than a fifth (17.0%, n = 1,012) among persons in the general population not admitted. Just 6.8% (n = 92) of all unnatural deaths post-discharge were due to transport accidents-the most common unnatural death type in the general population (53.4%, n = 3,184). Suicide risk was 32 times higher among discharged patients (IRR 32.3; 95% CI, 29.2-35.8) and was even higher during the first year post-discharge (IRR 70.4; 95% CI, 59.7-83.0). Among the suicide methods examined, relative risk values were significantly larger for intentional self-poisoning (IRR 40.8; 95% CI, 33.9-49.1) than for "violent" suicide methods (IRR 29.4; 95% CI, 26.1-33.2). The greatest relative risk observed was for fatal poisoning (irrespective of intent) by psychotropic medication (IRR 93.7; 95% CI, 62.5-140.5). The highest post-discharge mortality rate was for accidental self-poisoning among persons diagnosed with a psychoactive substance abuse disorder: 290.1 per 100,000 person-years. CONCLUSIONS:Closer liaison between inpatient services and community care, more effective early treatment for comorbid substance abuse, enhanced psychosocial assessment following self-harm, and tighter medication surveillance could decrease risk of unnatural death post-discharge.

SUBMITTER: Walter F

PROVIDER: S-EPMC6193537 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Suicide Methods and Specific Types of Accidental Death and Fatal Poisoning Among Discharged Psychiatric Patients: A National Cohort Study.

Walter Florian F Carr Matthew J MJ Mok Pearl L H PLH Antonsen Sussie S Pedersen Carsten B CB Shaw Jenny J Webb Roger T RT

The Journal of clinical psychiatry 20181002 6

<h4>Objective</h4>Persons discharged from inpatient psychiatric units are at greatly elevated risk of dying unnaturally. We conducted a comprehensive examination of specific causes of unnatural death post-discharge in a national register-based cohort.<h4>Method</h4>A cohort of 1,683,645 Danish residents born 1967-1996 was followed from their 15th birthday until death, emigration, or December 31, 2011, whichever came first. Survival analysis techniques were used to estimate incidence rate ratios ...[more]

PMID: 30289629

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Project description: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.

