Citations

AbstractAimsNew diastolic dysfunction (DD) guidelines were introduced in 2016 to replace the 2009 guidelines, but have not yet been evaluated in aortic stenosis (AS). We aimed to compare the 2009 and 2016 DD guidelines in severe AS patients in terms of association with left ventricular (LV) and left atrial (LA) remodeling, with pulmonary capillary wedge pressure (PCWP) at rest and exercise, and with prognosis.Methods and resultsWe included 212 patients with severe AS (112 undergoing AVR, 100 asymptomatic). Echocardiography, magnetic resonance imaging, and brain natriuretic peptides (BNP) were performed/measured. Thirty‐nine asymptomatic patients had PCWP measured during rest and maximal exertion. Asymptomatic patients were followed for 3.1 years for the combined endpoint of death, AVR or admission with heart failure. The 2009 and 2016 DD guidelines agreed poorly with each other (Cohens’ κ = .15). 2009 guidelines showed many ambiguous DD findings. With the 2016 guidelines, 20% of patients had indeterminate DD. DD grade 2 according to 2016 guidelines showed stronger association with symptom status, BNP, global longitudinal strain (GLS) and peak exercise PCWP than 2009 guidelines. For indeterminate DD patients according to 2009 guidelines, GLS above the median was associated with event‐free survival (HR .11 (95% CI .02–.53)). For neither guideline was DD associated with the combined endpoint in asymptomatic patients.ConclusionThe 2016 guidelines show a stronger association with BNP, GLS, and exercise PCWP than the 2009 guidelines. The 2016 guidelines result in 20% of patients with indeterminate DD; however, these patients may possibly be stratified according to GLS.

Abstract We characterized coronavirus disease 2019 (COVID-19) breakthrough cases admitted to a single center in Florida. With the emergence of delta variant, an increased number of hospitalizations was seen due to breakthrough infections. These patients were older and more likely to have comorbidities. Preventive measures should be maintained even after vaccination.

Abstract Background Cardiovascular disease (CVD) has been identified as a major threat to maternal health in the US and UK with cardiomyopathy being one of the most common acquired CVD in the pregnant and postpartum period. Diagnosing cardiomyopathy in pregnancy is challenging due to an overlap of cardiovascular symptoms with normal pregnancy symptoms. Purpose The purpose of this study was to evaluate the effectiveness of an ECG based deep learning model in identifying cardiomyopathy among pregnant and postpartum women. Methods We utilized an ECG based deep learning model to detect cardiomyopathy in a cohort of pregnant or postpartum women seen at multiple hospital sites. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, and specificity. We compared the diagnostic probabilities of the deep learning model with natriuretic peptides and a multivariable model consisting of demographic and clinical parameters. Results 1,807 women were included. 7%, 10% and 13% had LVEF ≤35%, <45% and <50% respectively. The ECG based deep learning model identified cardiomyopathy with an AUC of 0.92 for left ventricular ejection fraction (LVEF) ≤35%, 0.89 for LVEF <45% and 0.87 for LVEF <50%. For LVEF ≤35%, AUC was higher in Black (0.95) and Hispanic (0.98) women compared to white (0.91). Natriuretic peptides and the multivariable model had AUCs of 0.85 and 0.72 respectively. Conclusions A deep learning model effectively identifies cardiomyopathy in pregnant or postpartum women, outperforms natriuretic peptides and traditional clinical parameters with the potential to become a powerful initial screening tool for cardiomyopathy in the obstetric care setting. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): This study was made possible using resources supported by the Mayo Clinic Women's Health Research Center and the Mayo Clinic Building Interdisciplinary Research Careers in Women's Health (BIRCWH) Program funded by the National Institutes of Health (NIH), grant number K12 HD065987. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Mastocytosis is a hematologic neoplasm characterized by expansion and focal accumulation of neoplastic mast cells (MC) in diverse organs, including the skin, bone marrow (BM), spleen, liver, and gastrointestinal tract. The World Health Organization classification divides the disease into prognostically distinct variants of cutaneous mastocytosis (CM) and systemic mastocytosis (SM). Although this classification remains valid, recent developments in the field and the advent of new diagnostic and prognostic parameters created a need to update and refine definitions and diagnostic criteria in MC neoplasms. In addition, MC activation syndromes (MCAS) and genetic features predisposing to SM and MCAS have been identified. To discuss these developments and refinements in the classification, we organized a Working Conference comprised of experts from Europe and the United States in August 2020. This article reports on outcomes from this conference. Of particular note, we propose adjustments in the classification of CM and SM, refinements in diagnostic criteria of SM variants, including smoldering SM and BM mastocytosis (BMM), and updated criteria for MCAS and other conditions involving MC. CD30 expression in MC now qualifies as a minor SM criterion, and BMM is now defined by SM criteria, absence of skin lesions and absence of B- and C-findings. A basal serum tryptase level exceeding 20 ng/mL remains a minor SM criterion, with recognition that hereditary alpha-tryptasemia and various myeloid neoplasms may also cause elevations in tryptase. Our updated proposal will support diagnostic evaluations and prognostication in daily practice and the conduct of clinical trials in MC disorders.

