Computerized automated algorithm-based analyses of digitized paper ECGs in Brugada syndrome

Abstract

BackgroundBrugada syndrome is a rare inherited arrhythmic syndrome with a coved type 1 ST-segment elevation on ECG and an increased risk of sudden death. Many studies have evaluated risk stratification performance based on ECG-derived parameters. However, since historical Brugada patient cohorts included mostly paper ECGs, most studies have been based on manual ECG parameter measurements.We hypothesized that it would be possible to run automated algorithm-based analysis of paper ECGs.We aimed: 1) to validate the digitization process for paper ECGs in Brugada patients; and 2) to quantify the acute class I antiarrhythmic drug effect on relevant ECG parameters in Brugada syndrome. MethodsA total of 176 patients (30% female, 43 ± 13 years old) with induced type 1 Brugada syndrome ECG were included in the study. All of the patients had paper ECGs before and during class I antiarrhythmic drug challenge. Twenty patients also had a digital ECG, in whom printouts were used to validate the digitization process.Paper ECGs were scanned and then digitized using ECGScan software, version 3.4.0 (AMPS, LLC, New York, NY, USA) to obtain FDA HL7 XML format ECGs. Measurements were automatically performed using the Bravo (AMPS, LLC, New York, NY, USA) and Glasgow algorithms. ResultsECG parameters obtained from digital and digitized ECGs were closely correlated (r = 0.96 ± 0.07, R2 = 0.93 ± 0.12). Class I antiarrhythmic drugs significantly increased the global QRS duration (from 113 ± 20 to 138 ± 23, p < 0.0001). On lead V2, class I antiarrhythmic drugs increased ST-segment elevation (from 110 ± 84 to 338 ± 227 μV, p < 0.0001), decreased the ST slope (from 14.9 ± 23.3 to −27.4 ± 28.5, p < 0.0001) and increased the TpTe interval (from 88 ± 18 to 104 ± 33, p < 0.0001). ConclusionsAutomated algorithm-based measurements of depolarization and repolarization parameters from digitized paper ECGs are reliable and could quantify the acute effects of class 1 antiarrhythmic drug challenge in Brugada patients. Our results support using computerized automated algorithm-based analyses from digitized paper ECGs to establish risk stratification decision trees in Brugada syndrome.