Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Chen Q

PROVIDER: S-EPMC7647056 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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

Chen Qi Q Zhang-James Yanli Y Barnett Eric J EJ Lichtenstein Paul P Jokinen Jussi J D'Onofrio Brian M BM Faraone Stephen V SV Larsson Henrik H Fazel Seena S

PLoS medicine 20201106 11

<h4>Background</h4>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.<h4>Methods and findings</h4>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 psychiat ...[more]

PMID: 33156863

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Project description:ImportanceBecause more than one-third of people making nonfatal suicide attempts do not receive mental health treatment, it is essential to extend suicide attempt risk factors beyond high-risk clinical populations to the general adult population.ObjectiveTo identify future suicide attempt risk factors in the general population using a data-driven machine learning approach including more than 2500 questions from a large, nationally representative survey of US adults.Design, setting, and participantsData came from wave 1 (2001 to 2002) and wave 2 (2004 to 2005) of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). NESARC is a face-to-face longitudinal survey conducted with a national representative sample of noninstitutionalized civilian population 18 years and older in the US. The cumulative response rate across both waves was 70.2% resulting in 34 653 wave 2 interviews. A balanced random forest was trained using cross-validation to develop a suicide attempt risk model. Out-of-fold model prediction was used to assess model performance, including the area under the receiver operator curve, sensitivity, and specificity. Survey design and nonresponse weights allowed estimates to be representative of the US civilian population based on the 2000 census. Analyses were performed between May 15, 2019, and June 10, 2020.Main outcomes and measuresAttempted suicide in the 3 years between wave 1 and wave 2 interviews.ResultsOf 34 653 participants, 20 089 were female (weighted proportion, 52.1%). The weighted mean (SD) age was 45.1 (17.3) years at wave 1 and 48.2 (17.3) years at wave 2. Attempted suicide during the 3 years between wave 1 and wave 2 interviews was self-reported by 222 of 34 653 participants (0.6%). Using survey questions measured at wave 1, the suicide attempt risk model yielded a cross-validated area under the receiver operator characteristic curve of 0.857 with a sensitivity of 85.3% (95% CI, 79.8-89.7) and a specificity of 73.3% (95% CI, 72.8-73.8) at an optimized threshold. The model identified 1.8% of the US population to be at a 10% or greater risk of suicide attempt. The most important risk factors were 3 questions about previous suicidal ideation or behavior; 3 items from the 12-Item Short Form Health Survey, namely feeling downhearted, doing activities less carefully, or accomplishing less because of emotional problems; younger age; lower educational achievement; and recent financial crisis.Conclusions and relevanceIn this study, after searching through more than 2500 survey questions, several well-known risk factors of suicide attempt were confirmed, such as previous suicidal behaviors and ideation, and new risks were identified, including functional impairment resulting from mental disorders and socioeconomic disadvantage. These results may help guide future clinical assessment and the development of new suicide risk scales.

Project description:ImportanceAlthough longitudinal studies have reported associations between early life factors (ie, in-utero/perinatal/infancy) and long-term suicidal behavior, they have concentrated on 1 or few selected factors, and established associations, but did not investigate if early-life factors predict suicidal behavior.ObjectiveTo identify and evaluate the ability of early-life factors to predict suicide attempt in adolescents and young adults from the general population.Design, setting, and participantsThis prognostic study used data from the Québec Longitudinal Study of Child Development, a population-based longitudinal study from Québec province, Canada. Participants were followed-up from birth to age 20 years. Random forest classification algorithms were developed to predict suicide attempt. To avoid overfitting, prediction performance indices were assessed across 50 randomly split subsamples, and then the mean was calculated. Data were analyzed from November 2019 to June 2020.ExposuresFactors considered in the analysis included 150 variables, spanning virtually all early life domains, including pregnancy and birth information; child, parents, and neighborhood characteristics; parenting and family functioning; parents' mental health; and child temperament, as assessed by mothers, fathers, and hospital birth records.Main outcomes and measuresThe main outcome was self-reported suicide attempt by age 20 years.ResultsAmong 1623 included youths aged 20 years, 845 (52.1%) were female and 778 (47.9%) were male. Models show moderate prediction performance. The areas under the curve for the prediction of suicide attempt were 0.72 (95% CI, 0.71-0.73) for females and 0.62 (95% CI, 0.60-0.62) for males. The models showed low sensitivity (females, 0.50; males, 0.32), moderate positive predictive values (females, 0.60; males, 0.62), and good specificity (females, 0.76; males, 0.82) and negative predicted values (females, 0.75; males, 0.71). The most important factors contributing to the prediction included socioeconomic and demographic characteristics of the family (eg, mother and father education and age, socioeconomic status, neighborhood characteristics), parents' psychological state (specifically parents' antisocial behaviors) and parenting practices. Birth-related variables also contributed to the prediction of suicidal behavior (eg, prematurity). Sex differences were also identified, with family-related socioeconomic and demographic characteristics being the top factors for females and parents' antisocial behavior being the top factor for males.Conclusions and relevanceThese findings suggest that early life factors contributed modestly to the prediction of suicidal behavior in adolescence and young adulthood. Although these factors may inform the understanding of the etiological processes of suicide, their utility in the long-term prediction of suicide attempt was limited.

