Citations

Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): Danish Heart Association. Background First-phase ejection fraction (EF1), the ejection fraction (EF) until the time of peak systolic flow may be a sensitive marker of subclinical left ventricular (LV) dysfunction. This study investigated the prognostic value of EF1 in asymptomatic and symptomatic severe aortic stenosis (AS). Methods This study included 94 asymptomatic and 108 symptomatic patients with severe AS. The prognostic value of EF1 was compared with other echocardiographic markers and magnetic resonance imaging (MRI) measured end-systolic wall stress and late gadolinium enhancement fibrosis (LGE). Asymptomatic patients were followed up for 3.0 years (primary outcome death or aortic valve replacement). Symptomatic patients were followed up for 4.3 years (primary outcome death). Results In multivariate regression analysis wall stress (p < 0.001) and LGE (p = 0.03) were associated with EF1. In the asymptomatic cohort EF1 was significantly associated with the end-point, especially among the subgroup of patients with a mean gradient < 40 mmHg (HR 0.91, p = 0.005), while global longitudinal strain was not. In the surgical cohort, EF1 was borderline associated with death (p = 0.08) which was significant after correction for LGE (HR 0.90, p = 0.02). Conclusion EF1 is a predictor of death or AVR in asymptomatic AS, especially among discordantly graded patients with low area and low gradient. Univariate β (95% CI) p-value Multivariate β (95% CI) p-value Age (years) .03 (-.09 to .16) 0.58 .03 (-.12 to .18) 0.69 Sex (male) -.12 (-2.26 to 2.03) 0.91 .24 (-2.31 to 2.80) 0.85 Hypertension -.08 (-2.22 to 2.07) 0.95 Aortic valve area (0.01 cm2) .09 (.04 to .14) 0.001 Aortic mean gradient (mmHg) -.02 (-.08 to .04) 0.56 LV end-diastolic volume (ml) -.07 (-.11 to -.03) <0.001 LV end-systolic volume (ml) -.17 (-.22 to -.12) <0.001 -.07 (-.15 to .02) 0.11 LV ejection fraction (%) .28 (.19 to .38) <0.001 LV peak ejection time (ms) -.07 (-.16 to .02) 0.13 LGE fibrosis -3.04 (-5.67 to -.42) 0.02 -2.64 (-4.99 to -.30) 0.03 Wall stress (kdynes/m3) -.10 (-.13 to -.08) <0.001 -.08 (-.12 to -.04) <0.001 Multivariate linear regression analysis for associations with first phase ejection fraction. LV is left ventricular, LGE late gadolinium enhancement Abstract Figure. AVR-free survival according to EF1

Background: Heart rate–corrected QT interval (QTc) prolongation, whether secondary to drugs, genetics including congenital long QT syndrome, and/or systemic diseases including SARS-CoV-2–mediated coronavirus disease 2019 (COVID-19), can predispose to ventricular arrhythmias and sudden cardiac death. Currently, QTc assessment and monitoring relies largely on 12-lead electrocardiography. As such, we sought to train and validate an artificial intelligence (AI)–enabled 12-lead ECG algorithm to determine the QTc, and then prospectively test this algorithm on tracings acquired from a mobile ECG (mECG) device in a population enriched for repolarization abnormalities. Methods: Using >1.6 million 12-lead ECGs from 538 200 patients, a deep neural network (DNN) was derived (patients for training, n = 250 767; patients for testing, n = 107 920) and validated (n = 179 513 patients) to predict the QTc using cardiologist-overread QTc values as the “gold standard”. The ability of this DNN to detect clinically-relevant QTc prolongation (eg, QTc ≥500 ms) was then tested prospectively on 686 patients with genetic heart disease (50% with long QT syndrome) with QTc values obtained from both a 12-lead ECG and a prototype mECG device equivalent to the commercially-available AliveCor KardiaMobile 6L. Results: In the validation sample, strong agreement was observed between human over-read and DNN-predicted QTc values (−1.76±23.14 ms). Similarly, within the prospective, genetic heart disease–enriched dataset, the difference between DNN-predicted QTc values derived from mECG tracings and those annotated from 12-lead ECGs by a QT expert (−0.45±24.73 ms) and a commercial core ECG laboratory [10.52±25.64 ms] was nominal. When applied to mECG tracings, the DNN’s ability to detect a QTc value ≥500 ms yielded an area under the curve, sensitivity, and specificity of 0.97, 80.0%, and 94.4%, respectively. Conclusions: Using smartphone-enabled electrodes, an AI DNN can predict accurately the QTc of a standard 12-lead ECG. QTc estimation from an AI-enabled mECG device may provide a cost-effective means of screening for both acquired and congenital long QT syndrome in a variety of clinical settings where standard 12-lead electrocardiography is not accessible or cost-effective.

Identification of a CD4 + helper T cell population providing in situ help to B and CD8 + T cells in the respiratory mucosa.

Analysis of CD4 + T cell heterogeneity in the lung uncovers a Bcl6-dependent subset that supports mucosal antibody production.

