心脏病学
内科学
医学
心肌梗塞
西格玛
左束支阻滞
窦性心律
心向量图
心电图
心房颤动
心力衰竭
物理
量子力学
作者
Atsushi Nishiyama,Akira Suzuki,Hiroshi Hayashi,Seiji Shimizu,Masato Watarai,Makoto Saito,Yukio Shiga,Tatsuji Furuta,Fumimaro Takatsu,Masayoshi Adachi,Yoshio Ichihara,Makoto Tsuda,Makoto Hirai
标识
DOI:10.1016/0022-0736(93)90037-e
摘要
The authors compared the ability of QRST time-integral values (QRST values) from body surface potential maps (BSPM), 12-lead electrocardiograms (ECGs), and Frank lead vectorcardiograms (VCGs) in diagnosing a prior inferior myocardial infarction (MI) in simulated left bundle branch block (LBBB). The study included 32 patients whose digitized ECGs were recorded simultaneously for BSPM, ECGs, and VCGs during normal sinus rhythm and during right ventricular pacing simulating LBBB (18 with and 14 without an inferior MI). QRST values were calculated in each lead point of ECGs. Data on 608 normal subjects were used as controls; mean +/- 2 SD was regarded as the normal range. The following parameters were derived: sigma DM, sigma DE, sigma DV, the sum of the differences between the normal mean QRST value, and the QRST value of a given patient in leads where the QRST value was less than the normal range ("-2 SD area") in BSPM, ECGs, and VCGs (Y lead). The correlation coefficients for sigma DM, sigma DE, and sigma DV between the two activation sequences were highly significant. Sensitivity and specificity were as follows: 89% and 93% for sigma DM > 100 mV.ms, 89% and 93% for sigma DE > 50 mV.ms, and 56% and 100% for sigma DV > 10 mV.ms, respectively. Although sigma DM, sigma DE, and sigma DV were significantly (P < .01) correlated with the asynergy index calculated from left ventriculograms, sigma DM showed the best correlation. QRST values from BSPM, ECGs, and VCGs provide information that is useful in detecting an inferior MI and in estimating the severity of left ventricular wall motion abnormalities in the setting of LBBB. Of the three parameters, BSPM showed the best correlation with the severity of left ventricular wall motion abnormalities.
科研通智能强力驱动
Strongly Powered by AbleSci AI