利用贝叶斯统计原理提高医学诊断测试的性能

R. Warner
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引用次数: 1

摘要

该研究分析了432名因呼吸短促而在急诊科接受评估的患者的诊断数据。分析电子记录的心音和脑利钠肽(BNP),以确定左心室收缩功能障碍(LVSD)合并心力衰竭是否引起每位患者的症状。在每位患者的计算机数字心电图中,测量QRS复合物的持续时间。正如预期的那样,数据显示QRS复合持续时间本身并不能检测LVSD合并心力衰竭。然而,对于记录的心音和BNP数据,在QRS复杂持续时间延长的亚组中,LVSD合并心力衰竭的诊断敏感性(98%特异性)明显高于整个受试者组。在数字心电图上评估QRS复合持续时间可以评估潜在心脏病的先验概率,提高LVSD合并心力衰竭的诊断试验的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using the Principles of Bayesian Statistics to Improve the Performances of Medical Diagnostic Tests
The study analyzed diagnostic data from 432 patients evaluated in the emergency department for shortness of breath. Electronically recorded heart sounds and brain natriuretic peptide (BNP) were analyzed to determine whether left ventricular systolic dysfunction (LVSD) with heart failure caused each patient's symptoms. In each patient's computerized digital electrocardiogram, measurements of the duration of the QRS complex were made. As expected, the data show that the QRS complex duration itself does not detect LVSD with heart failure. However, for both the recorded heart sounds and the BNP data, the diagnostic sensitivities at 98% specificity for LVSD with heart failure are significantly greater in the subgroups with prolonged QRS complex duration than they are in the entire group of subjects. Evaluating QRS complex duration on the digital electrocardiogram can assess the prior probability of underlying heart disease and improves the performances of diagnostic tests for LVSD with heart failure.
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