Building and evaluating suicide attempt prediction models using risk factors

Abstract

This study identified risk factors of suicide attempts for the purpose of building prediction models and evaluating their performance. The participants of this secondary data analysis study were 11 671 adults aged 19 years or older. The prediction models consisted of risk factors identified through multiple logistic regression analysis, and performance was analyzed in terms of calibration, discrimination, and clinical usefulness. The risk factors for suicide attempts were suicide plan and suicidal ideation for males, and suicide plan and depression diagnosis for females. The prediction models constructed with these risk factors showed good calibration and discrimination, with over 0.90 of the area under the curves. At the cutoff point of 0.5%, the sensitivity of the full model was 90.9% for males and 82.4% for females. The net benefit was positive at a threshold probability under 30% for males and 40% for females. Given the acceptable performance of the suicide attempt prediction models, they can be used to assess suicide attempt risk and detect the population at high risk in the community at an early stage, with limited human resources.

Keywords: attempted suicide; clinical decision rules; model; regression analysis; risk factors; statistical; suicide.