EHRs Plus Self-Rating Scores May Help Predict Suicide Risk Following ED Visits
Investigators explored a new way to predict future suicide attempts.
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
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
Reference
1. Nock MK, Millner AJ, Ross EL, et al.
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