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Background: Infranodal conduction disorders are common after transcatheter aortic valve replacement (TAVR). Risk factors are incompletely understood., Objective: The purpose of this study was to assess the impact of valve implantation depth and calcium burden of the device landing zone on infranodal conduction intraprocedure pre- and post-TAVR., Methods: In all patients undergoing TAVR between June 2020 and June 2021, the His-ventricle (HV) interval was measured pre- and post-valve deployment. The difference between the 2 measurements defined delta HV, whereas infranodal conduction delay was defined as HV interval >55 ms. Valve implantation depth was measured as the distance between the aortic annular plane and the ventricular prosthesis end. Calcium burden was quantified as the volume of calcium in 6 regions of interest: the non-, right, and left coronary cusps (NCC, RCC, and LCC, respectively) and the corresponding regions of the left ventricular outflow tract (LVOT) underlying each cusp (LVOT NCC , LVOT RCC , LVOT LCC , respectively)., Results: Of 101 patients (mean age 81 ± 5.7 years; 47% women), 37 demonstrated infranodal conduction delay intraprocedure post-TAVR. Overall, mean implantation depth was 5 ± 3.1 mm, median calcium volume was 2080 mm 3 [interquartile range 632-2400]. Delta HV showed no correlation with implantation depth or calcium burden (r = -0.08 and r = 0.12, respectively). However, LVOT NCC calcification was a significant predictor for infranodal conduction delay post-valve deployment in a multivariable logistic regression model (odds ratio 1.62 per 100-mm 3 increase (95% confidence interval 1.06-2.69; P = .04)., Conclusion: Assessment of LVOT NCC calcification may identify patients at risk for infranodal conduction delay after TAVR, whereas implantation depth did not predict infranodal conduction delay., (© 2023 Heart Rhythm Society. Published by Elsevier Inc.)

Background: Self-reported exercise capacity is a well-established prognostic measure in stable ambulatory patients with cardiac and pulmonary disease., Objectives: The authors aimed to directly compare the prognostic accuracy of quantified self-reported exercise capacity using the Duke Activity Status Index (DASI) with the established objective disease-severity marker B-type natriuretic peptide (BNP) in patients presenting with acute dyspnea to the emergency department., Methods: The DASI was obtained in a prospective multicenter diagnostic study recruiting unselected patients presenting with acute dyspnea to the emergency department. The prognostic accuracy of DASI and BNP for 90-day and 720-day all-cause mortality was evaluated using C-index., Results: Among 1,019 patients eligible for this analysis, 75 (7%) and 297 (29%) patients died within 90 and 720 days after presentation, respectively. Unadjusted hazard ratios (HRs) and multivariable adjusted hazard ratios (aHRs) for 90- and 720-day mortality increased continuously from the fourth (best self-reported exercise capacity) to the first DASI quartile (worst self-reported exercise capacity). For 720-day mortality the HR of the first quartile vs the fourth was 9.1 (95% CI, 5.5-14.9) vs (aHR: 6.1, 95% CI: 3.7-10.1), of the second quartile 6.4 (95% CI: 3.9-10.6) vs (aHR: 4.4, 95% CI: 2.6-7.3), while of the third quartile the HR was 3.2 (95% CI: 1.9-5.5) vs (aHR: 2.4, 95% CI: 1.4-4.0). The prognostic accuracy of the DASI score was high, and higher than that of BNP concentrations (720-day mortality C-index: 0.67 vs 0.62; P  = 0.024)., Conclusions: Quantification of self-reported subjective exercise capacity using the DASI provides high prognostic accuracy and may aid physicians in risk stratification. (Basics in Acute Shortness of Breath EvaLuation [BASEL V] Study [BASEL V]; NCT01831115)., Competing Interests: This study was supported by research grants from the 10.13039/501100001711Swiss National Science Foundation, the 10.13039/501100004362Swiss Heart Foundation, the 10.13039/100008375University of Basel, the 10.13039/100016015University Hospital Basel, Critical Diagnostics, 10.13039/100000046Abbott, BRAHMS, 10.13039/100004337Roche and 10.13039/100016946Singulex. None of those supporters had any role in study design, the conduct of the study, the analysis of the data, or the decision to submit this manuscript for publication. Dr Breidthardt has received research grants from the Swiss National Science Foundation, the University Hospital Basel, the Department of Internal Medicine, University Hospital Basel, Abbott, and Roche. Dr Mueller has received research grants from the Swiss National Science Foundation, the Swiss Heart Foundation, the KTI, the University Hospital Basel, the University of Basel, Abbott, Beckman Coulter, Biomerieux, BRAHMS, Ortho Clinical, Quidel, Novartis, Roche, Siemens, Singulex, and Sphingotec. The sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (© 2023 The Authors.)

