Background: Drug safety precautions recommend monitoring of the corrected QT interval. To determine which QT correction formula to use in an automated QT ‐monitoring algorithm in our electronic medical record, we studied rate correction performance of different QT correction formulae and their impact on risk assessment for mortality. Methods and Results: All electrocardiograms ( ECG s) in patients >18 years with sinus rhythm, normal QRS duration and rate <90 beats per minute (bpm) in the University Hospitals of Leuven (Leuven, Belgium) during a 2‐month period were included. QT correction was performed with Bazett, Fridericia, Framingham, Hodges, and Rautaharju formulae. In total, 6609 patients were included (age, 59.8±16.2 years; 53.6% male and heart rate 68.8±10.6 bpm). Optimal rate correction was observed using Fridericia and Framingham; Bazett performed worst. A healthy subset showed 99% upper limits of normal for Bazett above current clinical standards: men 472 ms (95% CI , 464–478 ms) and women 482 ms (95% CI 474–490 ms). Multivariate Cox regression, including age, heart rate, and prolonged QT c, identified Framingham (hazard ratio [ HR ], 7.31; 95% CI , 4.10–13.05) and Fridericia ( HR , 5.95; 95% CI , 3.34–10.60) as significantly better predictors of 30‐day all‐cause mortality than Bazett ( HR , 4.49; 95% CI , 2.31–8.74). In a point‐prevalence study with haloperidol, the number of patients classified to be at risk for possibly harmful QT prolongation could be reduced by 50% using optimal QT rate correction. Conclusions: Fridericia and Framingham correction formulae showed the best rate correction and significantly improved prediction of 30‐day and 1‐year mortality. With current clinical standards, Bazett overestimated the number of patients with potential dangerous QT c prolongation, which could lead to unnecessary safety measurements as withholding the patient of first‐choice medication.

In mitral valve prolapse, myocardial fibrosis at cardiac MRI was associated with an increased risk of unexplained syncope, sustained ventricular tachycardia, or sudden cardiac death. Background Patients with mitral valve prolapse (MVP) may develop adverse outcomes even in the absence of mitral regurgitation or left ventricular (LV) dysfunction. Purpose To investigate the progno...

Objective: Fragmented QRS (fQRS) is an accessible biomarker and indication of myocardial scarring that can be detected from the electrocardiogram (ECG). Nowadays, fQRS scoring is done on a visual basis, which is time-consuming and leads to subjective results. This study proposes an automated method to detect and quantify fQRS in a continuous way using features extracted from Variational Mode Decomposition (VMD) and Phase-Rectified Signal Averaging (PRSA). Methods: In the proposed framework, QRS complexes in the ECG signals were first segmented using VMD. Then, 10 VMD- and PRSA-based features were computed and fed into well-known classifiers such as Support Vector Machine (SVM), K-nearest neighbors (KNN), Naive Bayesian (NB), and TreeBagger (TB) in order to compare their performance. The proposed method was evaluated with 12-lead ECG data of 616 patients from the University Hospitals Leuven. The presence of fQRS in each ECG lead was scored by five raters. Both detection and quantification of fQRS could be achieved in this way. Results: The experimental results indicated that the proposed method achieved AUC values of 0.95, 0.94, 0.90, and 0.89 using SVM, KNN, NB and TB classifiers respectively for detecting QRS fragmentation. Assessment of quantification performance was done by comparing the fQRS score with the total score, obtained by summing the scores from the individual raters. Results showed that the fQRS score clearly correlated with this estimate of fQRS certainty. Conclusion: The proposed method obtained good results in both fQRS detection and quantification, and is a novel way of assessing the certainty of QRS fragmentation in the ECG signal.