The development and validation of an easy to use automatic QT-interval algorithm

<div><p>Background</p><p>To evaluate QT-interval dynamics in patients and in drug safety analysis, beat-to-beat QT-interval measurements are increasingly used. However, interobserver differences, aberrant T-wave morphologies and changes in heart axis might hamper accurate QT-interval measurements.</p><p>Objective</p><p>To develop and validate a QT-interval algorithm robust to heart axis orientation and T-wave morphology that can be applied on a beat-to-beat basis.</p><p>Methods</p><p>Additionally to standard ECG leads, the root mean square (ECG_RMS), standard deviation and vectorcardiogram were used. QRS-onset was defined from the ECG_RMS. T-wave end was defined per individual lead and scalar ECG using an automated tangent method. A median of all T-wave ends was used as the general T-wave end per beat.</p><p>Supine-standing tests of 73 patients with Long-QT syndrome (LQTS) and 54 controls were used because they have wide ranges of RR and QT-intervals as well as changes in T-wave morphology and heart axis orientation. For each subject, automatically estimated QT-intervals in three random complexes chosen from the low, middle and high RR range, were compared with manually measured QT-intervals by three observers.</p><p>Results</p><p>After visual inspection of the randomly selected complexes, 21 complexes were excluded because of evident noise, too flat T-waves or premature ventricular beats. Bland-Altman analyses of automatically and manually determined QT-intervals showed a bias of <4ms and limits of agreement of ±25ms. Intra-class coefficient indicated excellent agreement (>0.9) between the algorithm and all observers individually as well as between the algorithm and the mean QT-interval of the observers.</p><p>Conclusion</p><p>Our automated algorithm provides reliable beat-to-beat QT-interval assessment, robust to heart axis and T-wave morphology.</p></div

Inter-observer variability.

<p>Inter-observer variability.</p

Validation results of the μQTobs VS QTalg.

<p><b>A</b> linear regression between μQTobs and QTalg. <b>B</b> Bland-Altman analysis shows no bias (solid black line) and narrow limit of agreements (dashed lines). <b>C</b> The Distribution of differences shows that the differences are normally distributed around zero. All numbers corresponding with this figure can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0184352#pone.0184352.t002" target="_blank">Table 2</a>. QTalg = QT-interval determined by the algorithm, μQTobs = mean QT-interval determined by three observers, SD = standard deviation, ms = milliseconds.</p

Characteristics of the study population.

<p>Characteristics of the study population.</p

Baseline characteristics of the AGNES case-control set.

<p>CK-MB, creatine kinase-MB; LAD, left anterior descending artery; LCX, left circumflex artery; RCA, right coronary artery. *In case of missing values, the sample sizes of the total, case and control sets (total, case, control) for which information was available are given. ^† Normally distributed continuous variables are presented as mean ± SD or as Median [interquartile range] otherwise. Categorical variables data are presented as number (%). ‡ <i>P</i> value for comparison of cases and controls using independent t-test, Mann-Whitney test, or chi-square test where appropriate.</p

ECG characteristics of AGNES cases and controls according to the artery harbouring the stenotic lesion.

<p>LAD, left anterior descending artery; LCX, left circumflex artery; RCA, right coronary artery</p>*<p><i>P</i> value of comparison between cases and controls using a logistic regression model adjusted for age and sex. (All patients with AV block or PR≥200 ms or QRS≥120 ms & AF are excluded)</p

An example of the results of our algorithm.

<p>The QRS onset and global Tend detected by the algorithm is shown for a healthy control and patients with LQT-1, 2 and 3. QTalg = QT-interval determined by the algorithm, μQTobs = mean QT-interval determined by three observers, ms = milliseconds.</p

Association analysis of SNPs with ECG indices of conduction and repolarization during myocardial ischemia.

<p>SE, Standard Error * Direction of effect estimate per copy coded allele; Inc, Increasing effect; Dec, Decreasing effect; data from previous GWA studies † Effect estimate is given per copy of the coded allele adjusted for age, sex and culprit artery (all patients with AV block or PR ≥ 200 ms or QRS ≥ 120 ms are excluded).</p

Association analysis of SNPs with VF in AGNES cases versus AGNES controls.

*<p>effect estimate is given per copy of the coded allele adjusted for age, sex and culprit artery. † <i>P</i> values for interaction between SNPs and culprit artery on risk of VF</p

Population structure of the Caucasian cases and controls.

<p>The red dots represent diTdP cases and blue dots represent controls (drug-exposed patients and population [POPRES] controls). The plot shows the first and second eigen vectors, which clearly separate the Caucasians into a Northwestern group (top) and other groups from Southern and Eastern Europe. The dense cluster on the lower left represents the subjects of Spanish origin from the POPRES collection. The final analysis included subjects with PC1<–0.03.</p