A QT Interval Inaccuracy Index (QTI) for Highly Automated TQT Studies

Automatic QT interval measurements during Thorough QT studies (TQT) are usually overread by experts and adjusted if necessary. Our study aimed to provide a QT Interval Inaccuracy index to automatically identify inaccurate automated QT interval estimations. 12-lead ECG recordings and their manual interval measurements were obtained from 2 TQT study databases in PhysioNet (ECGDMMLD and ECGRDVQ). We derived 268 single-lead features that might relate to the accuracy of automatic QT intervals computed by convolutional neural network (CNN) based QT estimators previously trained on our own ECG database. Classification of accurate and inaccurate automatic QT intervals was performed with a regularized logistic regression algorithm trained on ECGDMMLD. The QTI was then defined for each ECG recording as the average probability of the QT being inaccurate across the 12 leads. 41% inaccurate automatic QT intervals were identified in ECGRDVQ (QTI threshold = 0.5). Better estimates of the drug-induced QTc changes were obtained by correcting these QT intervals. The QTc prolongation obtained with this semi-automated method differed from the manual one by on average 2.98 ms (std = 1.82 ms) across the 4 drugs studied. The QTI would therefore allow a faster and more robust analysis of ECG signals from TQT studies.