Predicting suicidal ideation in primary care: An approach to identify easily assessable key variables
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Predicting suicidal ideation in primary care: An approach to identify easily assessable key variables. / Jordan, Pascal; Shedden-Mora, Meike C; Löwe, Bernd.
in: GEN HOSP PSYCHIAT, Jahrgang 51, 13.02.2018, S. 106-111.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - Predicting suicidal ideation in primary care: An approach to identify easily assessable key variables
AU - Jordan, Pascal
AU - Shedden-Mora, Meike C
AU - Löwe, Bernd
N1 - Copyright © 2018 Elsevier Inc. All rights reserved.
PY - 2018/2/13
Y1 - 2018/2/13
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.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.
AB - 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.
KW - Adult
KW - Anxiety Disorders
KW - Cross-Sectional Studies
KW - Depressive Disorder
KW - Female
KW - Humans
KW - Male
KW - Middle Aged
KW - Neural Networks (Computer)
KW - Pattern Recognition, Automated
KW - Primary Health Care
KW - Prognosis
KW - Risk Assessment
KW - Somatoform Disorders
KW - Suicidal Ideation
KW - Support Vector Machine
KW - Journal Article
KW - Research Support, Non-U.S. Gov't
U2 - 10.1016/j.genhosppsych.2018.02.002
DO - 10.1016/j.genhosppsych.2018.02.002
M3 - SCORING: Journal article
C2 - 29428582
VL - 51
SP - 106
EP - 111
JO - GEN HOSP PSYCHIAT
JF - GEN HOSP PSYCHIAT
SN - 0163-8343
ER -