Malik M, Farrell T, Poloniecki J, Camm A J
Department of Cardiological Sciences, St. George's Hospital Medical School, London, U.K.
Int J Biomed Comput. 1991 Mar-Apr;27(3-4):175-92. doi: 10.1016/0020-7101(91)90061-i.
The study reports a mathematical method for deciding which clinical data are of importance for treatment decisions in a given clinical setting. The method comprises the following steps: (A) the receiver operator characteristic (ROC) functions of the compared sets of data are computed; (B) the design and aim of the clinical study is expressed as an integral measure on the space of sensitivity values (this reflects the preference of low or high sensitivity values dependent on the clinical targets); (C) the sets of data being compared are characterised by the non-linear integrals of their ROC functions. The approach has been used to compare mean heart rate (HR) and heart rate variability (HRV) data calculated in 5113 different portions of 24-h ECG recordings and assessed in 365 patients surviving acute myocardial infarction, in order to evaluate the utility of Holter recording of varying lengths and starting times for the prediction of sudden cardiac death and/or serious arrhythmic events. The results of the study show that this approach is capable of evaluating and comparing the sets of medical data used for identification of patients who are at increased risk. The experimental part of the study showed that the optimum recording interval for the assessment of HR and HRV data in patients who survived acute myocardial infarction depends on the aim of the identification of increased risk patients. The optimum interval of recording is different for an identification which requires a low number of false negative cases and permits a higher number of false positive cases, than for the situation where a low number of false positive cases are required and a higher number of false negative cases are permissible.
该研究报告了一种数学方法，用于确定在给定临床环境中哪些临床数据对治疗决策具有重要意义。该方法包括以下步骤：（A）计算所比较数据集的接收者操作特征（ROC）函数；（B）将临床研究的设计和目的表示为敏感度值空间上的积分度量（这反映了根据临床目标对低或高敏感度值的偏好）；（C）通过其ROC函数的非线性积分来表征所比较的数据集。该方法已用于比较在24小时心电图记录的5113个不同部分中计算出的平均心率（HR）和心率变异性（HRV）数据，并在365例急性心肌梗死存活患者中进行评估，以评估不同长度和起始时间的动态心电图记录对预测心源性猝死和/或严重心律失常事件的效用。研究结果表明，这种方法能够评估和比较用于识别风险增加患者的医学数据集。该研究的实验部分表明，对于急性心肌梗死存活患者，评估HR和HRV数据的最佳记录间隔取决于识别风险增加患者的目的。与需要较少假阳性病例且允许较多假阴性病例的情况相比，对于需要较少假阴性病例且允许较多假阳性病例的识别，最佳记录间隔是不同的。