Project description:OBJECTIVE:More than 90% of people who attempt suicide have a psychiatric diagnosis; however, twin and family studies suggest that the genetic etiology of suicide attempt is partially distinct from that of the psychiatric disorders themselves. The authors present the largest genome-wide association study (GWAS) on suicide attempt, using cohorts of individuals with major depressive disorder, bipolar disorder, and schizophrenia from the Psychiatric Genomics Consortium. METHODS:The samples comprised 1,622 suicide attempters and 8,786 nonattempters with major depressive disorder; 3,264 attempters and 5,500 nonattempters with bipolar disorder; and 1,683 attempters and 2,946 nonattempters with schizophrenia. A GWAS on suicide attempt was performed by comparing attempters to nonattempters with each disorder, followed by a meta-analysis across disorders. Polygenic risk scoring was used to investigate the genetic relationship between suicide attempt and the psychiatric disorders. RESULTS:Three genome-wide significant loci for suicide attempt were found: one associated with suicide attempt in major depressive disorder, one associated with suicide attempt in bipolar disorder, and one in the meta-analysis of suicide attempt in mood disorders. These associations were not replicated in independent mood disorder cohorts from the UK Biobank and iPSYCH. No significant associations were found in the meta-analysis of all three disorders. Polygenic risk scores for major depression were significantly associated with suicide attempt in major depressive disorder (R2=0.25%), bipolar disorder (R2=0.24%), and schizophrenia (R2=0.40%). CONCLUSIONS:This study provides new information on genetic associations and demonstrates that genetic liability for major depression increases risk for suicide attempt across psychiatric disorders. Further collaborative efforts to increase sample size may help to robustly identify genetic associations and provide biological insights into the etiology of suicide attempt.

Project description:ImportanceInflammatory bowel disease (IBD) has been associated with psychiatric morbidity in adults, although previous studies have not accounted for familial confounding. In children, IBD has an even more severe course, but the association between childhood-onset IBD and psychiatric morbidity remains unclear.ObjectiveTo examine the risk of psychiatric morbidity in individuals with childhood-onset IBD, controlling for potential confounding shared between siblings.Design, setting, and participantsA population-based cohort study was conducted using data from the Swedish national health care and population registers of all children younger than 18 years born from 1973 to 2013. The study included 6464 individuals with a diagnosis of childhood-onset IBD (3228 with ulcerative colitis, 2536 with Crohn disease, and 700 with IBD unclassified) who were compared with 323 200 matched reference individuals from the general population and 6999 siblings of patients with IBD. Cox proportional hazards regression was used to estimate hazard ratios (HRs) with 95% CIs. Statistical analysis was performed from January 1, 1973, to December 1, 2013.Main outcomes and measuresThe primary outcome was any psychiatric disorder and suicide attempt. Secondary outcomes were the following specific psychiatric disorders: psychotic, mood, anxiety, eating, personality, and behavioral disorders; substance misuse; attention-deficit/hyperactivity disorder; autism spectrum disorders; and intellectual disability.ResultsThe study included 6464 individuals with a diagnosis of childhood-onset IBD (2831 girls and 3633 boys; mean [SD] age at diagnosis of IBD, 13 [4] years). During a median follow-up time of 9 years, 1117 individuals with IBD (17.3%) received a diagnosis of any psychiatric disorder (incidence rate, 17.1 per 1000 person-years), compared with 38 044 of 323 200 individuals (11.8%) in the general population (incidence rate, 11.2 per 1000 person-years), corresponding to an HR of 1.6 (95% CI, 1.5-1.7), equaling 1 extra case of any psychiatric disorder per 170 person-years. Inflammatory bowel disease was significantly associated with suicide attempt (HR, 1.4; 95% CI, 1.2-1.7) as well as mood disorders (HR, 1.6; 95% CI, 1.4-1.7), anxiety disorders (HR, 1.9; 95% CI, 1.7-2.0) eating disorders (HR, 1.6; 95% CI, 1.3-2.0), personality disorders (HR, 1.4; 95% CI, 1.1-1.8), attention-deficit/hyperactivity disorder (HR, 1.2; 95% CI, 1.1-1.4), and autism spectrum disorders (HR, 1.4; 95% CI, 1.1-1.7) Results were similar for boys and girls. Hazard ratios for any psychiatric disorder were highest in the first year of follow-up but remained statistically significant after more than 5 years. Psychiatric disorders were particularly common for patients with very early-onset IBD (<6 years) and for patients with a parental psychiatric history. Results were largely confirmed by sibling comparison, with similar estimates noted for any psychiatric disorder (HR, 1.6; 95% CI, 1.5-1.8) and suicide attempt (HR, 1.7; 95% CI, 1.2-2.3).Conclusions and relevanceOverall, childhood-onset IBD was associated with psychiatric morbidity, confirmed by between-sibling results. Particularly concerning is the increased risk of suicide attempt, suggesting that long-term psychological support be considered for patients with childhood-onset IBD.