Project description:ImportanceThere is a need for representative research on serious adverse outcomes following discharge from psychiatric hospitalization.ObjectiveTo compare rates of premature death, suicide, and nonlethal intentional self-harm after psychiatric discharge with rates in the general population and investigate associations of these outcomes with relevant variables associated with the index psychiatric hospitalization.Design, setting, and participantsThis retrospective cohort study included all residents from Catalonia, Spain (7.6 million population), who had psychiatric hospitalizations between January 1, 2014, and December 31, 2018, and were older than 10 years at the index (first) hospitalization. Follow-up was until December 31, 2019. Statistical analysis was performed from December 1, 2022, through April 11, 2024.ExposuresSocioeconomic status, psychiatric diagnoses, duration of index hospitalization, and number of previous psychiatric hospitalizations.Main outcomes and measuresPostdischarge premature death (ie, all-cause death before age 70 years) and suicide (International Statistical Classification of Diseases and Related Health Problems, Tenth Revision [ICD-10] code range X60-X84), identified using mortality data, and postdischarge nonlethal intentional self-harm, identified using electronic health record and self-harm case register data. Standardized mortality ratios (SMRs) compared rates of premature death and suicide between the cohort and the general population. Fully adjusted, multivariable, cause-specific Cox proportional hazards regression models for the 3 outcomes were fitted.ResultsA total of 49 108 patients discharged from psychiatric hospitalization were included (25 833 males [52.6%]; mean [SD] age at discharge, 44.2 [18.2] years). During follow-up, 2260 patients (4.6%) died prematurely, 437 (0.9%) died by suicide, and 4752 (9.7%) had an episode of nonlethal intentional self-harm. The overall SMR for premature death was 7.5 (95% CI, 7.2-7.9). For suicide, SMR was 32.9 (95% CI, 29.9-36.0) overall and was especially high among females (47.6 [95% CI, 40.2-54.9]). In fully adjusted sex-stratified hazard models, postdischarge premature death was associated with cognitive disorders (adjusted hazard ratio [AHR], 2.89 [95% CI, 2.24-3.74] for females; 2.59 [95% CI, 2.17-3.08] for males) and alcohol-related disorders (AHR, 1.41 [95% CI, 1.18-1.70] for females; 1.22 [95% CI, 1.09-1.37] for males). Postdischarge suicide was associated with postdischarge intentional self-harm (AHR, 2.83 [95% CI, 1.97-4.05] for females; 3.29 [95% CI, 2.47-4.40] for males), with depressive disorders (AHR, 2.13 [95% CI, 1.52-2.97]) and adjustment disorders (AHR, 1.94 [95% CI, 1.32-2.83]) among males, and with bipolar disorder among females (AHR, 1.94 [95% CI, 1.21-3.09]). Postdischarge intentional self-harm was associated with index admissions for intentional self-harm (AHR, 1.95 [95% CI, 1.73-2.21] for females; 2.62 [95% CI, 2.20-3.13] for males) as well as for adjustment disorders (AHR, 1.48 [95% CI, 1.33-1.65] for females; 1.99 [95% CI, 1.74-2.27] for males), anxiety disorders (AHR, 1.24 [95% CI, 1.10-1.39] for females; 1.36 [95% CI, 1.18-1.58] for males), depressive disorders (AHR, 1.54 [95% CI, 1.40-1.69] for females; 1.80 [95% CI, 1.58-2.04] for males), and personality disorders (AHR, 1.59 [95% CI, 1.46-1.73] for females; 1.43 [95% CI, 1.28-1.60] for males).Conclusions and relevanceIn this cohort study of patients discharged from psychiatric hospitalization, risk for premature death and suicide was significantly higher compared with the general population, suggesting individuals discharged from psychiatric inpatient care are a vulnerable population for premature death and suicidal behavior.

Project description:ImportanceSuicide represents the 10th leading cause of death across age groups in the United States (12.6 cases per 100 000) and remains challenging to predict. While many individuals who die by suicide are seen by physicians before their attempt, they may not seek psychiatric care.ObjectiveTo determine the extent to which incorporating natural language processing of narrative discharge notes improves stratification of risk for death by suicide after medical or surgical hospital discharge.Design, setting, and participantsIn this retrospective health care use study, clinical data were analyzed from individuals with discharges from 2 large academic medical centers between January 1, 2005, and December 31, 2013.Main outcomes and measuresThe primary outcome was suicide as a reported cause of death based on Massachusetts Department of Public Health records. Regression models for prediction of death by suicide or accidental death were compared relying solely on coded clinical data and those using natural language processing of hospital discharge notes.ResultsThere were 845 417 hospital discharges represented in the cohort, including 458 053 unique individuals. Overall, all-cause mortality was 18% during 9 years, and the median follow-up was 5.2 years. The cohort included 235 (0.1%) who died by suicide during 2.4 million patient-years of follow-up. Positive valence reflected in narrative notes was associated with a 30% reduction in risk for suicide in models adjusted for coded sociodemographic and clinical features (hazard ratio, 0.70; 95% CI, 0.58-0.85; P < .001) and improved model fit (χ22 = 14.843, P < .001 by log-likelihood test). The C statistic was 0.741 (95% CI, 0.738-0.744) for models of suicide with or without inclusion of accidental death.Conclusions and relevanceMultiple clinical features available at hospital discharge identified a cohort of individuals at substantially increased risk for suicide. Greater positive valence expressed in narrative discharge summaries was associated with substantially diminished risk. Automated tools to aid clinicians in evaluating these risks may assist in identifying high-risk individuals.

Dataset Information

Suicide Methods and Specific Types of Accidental Death and Fatal Poisoning Among Discharged Psychiatric Patients: A National Cohort Study.

Publications

Suicide Methods and Specific Types of Accidental Death and Fatal Poisoning Among Discharged Psychiatric Patients: A National Cohort Study.

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