Recognition of pre-hypertrophic cardiac involvement in Fabry Disease based on automated electrocardiographic measures. (1st September 2021) - Namdar, Mehdi; Richardot, Philippe; Johner, Nicolas; Shah, Dipen; Nordbeck, Peter; Olivotto, Iacopo; Macfarlane, Peter
Abstract: Background: Various electrocardiographic (ECG) indices have been shown to be useful for early recognition and staging of cardiac involvement in Fabry Disease (FD). However, many of them lack acceptable sensitivity and specificity. We assessed the value of automated ECG measures to discriminate between pre-hypertrophic FD and healthy individuals. Methods and results: Normal ECGs from 1496 healthy individuals (57.4% male, age 37.4 ± 13 years) were compared to those of 142 FD patients without LVH (37.3% male, age 41.5 ± 18 years). All ECGs were analyzed centrally and a total of 429 automated ECG measures per individual were included for step-wise analysis. The Cramer V statistic was first used to pick out those parameters which were helpful in discriminating between the two groups and a final selection was made by using two models, namely the FLD (Fisher Linear Discrimination) and the Logistic model, to optimise diagnostic performance for the detection of cardiac involvement in FD patients vs. specificity in healthy individuals. The three-step statistical analysis identified 9 ECG parameters as most significant for the discrimination between the groups. The combined discriminant score yielded 64% sensitivity and 97% specificity for correct classification of FD patients in the test sample with a logistic area under curve of the ROC analysis of 0.97. Conclusion: The combination of automated ECG measures identified via a stepwise statistical approach may be useful for detection of FD patients in the pre-hypertrophic stage. These data are promising for screening purposes in the very early stages of FD cardiomyopathy and warrant prospective confirmation. Highlights: Pre-hypertrophic stages of Fabry cardiomyopathy remain often unrecognized and disease suspicion is raised only much later. Automated ECG measures are capable to detect very early myocardial manifestations. Such an approach with its excellent specificity and reproducibility may pave the way for an ECG based population screening.