Citations

The National Heart, Lung, and Blood Institute (NHLBI) convened a workshop of international experts to discuss new research opportunities for the prevention, detection, and intervention of myocarditis in May 2021. These experts reviewed the current state of science and identified key gaps and opportunities in basic, diagnostic, translational, and therapeutic frontiers to guide future research in myocarditis. In addition to addressing community-acquired myocarditis, the workshop also focused on emerging causes of myocarditis including immune checkpoint inhibitors and SARS-CoV-2 related myocardial injuries and considered the use of systems biology and artificial intelligence methodologies to define workflows to identify novel mechanisms of disease and new therapeutic targets. A new priority is the investigation of the relationship between social determinants of health (SDoH), including race and economic status, and inflammatory response and outcomes in myocarditis. The result is a proposal for the reclassification of myocarditis that integrates the latest knowledge of immunological pathogenesis to refine estimates of prognosis and target pathway-specific treatments.

ABSTRACT Aim Intrinsic renal sodium avidity (IRSA) is a hallmark feature of acute heart failure (AHF) and can be measured by evaluating the urinary sodium (UNa) concentration. The aim of this study is to assess the role of measuring IRSA through a random Una‐sample and its association with decongestive response. Methods and results A post‐hoc analysis of the ROSE‐AHF trial was performed in all patients with a random UNa spot sample before randomization ( n = 339/360). Patients were categorized according to tertiles of UNa as high (range 19–40 mmol/L), intermediate (range 41–68 mmol/L), or low (range 69–139 mmol/L) IRSA. Linear mixed effect models and ANCOVA were used to assess the relation with decongestive effectiveness measured by: (i) weight change, (ii) visual analogue scale (VAS) improvement, (iii) N‐terminal pro‐B‐type natriuretic peptide (NT‐proBNP) change, (iv) natriuretic response (UNa in mmol/L), (v) 72 h natriuresis (mmol), (vi) oedema resolution, and (vii) length of stay. High IRSA patients had less improvement in decongestive metrics, including weight loss ( p = 0.028), VAS improvement, NT‐proBNP decrease, natriuretic response ( p ‐time interaction <0.001 for all), had lower total natriuresis (high IRSA 438 ± 141 mmol, intermediate IRSA 526 ± 320 mmol, and low IRSA 603 ± 276 mmol; p < 0.001), exhibited more oedema at 72 h ( p = 0.005), and had a longer length of stay ( p = 0.015). Incremental loop diuretic dose titration (± 4 times home dose) after >24 h, resulted in an increase in natriuretic response in the high IRSA group, however cumulative natriuresis still remained lower at 72 h ( p < 0.001). Longitudinal UNa profiling of patients with low IRSA showed physiologic breaking in the UNa pattern, associated with attaining decongestion and slight increase in creatinine and cystatin C, forming a potential signal of complete decongestion. Conclusions A simple random UNa sample at the time of AHF, gives insight into IRSA which is consistently associated with decongestive effectiveness across multiple metrics, offering an opportunity for early AHF care improvement.

Introduction: Theobromine is a naturally occurring substance found in cocoa products. Theobromine may have beneficial cardiovascular effects through various mechanisms. We used the NHANES database to analyze associations between theobromine consumption and cardiovascular health outcomes. Methods: The NHANES database represents a nationally representative population which was used to construct the study population from 1996-2016. We assessed multiple demographic factors and compared differences amongst those who consumed various amounts of theobromine to those who did not. The primary outcome measures were hypertension and lipid profiles. Results: Among 42,038 NHANES survey participants, 37569 and 4469 of the participants reported never consuming theobromine and consuming theobromine, respectively. Table 1 demonstrates baseline characteristics of those reporting no theobromine consumption in comparison to the theobromine consuming participants (100-400md/g and 400-850mg/d). Moderate to high amounts of theobromine consumption was observed in participants who were older with higher BMI and more education and were more likely to be tobacco users in comparison to those with no theobromine consumption (all p-values <0.05). Moderate to high theobromine consumption was also observed to be associated with a higher hemoglobin level, higher LDL cholesterol, lower HDL cholesterol, and prevalence of hypertension (all p-values <0.05). Conclusions: We observed that individuals who consumed moderate to high amounts of theobromine had associated improvements in lipid profile parameters as well as reduced blood pressure. Theobromine may represent a complementary non-pharmacologic supplementation to typical lifestyle modifications to improve cardiovascular outcomes. Further prospective studies are needed to better clarify this potential association.

