Early risk stratification after ST-segment–elevation myocardial infarction (STEMI) is of major clinical importance. Strain quantifies myocardial deformation and can demonstrate abnormal global and segmental myocardial function in acute ischaemia. Native T1-mapping allows assessment of the severity of acute ischemic injury, however its clinical applicability early post MI is limited by the complex dynamic changes happening in the myocardium post MI. We aimed to explore relationship between T1-mapping and feature tracking imaging, to establish whether combined analysis of these parameters could predict recovery after STEMI. 96 STEMI patients (aged 60 ± 11) prospectively recruited in the Oxford Acute Myocardial Infarction (OxAMI) study underwent 3T-CMR scans acutely (within 53 ± 32 h from primary percutaneous coronary intervention) and at 6 months (6M). The imaging protocol included: cine, ShMOLLI T1-mapping and late gadolinium enhancement (LGE). Segments were divided in the infarct, adjacent and remote zones based on the presence of LGE. Peak circumferential (Ecc) and radial (Err) strain was assessed using cvi42 software. Acute segmental strain correlated with segmental T1-mapping values (T1 vs. Err − 0.75 ± 0.25, p < 0.01; T1 vs. Ecc 0.72 ± 0.32, p < 0.01) and with LGE segmental injury (LGE vs. Err − 0.56 ± 0.29, p < 0.01; LGE vs. Ecc 0.54 ± 0.35, p < 0.01). Moreover, acute segmental T1 and strain predicted segmental LGE transmurality on 6M scans (p < 0.001, r = 0.5). Multiple regression analysis confirmed combined analysis of global Ecc and T1-mapping was significantly better than either method alone in predicting final infarct size at 6M (r = 0.556 vs r = 0.473 for global T1 only and r = 0.476 for global Ecc only, p < 0.001). This novel CMR method combining T1-mapping and feature tracking analysis of acute CMR scans predicts LGE transmurality and infarct size at 6M following STEMI.

Background Charge density mapping of atrial fibrillation (AF) reveals dynamic localized rotational activation (LRA), irregular activation (LIA) and focal firing (FF). Their spatial stability, conduction characteristics and the optimal duration of mapping required to reveal these phenomena and has not been explored. Methods Bi‐atrial mapping of AF propagation was undertaken using AcQMap (Acutus Medical) and variability of activation patterns quantified up to a duration of 30 s. The frequency of each pattern was quantified at each unique point of the chamber over two separate 30‐s recordings before ablation and R2 calculated to quantify spatial stability. Regions with the highest frequency were identified at increasing time durations and compared to the result over 30 s using Cohen's kappa. Properties of regions with the most stable patterns were assessed during sinus rhythm and extrastimulus pacing. Results In 21 patients, 62 paired LA and RA maps were obtained. LIA was highly spatially stable with R2 between maps of 0.83 (0.71–0.88) compared to 0.39 (0.24–0.57), and 0.64 (0.54–0.73) for LRA and FF, respectively. LIA was most temporally stable with a kappa of >0.8 reached by 12 s. LRA showed greatest variability with kappa >0.8 only after 22 s. Regions of LIA were of normal voltage amplitude (1.09 mv) but showed increased conduction heterogeneity during extrastimulus pacing (p = .0480). Conclusion Irregular activation patterns characterized by changing wavefront direction are temporally and spatially stable in contrast with LRA that is transient with least spatial stability. Focal activation appears of intermediate stability. Regions of LIA show increased heterogeneity following extrastimulus pacing and may represent fixed anatomical substrate.

Aims SuperMap is a novel non-contact algorithm for the mapping of organized atrial arrhythmias. We prospectively evaluated SuperMap during mapping and ablation of atrial tachycardias (ATs) and paced rhythms and compared to conventional high-density contact mapping. Methods and results Consecutive patients undergoing SuperMap guided ablation of pre-existing ATs or AT developed during atrial fibrillation ablation procedures were included together with maps obtained during pacing to assess block in linear lesions. The time taken to obtain diagnostic maps was measured together with the number of electrogram (EGM) points and accuracy compared to the arrhythmia diagnosis confirmed using a combination of map findings, entrainment, and response to ablation. In a subgroup of patients, concurrent contact mapping was performed with contact and SuperMap analysed by separate operators blinded to the other technique. The time taken to generate a diagnostic map, EGM number, and map accuracy was compared. Thirty-one patients (62 maps) were included with contact mapping performed in 19 [39 maps (33 for AT)]. SuperMap acquisition time was 314 s [interquartile range (IQR) 239–436]. The median number of EGM points used per map was 5399 (IQR 3279–8677). SuperMap was faster than contact mapping [394 ± 219 s vs. 611 ± 331 s; difference 217 s, 95% confidence interval (CI) 116–318, P &lt; 0.0005]. The number of EGM points used per map was higher for SuperMap (7351 ± 5054 vs. 3620 ± 3211; difference 3731, 95% CI 2073–5388, P &lt; 0.0005). SuperMap and contact mapping were accurate in 92% and 85% of maps, respectively, P = 0.4805. Conclusion SuperMap non-contact charge density mapping is a rapid and reliable approach to guide the ablation of complex atrial arrhythmias.

