Albert A, Harris E K, Chapelle J P, Heusghem C, Kulbertus H E
Clin Chem. 1984 Jan;30(1):69-76.
Serial laboratory determinations are now routinely performed on patients admitted to intensive-care units. Adequate interpretation of such cumulative information for clinical decision-making purposes is a challenging problem. We describe a statistical method for predicting--sequentially as the data become available--the patient's outcome, death or survival. Thus, the method goes beyond previously reported techniques that base such prediction on only a single multivariate observation. The method has been applied to daily measurements of serum urea and lactate dehydrogenase, performed during one week on patients hospitalized in the coronary-care unit with acute myocardial infarction. Two baseline variables were also included in the dynamic risk index so derived: the age of the patient and the number of previous myocardial infarctions recorded on admission. We also discuss the problems of selecting the most-predictive laboratory tests and of determining for each test the amount of past data needed to achieve satisfactory prediction. We distinguish between global evaluation of the dynamic risk index obtained (in terms of specificity and sensitivity) and individual interpretation (in terms of posterior/prior probability ratio) of a given risk score for a particular patient. The approach described may contribute to more effective use of results of repeated laboratory tests on critically ill patients.
目前，对入住重症监护病房的患者常规进行系列实验室检测。为临床决策目的充分解读此类累积信息是一个具有挑战性的问题。我们描述了一种统计方法，可在数据可用时依次预测患者的结局，即死亡或存活。因此，该方法超越了先前报道的仅基于单一多变量观察进行此类预测的技术。该方法已应用于对冠心病监护病房中因急性心肌梗死住院的患者在一周内每日进行的血清尿素和乳酸脱氢酶测量。由此得出的动态风险指数中还纳入了两个基线变量：患者年龄和入院时记录的既往心肌梗死次数。我们还讨论了选择最具预测性的实验室检测以及为每项检测确定实现满意预测所需的既往数据量的问题。我们区分了对所获得的动态风险指数的整体评估（根据特异性和敏感性）以及对特定患者给定风险评分的个体解读（根据后验/先验概率比）。所描述的方法可能有助于更有效地利用对重症患者重复进行实验室检测的结果。
