Mechanisms and Prediction of Ischemic Stroke in Atrial Fibrillation Patients.

Atrial fibrillation (AF) is the most common arrhythmia in adults worldwide and represents an important burden for patients, physicians, and healthcare systems. AF is associated with substantial mortality and morbidity, due to the disease itself and its specific complications, such as the increased risk of stroke and thromboembolic events associated with AF. The temporal relation between AF episodes and stroke is nonetheless incompletely understood. The factors associated with an increased thromboembolic risk remain unclear, as well as the stroke risk stratification. Therefore, in this review, we intend to expose the mechanisms and physiopathology leading to intracardiac thrombus formation and stroke in AF patients, together with the evidence supporting the causal hypothesis. We also expose the risk factors associated with increased risk of stroke, the current different risk stratification tools as well as future prospects for improving this risk stratification.

Diaphragmatic CMAP Monitoring During Cryoballoon Procedures: Surface vs. Hepatic Recording Comparison and Limitations of This Approach.

BACKGROUND: Compound motor action potential (CMAP) monitoring is a common method used to prevent right phrenic nerve palsy during cryoballoon ablation for atrial fibrillation. OBJECTIVE: We compared recordings simultaneously obtained with surface and hepatic electrodes. METHODS: We included 114 consecutive patients (mean age 61.7 ± 10.9 years) admitted to our department for cryoballoon ablation. CMAP was monitored simultaneously with a hepatic catheter and a modified lead I ECG, whilst right phrenic nerve was paced before (stage 1) and during (stage 2) the right-sided freezes. If phrenic threat was detected with hepatic recordings (CMAP amplitude drop >30%) the application was discontinued with forced deflation. RESULTS: The ratio of CMAP/QRS was 4.63 (2.67-9.46) for hepatic and 0.76 (0.55-1.14) for surface (p < 0.0001). Signal coefficients of variation during stage 1 were 3.92% (2.48-6.74) and 4.10% (2.85-5.96) (p = 0.2177), respectively. Uninterpretable signals were more frequent on surface (median 10 vs. 0; p < 0.0001). For the 14 phrenic threats, the CMAP amplitude dropped by 35.61 ± 8.27% on hepatic signal and by 33.42 ± 11.58% concomitantly on surface (p = 0.5417). Our main limitation was to achieve to obtain stable phrenic capture (57%). CMAP monitoring was not reliable because of pacing instability in 15 patients (13.16%). A palsy occurred in 4 patients (3.51%) because cryoapplication was halted too late. CONCLUSION: Both methods are feasible with the same signal stability and amplitude drop precocity during phrenic threats. Clarity and legibility are significantly better with hepatic recording (sharper signals, less far-field QRS). The two main limitations were pacing instability and delay between 30% CMAP decrease and cryoapplication discontinuation.

Advantages and disadvantages of drug challenge during electrophysiological study in patients with new left bundle branch block after transaortic valve implantation.

AIMS: Electrophysiological study (EPS) is recommended in case of new-onset persistent left bundle branch block (NOP-LBBB) after transaortic valve implantation (TAVI) to identify patients at high risk of delayed atrioventricular block (D-AVB). We evaluated the added value of drug challenge, after normal baseline EPS, to predict D-AVB in such patients. METHODS: We conducted a comparative single-centre study of two successive periods, during which we used baseline EPS alone (first period) or drug challenge in case of normal baseline EPS (second period), for patients with NOP-LBBB after TAVI. The primary endpoint was a composite of pacemaker use, documented D-AVB, cardiac syncope, sudden death, or delayed pacemaker implantation. RESULTS: Among 736 patients with TAVI implantation between January 2016 and September 2019, 64 with NOP-LBBB were included. During the first period, 4/22 (18.2%) presented with a positive baseline EPS. After a mean (standard deviation [SD]) of 15.6 (8.3) months, 7/22 (31.8%) reached the primary endpoint. During the second period, 19/42 (45.2%) presented with a positive EPS. After a mean (SD) of 12.8 (3.5) months, 8/42 (19.0%) reached the primary endpoint. There was a tendency to increased sensitivity (42.9-87.5%; P = 0.12) and negative predictive value (77.8-95.7%; P = 0.15) of the EPS, respectively during the first to the second period. However, the specificity decreased (93.3-64.7%; P = 0.04). CONCLUSION: Diagnostic yield improved with drug challenge in case of normal baseline EPS. However, the decrease in specificity led to a high rate of unnecessary pacemaker implantation.

Smartwatch Electrocardiogram and Artificial Intelligence for Assessing Cardiac-Rhythm Safety of Drug Therapy in the COVID-19 Pandemic. The QT-logs study.

BACKGROUND: QTc interval monitoring, for the prevention of drug-induced arrhythmias is necessary, especially in the context of coronavirus disease 2019 (COVID-19). For the provision of widespread use, surrogates for 12­lead ECG QTc assessment may be useful. This prospective observational study compared QTc duration assessed by artificial intelligence (AI-QTc) (Cardiologs®, Paris, France) on smartwatch single­lead electrocardiograms (SW-ECGs) with those measured on 12­lead ECGs, in patients with early stage COVID-19 treated with a hydroxychloroquine-azithromycin regimen. METHODS: Consecutive patients with COVID-19 who needed hydroxychloroquine-azithromycin therapy, received a smartwatch (Withings Move ECG®, Withings, France). At baseline, day-6 and day-10, a 12­lead ECG was recorded, and a SW-ECG was transmitted thereafter. Throughout the drug regimen, a SW-ECG was transmitted every morning at rest. Agreement between manual QTc measurement on a 12­lead ECG and AI-QTc on the corresponding SW-ECG was assessed by the Bland-Altman method. RESULTS: 85 patients (30 men, mean age 38.3 ± 12.2 years) were included in the study. Fair agreement between manual and AI-QTc values was observed, particularly at day-10, where the delay between the 12­lead ECG and the SW-ECG was the shortest (-2.6 ± 64.7 min): 407 ± 26 ms on the 12­lead ECG vs 407 ± 22 ms on SW-ECG, bias -1 ms, limits of agreement -46 ms to +45 ms; the difference between the two measures was <50 ms in 98.2% of patients. CONCLUSION: In real-world epidemic conditions, AI-QTc duration measured by SW-ECG is in fair agreement with manual measurements on 12­lead ECGs. Following further validation, AI-assisted SW-ECGs may be suitable for QTc interval monitoring. REGISTRATION: ClinicalTrial.govNCT04371744.