Citation

Jordan P, Shedden-Mora MC, Lowe B. Gen. Hosp. Psychiatry 2018; 51: 106-111.

Affiliation

Department of Psychosomatic Medicine and Psychotherapy, University Medical Center Hamburg-Eppendorf and Schön Klinik Hamburg Eilbek, Hamburg, Germany. Electronic address: b.loewe@uke.de.

Copyright

(Copyright © 2018, Elsevier Publishing)

DOI

10.1016/j.genhosppsych.2018.02.002

PMID

29428582

Abstract

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.

Copyright © 2018 Elsevier Inc. All rights reserved.

Language: en

Keywords

Anxiety; Depression; Primary care; Somatic symptoms; Suicidal ideation; Suicide