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Use of emergency department electronic medical records for automated epidemiological surveillance of suicide attempts: a French pilot study.

ABSTRACT: The aim of this study was to determine whether an expert system based on automated processing of electronic health records (EHRs) could provide a more accurate estimate of the annual rate of emergency department (ED) visits for suicide attempts in France, as compared to the current national surveillance system based on manual coding by emergency practitioners. A feasibility study was conducted at Lyon University Hospital, using data for all ED patient visits in 2012. After automatic data extraction and pre-processing, including automatic coding of medical free-text through use of the Unified Medical Language System, seven different machine-learning methods were used to classify the reasons for ED visits into "suicide attempts" versus "other reasons". The performance of these different methods was compared by using the F-measure. In a test sample of 444 patients admitted to the ED in 2012 (98 suicide attempts, 48 cases of suicidal ideation, and 292 controls with no recorded non-fatal suicidal behaviour), the F-measure for automatic detection of suicide attempts ranged from 70.4% to 95.3%. The random forest and naïve Bayes methods performed best. This study demonstrates that machine-learning methods can improve the quality of epidemiological indicators as compared to current national surveillance of suicide attempts.

SUBMITTER: Metzger MH

PROVIDER: S-EPMC6877202 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Use of emergency department electronic medical records for automated epidemiological surveillance of suicide attempts: a French pilot study.

Metzger Marie-Hélène MH Tvardik Nastassia N Gicquel Quentin Q Bouvry Côme C Poulet Emmanuel E Potinet-Pagliaroli Véronique V

International journal of methods in psychiatric research 20160915 2

The aim of this study was to determine whether an expert system based on automated processing of electronic health records (EHRs) could provide a more accurate estimate of the annual rate of emergency department (ED) visits for suicide attempts in France, as compared to the current national surveillance system based on manual coding by emergency practitioners. A feasibility study was conducted at Lyon University Hospital, using data for all ED patient visits in 2012. After automatic data extract ...[more]

PMID: 27634457

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Project description:ObjectiveTo analyze the number and characteristics of suicide attempts by reviewing records of the public emergency healthcare service information system.MethodA retrospective observational study was conducted of emergency telephone calls received between January 1 2007 and December 31 2013 throughout the Andalusia region (Southern Spain). Cases were selected based on phone operator or healthcare team labeling. Data were analyzed on the characteristics of the individuals, the timing and severity of attempts, their prioritization, and their outcome.ResultsBetween January 1 2007 and December 31 2013, 20.942 calls related to suicide attempts were recorded, a rate of 34.7 attempts per 100,000 inhabitants. Most cases were classified by the public emergency healthcare service (Empresa Pública de Emergencias Sanitarias, EPES) as code X84 (The International Statistical Classification of Diseases, tenth revision, ICD-10) or 305(The International Statistical Classification of Diseases, ninth revision, ICD-9). Attempts were more frequent in the 35-49-year age group and there were similar proportions of males and females. The lowest number of calls for suicide attempts were in 2007 and the highest in 2013. Calls were more frequent during the summer months, at weekends, and between 16:00 and 23:00 h. The likelihood of evacuation to the hospital emergency department was almost two-fold lower in over 65-yr-olds than in younger individuals. Significant (ƿ = 0.001) gender differences were found in call outcome and prioritization. The most influential factor for evacuation to a hospital emergency department was the code assigned by the attending healthcare team.ConclusionsInformation obtained from extra-hospital emergency services provides valuable data on the characteristics and timing of calls related to suicide attempts, complementing information from hospital emergency departments or population surveys. There is a need to standardize the definition and recording of a suicide attempt.

