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在全面的QT/QTc研究中,两种估计QT间期测量中读者变异性方法的比较。

Comparison of two methods of estimating reader variability in QT interval measurements in thorough QT/QTc studies.

作者信息

Salvi Vaibhav, Karnad Dilip R, Kerkar Vaibhav, Panicker Gopi Krishna, Natekar Mili, Kothari Snehal

机构信息

Research Section, Quintiles Cardiac Safety Services, Mumbai, India.

出版信息

Ann Noninvasive Electrocardiol. 2014 Mar;19(2):182-9. doi: 10.1111/anec.12136. Epub 2014 Feb 13.

DOI:10.1111/anec.12136
PMID:24521536
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6932446/
Abstract

BACKGROUND

Two methods of estimating reader variability (RV) in QT measurements between 12 readers were compared.

METHODS

Using data from 500 electrocardiograms (ECGs) analyzed twice by 12 readers, we bootstrapped 1000 datasets each for both methods. In grouped analysis design (GAD), the same 40 ECGs were read twice by all readers. In pairwise analysis design (PAD), 40 ECGs analyzed by each reader in a clinical trial were reanalyzed by the same reader (intra-RV) and also by another reader (inter-RV); thus, variability between each pair of readers was estimated using different ECGs.

RESULTS

Inter-RV (mean [95% CI]) between pairs of readers by GAD and PAD was 3.9 ms (2.1-5.5 ms) and 4.1 ms (2.6-5.4 ms), respectively, using ANOVA, 0 ms (-0.0 to 0.4 ms), and 0 ms (-0.7 to 0.6 ms), respectively, by actual difference between readers and 7.7 ms (6.2-9.8 ms) and 7.7 ms (6.6-9.1 ms), respectively, by absolute difference between readers. Intra-RV too was comparable.

CONCLUSIONS

RV estimates by the grouped- and pairwise analysis designs are comparable.

摘要

背景

比较了两种估计12位读者QT测量中读者变异性(RV)的方法。

方法

利用12位读者对500份心电图(ECG)进行两次分析的数据,我们对两种方法分别进行了1000次数据集的自助抽样。在分组分析设计(GAD)中,所有读者对相同的40份心电图进行两次阅读。在成对分析设计(PAD)中,每位读者在一项临床试验中分析的40份心电图由同一位读者重新分析(内部RV),也由另一位读者分析(外部RV);因此,使用不同的心电图估计每对读者之间的变异性。

结果

采用方差分析时,GAD和PAD中读者对之间的外部RV(均值[95%CI])分别为3.9毫秒(2.1 - 5.5毫秒)和4.1毫秒(2.6 - 5.4毫秒),根据读者之间的实际差异分别为0毫秒(-0.0至0.4毫秒)和0毫秒(-0.7至0.6毫秒),根据读者之间的绝对差异分别为7.7毫秒(6.2 - 9.8毫秒)和7.7毫秒(6.6 - 9.1毫秒)。内部RV也具有可比性。

结论

分组分析设计和成对分析设计的RV估计具有可比性。

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