Aims None of the conventional echocardiographic parameters alone predict increased NTproBNP level and symptoms, making diagnosis of heart failure with preserved ejection fraction (HFpEF) very difficult in some cases, in resting condition. We evaluated LA functions by 2D speckle tracking echocardiography (STE) on top of conventional parameters in HFpEF and preHF patients with diastolic dysfunction (DD), in order to establish the added value of the LA deformation parameters in the diagnosis of HFpEF. Methods We prospectively enrolled 125 patients, 88 with HFpEF (68±9 yrs), and 37 asymptomatic with similar risk factors with DD (preHF) (61±8 yrs). We evaluated them by NTproBNP, conventional DD parameters, and STE. Global longitudinal strain (GS) was added. LA reservoir (R), conduit (C), and pump function (CT) were assessed both by volumetric and STE. 2 reservoir strain (S) derived indices were also measured, stiffness (SI) and distensibility index (DI). Results LA R and CT functions were significantly reduced in HFpEF compared to preHF group (all p<0.001), whereas conduit was similarly in both groups. SI was increased, whereas DI was reduced in HFpEF group (p<0.001). By adding LA strain analysis, from all echocardiographic parameters, SR_CT<-1.66/s and DI<0.57 (AUC = 0.76, p<0.001) demonstrated the highest accuracy to identify HFpEF diagnosis. However, by multivariate logistic regression, the model that best identifies HFpEF included only SR_CT, GS and sPAP (R2 = 0.506, p<0.001). Moreover, SR_CT, DI, and sPAP registered significant correlation with NTproBNP level. Conclusions By adding LA functional analysis, we might improve the HFpEF diagnosis accuracy, compared to present guidelines. LA pump function is the only one able to differentiates preHF from HFpEF patients at rest. A value of SR_CT < -1.66/s outperformed conventional parameters from the scoring system, reservoir strain, and LA overload indices in HFpEF diagnosis. We suggest that LA function by STE could be incorporated in the current protocol for HFpEF diagnosis at rest as a major functional criterion, in order to improve diagnostic algorithm, and also in the follow-up of patients with risk factors and DD, as a prognostic marker. Future studies are needed to validate our findings.

Acute cardiovascular pathology can frequently resemble the clinical and paraclinical picture of SARS-CoV-2 infection. The present paper aims to present the experience of a cardiology clinic during this pandemic and describe the way in which the clinical station was organized in order to limit in-hospital transmission of the virus. Methods Patients admitted to an emergency cardiology department between May 1, 2020 and December 31, 2020 were retrospectively identified and divided into two groups: (1) those positive for SARS-CoV2 infection and (2) those with an initial negative test, but high suspicion for the infection, who were tested at least twice by RT-PCR. We followed the motivation for retesting as well as possible correlations between clinical and paraclinical parameters and the decision to retest. Results A number of 334 patients were identified, 51 with a first positive RT-PCR test for SARS-CoV2 infection, and 276 who were tested for infection at least twice. The most common reasons for retesting were lung imaging and existence of subfever. The best predictive model for the outcome of the second RT-PCR test included the presence of lymphopenia, subpleural condensation, highest temperature during hospitalization, and the presence of at least two COVID-19 symptoms. Conclusion The balance between prompt detection of patients with high suspicion of SARS-CoV2 infection (through PCR re-testing) and misuse of material resources should be guided by clinical algorithms.

Sustained ventricular arrhythmias that occur early post-myocardial infarction (MI) are generally considered epiphenomena of the MI and are not consistently associated with long-term prognosis. The lack of association with long-term prognosis is more clearly established for early ventricular fibrillation (VF) and polymorphic ventricular tachycardia (PVT). Sustained monomorphic ventricular tachycardia (SMVT), even when it occurs early, however, may reflect a permanent arrhythmic substrate1. Patients with COVID-19 have a high risk of thromboembolic events, and the virus has also been shown to have extensive effects on the cardiovascular system2,3,4. A 62-year-old woman, recently hospitalized for COVID-19 pneumonia, was brought to the emergency department with pulseless SMVT having been successfully resuscitated in the prehospital setting. The patient has a history of an old MI treated with thrombolysis and percutaneous coronary intervention (PCI) that was complicated with early SMVT, but with preserved left ventricular function and without heart failure. The patient underwent implantation of a cardioverter defibrillator (ICD). During the hospitalization, she developed dyspnea and was diagnosed with minor pulmonary embolism. It may be appropriate to consider early SMVT as a predictor of adverse late outcomes that would necessitate rigorous follow-up and maybe an early invasive primary prevention strategy. This case also reflects the possibility of long-term cardiac involvement and increased thromboembolic risk in patients recovering from COVID-19.

Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): PN-III-P1-1-TE-2016-0669, within PNCDI III Background Left ventricular non-compaction (LVNC) is associated with an increased risk of heart failure (HF). The presence of a real LVNC with HF with preserved ejection fraction (HFpEF), is still controverted. Methods We evaluated prospectively 42 patients with HFpEF, 21 with LVNC (61 ± 9 years) and 21 without LVNC (LVC), aged and risk factor matched, by cardiac magnetic resonance (CMR) 1.5T. LVNC diagnosis was confirmed by Petersen and Jacquier criteria (NC/C ratio and the percentage of NC myocardium). We performed myocardial T1 mapping (normal value of 950 ± 21ms). We calculated a mean value of all native T1 (T1mean), and also for apical (apicalT1) and basal segments (basalT1). We also calculated ECV mean, basal and apical. All patients had NTproBNP and biomarkers for systemic inflammation (hsCRP, IL6, cystatin C and sST2), endothelial dysfunction: VCAM, von Willebrand factor (vWf), vWF metalloproteinase-ADAMTS13, and myocardial fibrosis: vascular peroxidase (VPO), and Galectin-3. Results In the LVNC, mean NC/C ratio was 2.9 ± 0.5 mm and the percentage of NC myocardium was 24.41 ± 8.8%. LVNC patients had significantly higher T1apical, higher ECVmean, ECV basal and apical (Table) by comparison with LVC group, suggesting an extensive fibrosis in LVNC group with significantly higher apical fibrosis.  Inflammatory markers were similar between groups, LVNC patients had lower values of ADAMTS13, suggesting endothelial dysfunction, and higher values of Galectin-3, suggesting increased myocardial fibrosis (Table). Galectin-3 correlated positively only with apicalT1 (R = 0.49, p = 0.04). NTproBNP significantly correlated with VPO, a promotor of fibrosis (r = 0.61, p = 0.009) in LVNC group, whereas in LVC group correlated with cystatin C (r = 0.62, p = 0003) and VCAM (r = 0.4, p = 0.05). Native apical T1 cut off &gt;1021 ms provided the highest sensibility and specificity to differentiate segments with and without NC in HFpEF (p = 0.002) (Figure). Conclusion  HFpEF patients with LVNC have significant higher NTproBNP, higher fibrosis than patients without LVNC, more extensive in non-compacted apical segments. Galectin-3 level correlates only with apical fibrosis on CMR, expressed by apicalT1 time. Moreover, endothelial dysfunction seems to play an important role in HFpEF generation in LVNC. All findings suggests that LVNC is a stand alone condition, not an adaptive hyper-trabeculation in HFpEF. Table.Comparison between groups NTproBNP (pg/ml) Galectin3 (ng/ml) ADAMTS13 (ng/ml) T1mean (ms) basalT1 (ms) apicalT1 (ms) ECV mean (%) ECV basal (%) ECV apical (%) LVNC 294 ± 282 8.44 ± 3.45 767.35 ± 335.56 1013.8 ± 31.8 1002.8 ± 27.2 1059 ± 73 27.2 ± 2.9 26.2 ± 2.9 29.6 ± 3.9 LVC 163 ± 71 6.67 ± 2.88 962.33 ± 253.78 1003.2 ± 28.1 1004.3 ± 29.5 1007 ± 40 24.3 ± 2.5 24.2 ± 2.7 25.2 ± 2.8 P value 0.031 0.048 0.049 0.26 0.865 0.007 0.002 0.033 &lt;0.001 Abstract Figure