期刊
GENERAL HOSPITAL PSYCHIATRY
卷 51, 期 -, 页码 106-111出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.genhosppsych.2018.02.002
关键词
Suicide; Suicidal ideation; Primary care; Depression; Anxiety; Somatic symptoms
类别
资金
- German Ministry of Education and Research (BMBF) [01KQ1002B]
Objective: To obtain predictors of suicidal ideation, which can also be used for an indirect assessment of suicidal ideation (SI). To create a classifier for SI based on variables of the Patient Health Questionnaire (PHQ) and sociodemographic variables, and to obtain an upper bound on the best possible performance of a predictor based on those variables. Methods: From a consecutive sample of 9025 primary care patients, 6805 eligible patients (60% female; mean age=51.5 years) participated. Advanced methods of machine learning were used to derive the prediction equation. Various classifiers were applied and the area under the curve (AUC) was computed as a performance measure. Results: Classifiers based on methods of machine learning outperformed ordinary regression methods and achieved AUCs around 0.87. The key variables in the prediction equation comprised four items - namely feelings of depression/hopelessness, low self-esteem, worrying, and severe sleep disturbances. The generalized anxiety disorder scale (GAD-7) and the somatic symptom subscale (PHQ-15) did not enhance prediction substantially. Conclusions: In predicting suicidal ideation researchers should refrain from using ordinary regression tools. The relevant information is primarily captured by the depression subscale and should be incorporated in a nonlinear model. For clinical practice, a classification tree using only four items of the whole PHQ may be advocated.
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