Duke University Medical Center and Duke Clinical Research Institute, Durham, NC.
Am Heart J. 2012 Mar;163(3):365-71. doi: 10.1016/j.ahj.2011.11.009. Epub 2012 Feb 27.
The Cardiac Safety Research Consortium (CSRC) provides both "learning" and blinded "testing" digital electrocardiographic (ECG) data sets from thorough QT (TQT) studies annotated for submission to the US Food and Drug Administration (FDA) to developers of ECG analysis technologies. This article reports the first results from a blinded testing data set that examines developer reanalysis of original sponsor-reported core laboratory data.
A total of 11,925 anonymized ECGs including both moxifloxacin and placebo arms of a parallel-group TQT in 181 subjects were blindly analyzed using a novel ECG analysis algorithm applying intelligent automation. Developer-measured ECG intervals were submitted to CSRC for unblinding, temporal reconstruction of the TQT exposures, and statistical comparison to core laboratory findings previously submitted to FDA by the pharmaceutical sponsor. Primary comparisons included baseline-adjusted interval measurements, baseline- and placebo-adjusted moxifloxacin QTcF changes (ddQTcF), and associated variability measures.
Developer and sponsor-reported baseline-adjusted data were similar with average differences <1 ms for all intervals. Both developer- and sponsor-reported data demonstrated assay sensitivity with similar ddQTcF changes. Average within-subject SD for triplicate QTcF measurements was significantly lower for developer- than sponsor-reported data (5.4 and 7.2 ms, respectively; P < .001).
The virtually automated ECG algorithm used for this analysis produced similar yet less variable TQT results compared with the sponsor-reported study, without the use of a manual core laboratory. These findings indicate that CSRC ECG data sets can be useful for evaluating novel methods and algorithms for determining drug-induced QT/QTc prolongation. Although the results should not constitute endorsement of specific algorithms by either CSRC or FDA, the value of a public domain digital ECG warehouse to provide prospective, blinded comparisons of ECG technologies applied for QT/QTc measurement is illustrated.
心脏安全研究联盟（CSRC）为心电图（ECG）分析技术的开发者提供了来自充分 QT（TQT）研究的“学习”和盲法“测试”数字 ECG 数据集，这些数据集经过注释，可提交给美国食品和药物管理局（FDA）。本文报告了首个来自盲法测试数据集的结果，该数据集检查了开发者对原始赞助商报告的核心实验室数据的重新分析。
共有 181 名受试者的 11925 份匿名 ECG 数据，包括莫西沙星和安慰剂的平行组 TQT 双臂，使用一种新的 ECG 分析算法进行盲法分析，该算法应用了智能自动化。开发者测量的 ECG 间期被提交给 CSRC 进行去盲，对 TQT 暴露的时间进行重构，并与之前由制药赞助商提交给 FDA 的核心实验室结果进行统计比较。主要比较包括基线校正的间期测量、基线和安慰剂校正的莫西沙星 QTcF 变化（ddQTcF）以及相关的变异性测量。
开发者和赞助商报告的基线校正数据相似，所有间期的平均差异<1ms。开发者和赞助商报告的数据均显示出了检测的灵敏度，ddQTcF 变化相似。三重复测 QTcF 测量的个体内标准差（SD）显著低于赞助商报告的数据（分别为 5.4ms 和 7.2ms；P<0.001）。
与赞助商报告的研究相比，用于此分析的几乎自动 ECG 算法产生了相似但变异性更小的 TQT 结果，而无需使用手动核心实验室。这些发现表明，CSRC ECG 数据集可用于评估用于确定药物引起 QT/QTc 延长的新方法和算法。虽然结果不应构成 CSRC 或 FDA 对特定算法的认可，但公共领域数字 ECG 仓库提供了用于前瞻性、盲法比较应用于 QT/QTc 测量的 ECG 技术的价值。
