Predicting suicidal ideation in primary care: An approach to identify easily assessable key variables

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 -