TY  - JOUR
PY  - 2018//
TI  - Predicting suicidal ideation in primary care: an approach to identify easily assessable key variables
JO  - General hospital psychiatry
A1  - Jordan, Pascal
A1  - Shedden-Mora, Meike C.
A1  - Lowe, Bernd
SP  - 106
EP  - 111
VL  - 51
IS  - 
N2  - 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>
LA  - en
SN  - 0163-8343
UR  - http://dx.doi.org/10.1016/j.genhosppsych.2018.02.002
ID  - ref1
ER  -