Pattern recognition analysis of digital ECGs: Decreased QT measurement error and improved precision compared to semi-automated methods☆
Introduction
Currently, the accurate clinical assessment of potential drug-induced QT interval prolongation remains a major concern for regulatory agencies. Rarely, prolongation of ventricular repolarization may lead to torsade de pointes (TdP), a life-threatening polymorphic ventricular tachycardia. Despite several attempts to develop new and more reliable biomarkers predictive of TdP,1, 2, 3 the assessment of cardiac repolarization during a thorough QT (TQT) study remains the gold standard where virtually all new drug candidates are required to demonstrate relative safety for the risk of inducing TdP, indirectly assessed as an estimation of their potential to prolong the QT interval.
Pharmaceutical companies generally outsource TQT data analysis to ECG core laboratories which specialize in semi-automated (SA) ECG annotations performed by well-trained cardiologists. The analysis performed by such laboratories typically consists of semi-automatically adjudicated annotations of 3–9 beats obtained during periods of heart rate stability (± 2 bpm) extracted at predefined time points prospectively defined in the study protocol.4 Although this methodology provides reliable QT interval measurements during the specified intervals, it has many well recognized and, to date, unresolved limitations including the exclusion of the majority of the recorded data, insufficient temporal resolution, substantial time and resource (manpower, cost) requirements, and inter-observer variability.5, 6, 7, 8 Importantly, the sparse QT–RR data derived from fully manual or SA ECG analysis precludes the derivation and use of individual QT rate corrections, now generally accepted as the most accurate and reliable method to control for the effects of heart rate on the QT interval.9
Most of the fully automated T-end detection algorithms employ traditional analytical approaches (e.g. first-derivative adaptive threshold, tangent-based return to baseline) which usually perform well in noise-free, stable ECG recordings.10, 11 However, electrical noise, baseline drift, and T-wave morphological changes will unavoidably lower the accuracy of algorithm-based ECG interval measurements, particularly affecting the reproducible determination of the T-end offset.12, 13 Recently developed pattern recognition software enables reliable machine-read analysis of continuous ECGs by matching individual ECG waveforms to user-selected, noise-free, well-inscribed, and manually adjudicated ECG complexes which serve as reference waveform analysis templates.
In this study, we considered a well-characterized automated ECG analysis algorithm,14 and assessed potential improvements in raw QT interval measurement accuracy and precision with the addition of an ECG pattern recognition module (Ponemah Physiology Platform, ver. 5.0, Data Sciences International, St. Paul, MN). The algorithm (ALG) and pattern recognition (PRO) modules were compared by performing parallel analyses of continuous ECG data collected from all periods of a TQT study which included placebo, two doses of saquinavir, a QT-prolonging drug that induces changes in T-wave morphology,15 and the standard clinical reference agent, moxifloxacin. The current machine-read methods were validated by direct comparison of the reference moxifloxacin-induced QT-prolonging effect with parallel time-matched values obtained by an ECG core laboratory during the primary TQT analysis. Confirmation of accurate and precise fully automated QT analytical method, particularly in the setting of variable T-wave morphology,16 would provide a powerful new method for continuous beat-to-beat ECG analysis, possibly supplanting the current labor intensive manual adjudication of sparse ECG samples. Accordingly, the goals of the current study were twofold: (1) to directly compare the accuracy and precision of ALG and PRO T-end determinations in the presence of drug-induced QT prolongation and/or T-wave morphological changes and (2) to characterize and compare the ability of both machine-read analyses to detect benchmark moxifloxacin-induced QTc changes previously adjudicated by an ECG core laboratory employing SA analysis of beats extracted from a common data set.
Section snippets
Study design
Data used in this study were extracted from a TQT study previously evaluated by an ECG core laboratory. Sixty healthy subjects were enrolled and 24-h continuous 12-lead digital ECGs (1000 Hz, Mortara H12 + Holter devices, Mortara Instruments, Milwaukee, WI) were acquired on day − 1 (pretreatment baseline) and day 3 (on-drug) for each period. The study was performed at a single center, employing a double-blind, randomized, four-period, four-way crossover design. All subjects received each of four
Raw QT and RR interval analysis
For all subjects and periods, PRO analysis matched 91% ± 10% of all beats employing 4.2 ± 1.3 templates (range 2–7), yielding ~ 72-K valid beats for each 24-h period. Generally, unmatched complexes were due to either electrical noise or environmental disturbances.
Visual characterization of T-end adjudication with both ALG and PRO yielded 671 and 10 measurement errors, corresponding to incidence rates of 5.6% and 0.1%, respectively (p < 0.001, chi-squared test). ALG measurement errors usually consisted
Discussion
While the feasibility of reliable machine-read QT interval measurements has been previously demonstrated,5, 6, 7, 8, 10, 13 this methodology is not yet routinely accepted by the regulatory agencies as a standalone clinical ECG evaluation method. Natural and drug-induced changes in T-wave morphology remain a major concern when employing machine-read analysis of continuously digitized ECGs. Currently, there is no clinical evidence to support the notion that a machine-read analysis, which performs
Conclusions
Machine-read ECG methodologies are rapidly evolving and, as demonstrated for the current TQT study, advanced measurement algorithms can precisely replicate the results of semi-automated visual interpretations currently performed by core laboratory cardiologists, but with reduced QT variability and improved temporal resolution. The current results support the use of pattern recognition methods when evaluating continuously recorded digital ECGs, especially when the study involves the analysis of
Limitations
The current study assessed moxifloxacin-induced QTca changes based on machine-read values derived from continuous QT measurements from lead V4 which were then compared to moxifloxacin-induced QTcS changes based on semi-automated lead II QT measurements performed by an ECG core laboratory. Comparison of repolarization results obtained in different leads and employing different methodologies should be interpreted with caution. In the current study, lead V4 offered optimal waveform amplitudes for
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Cited by (7)
QT interval correction assessment in the anesthetized guinea pig
2015, Journal of Pharmacological and Toxicological MethodsPreclinical QT safety assessment: Cross-species comparisons and human translation from an industry consortium
2014, Journal of Pharmacological and Toxicological MethodsCitation Excerpt :Employing a common analytical scheme, the current data present a parallel QT–RR comparison across all species and clearly demonstrate that preclinical variability can be reduced to the same extent as in man when the species-specific QT–RR domains are considered in the rate-correction model. Assays conducted across diverse platforms must be referenced to a common gold standard to accurately assess translation (Meyer et al., 2013). For repolarization studies, moxifloxacin is the most common clinical reference agent and was thus used to benchmark all current preclinical results.
Comparative TQT analysis with three fully-automated platforms: Comparison to core laboratory semi-automated results
2013, Journal of ElectrocardiologyCitation Excerpt :Briefly, data used in this study were extracted from a TQT study previously evaluated by an ECG core laboratory. Both the current study design and semi-automated methodologies have been described in detail elsewhere.4 It is well-recognized that selection of the lead to be analyzed with machine-read methods is critical as electrical noise, baseline drift, and T-wave morphological changes will impact the accurate and reproducible determination of T-end offset.2,15
The "thorough QT study": A valid paradigm to test new algorithms for QT interval measurements?
2013, Journal of ElectrocardiologyNeonatal ECG screening andQT correction: Themarch towards consistency and accuracy
2018, European Heart JournalElectrocardiographic data quality in thorough QT/QTc studies
2014, Drug Safety
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No declared financial support or conflicts of interest.