EHRs Plus Self-Rating Scores May Help Predict Suicide Risk Following ED Visits

Matthieu/Adobestock

A new machine learning technique may help predict which patients are most at-risk for suicide following an emergency department (ED) visit.¹

Led by Matthew K. Nock, PhD, Department of Psychology, Harvard University, the investigators identified a way to forecast suicide attempts within 1 and 6 months of presentation at an ED for psychiatric issues. The prognostic study assessed the 1-month and 6-month risk of suicide for patients (N=11818) presenting at the emergency department at Massachusetts General Hospital between February 4, 2015 and March 13, 2017. Data were obtained from electronic health records (EHR) with patient 1-month (n = 1102) and 6-month (n = 1220) through follow-up surveys; and the team used an ensemble machine learning technique to develop predictive models and a risk score for suicide. At month 1 following the ED visit, 12.9% (n = 137) participants attempted suicide; 22% (n = 268) attempted suicide within 6 months.

Nock et al found that clinician assessments alone was a little better than chance at predicting suicide attempts, with externally validated area under the receiver operating characteristic curve of 0.67 for the 1-month model and 0.60 for the 6-month model. Prediction accuracy was slightly higher in the EHR models (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). This was particularly true when patient-self-reports were combined with EHR data and/or clinician data (1-month model: AUC, 0.77; and 6 month model: AUC, 0.79).

“The results of this prognostic study suggest that suicide risk assessments made using EHR-based and self-report–based risk scores may yield relatively accurate and clinically actionable predictions about the risk of suicide attempts by patients after presenting to an ED,” Nock and colleagues wrote. “These results highlight the need for tests of the implementation of such risk assessment tools to target preventive interventions.”

A version of this article originally appeared with our sister publication HCPLive.

Reference

1. 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. 2022 Jan 4. Accessed January 28, 2022.