Project description:OBJECTIVE:The authors sought to develop and validate models using electronic health records to predict suicide attempt and suicide death following an outpatient visit. METHOD:Across seven health systems, 2,960,929 patients age 13 or older (mean age, 46 years; 62% female) made 10,275,853 specialty mental health visits and 9,685,206 primary care visits with mental health diagnoses between Jan. 1, 2009, and June 30, 2015. Health system records and state death certificate data identified suicide attempts (N=24,133) and suicide deaths (N=1,240) over 90 days following each visit. Potential predictors included 313 demographic and clinical characteristics extracted from records for up to 5 years before each visit: prior suicide attempts, mental health and substance use diagnoses, medical diagnoses, psychiatric medications dispensed, inpatient or emergency department care, and routinely administered depression questionnaires. Logistic regression models predicting suicide attempt and death were developed using penalized LASSO (least absolute shrinkage and selection operator) variable selection in a random sample of 65% of the visits and validated in the remaining 35%. RESULTS:Mental health specialty visits with risk scores in the top 5% accounted for 43% of subsequent suicide attempts and 48% of suicide deaths. Of patients scoring in the top 5%, 5.4% attempted suicide and 0.26% died by suicide within 90 days. C-statistics (equivalent to area under the curve) for prediction of suicide attempt and suicide death were 0.851 (95% CI=0.848, 0.853) and 0.861 (95% CI=0.848, 0.875), respectively. Primary care visits with scores in the top 5% accounted for 48% of subsequent suicide attempts and 43% of suicide deaths. C-statistics for prediction of suicide attempt and suicide death were 0.853 (95% CI=0.849, 0.857) and 0.833 (95% CI=0.813, 0.853), respectively. CONCLUSIONS:Prediction models incorporating both health record data and responses to self-report questionnaires substantially outperform existing suicide risk prediction tools.

Project description:BACKGROUND:The incidence of adolescent suicide is rising in the United States, yet we have limited information regarding short-term prediction of suicide attempts. Our aim was to identify predictors of suicide attempts within 3-months of an emergency department (ED) visit. METHODS:Adolescents, ages 12-17, seeking health care at 13 pediatric EDs (Pediatric Emergency Care Applied Research Network) and one Indian Health Service Hospital in the United States were consecutively recruited. Among 10,664 approached patients, 6,448 (60%) were enrolled and completed a suicide risk survey. A subset of participants (n = 2,897) was assigned to a 3-month telephone follow-up, and 2,104 participants completed this follow-up (73% retention). Our primary outcome was a suicide attempt between the ED visit and 3-month follow-up. RESULTS:One hundred four adolescents (4.9%) made a suicide attempt between enrollment and 3-month follow-up. A large number of baseline predictors of suicide attempt were identified in bivariate analyses. The final multivariable model for the full sample included the presence of suicidal ideation during the past week, lifetime severity of suicidal ideation, lifetime history of suicidal behavior, and school connectedness. For the subgroup of adolescents who did not report recent suicidal ideation at baseline, the final model included only lifetime severity of suicidal ideation and social connectedness. Among males, the final model included only lifetime severity of suicidal ideation and past week suicidal ideation. For females, the final model included past week suicidal ideation, lifetime severity of suicidal ideation, number of past-year nonsuicidal self-injury (NSSI) incidents, and social connectedness. CONCLUSIONS:Results indicate that the key risk factors for adolescent suicide attempts differ for subgroups of adolescents defined by sex and whether or not they report recent suicidal thoughts. Results also point to the importance of school and social connectedness as protective factors against suicide attempts.