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Background Charge density mapping of atrial fibrillation (AF) reveals dynamic patterns of localised rotational activation (LRA), irregular activation (LIA) and focal firing (FF). Their spatial stability, conduction characteristics and the optimal duration of mapping required to reveal these phenomena and has not been explored. Methods Bi-atrial mapping of AF propagation was undertaken and variability of activation patterns quantified up to a duration of 30-seconds(s). The frequency of each pattern was quantified at each vertex of the chamber over 2 separate 30s recordings prior to ablation and R2 calculated to quantify spatial stability. Regions with the highest frequency were identified at increasing time durations and compared to the result over 30s using Cohen’s kappa. Properties of regions with the most stable patterns were assessed during sinus rhythm and extrastimulus pacing. Results In twenty-one patients, 62 paired LA and RA maps were obtained. LIA was highly spatially stable with R2 between maps of 0.83(0.71-0.88) compared to 0.39(0.24-0.57) and 0.64(0.54-0.73) for LRA and FF, respectively. LIA was also most temporally stable with a kappa of >0.8 reached by 12s. LRA showed greatest variability with kappa>0.8 only after 22s. Regions of LIA were of normal voltage amplitude (1.09mv) but showed increased conduction heterogeneity during extrastimulus pacing (p=0.0480). Conclusion Irregular activation patterns characterised by changing wavefront direction are temporally and spatially stable in contrast with rotational patterns that are transient with least spatial stability. Focal activation appears of intermediate stability. Regions of LIA show increased heterogeneity following extrastimulus pacing and may represent fixed anatomical substrate.

The reduction in patients presenting with ST-elevation myocardial infarction (STEMI) during the COVID19 crisis could have resulted from fears about developing COVID-19 infection in hospital. Patients who delay presenting with STEMI are more likely to develop mechanical complications, including acute ischemic mitral regurgitation (MR). We present a 69-year-old women with an inferior STEMI and cardiogenic shock due to acute ischemic MR who delayed presenting to hospital due to the fear of COVID-19. Early identification of this mechanical complication using transthoracic echocardiography in the Emergency Department enabled the team to target her optimisation. Ultimately these patients require urgent surgery to repair the mitral valve and revascularize the myocardium but they are often too unwell to undergo surgery and even when it is feasible the outcomes are poor.

Background Adenosine shortens action potential duration and refractoriness and provokes atrial fibrillation. This study aimed to evaluate the effect of adenosine on mechanisms of wavefront propagation during atrial fibrillation. Methods and Results The study included 22 patients undergoing catheter ablation for persistent atrial fibrillation. Left atrial mapping was performed using the AcQMap charge density system before and after administration of intravenous adenosine at 1 or more of 3 time points during the procedure (before pulmonary vein isolation, after pulmonary vein isolation, and after nonpulmonary vein isolation ablation). Wave‐front propagation patterns were evaluated allowing identification and quantification of localized rotational activation (LRA), localized irregular activation, and focal firing. Additional signal processing was performed to identify phase singularities and calculate global atrial fibrillation cycle length and dominant frequency. A total of 35 paired maps were analyzed. Adenosine shortened mean atrial fibrillation cycle length from 181.7±14.3 to 165.1±16.3, (mean difference 16.6 ms; 95% CI, 11.3–21.9, P <0.0005) and increased dominant frequency from 6.0±0.7 Hz to 6.6±0.8 Hz (95% CI, 0.4–0.9, P <0.0005). This was associated with a 50% increase in the number of LRA occurrences (16.1±7.6–24.2±8.1; mean difference 8.1, 95% CI, 4.1–12, P <0.0005) as well as a 20% increase in the number of phase singularities detected (30.1±7.8–36.6±9.3; mean difference 6.5; 95% CI, 2.6–10.0, P =0.002). The percentage of left atrial surface area with LRA increased with adenosine and 42 of 70 zones (60%) with highest density of LRA coincided with high density LRA zones at baseline with only 28% stable across multiple maps. Conclusions Adenosine accelerates atrial fibrillation and promotes rotational activation patterns with no impact on focal activation. There is little evidence that rotational activation seen with adenosine represents promising targets for ablation aimed at sites of stable arrhythmogenic sources in the left atrium.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and a growing public health epidemic. In the UK, over 1.3 million people have a diagnosis of AF and an estimated 400,000 remain undiagnosed. AF-related strokes account for a quarter of all strokes and, as AF episodes are often asymptomatic, are still often the first manifestation of AF. Early diagnosis and initiation of oral anticoagulation, where appropriate, may prevent some of these thromboembolic strokes. Public Health England is committed to decrease the incidence of AF-related strokes and has sponsored initiatives aimed at improving AF detection by promoting the uptake of wearable technologies. However, the National Institute for Health and Care Excellence (NICE) has not recommended wearable technology in their recent AF diagnosis and management guidelines (NG196). Diagnostic accuracy of single-lead electrocardiography (ECG) generated by the latest iteration of wearable devices is excellent and, in many cases, superior to general practitioner interpretation of the 12-lead ECG. High-quality ECG from wearable devices that unequivocally shows AF can expedite AF detection. Otherwise, there is a real risk of delaying AF diagnosis with the potential of devastating consequences for patients and their families.