Predicting Suicide Risk

Researchers developed an approach that may help to identify patients most likely to attempt suicide. The experimental technique still must be tested in larger groups of people to assess its effectiveness.

Scientists have long searched for reliable ways to identify those at risk for suicide, which claims the lives of about 40,000 Americans each year. Some researchers are developing questionnaires that measure the likelihood of someone committing suicide. Others are looking for molecular clues in the blood.

An NIH-funded research team decided to combine these methods. To identify molecular clues, they studied 217 male psychiatric patients who made several visits to a medical center. Interviews helped the scientists identify 37 patients whose thoughts of suicide increased between visits. Blood analyses found molecules with different levels between visits. The scientists then measured these molecules in blood samples from 26 men who had committed suicide.

The team also developed 2 apps that use questionnaires to measure suicide risk. The apps collect information on a patient’s emotional state, life events, stress, and mental health. Both apps were able to predict thoughts of suicide more than 85% of the time. The researchers combined the questionnaires with the most predictive blood-based molecules to create a tool called UP-Suicide.

In separate groups of psychiatric patients, the UP-Suicide tool predicted which patients would go on to have serious suicidal thoughts with 92% accuracy. It also predicted with 71% accuracy which patients would be hospitalized for suicidal behaviors in the year following testing.

“We believe that widespread adoption of risk prediction tests based on these findings during health care assessments will enable clinicians to intervene with lifestyle changes or treatments that can save lives,” says lead researcher Dr. Alexander B. Niculescu of Indiana University School of Medicine.

Because the team studied only male psychiatric patients, further research will be needed to understand how well this approach can predict suicidal thoughts and behaviors in other populations, such as women and those who aren’t psychiatric patients.