@article{ref1,
title="Predicting suicidal ideation in primary care: an approach to identify easily assessable key variables",
journal="General hospital psychiatry",
year="2018",
author="Jordan, Pascal and Shedden-Mora, Meike C. and Lowe, Bernd",
volume="51",
number="",
pages="106-111",
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. <br><br>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. <br><br>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. <br><br>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.<br><br>Copyright © 2018 Elsevier Inc. All rights reserved.<p /> <p>Language: en</p>",
language="en",
issn="0163-8343",
doi="10.1016/j.genhosppsych.2018.02.002",
url="http://dx.doi.org/10.1016/j.genhosppsych.2018.02.002"
}