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Computerized Extraction of Electrocardiograms From Continuous 12‐Lead Holter Recordings Reduces Measurement Variability in a Thorough QT Study
Author(s) -
George Samuel,
Rodriguez Ignacio,
Ipe David,
Sager Philip T.,
Gussak Ihor,
Vajdic Branislav
Publication year - 2012
Publication title -
the journal of clinical pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 116
eISSN - 1552-4604
pISSN - 0091-2700
DOI - 10.1177/0091270011430505
Subject(s) - holter monitor , qt interval , electrocardiography , medicine , selection (genetic algorithm) , computer science , cardiology , artificial intelligence
Continuous Holter recordings are often used in thorough QT studies (TQTS), with multiple 10‐second electrocardiograms (ECGs) visually selected around predesignated time points. The authors hypothesized that computer‐automated ECG selection would reduce within‐subject variability, improve study data precision, and increase study power. Using the moxifloxacin and placebo arms of a Holter‐based crossover TQTS, the authors compared interval duration measurements (IDMs) from manually selected to computer‐selected ECGs. All IDMs were made with a fully automated computer algorithm. Moxifloxacin‐induced changes in baseline‐ and placebo‐subtracted QT intervals were similar for manual and computer ECG selection. Mean 90% confidence intervals were narrower, and within‐subject variability by mixed‐model covariance was lower for computer‐selected than for manual‐selected ECGs. Computer ECG selection reduced the number of subjects needed to achieve 80% power by 40% to 50% over manual. Computer ECG selection returns accurate ddQTcF values with less measurement variability than manual ECG selection by a variety of metrics. This results in increased study power and reduces the number of subjects needed to achieve desired power, which represents a significant potential source cost savings in clinical drug trials.