Detection of QT prolongation using a novel electrocardiographic analysis algorithm applying intelligent automation: prospective blinded evaluation using the Cardiac Safety Research Consortium electrocardiographic database. Academic Article uri icon

Overview

abstract

  • BACKGROUND: 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. METHODS: 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. RESULTS: 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). CONCLUSION: 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.

publication date

  • February 27, 2012

Research

keywords

  • Algorithms
  • Automation
  • Aza Compounds
  • Electrocardiography
  • Long QT Syndrome
  • Quinolines

Identity

PubMed Central ID

  • PMC3638766

Scopus Document Identifier

  • 84858338629

Digital Object Identifier (DOI)

  • 10.1016/j.ahj.2011.11.009

PubMed ID

  • 22424006

Additional Document Info

volume

  • 163

issue

  • 3