Polygenic diseases, which are genetic disorders caused by the combined action of multiple genes, pose unique and significant challenges for the diagnosis and management of affected patients. A major goal of cardiovascular medicine has been to understand how genetic variation leads to the clinical heterogeneity seen in polygenic cardiovascular diseases (CVDs). Recent advances and emerging technologies in artificial intelligence (AI), coupled with the ever-increasing availability of next generation sequencing (NGS) technologies, now provide researchers with unprecedented possibilities for dynamic and complex biological genomic analyses. Combining these technologies may lead to a deeper understanding of heterogeneous polygenic CVDs, better prognostic guidance, and, ultimately, greater personalized medicine. Advances will likely be achieved through increasingly frequent and robust genomic characterization of patients, as well the integration of genomic data with other clinical data, such as cardiac imaging, coronary angiography, and clinical biomarkers. This review discusses the current opportunities and limitations of genomics; provides a brief overview of AI; and identifies the current applications, limitations, and future directions of AI in genomics.

3072 Background: The Circulating Cell-free Genome Atlas study (CCGA; NCT02889978) previously demonstrated that a blood-based multi-cancer early detection (MCED) test utilizing cell-free DNA (cfDNA) sequencing in combination with machine learning could detect cancer signals across multiple cancer types and predict cancer signal origin. Cancer classes were defined within the CCGA study for sensitivity reporting. Separately, cancer types defined by the American Joint Committee on Cancer (AJCC) criteria, which outline unique staging requirements and reflect a distinct combination of anatomic site, histology and other biologic features, were assigned to each cancer participant using the same source data for primary site of origin and histologic type. Here, we report CCGA ‘cancer class’ designation and AJCC ‘cancer type’ assignment within the third and final CCGA3 validation substudy to better characterize the diversity of tumors across which a cancer signal could be detected with the MCED test that is nearing clinical availability. Methods: CCGA is a prospective, multicenter, case-control, observational study with longitudinal follow-up (overall population N = 15,254). Plasma cfDNA from evaluable samples was analyzed using a targeted methylation bisulfite sequencing assay and a machine learning approach, and test performance, including sensitivity, was assessed. For sensitivity reporting, CCGA cancer classes were assigned to cancer participants using a combination of the type of primary cancer reported by the site and tumor characteristics abstracted from the site pathology reports by GRAIL pathologists. Each cancer participant also was separately assigned an AJCC cancer type based on the same source data using AJCC staging manual (8th edition) classifications. Results: A total of 4077 participants comprised the independent validation set with confirmed status (cancer: n = 2823; non-cancer: n = 1254 with non-cancer status confirmed at year-one follow-up). Sensitivity was reported for 24 cancer classes (sample sizes ranged from 10 to 524 participants), as well as an “other” cancer class (59 participants). According to AJCC classification, the MCED test was found to detect cancer signals across 50+ AJCC cancer types, including some types not present in the training set; some cancer types had limited representation. Conclusions: This MCED test that is nearing clinical availability and was evaluated in the third CCGA substudy detected cancer signals across 50+ AJCC cancer types. Reporting CCGA cancer classes and AJCC cancer types demonstrates the ability of the MCED test to detect cancer signals across a set of diverse cancer types representing a wide range of biologic characteristics, including cancer types that the classifier has not been trained on, and supports its use on a population-wide scale. Clinical trial information: NCT02889978.

