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Fragmented QRS (fQRS) is an electrocardiographic (ECG) marker of myocardial conduction abnormality, characterized by additional notches in the QRS complex. The presence of fQRS has been associated with an increased risk of all-cause mortality and arrhythmia in patients with cardiovascular disease. However, current binary visual analysis is prone to intra- and inter-observer variability and different definitions are problematic in clinical practice. Therefore, objective quantification of fQRS is needed and could further improve risk stratification of these patients. We present an automated method for fQRS detection and quantification. First, a novel robust QRS complex segmentation strategy is proposed, which combines multi-lead information and excludes abnormal heartbeats automatically. Afterwards extracted features, based on variational mode decomposition (VMD), phase-rectified signal averaging (PRSA) and the number of baseline-crossings of the ECG, were used to train a machine learning classifier (Support Vector Machine) to discriminate fragmented from non-fragmented ECG-traces using multi-center data and combining different fQRS criteria used in clinical settings. The best model was trained on the combination of two independent previously annotated datasets and, compared to these visual fQRS annotations, achieved Kappa scores of 0.68 and 0.44, respectively. We also show that the algorithm might be used in both regular sinus rhythm and irregular beats during atrial fibrillation. These results demonstrate that the proposed approach could be relevant for clinical practice by objectively assessing and quantifying fQRS. The study sets the path for further clinical application of the developed automated fQRS algorithm.

Objective: Diabetes increases the risk of all-cause mortality and sudden cardiac death (SCD). The exact mechanisms leading to sudden death in diabetes are not well known. We compared the incidence of appropriate shocks and mortality in patients with versus without diabetes with a prophylactic implantable cardioverter defibrillator (ICD) included in the retrospective EU-CERT-ICD registry. Research design and methods and results: A total of 3,535 patients from 12 European EU-CERT-ICD centers with a mean age of 63.7 ± 11.2 years (82% males) at the time of ICD implantation were included in the analysis. A total of 995 patients (28%) had a history of diabetes. All patients had an ICD implanted for primary SCD prevention. End points were appropriate shock and all-cause mortality. Mean follow-up time was 3.2 ± 2.3 years. Diabetes was associated with a lower risk of appropriate shocks (adjusted hazard ratio [HR] 0.77 [95% CI 0.62–0.96], P = 0.02). However, patients with diabetes had significantly higher mortality (adjusted HR 1.30 [95% CI 1.11–1.53], P = 0.001). Conclusions: All-cause mortality is higher in patients with diabetes than in patients without diabetes with primary prophylactic ICDs. Subsequently, patients with diabetes have a lower incidence of appropriate ICD shocks, indicating that the excess mortality might not be caused primarily by ventricular tachyarrhythmias. These findings suggest a limitation of the potential of prophylactic ICD therapy to improve survival in patients with diabetes with impaired left ventricular function.