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首页> 外文期刊>The journal of clinical psychiatry >Classification Trees Distinguish Suicide Attempters in Major Psychiatric Disorders: A Model of Clinical Decision Making.
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Classification Trees Distinguish Suicide Attempters in Major Psychiatric Disorders: A Model of Clinical Decision Making.

机译：分类树区分主要精神疾病的自杀企图：临床决策模型。

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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 ( 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 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 = 44%). 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 specificity.
机译：目的：要确定精神病患者自杀未遂的风险，需要评估多种风险因素并了解其相对重要性。分类和回归树（CART）分析生成决策树，这些决策树选择在识别目标群体和模型临床决策方面表现最佳的变量。使用过去的自杀行为相关性，为精神病患者生成了假想决策树，以识别最近和偏远的自杀企图，并对其进行加权以提高敏感性。方法：在408名患有情绪，精神分裂症谱系或人格障碍（DSM-IV）的患者中鉴定出过去自杀未遂的相关性。将相关变量输入到递归分区统计模型中，以产生相等加权和不相等加权的假设决策树，以区分最近（自杀前为研究前30天）和远程自杀未遂者（为研究前250天）与非自杀未遂者。该研究于1989年12月至1998年11月进行。结果：在同等权重的树木中，当前的自杀意念最好地预测了最近的自杀企图（敏感性为56％，对远程尝试具有特异性。在不等权重的模型中，最近的尝试是通过自杀意念和合并性边缘人格障碍进行识别（敏感性= 73％，特异性= 80％，阳性预测值= 58％）;通过终生侵略和当前的主观抑郁来识别远程尝试者（敏感性= 89％，特异性= 36％，阳性预测值= 44％）结论：当前的自杀意念是近期精神病患者自杀未遂的最好指标;远程尝试的指标是攻击性特征和当前的抑郁症;加权决策树可以提高敏感性，减少未遂者，但是专门的成本。

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