New Models For Predicting Suicide Risk

Posted on June 5, 2018

Combining a variety of information from the past five years of people's electronic health records and answers to questionnaires, new models predicted suicide risk more accurately than before, according to the authors. The strongest predictors include prior suicide attempts, mental health and substance use diagnoses, medical diagnoses, psychiatric medications dispensed, inpatient or emergency room care, and scores on a standardized depression questionnaire.

It was shown that suicide attempts and deaths among patients whose visits were in the highest 1 percent of predicted risk were 200 times more common than among those in the bottom half of predicted risk. Also, patients with mental health specialty visits who had risk scores in the top 5 percent accounted for 43 percent of suicide attempts and 48 percent of suicide deaths. Finally, patients with primary care visits who had scores in the top 5 percent accounted for 48 percent of suicide attempts and 43 percent of suicide deaths.

The study demonstrated that electronic health record data in combination with other tools can be used to accurately identify people at high risk for suicide attempt or suicide death. This study builds on previous models in other health systems that used fewer potential predictors from patients' records. Using those models, people in the top 5 percent of risk accounted for only a quarter to a third of subsequent suicide attempts and deaths. More traditional suicide risk assessment, which relies on questionnaires or clinical interviews only, is even less accurate.

Better prediction of suicide risk can inform decisions by health care providers and health systems. Such decisions include how often to follow up with patients, refer them for intensive treatment, reach out to them after missed or canceled appointments, and to help them create a personal safety plan and counsel them about reducing access to means of self-harm.


Category(s):Suicide Prevention

Source material from Science Daily