Project description:Importance:Predicting suicidal behavior continues to be among the most challenging tasks in psychiatry. Objectives:To examine the trajectories of clinical predictors of suicide attempt (specifically, depression symptoms, hopelessness, impulsivity, aggression, impulsive aggression, and irritability) for their ability to predict suicide attempt and to compute a risk score for suicide attempts. Design, Setting, and Participants:This is a longitudinal study of the offspring of parents (or probands) with mood disorders who were recruited from inpatient units at Western Psychiatric Institute and Clinic (Pittsburgh) and New York State Psychiatric Institute. Participants were recruited from July 15, 1997, to September 6, 2005, and were followed up through January 21, 2014. Probands and offspring (n = 663) were interviewed at baseline and at yearly follow-ups for 12 years. Lifetime and current psychiatric disorders were assessed, and self-reported questionnaires were administered. Model evaluation used 10-fold cross-validation, which split the entire data set into 10 equal parts, fit the model to 90% of the data (training set), and assessed it on the remaining 10% (test set) and repeated that process 10 times. Preliminary analyses were performed from July 20, 2015, to October 5, 2016. Additional analyses were conducted from July 26, 2017, to July 24, 2018. Main Outcomes and Measures:The broad definition of suicide attempt included actual, interrupted, and aborted attempts as well as suicidal ideation that prompted emergency referrals during the study. The narrow definition referred to actual attempt only. Results:The sample of offspring (n = 663) was almost equally distributed by sex (316 female [47.7%]) and had a mean (SD) age of 23.8 (8.5) years at the time of censored observations. Among the 663 offspring, 71 (10.7%) had suicide attempts over the course of the study. The trajectory of depression symptoms with the highest mean scores and variability over time was the only trajectory to predict suicide attempt (odds ratio [OR], 4.72; 95% CI, 1.47-15.21; P = .01). In addition, we identified the following predictors: younger age (OR, 0.82; 95% CI, 0.74-0.90; P < .001), lifetime history of unipolar disorder (OR, 4.71; 95% CI, 1.63-13.58; P = .004), lifetime history of bipolar disorder (OR, 3.4; 95% CI, 0.96-12.04; P = .06), history of childhood abuse (OR, 2.98; 95% CI, 1.40-6.38; P = .01), and proband actual attempt (OR, 2.24; 95% CI, 1.06-4.75; P = .04). Endorsing a score of 3 or higher on the risk score tool resulted in high sensitivity (87.3%) and moderate specificity (63%; area under the curve = 0.80). Conclusions and Relevance:The specific predictors of suicide attempt identified are those that clinicians already assess during routine psychiatric evaluations; monitoring and treating depression symptoms to reduce their severity and fluctuation may attenuate the risk for suicidal behavior.

Dataset Information

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

Publications

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

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