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Comparison of exercise algorithms for diagnosis of coronary artery disease

机译:术冠状动脉疾病诊断的运动算法比较

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The following previously proposed exercise algorithms were evaluated: (a) the cumulative area of ST-segment depression during exercise, (b) discriminant analysis of various exercise variables and (c) heart-rate-adjusted ST-segment amplitude changes. The study population comprised 345 males without a history of myocardial infarction. Prevalence of coronary artery disease was 51%. All had a normal ECG at rest. Frank-lead ECG were computer processed during symptom-limited bicycle ergometry. Discriminant analysis and heart-rate-adjusted ST-segment amplitude changes proved to have excellent diagnostic characteristics: sensitivity amounted 80%; specificity to 90%. Both methods seem well suited for diagnostic applications in clinical practice.
机译:评估以下先前提出的运动算法:(a)运动期间ST段抑郁症的累积区域,(b)各种运动变量的判别分析和(c)心率调整的st段幅度变化。研究人群组成345名男性,没有心肌梗死病史。冠状动脉疾病的患病率为51%。所有人都在休息时有一个正常的心电图。弗兰克 - 领导ECG是在症状限制的自行车计时器中加工的电脑。判别分析和心率调整后的ST段振幅变化已经证明具有优异的诊断特性:灵敏度为80%;特异性达到90%。这两种方法似乎很适用于临床实践中的诊断应用。

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