Evaluation of clinical prognostic models for suicide attempts after a major depressive episode

Objective:  In this study, we compare the performance of prognostic models of increasing complexity for prediction of future suicide attempt. Method:  Using data from a 2‐year prospective study of 304 depressed subjects, a series of Cox proportional hazard regression models were developed to predict...

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Bibliographic Details
Published in:Acta psychiatrica Scandinavica 2008-04, Vol.117 (4), p.244-252
Main Authors: Galfalvy, H. C., Oquendo, M. A., Mann, J. J.
Format: Article
Language:English
Subjects:
Adult
Adult and adolescent clinical studies
Aggression - psychology
attempted suicide
Biological and medical sciences
Bipolar Disorder - diagnosis
Bipolar Disorder - epidemiology
Bipolar Disorder - psychology
Borderline Personality Disorder - diagnosis
Borderline Personality Disorder - epidemiology
Borderline Personality Disorder - psychology
Cox proportional hazards models
Depression
Depressive Disorder, Major - diagnosis
Depressive Disorder, Major - epidemiology
Depressive Disorder, Major - psychology
Disruptive, Impulse Control, and Conduct Disorders - diagnosis
Disruptive, Impulse Control, and Conduct Disorders - epidemiology
Disruptive, Impulse Control, and Conduct Disorders - psychology
Female
Hostility
Humans
Life Change Events
Male
Medical prognosis
Medical sciences
Mental depression
Mood disorders
prediction
Predictions
Proportional Hazards Models
Prospective Studies
Psychiatry
Psychology. Psychoanalysis. Psychiatry
Psychopathology. Psychiatry
Risk Assessment
Risk factors
Severity of Illness Index
Suicide
Suicide, Attempted - psychology
Suicide, Attempted - statistics & numerical data
Suicides & suicide attempts
Surveys and Questionnaires
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Summary:Objective:  In this study, we compare the performance of prognostic models of increasing complexity for prediction of future suicide attempt. Method:  Using data from a 2‐year prospective study of 304 depressed subjects, a series of Cox proportional hazard regression models were developed to predict future suicide attempt. The models were evaluated in terms of discrimination (the ability to rank subjects in order of risk), calibration (accuracy of predicted probabilities of attempt), and sensitivity and specificity of risk group stratification based on cross‐validated predicted probabilities. Results:  Although an additive model with past attempt, smoking status, and suicidal ideation achieved 75% (cross‐validated) sensitivity and specificity, models that performed best in terms of discrimination included interactions between predictor variables. Conclusion:  As several models had similar predictive power, clinical considerations and ease of interpretation may have a significant role in the final stage of model selection for assessing future suicide attempt risk.
ISSN:0001-690X
1600-0447
DOI:10.1111/j.1600-0447.2008.01162.x