11 research outputs found
Evaluation of Differences in Automated QT/QTc Measurements between Fukuda Denshi and Nihon Koden Systems
<div><p>Background</p><p>Automatic measurement becomes a preference, and indeed a necessity, when analyzing 1000 s of ECGs in the setting of either drug-inducing QT prolongation screening or genome-wide association studies of QT interval. The problem is that individual manufacturers apply different computerized algorithms to measure QT interval. We conducted a comparative study to assess the outcomes with different automated measurements of QT interval between ECG machine manufacturers and validated the related heart rate correction methods.</p><p>Methods and Results</p><p>Herein, we directly compared these different commercial systems using 10,529 Fukuda Denshi ECGs and 72,754 Nihon Kohden ECGs taken in healthy Japanese volunteers. Log-transformed data revealed an equal optimal heart rate correction formula of QT interval for Fukuda Denshi and Nihon Kohden, in the form of QTc = QT/RR<sup>−0.347</sup>. However, with the raw data, the optimal heart rate correction formula of QT interval was in the form of QTc = QT+0.156×(1-RR) for Fukuda Denshi and QTc = QT+0.152×(1-RR) for Nihon Kohden. After optimization of heart rate correction of QT interval by the linear regression model using either log-transformed data or raw data, QTc interval was ∼10 ms longer in Nihon Kohden ECGs than in those recorded on Fukuda Denshi machines. Indeed, regression analysis revealed that differences in the ECG machine used had up to a two-fold larger impact on QT variation than gender difference. Such an impact is likely to be of considerable importance when ECGs for a given individual are recorded on different machines in the setting of multi-institutional joint research.</p><p>Conclusions</p><p>We recommend that ECG machines of the same manufacturer should be used to measure QT and RR intervals in the setting of multi-institutional joint research. It is desirable to unify the computer algorithm for automatic QT and RR measurements from an ECG.</p></div
Analysis of resting Fukuda Denshi ECGs.
<p>(A) Histograms of QT, log-transformed QT, RR, and log-transformed RR intervals. (B) Scatter plots of log QT versus log RR and log QTc_ours log versus log RR. (C) QT versus RR and QTc_ours raw versus RR. Units of all variables are ms.</p
Analysis of resting Nihon Kohden ECGs.
<p>(A) Histograms of QT, log-transformed QT, RR, and log-transformed RR intervals. (B) Scatter plots of log QT versus log RR and log QTc_ours log versus log RR. (C) QT versus RR and QTc_ours raw versus RR. Units of all variables are ms.</p
Comparison of automatic QT measurements in adult resting ECGs between Fukuda Denshi and Nihon Kohden.
<p>Units of RR, QT and all corrected forms of QT in this table are ms.</p><p>The results of the tests of differences in QT, RR, QTc(ours_log), Fredericia, Bazett, QTc(ours_raw), Framingham and ECAPs12 between Nihon Koden and Fukuda Denshi for each gender were all P<2.2×10<sup>−16</sup> (Student t-test).</p><p>Comparison of automatic QT measurements in adult resting ECGs between Fukuda Denshi and Nihon Kohden.</p
Schematic view of RYR2 mutations in long QT syndrome.
Only those predicted to be damaging in silico are shown. The number in the parentheses indicates the reference number in the manuscript. Those with surrounding lines are the novel variants identified in this study. Gray boxes indicate the transmembrane region and “P” indicates the pore-forming region.</p
Characteristics of newly detected variants and their functional prediction scores.
Characteristics of newly detected variants and their functional prediction scores.</p
Variants in the upstream region that could alter the binding affinity of transcription factors predicted by TRAP.
Variants in the upstream region that could alter the binding affinity of transcription factors predicted by TRAP.</p