Madsen E B, Gilpin E, Henning H
J Am Coll Cardiol. 1983 Apr;1(4):985-93. doi: 10.1016/s0735-1097(83)80099-0.
Three multivariate methods for predicting death within 1 year for patients discharged after acute myocardial infarction were evaluated: Cox model, discriminant function analysis and recursive partitioning. Discriminant function analysis was utilized to predict a new myocardial infarction (any new or nonfatal infarction). A Cox classification model developed in a population of 260 patients (group 1) discharged after myocardial infarction was tested in 886 patients from the same institution (group 2) and 582 patients from another institution (group 3). Discriminant function analysis and recursive partitioning were developed in group 2 and tested in group 3. Data gathered during the entire period of hospitalization were utilized. The important variables (ordered as selected by the analyses) for the end point death were: heart failure, ventricular tachycardia and atrioventricular block in the Cox model and heart failure, previous myocardial infarction, age and ventricular premature beats in the discriminant function analysis. For the end point new myocardial infarction, the important variables were: previous myocardial infarction, heart failure, extension of infarction during the acute phase and infarct site. For predicting death and survival within 1 year, each of the three schemes was comparable. For estimating the actual risk of death, the Cox model was best. Recursive partitioning had the advantage of using only one variable--heart failure. Total correct classification ranged from 65.4 (Cox model) to 71.6% (discriminant function analysis) for the original population (groups 1 and 2) and from 47.9 (discriminant function analysis) to 54.3% (recursive partioning) when the schemes were tested in patients in group 3. The Cox model and discriminant function analysis were able to correctly predict over half of the new infarctions within 1 year.
对三种用于预测急性心肌梗死后出院患者1年内死亡情况的多变量方法进行了评估:Cox模型、判别函数分析和递归分割法。判别函数分析用于预测新发心肌梗死(任何新发或非致命性梗死)。在260例心肌梗死后出院的患者群体(第1组)中开发的Cox分类模型,在来自同一机构的886例患者(第2组)和来自另一机构的582例患者(第3组)中进行了测试。判别函数分析和递归分割法在第2组中开发,并在第3组中进行了测试。使用了住院期间收集的全部数据。对于终点死亡而言,重要变量(按分析选定的顺序排列)在Cox模型中为:心力衰竭、室性心动过速和房室传导阻滞;在判别函数分析中为:心力衰竭、既往心肌梗死、年龄和室性早搏。对于终点新发心肌梗死而言,重要变量为:既往心肌梗死、心力衰竭、急性期梗死扩展和梗死部位。对于预测1年内的死亡和生存情况,三种方案中的每一种都具有可比性。对于估计实际死亡风险,Cox模型最佳。递归分割法的优点是仅使用一个变量——心力衰竭。对于原始群体(第1组和第2组),总正确分类率范围从65.4%(Cox模型)到71.6%(判别函数分析);当在第3组患者中测试这些方案时,总正确分类率范围从47.9%(判别函数分析)到54.3%(递归分割法)。Cox模型和判别函数分析能够正确预测1年内超过一半的新发梗死。