Prognosis After Acute Myocardial Infarction A Multivariate Analysis of Mortality and Survival
Prognosis After Acute Myocardial Infarction A Multivariate Analysis of Mortality and Survival
复制标题
急性心肌梗死后的预后死亡率和生存率的多变量分析
DOI：
10.1161/01.cir.59.6.1124
复制
发表时间：
1979
期刊：
影响因子：
37.8
通讯作者：
J. Ross
中科院分区：
文献类型：
--
作者：
H. Henning;E. Gilpin;J. Covell;E. A. Swan;R. O'rourke;J. Ross
我们对221例急性心肌梗死患者的早期死亡率（30天内）和生存率（超过30天）进行了研究，筛选了患者入院后不久测量的158个变量。其中19个测量值具有预测价值，但每个变量本身相对不敏感。因此，我们对变量组进行逐步判别函数分析，并通过使用折刀法计算95%置信区间来估计分类率。当综合病史、体格检查和非侵入性评估的因素时，我们确定了70%的死亡（置信区间48-80%）和94%（90-98%）的幸存者;当包括血液动力学数据的11个选定变量被组合时，我们确定了86%（66-98%）的死亡和96%（92-100%）的幸存者（93%的总体准确率）。我们在随后的150名患者系列中进一步测试了该方法的有效性。使用原始判别函数，基于无创和血流动力学数据的分类率在预测范围内，尽管血流动力学研究的患者数量不具代表性，而且太少，无法实现整体预测准确性。因此，我们将整个人群（371名患者）随机分为一个基础样本，从中我们构建了新的判别函数，并对其余患者进行了分类。验证样本的分类率在预测的置信区间内。因此，我们的方法提供了一个可靠的方法来预测早期死亡的风险或急性心肌梗死后不久的患者生存的可能性。
We examined early mortality (within 30 days) and survival (beyond 30 days) after acute myocardial infarction in 221 patients by screening 158 variables measured soon after the patient's admission to the hospital. Nineteen of these measurements had predictive value, but each variable alone was relatively insensitive. Therefore, we subjected groups of variables to stepwise discriminant function analysis and classification rates were estimated by calculating 95% confidence intervals using a jackknife procedure. When factors from the history, physical examination, and noninvasive assessment were combined, we identified 70% of deaths (confidence interval 48-80%) and 94% (90-98%) of survivors; when 11 selected variables including hemodynamic data were combined, we identified 86% (66-98%) of deaths and 96% (92-100%) of survivors (93% overall accuracy). We further tested the validity of this method in a subsequent series of 150 patients. Using the original discriminant functions, classification rates based on noninvasive and hemodynamic data fell within predicted limits, although the number of patients studied hemodynamically was unrepresentative and too small to allow overall predictive accuracy. Therefore, we randomly divided the entire population (371 patients) into a base sample from which we constructed new discriminant functions, with which we classified the remaining patients. The classification rates for the validation sample fell within the predicted confidence intervals. Thus, our method provides a reliable approach for predicting the risk of early death or the likelihood of survival in patients soon after acute myocardial infarction.