Project description:ImportanceHalf of the people who die by suicide make a health care visit within 1 month of their death. However, clinicians lack the tools to identify these patients.ObjectiveTo predict suicide attempts within 1 and 6 months of presentation at an emergency department (ED) for psychiatric problems.Design, setting, and participantsThis prognostic study assessed the 1-month and 6-month risk of suicide attempts among 1818 patients presenting to an ED between February 4, 2015, and March 13, 2017, with psychiatric problems. Data analysis was performed from May 1, 2020, to November 19, 2021.Main outcomes and measuresSuicide attempts 1 and 6 months after presentation to the ED were defined by combining data from electronic health records (EHRs) with patient 1-month (n = 1102) and 6-month (n = 1220) follow-up surveys. Ensemble machine learning was used to develop predictive models and a risk score for suicide.ResultsA total of 1818 patients participated in this study (1016 men [55.9%]; median age, 33 years [IQR, 24-46 years]; 266 Hispanic patients [14.6%]; 1221 non-Hispanic White patients [67.2%], 142 non-Hispanic Black patients [7.8%], 64 non-Hispanic Asian patients [3.5%], and 125 non-Hispanic patients of other race and ethnicity [6.9%]). A total of 137 of 1102 patients (12.9%; weighted prevalence) attempted suicide within 1 month, and a total of 268 of 1220 patients (22.0%; weighted prevalence) attempted suicide within 6 months. Clinicians' assessment alone was little better than chance at predicting suicide attempts, with externally validated area under the receiver operating characteristic curve (AUC) of 0.67 for the 1-month model and 0.60 for the 6-month model. Prediction accuracy was slightly higher for models based on EHR data (1-month model: AUC, 0.71; 6 month model: AUC, 0.65) and was best using patient self-reports (1-month model: AUC, 0.76; 6-month model: AUC, 0.77), especially when patient self-reports were combined with EHR and/or clinician data (1-month model: AUC, 0.77; and 6 month model: AUC, 0.79). A model that used only 20 patient self-report questions and an EHR-based risk score performed similarly well (1-month model: AUC, 0.77; 6 month model: AUC, 0.78). In the best 1-month model, 30.7% (positive predicted value) of the patients classified as having highest risk (top 25% of the sample) made a suicide attempt within 1 month of their ED visit, accounting for 64.8% (sensitivity) of all 1-month attempts. In the best 6-month model, 46.0% (positive predicted value) of the patients classified at highest risk made a suicide attempt within 6 months of their ED visit, accounting for 50.2% (sensitivity) of all 6-month attempts.Conclusions and relevanceThis prognostic study suggests that the ability to identify patients at high risk of suicide attempt after an ED visit for psychiatric problems improved using a combination of patient self-reports and EHR data.

Project description:Background. While often first treated in the emergency department (ED), identification of sepsis is difficult. Electronic medical record (EMR) clinical decision tools offer a novel strategy for identifying patients with sepsis. The objective of this study was to test the accuracy of an EMR-based, automated sepsis identification system. Methods. We tested an EMR-based sepsis identification tool at a major academic, urban ED with 64,000 annual visits. The EMR system collected vital sign and laboratory test information on all ED patients, triggering a "sepsis alert" for those with ?2 SIRS (systemic inflammatory response syndrome) criteria (fever, tachycardia, tachypnea, leukocytosis) plus ?1 major organ dysfunction (SBP ? 90 mm Hg, lactic acid ?2.0 mg/dL). We confirmed the presence of sepsis through manual review of physician, nursing, and laboratory records. We also reviewed a random selection of ED cases that did not trigger a sepsis alert. We evaluated the diagnostic accuracy of the sepsis identification tool. Results. From January 1 through March 31, 2012, there were 795 automated sepsis alerts. We randomly selected 300 cases without a sepsis alert from the same period. The true prevalence of sepsis was 355/795 (44.7%) among alerts and 0/300 (0%) among non-alerts. The positive predictive value of the sepsis alert was 44.7% (95% CI [41.2-48.2%]). Pneumonia and respiratory infections (38%) and urinary tract infection (32.7%) were the most common infections among the 355 patients with true sepsis (true positives). Among false-positive sepsis alerts, the most common medical conditions were gastrointestinal (26.1%), traumatic (25.7%), and cardiovascular (20.0%) conditions. Rates of hospital admission were: true-positive sepsis alert 91.0%, false-positive alert 83.0%, no sepsis alert 5.7%. Conclusions. This ED EMR-based automated sepsis identification system was able to detect cases with sepsis. Automated EMR-based detection may provide a viable strategy for identifying sepsis in the ED.

Dataset Information

Use of emergency department electronic medical records for automated epidemiological surveillance of suicide attempts: a French pilot study.

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Use of emergency department electronic medical records for automated epidemiological surveillance of suicide attempts: a French pilot study.

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