3071 Background: Disparities in cancer screening and outcomes based on factors such as gender, socioeconomic status, and race/ethnicity are well documented.1 The Circulating Cell-free Genome Atlas study (CCGA; NCT02889978) was designed to develop and validate a blood-based multi-cancer early detection (MCED) test analyzing plasma cell-free DNA (cfDNA) to detect cancer signals across multiple cancer types and simultaneously predict cancer signal origin. Findings stratified by race/ethnicity from the third and final CCGA validation sub-study are reported. Methods: CCGA is a prospective, multicenter, case-control, observational study with longitudinal follow-up (overall N = 15,254). In this pre-specified exploratory analysis from the third substudy, key objectives were to evaluate test performance for cancer signal detection (specificity, overall sensitivity, and sensitivity by clinical stage) among racial/ethnic groups. Plasma cfDNA from evaluable samples was analyzed using a targeted methylation bisulfite sequencing assay and a machine learning approach. Overall, 4077 participants comprised the independent validation set with confirmed status (cancer: n = 2823; non-cancer: n = 1254). The groups stratified by race/ethnicity were White Non-Hispanic, Black Non-Hispanic, Other Non-Hispanic (including but not limited to Asian, Native Hawaiian, Pacific Islander, American Indian, Alaska Native), Hispanic (all races), and Other/unknown. The study was not powered to detect statistical differences between groups. Results: Cancer and non-cancer groups were predominantly White (2316/2823, 82.0% and 996/1254, 79.4%, respectively). Across racial/ethnic groups, specificity for cancer signal detection was 99.6% (White Non-Hispanic: 992/996, 95% confidence interval [99.0-99.8%]), 100.0% (Black Non-Hispanic: 85/85 [95.7-100.0%]), 100.0% (Other Non-Hispanic: 33/33 [89.6-100.0%]), 98.1% (Hispanic: 101/103 [93.2-99.5%]), and 100% (Other/unknown: 37/37 [90.6-100.0%]). Despite slight differences in cancer type and staging across racial/ethnic groups, overall sensitivity for cancer signal detection among groups ranged from 43.9% to 63.0% (White Non-Hispanic: 50.5%, 1169/2316 [48.4-52.5%], Black Non-Hispanic: 53.9%, 104/193 [46.8-60.8%], Other Non-Hispanic: 43.9%, 25/57 [31.8-56.7%], Hispanic: 63.0%, 121/192 [56.0-69.5%], and Other/unknown: 52.3%, 34/65 [40.4-64.0%]). For all racial/ethnic groups, sensitivity generally increased with clinical stage (with limited exceptions at Stage IV in some groups with small sample sizes). Conclusions: The MCED test demonstrated consistent specificity and sensitivity across racial/ethnic groups, though results are limited by sample size for some groups. These findings indicate broad applicability and support clinical implementation of this MCED test on a population scale. 1. Zavela et al. Brit J Cancer 2021. Clinical trial information: NCT02889978.

Introduction: Heart failure (HF) is a leading cause of hospitalization, morbidity and mortality. Deep learning (DL) techniques appear to show promising results in risk stratification and prognosis in several conditions in medicine. However, few methods using DL exist to help quantitatively estimate prognosis of HF. We hypothesized that deep learning (DL) techniques could prognosis of HF using simple variables. We propose application of a custom-built deep-neural-network model to identify mortality in HF patients. Methods: Custom-built deep-neural-networks were assessed using survey data from 42,147 participants from the National Health and Nutrition Examination Survey 1999-2016 (NHANES). Variables were selected using clinical judgment and stepwise backward regressions to develop prediction models. We partitioned the data into training and testing sets and repetitive experiments. We then evaluated model performance based on discrimination and calibration including the area under the receiver-operator characteristics curve (C-statistics), balanced accuracy, probability calibration with sigmoid, and the Brier score, respectively. As sensitivity analyses, we examined results limited to cases with complete clinical information available. We validated models’ performance using Mount Sinai database. Results: Of 42,147 participants with 4,060 variables, 1,491 (3.5%) had HF and HF mortality was 51.8%. In validation cohort, of 26,333 HF patients, the mortality in HF patients was 405 (1.5%). Final model using only 20 variables (age, race, gender, BMI, smoking, alcohol consumption, HTN, COPD, SBP, DBP, HR, HDL, LDL, CRP, A1C, BUN, creatinine, hemoglobin, sodium level, on statin) was tested. A state-of-the-art deep learning models achieved high accuracy for predicting mortality in HF patients with an AUC of 0.96 (95% CI: 0.95-0.99) in the first cohort and AUC of 0.93 (95% CI: 0.91-0.96) in validation cohort. Conclusions: A deep neural network model has shown to have high predictive accuracy and discriminative and calibrative power for prediction of HF mortality. Further research can delineate the clinical implications of DL in predicting HF mortality.

Abstract Aims There is a wide spectrum of diseases associated with pulmonary hypertension, pulmonary vascular remodelling, and right ventricular dysfunction. The NIH-sponsored PVDOMICS network seeks to perform comprehensive clinical phenotyping and endophenotyping across these disorders to further evaluate and define pulmonary vascular disease. Methods and results Echocardiography represents the primary non-invasive method to phenotype cardiac anatomy, function, and haemodynamics in these complex patients. However, comprehensive right heart evaluation requires the use of multiple echocardiographic parameters and optimized techniques to ensure optimal image acquisition. The PVDOMICS echo protocol outlines the best practice approach to echo phenotypic assessment of the right heart/pulmonary artery unit. Conclusion Novel workflow processes, methods for quality control, data for feasibility of measurements, and reproducibility of right heart parameters derived from this study provide a benchmark frame of reference. Lessons learned from this protocol will serve as a best practice guide for echocardiographic image acquisition and analysis across the spectrum of right heart/pulmonary vascular disease.