Background: Multiple smart devices capable to detect atrial fibrillation (AF) are presently available. Sensitivity and specificity for the detection of AF may differ between available smart devices, and this has not yet been adequately investigated., Objectives: The aim was to assess the accuracy of 5 smart devices in identifying AF compared with a physician-interpreted 12-lead electrocardiogram as the reference standard in a real-world cohort of patients., Methods: We consecutively enrolled patients presenting to a cardiology service at a tertiary referral center in a prospective, diagnostic study., Results: We prospectively analyzed 201 patients (31% women, median age 66.7 years). AF was present in 62 (31%) patients. Sensitivity and specificity for the detection of AF were comparable between devices: 85% and 75% for the Apple Watch 6, 85% and 75% for the Samsung Galaxy Watch 3, 58% and 75% for the Withings Scanwatch, 66% and 79% for the Fitbit Sense, and 79% and 69% for the AliveCor KardiaMobile, respectively. The rate of inconclusive tracings (the algorithm was unable to determine the heart rhythm) was 18%, 17%, 24%, 21%, and 26% for the Apple Watch 6, Samsung Galaxy Watch 3, Withings Scan Watch, Fitbit Sense, and AliveCor KardiaMobile (P < 0.01 for pairwise comparison), respectively. By manual review of inconclusive tracings, the rhythm could be determined in 955 (99%) of 969 single-lead electrocardiograms. Regarding patient acceptance, the Apple Watch was ranked first (39% of participants)., Conclusions: In this clinical validation of 5 direct-to-consumer smart devices, we found differences in the amount of inconclusive tracings diminishing sensitivity and specificity of the smart devices. In a clinical setting, manual review of tracings is required in about one-fourth of cases., Competing Interests: Funding Support and Author Disclosures Dr Knecht has received funding from the Stiftung für kardiovaskuläre Forschung. Dr Schaer has served on the Speakers Bureau for Medtronic. Dr Mueller has received research support from the Swiss National Science Foundation, the Swiss Heart Foundation, the KTI, the University Hospital Basel, the University of Basel, Abbott, Beckman Coulter, Brahms, Idorsia, Novartis, Ortho Diagnostics, Quidel, Roche, Siemens, Singulex, and Sphingotec, outside the submitted work; and speaker/consulting honoraria from Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, BMS, Novartis, Osler, Roche, and Sanofi, all paid to the institution. Dr Kühne has received personal fees from Bayer, Boehringer Ingelheim, Pfizer BMS, Daiichi Sankyo, Medtronic, Biotronik, Boston Scientific, Johnson & Johnson, Roche; and grants from Bayer, Pfizer, Boston Scientific, BMS, Biotronik, and Daiichi Sankyo, all outside the submitted work. Dr Sticherling has served on the advisory board for Medtronic Europe and Boston Scientitic Europe; received educational grants from Biosense Webster and Biotronik; received a research grant from the European Union’s FP7 program and Biosense Webster; and received lecture and consulting fees from Abbott, Medtronic, Biosense Webster, Boston Scientific, MicroPort, and Biotronik, all outside the submitted work. Dr Badertscher has received research funding from the University of Basel, the Stiftung für Herzschrittmacher und Elektrophysiologie, the Freiwillige Akademische Gesellschaft Basel, and Johnson & Johnson, all outside the submitted work; and received personal fees from Abbott. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2023. Published by Elsevier Inc.)

Background: Prompt differential diagnosis of wide QRS complex tachycardia (WCT) is crucial to patient management. However, distinguishing ventricular tachycardia (VT) from supraventricular tachycardia (SVT) with wide QRS complexes remains problematic, especially for nonelectrophysiologists., Objectives: This study aimed to develop a simple-to-use algorithm with integration of clinical and electrocardiographic (ECG) parameters for the differential diagnosis of WCT., Methods: The 12-lead ECGs of 206 monomorphic WCTs (153 VT, 53 SVT) with electrophysiology-confirmed diagnoses were analyzed. In the novel Basel algorithm, VT was diagnosed in the presence of at least 2 of the following criteria: 1) clinical high risk features; 2) lead II time to first peak >40 ms; and 3) lead aVR time to first peak >40 ms. The algorithm was externally validated in 203 consecutive WCT cases (151 VT, 52 SVT). Its' diagnostic performance and clinical applicability were compared with those of the Brugada and Vereckei algorithms., Results: The Basel algorithm showed a sensitivity, specificity, and accuracy of 92%, 89%, and 91%, respectively, in the derivation cohort and 93%, 90%, and 93%, respectively, in the validation cohort. There were no significant differences in the performance characteristics between the 3 algorithms. The evaluation of the clinical applicability of the Basel algorithm showed similar diagnostic accuracy compared with the Brugada algorithm (80% vs 81%; P = 1.00), but superiority compared with the Vereckei algorithm (72%; P = 0.03). The Basel algorithm, however, enabled a faster diagnosis (median 36 seconds vs 105 seconds for the Brugada algorithm [P = 0.002] and 50 seconds for the Vereckei algorithm [P = 0.02])., Conclusions: The novel Basel algorithm based on simple clinical and ECG criteria allows for a rapid and accurate differential diagnosis of WCT., Competing Interests: Funding Support and Author Disclosures Dr Schaer has received personal fees from Medtronic. Dr Kühne has received grants from the Swiss National Science Foundation, the Swiss Heart Foundation, Daiichi-Sankyo, Bayer, Pfizer BMS, and Boston Scientific; and has received personal fees from Bayer, Boehringer Ingelheim, Pfizer BMS, Daiichi-Sankyo, Medtronic, Biotronik, Boston Scientific, and Johnson & Johnson, all outside the submitted work. Dr Henrikson has received fellowship support from Abbott, Boston Scientific, and Medtronic; and has served as chair of the clinical endpoints committee for Biotronik. Dr Sticherling has received grants from Biosense-Webster; and has received lecture fees from Abbott, Medtronic, Biosense-Webster, Boston Scientific, Microport, and Biotronik. Dr Reichlin has received speaker/consulting honoraria or travel support from Abbott/SJM, AstraZeneca, Brahms, Bayer, Biosense-Webster, Biotronik, Boston-Scientific, Daiichi-Sankyo, Medtronic, Pfizer BMS, and Roche, all for work outside the submitted study; and has received support for his institution’s fellowship program from Abbott/SJM, Biosense-Webster, Biotronik, Boston-Scientific, and Medtronic for work outside the submitted study. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2022 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)