Automated Algorithm for J-Tpeak and Tpeak-Tend Assessment of Drug-Induced Proarrhythmia Risk [electronic resource]

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Tác giả:

Ngôn ngữ: eng

Ký hiệu phân loại: 612.01 Biophysics and biochemistry

Thông tin xuất bản: Washington, D.C. : Oak Ridge, Tenn. : United States. Food and Drug Administration ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2016

Mô tả vật lý: Size: Article No. e0166925 : , digital, PDF file.

Bộ sưu tập: Metadata

ID: 260668

 Prolongation of the heart rate corrected QT (QTc) interval is a sensitive marker of torsade de pointes risk
  however it is not specific as QTc prolonging drugs that block inward currents are often not associated with torsade. Recent work demonstrated that separate analysis of the heart rate corrected J-T<
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 c (J-T<
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 c) and T<
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  intervals can identify QTc prolonging drugs with inward current block and is being proposed as a part of a new cardiac safety paradigm for new drugs (the ?CiPA? initiative). In this work, we describe an automated measurement methodology for assessment of the J-T<
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  intervals using the vector magnitude lead. The automated measurement methodology was developed using data from one clinical trial and was evaluated using independent data from a second clinical trial. Comparison between the automated and the prior semi-automated measurements shows that the automated algorithm reproduces the semi-automated measurements with a mean difference of single-deltas <
 1 ms and no difference in intra-time point variability (p for all >
  0.39). In addition, the time-profile of the baseline and placebo-adjusted changes are within 1 ms for 63% of the time-points (86% within 2 ms). Importantly, the automated results lead to the same conclusions about the electrophysiological mechanisms of the studied drugs. We have developed an automated algorithm for assessment of J-T<
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 c and T<
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  intervals that can be applied in clinical drug trials. Under the CiPA initiative this ECG assessment would determine if there are unexpected ion channel effects in humans compared to preclinical studies. In conclusion, the algorithm is being released as open-source software.
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