Classification Trees Distinguish Suicide Attempters in Major Psychiatric Disorders: A Model of Clinical Decision Making
J Clin Psychiatry 2008;69(1):23-31
© Copyright 2014 Physicians Postgraduate Press, Inc.
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Objective: Determining risk for a suicide attempt in
psychiatric patients requires assessment of multiple risk factors and knowledge
of their relative importance. Classification and regression tree (CART)
analysis generates decision trees that select the variables that perform best
in identifying the group of interest and model clinical decision making.
Hypothetical decision trees to identify recent and remote suicide attempters,
weighted to increase sensitivity, were generated for psychiatric patients using
correlates of past suicidal behavior.
Method: Correlates of past suicide attempts were identified in
408 patients with mood, schizophrenia spectrum, or personality disorders
(DSM-IV). Correlated variables were entered into recursive partitioning
statistical models to generate equally weighted and unequally weighted
hypothetical decision trees for distinguishing recent (<= 30 days prior to
study) and remote (> 250 days prior to study) suicide attempters from
nonattempters. The study was conducted from December 1989 to November 1998.
Results: In equally weighted trees, a recent past suicide
attempt was best predicted by current suicidal ideation (sensitivity = 56%,
specificity = 91%, positive predictive value = 69%), and no adequate model was
found for remote attempts. In unequally weighted models, recent attempters were
identified by suicidal ideation and comorbid borderline personality disorder
(sensitivity = 73%, specificity = 80%, positive predictive value = 58%). Remote
attempters were identified by lifetime aggression and current subjective
depression (sensitivity = 89%, specificity = 36%, positive predictive value =
Conclusion: Current suicidal ideation is the best indicator of
a recent suicide attempt in psychiatric patients. Indicators of a remote
attempt are aggressive traits and current depression. Weighted decision trees
can improve sensitivity and miss fewer attempters but with a cost in