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Prediction of Suicide Attempts Using Clinician Assessment, Patient Self-report, and Electronic Health Records.
JAMA Netw Open. 2022 01 04; 5(1):e2144373.JN

Abstract

Importance

Half 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.

Objective

To predict suicide attempts within 1 and 6 months of presentation at an emergency department (ED) for psychiatric problems.

Design, Setting, and Participants

This 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 Measures

Suicide 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.

Results

A 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 Relevance

This 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.

Authors+Show Affiliations

Department of Psychology, Harvard University, Cambridge, Massachusetts. Mental Health Research Program, Franciscan Children's, Brighton, Massachusetts. Department of Psychiatry, Massachusetts General Hospital, Boston.Department of Psychology, Harvard University, Cambridge, Massachusetts. Mental Health Research Program, Franciscan Children's, Brighton, Massachusetts.Department of Psychiatry, Massachusetts General Hospital, Boston. Department of Psychiatry, Harvard Medical School, Boston, Massachusetts.Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts.Department of Psychology, Harvard University, Cambridge, Massachusetts.Department of Bioinformatics, Boston Children's Hospital, Boston, Massachusetts.Department of Psychiatry, Massachusetts General Hospital, Boston.Department of Psychology, Harvard University, Cambridge, Massachusetts.Department of Psychology, Harvard University, Cambridge, Massachusetts.Department of Psychology, Harvard University, Cambridge, Massachusetts.Department of Healthcare Policy, Harvard Medical School, Boston, Massachusetts.Department of Psychiatry, Massachusetts General Hospital, Boston.Department of Bioinformatics, Boston Children's Hospital, Boston, Massachusetts.Department of Psychiatry, Harvard Medical School, Boston, Massachusetts. Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston.Department of Healthcare Policy, Harvard Medical School, Boston, Massachusetts.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

35084483

Citation

Nock, Matthew K., et al. "Prediction of Suicide Attempts Using Clinician Assessment, Patient Self-report, and Electronic Health Records." JAMA Network Open, vol. 5, no. 1, 2022, pp. e2144373.
Nock MK, Millner AJ, Ross EL, et al. Prediction of Suicide Attempts Using Clinician Assessment, Patient Self-report, and Electronic Health Records. JAMA Netw Open. 2022;5(1):e2144373.
Nock, M. K., Millner, A. J., Ross, E. L., Kennedy, C. J., Al-Suwaidi, M., Barak-Corren, Y., Castro, V. M., Castro-Ramirez, F., Lauricella, T., Murman, N., Petukhova, M., Bird, S. A., Reis, B., Smoller, J. W., & Kessler, R. C. (2022). Prediction of Suicide Attempts Using Clinician Assessment, Patient Self-report, and Electronic Health Records. JAMA Network Open, 5(1), e2144373. https://doi.org/10.1001/jamanetworkopen.2021.44373
Nock MK, et al. Prediction of Suicide Attempts Using Clinician Assessment, Patient Self-report, and Electronic Health Records. JAMA Netw Open. 2022 01 4;5(1):e2144373. PubMed PMID: 35084483.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Prediction of Suicide Attempts Using Clinician Assessment, Patient Self-report, and Electronic Health Records. AU - Nock,Matthew K, AU - Millner,Alexander J, AU - Ross,Eric L, AU - Kennedy,Chris J, AU - Al-Suwaidi,Maha, AU - Barak-Corren,Yuval, AU - Castro,Victor M, AU - Castro-Ramirez,Franchesca, AU - Lauricella,Tess, AU - Murman,Nicole, AU - Petukhova,Maria, AU - Bird,Suzanne A, AU - Reis,Ben, AU - Smoller,Jordan W, AU - Kessler,Ronald C, Y1 - 2022/01/04/ PY - 2022/1/27/entrez PY - 2022/1/28/pubmed PY - 2022/2/25/medline SP - e2144373 EP - e2144373 JF - JAMA network open JO - JAMA Netw Open VL - 5 IS - 1 N2 - Importance: Half 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. Objective: To predict suicide attempts within 1 and 6 months of presentation at an emergency department (ED) for psychiatric problems. Design, Setting, and Participants: This 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 Measures: Suicide 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. Results: A 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 Relevance: This 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. SN - 2574-3805 UR - https://www.unboundmedicine.com/medline/citation/35084483/Prediction_of_Suicide_Attempts_Using_Clinician_Assessment_Patient_Self_report_and_Electronic_Health_Records_ L2 - https://jamanetwork.com/journals/jamanetworkopen/fullarticle/10.1001/jamanetworkopen.2021.44373 DB - PRIME DP - Unbound Medicine ER -