AIMS: Non-cardiologists (NCs) are often responsible for the preliminary diagnosis and early management of patients presenting with ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT). At present, the Vereckei aVR and Brugada algorithms are the most widely recognized and frequently relied upon wide complex tachycardia (WCT) differentiation criteria by NCs. This study aimed to determine the diagnostic efficacy of the Vereckei aVR and Brugada algorithms when applied by NCs. METHODS: In a blinded fashion, three internal medicine residents prospectively interpreted WCTs using the Vereckei aVR and Brugada algorithms. The diagnostic performance of each method was evaluated according to their agreement with the correct rhythm diagnosis. RESULTS: Two-hundred sixty-nine WCTs (160 VT, 109 SWCT) from 186 patients were independently interpreted by each participant (807 separate interpretations per algorithm). The aVR and Brugada algorithms accurately classified 546 out of 807 (67.7%) and 622 out of 807 (77.1%) interpreted WCTs, respectively. Overall sensitivity and specificity of the aVR algorithm for VT was 92.1% and 31.8%, respectively. Overall sensitivity and specificity of the Brugada algorithm for VT was 89.4% and 59.0%, respectively. Both algorithms yielded modestly favorable overall positive predictive values (aVR 66.5%; Brugada 76.2%) and negative predictive values (73.3%; Brugada 79.1%). CONCLUSION: Non-cardiologist algorithm users correctly identified most "actual" VTs, but did not sufficiently revise VT probability to conclusively distinguish VT and SWCT. Newer WCT differentiation methods are needed to improve NC's ability to accurately differentiate WCTs.
AIMS: Non-cardiologists (NCs) are often responsible for the preliminary diagnosis and early management of patients presenting with ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT). At present, the Vereckei aVR and Brugada algorithms are the most widely recognized and frequently relied upon wide complex tachycardia (WCT) differentiation criteria by NCs. This study aimed to determine the diagnostic efficacy of the Vereckei aVR and Brugada algorithms when applied by NCs. METHODS: In a blinded fashion, three internal medicine residents prospectively interpreted WCTs using the Vereckei aVR and Brugada algorithms. The diagnostic performance of each method was evaluated according to their agreement with the correct rhythm diagnosis. RESULTS: Two-hundred sixty-nine WCTs (160 VT, 109 SWCT) from 186 patients were independently interpreted by each participant (807 separate interpretations per algorithm). The aVR and Brugada algorithms accurately classified 546 out of 807 (67.7%) and 622 out of 807 (77.1%) interpreted WCTs, respectively. Overall sensitivity and specificity of the aVR algorithm for VT was 92.1% and 31.8%, respectively. Overall sensitivity and specificity of the Brugada algorithm for VT was 89.4% and 59.0%, respectively. Both algorithms yielded modestly favorable overall positive predictive values (aVR 66.5%; Brugada 76.2%) and negative predictive values (73.3%; Brugada 79.1%). CONCLUSION: Non-cardiologist algorithm users correctly identified most "actual" VTs, but did not sufficiently revise VT probability to conclusively distinguish VT and SWCT. Newer WCT differentiation methods are needed to improve NC's ability to accurately differentiate WCTs.
Authors: Anthony H Kashou; Peter A Noseworthy; Christopher V DeSimone; Abhishek J Deshmukh; Samuel J Asirvatham; Adam M May Journal: J Am Heart Assoc Date: 2020-05-19 Impact factor: 5.501
Authors: Anthony H Kashou; Christopher M Evenson; Peter A Noseworthy; Thoddi R Muralidharan; Christopher V DeSimone; Abhishek J Deshmukh; Samuel J Asirvatham; Adam M May Journal: Indian Heart J Date: 2020-09-23