AbstractBackgroundUsing an objective mathematical algorithm to assess corticolimbic involvement of neurofibrillary tangle pathology, we identified three AD subtypes: hippocampal sparing (HpSp) AD, typical AD, and limbic predominant AD. Typical AD brains were representative of the expected patterns of hippocampal and cortical involvement as outlined by Braak tangle stage. In contrast, we discovered an extreme phenotype that exists outside of the Braakian‐concept of neurofibrillary tangle patterns. Hippocampal sparing AD cases demonstrate unexpected sparing of the hippocampus relative to severely involved association cortices and limbic predominant AD cases demonstrate inundation of the hippocampus relative to mildly involved association cortices. Using these AD subtypes, our objective was to leverage our understanding of disease spectrum to uncover transcriptomic changes that underlie selective vulnerability of the hippocampus in AD.MethodWe performed RNA‐sequencing in 40 neuropathologically diagnosed AD cases (10 HpSp, 20 Typical and 10 Limbic) and 15 controls to uncover gene expression changes associated with phenotype differences allowing us to prioritize 44 genes for further exploration. Validation with NanoString and quantification with digital pathology were performed in an expanded cohort of 158 AD cases (36 HpSp, 79 Typical and 35 Limbic) and 32 controls. We quantified digital pathology measures of early tangle pathology (CP13), mature tangle pathology (Ab39), amyloid‐β (33.1.1). In addition, we measured cellular markers for microglia (CD68), endothelia (CD34), and astroglia (GFAP). Deep learning based prediction models were employed, which nominated the SLC38A2 gene as a predictor of AD.ResultThe SLC38A2 gene was originally prioritized in our RNA‐sequencing data based upon differential expression between controls and typical AD. We observed upregulation of SLC38A2 in all AD subtypes. RNA‐seq gene expression measures validated well with NanoString for SLC38A2 (R=0.97, p<0.001). Regression analysis identified early tangle (p=0.011) and late tangle measures (p=0.026) as significant predictors of gene expression, but not amyloid measures (p=0.74). Microgliosis approached significance (p=0.052), but with no contribution from endothelial cell burden (p=0.17) or astrogliosis (p=0.58). Age approached significance (p=0.06), but not sex (p=0.88) or APOE‐ε4 status (p=0.10).ConclusionOur data supports consideration of intra‐disease divergence with regard to case stratification and may reveal genes previously masked by heterogeneity of cohorts.

Abstract Background The HFA-PEFF algorithm has been recently developed and is recommended by the European Society of Cardiology to make a diagnosis of heart failure with preserved ejection fraction (HFpEF). Based on functional echocardiography criteria, morphological criteria, and natriuretic peptide levels, a score from 1 to 6 is obtained with higher scores indicating a higher probability of HFpEF. Purpose This study investigates the prognostic value of the HFA-PEFF score in HFpEF patients hospitalised for decompensation. Methods This retrospective cohort study includes consecutive patients with HFpEF, admitted to Mayo Clinic Rochester between 2010–2015 and requiring treatment with intravenous loop diuretics for congestion. The HFA-PEFF score was calculated for all patients with echocardiography data available within 1 year and natriuretic peptide levels obtained upon admission. The study population was subsequently stratified according to HFA-PEFF score ≤4 (n=79), =5 (n=93), or =6 (n=271). Incidence rates of recurrent heart failure admissions per 100 patient-years of follow-up, time to all-cause mortality, and mortality causes were compared among strata. Results In 443 patients with HFpEF (78±12 years; 60% women), higher HFA-PEFF score was associated with older age, female gender, higher systolic blood pressure, and worse renal function. Heart failure readmission rates were higher in patients with HFA-PEFF score =5 [46.0 (38.5–53.5)] or HFA-PEFF score =6 [45.0 (40.1–49.8)] versus HFA-PEFF score ≤4 [28.9 (22.7–35.0); P-value&lt;0.001 for differences; see upper panel figure]. Increasing HFA-PEFF score was associated with a shorter median survival (P-value&lt;0.001), which was 65 months (36–89 months) in HFA-PEFF score ≤4, 45 months (26–59 months) in HFA-PEFF score =5, and 28 months (22–42 months) in HFA-PEFF score =6 (see lower panel figure). The proportions of deaths directly related to heart failure were comparable among HFA-PEFF strata (49% versus 49% versus 52%, respectively; P-value=0.903), as was the contribution of cardiovascular death (60% versus 57% versus 63%, respectively; P-value=0.662). Overall, few differences in patterns of mortality causes were observed among strata. There was a lower contribution of primary respiratory disease (mainly chronic obstructive pulmonary disease) to mortality in patients with a higher HFA-PEFF score (26% versus 19% versus 6%, respectively; P-value&lt;0.001). Conclusions Higher HFA-PEFF score in hospitalized patients with HFpEF is associated with increased risk of subsequent heart failure readmissions and all-cause mortality, which includes both heart failure specific and non-heart failure mortality. Heart failure readmissions & mortality Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Belgian American Educational Foundation (B.A.E.F.); Special Research Fund (BOF) of Hasselt University (Hasselt, Belgium)