Respiratory tissue resident–like CD8 + T cells are associated with chronic lung sequelae post-acute COVID-19.

Objectives: The objectives of the study were to estimate the impact of high matrix image reconstruction on chest computed tomography (CT) compared to standard image reconstruction. Material and Methods: This retrospective study included patients with interstitial or parenchymal lung disease, airway disease, and pulmonary nodules who underwent chest CT. Chest CT images were reconstructed using high matrix (1024 × 1024) or standard matrix (512 × 512), with all other parameters matched. Two radiologists, blinded to reconstruction technique, independently examined each lung, viewing image sets side by side and rating the conspicuity of imaging findings using a 5-point relative conspicuity scale. The presence of pulmonary nodules and confidence in classification of internal attenuation was also graded. Overall image quality and subjective noise/artifacts were assessed. Results: Thirty-four patients with 68 lungs were evaluated. Relative conspicuity scores were significantly higher using high matrix image reconstruction for all imaging findings indicative of idiopathic lung fibrosis (peripheral airway visualization, interlobular septal thickening, intralobular reticular opacity, and end-stage fibrotic change; P ≤ 0.001) along with emphysema, mosaic attenuation, and fourth order bronchi for both readers (P ≤ 0.001). High matrix reconstruction did not improve confidence in the presence or classification of internal nodule attenuation for either reader. Overall image quality was increased but not subjective noise/artifacts with high matrix image reconstruction for both readers (P < 0.001). Conclusion: High matrix image reconstruction significantly improves the conspicuity of imaging findings reflecting interstitial lung disease and may be useful for diagnosis or treatment response assessment.

Abstract Variable differences of nerve conduction amplitudes vs velocities and distal latencies (DLs) of healthy subjects assessed in ethnic cohorts. Introduction/Aims The variables affecting reference compound muscle (CMAP) and sensory nerve action potential (SNAP) amplitudes as compared to ones affecting conduction velocities and DLs have not been adequately evaluated in previous studies. In this report, this subject is studied in healthy subject cohorts mainly of Northern European extraction, Northern Plains Indians, and Latinos. Methods Nineteen variables and 18 attributes of nerve conductions (NCs) were assessed using highly standard testing conditions and techniques. Classification and Regression Tree analyses were used to assess variable differences among amplitudes, conduction velocities, and DLs. Results The most important variable affecting CMAP and SNAP amplitudes was age. For conduction velocities (CVs) and DLs, the variables were height, ethnic cohort, and age. Discussion The variables affecting attributes of NCs were similar for the three ethnic cohorts evaluated. The differences of variables affecting amplitudes compared to CVs and DLs need to be taken into account in interpretation of NC results and in setting reference limits for use in medical practice, epidemiology surveys, and therapeutic trials. Scores of CMAP and SNAP amplitudes are suitable measures of sensorimotor polyneuropathy severity, whereas conduction velocities and DLs reflect physiologic/pathologic abnormality of nerve fibers.