BACKGROUND Cardiac sarcoidosis (CS) with right ventricular (RV) involvement can mimic arrhythmogenic right ventricular cardiomyopathy (ARVC). Histopathological differences may result in disease... Show moreBACKGROUND Cardiac sarcoidosis (CS) with right ventricular (RV) involvement can mimic arrhythmogenic right ventricular cardiomyopathy (ARVC). Histopathological differences may result in disease-specific RV activation patterns detectable on the 12-lead electrocardiogram. Dominant subepicardial scar in ARVC leads to delayed activation of areas with reduced voltages, translating into terminal activation delay and occasionally (epsilon) waves with a small amplitude. Conversely, patchy transmural RV scar in CS may lead to conduction block and therefore late activated areas with preserved voltages reflected as preserved R' waves.OBJECTIVE The purpose of this study was to evaluate the distinct terminal activation patterns in precordial leads V-1 through V-3 as a discriminator between CS and ARVC.METHODS Thirteen patients with CS affecting the RV and 23 patients with gene-positive ARVC referred for ventricular tachycardia ablation were retrospectively included in a multicenter approach. A non-ventricular-paced 12-lead surface electrocardiogram was analyzed for the presence and the surface area of the R' wave (any positive deflection from baseline after an S wave) in leads V-1 through V-3.RESULTS An R' wave in leads V-1 through V-3 was present in all patients with CS compared to 11 (48%) patients with ARVC (P=.002). An algorithm including a PR interval of >= 220 ms, the presence of an R' wave, and the surface area of the maximum R' wave in leads V-1 through V-3 of similar to 1.65 mm(2) had 85% sensitivity and 96% specificity for diagnosing CS, validated in a second cohort (18 CS and 40 ARVC) with 83% sensitivity and 88% specificity.CONCLUSION An easily applicable algorithm including PR prolongation and the surface area of the maximum R' wave in leads V-1 through V-3 of similar to 1.65 mm(2) distinguishes CS from ARVC. This QRS terminal activation in precordial leads V-1 through V-3 may reflect disease-specific scar patterns. Show less
Background In the prehospital triage of patients presenting with symptoms suggestive of acute myocardial ischemia, reliable myocardial ischemia detection in the electrocardiogram (ECG) is pivotal.... Show moreBackground In the prehospital triage of patients presenting with symptoms suggestive of acute myocardial ischemia, reliable myocardial ischemia detection in the electrocardiogram (ECG) is pivotal. Due to large interindividual variability and overlap between ischemic and nonischemic ECG-patterns, incorporation of a previous elective (reference) ECG may improve accuracy. The aim of the current study was to explore the potential value of serial ECG analysis using subtraction electrocardiography.Methods SUBTRACT is a multicenter retrospective observational study, including patients who were prehospitally evaluated for acute myocardial ischemia. For each patient, an elective previously recorded reference ECG was subtracted from the ambulance ECG. Patients were classified as myocardial ischemia cases or controls, based on the in-hospital diagnosis. The diagnostic performance of subtraction electrocardiography was tested using logistic regression of 28 variables describing the differences between the reference and ambulance ECGs. The Uni-G ECG Analysis Program was used for state-of-the-art single-ECG interpretation of the ambulance ECG.Results In 1,229 patients, the mean area-under-the-curve of subtraction electrocardiography was 0.80 (95%CI: 0.77-0.82). The performance of our new method was comparable to single-ECG analysis using the Uni-G algorithm: sensitivities were 66% versus 67% (p-value > .05), respectively; specificities were 80% versus 81% (p-value > .05), respectively.Conclusions In our initial exploration, the diagnostic performance of subtraction electrocardiography for the detection of acute myocardial ischemia proved equal to that of state-of-the-art automated single-ECG analysis by the Uni-G algorithm. Possibly, refinement of both algorithms, or even integration of the two, could surpass current electrocardiographic myocardial ischemia detection. Show less
Man, S.; Duffhues, G.S.; Dijke, P. ten; Baker, D. 2019
Background: Serial electrocardiography aims to contribute to electrocardiogram (ECG) diagnosis by comparing the ECG under consideration with a previously made ECG in the same individual. Here, we... Show moreBackground: Serial electrocardiography aims to contribute to electrocardiogram (ECG) diagnosis by comparing the ECG under consideration with a previously made ECG in the same individual. Here, we present a novel algorithm to construct dedicated deep-learning neural networks (NNs) that are specialized in detecting newly emerging or aggravating existing cardiac pathology in serial ECGs.Methods: We developed a novel deep-learning method for serial ECG analysis and tested its performance in detection of heart failure in post-infarction patients, and in the detection of ischemia in patients who underwent elective percutaneous coronary intervention. Core of the method is the repeated structuring and learning procedure that, when fed with 13 serial ECG difference features (intra-individual differences in: QRS duration; QT interval; QRS maximum; T-wave maximum; QRS integral; T-wave integral; QRS complexity; T-wave complexity; ventricular gradient; QRS-T spatial angle; heart rate; J-point amplitude; and T-wave symmetry), dynamically creates a NN of at most three hidden layers. An optimization process reduces the possibility of obtaining an inefficient NN due to adverse initialization.Results: Application of our method to the two clinical ECG databases yielded 3-layer NN architectures, both showing high testing performances (areas under the receiver operating curves were 84% and 83%, respectively).Conclusions: Our method was successful in two different clinical serial ECG applications. Further studies will investigate if other problem-specific NNs can successfully be constructed, and even if it will be possible to construct a universal NN to detect any pathologic ECG change. Show less
