A Blueprint for Pediatric Emergency Resource Reallocation During the COVID-19 Pandemic: An NYC Hospital Experience.

Objective: We present a blueprint for the reallocation of pediatric emergency resources in response to the COVID-19 pandemic.

Methods: New York-Presbyterian Hospital - Weill Cornell Medical Center is an urban, quaternary, academic medical center, a level 1 trauma center, and a regional burn center located in New York City. The novel coronavirus (COVID-19) pandemic created a unique challenge for pediatric emergency medicine.

Association of Cardiovascular Disease Risk Factor Burden With Progression of Coronary Atherosclerosis Assessed by Serial Coronary Computed Tomographic Angiography.

Importance: Several studies have reported that the progression of coronary atherosclerosis, as measured by serial coronary computed tomographic (CT) angiography, is associated with the risk of future cardiovascular events. However, the cumulative consequences of multiple risk factors for plaque progression and the development of adverse plaque characteristics have not been well characterized.

Objectives: To examine the association of cardiovascular risk factor burden, as assessed by atherosclerotic cardiovascular disease (ASCVD) risk score, with the progression of coronary atherosclerosis and the development of adverse plaque characteristics.

A Boosted Ensemble Algorithm for Determination of Plaque Stability in High-Risk Patients on Coronary CTA.

Objectives: This study sought to identify culprit lesion (CL) precursors among acute coronary syndrome (ACS) patients based on qualitative and quantitative computed tomography-based plaque characteristics.

Background: Coronary computed tomography angiography (CTA) has been validated for patient-level prediction of ACS. However, the applicability of coronary CTA to CL assessment is not known.

Machine learning insight into the role of imaging and clinical variables for the prediction of obstructive coronary artery disease and revascularization: An exploratory analysis of the CONSERVE study.

Background: Machine learning (ML) is able to extract patterns and develop algorithms to construct data-driven models. We use ML models to gain insight into the relative importance of variables to predict obstructive coronary artery disease (CAD) using the Coronary Computed Tomographic Angiography for Selective Cardiac Catheterization (CONSERVE) study, as well as to compare prediction of obstructive CAD to the CAD consortium clinical score (CAD2). We further perform ML analysis to gain insight into the role of imaging and clinical variables for revascularization.

Statement from the North American Society for Cardiovascular Imaging on imaging strategies to reduce the scarcity of healthcare resources during the COVID-19 outbreak.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is an evolving global pandemic that is predicted to strain healthcare resources at multiple locations throughout North America and the World. As of April 6, 2020, the apex of infection rates is predicted to occur within 1 to 5 weeks at various locations. Widespread reports of personal protective equipment (PPE) shortages, and healthcare worker exposure to disease have become commonplace.

Remdesivir for 5 or 10 Days in Patients with Severe Covid-19.

Background: Remdesivir is an RNA polymerase inhibitor with potent antiviral activity in vitro and efficacy in animal models of coronavirus disease 2019 (Covid-19).

Methods: We conducted a randomized, open-label, phase 3 trial involving hospitalized patients with confirmed SARS-CoV-2 infection, oxygen saturation of 94% or less while they were breathing ambient air, and radiologic evidence of pneumonia. Patients were randomly assigned in a 1:1 ratio to receive intravenous remdesivir for either 5 days or 10 days.

Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning.

Objectives: To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation.

Background: Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images.

Relationship of soluble ST2 to pulmonary hypertension severity in patients undergoing cardiac resynchronization therapy.

Background: Pulmonary hypertension (PH) is an adverse prognostic marker in patients undergoing cardiac resynchronization therapy (CRT). We sought to determine the relation of biomarkers of fibrosis [soluble ST2 (sST2), galectin-3], wall stretch [amino terminal pro-brain natriuretic peptide (NT-proBNP)], and necrosis [high-sensitivity troponin-I (hsTnI)] to PH severity in CRT patients.

Methods: Biomarkers and right ventricular systolic pressure (RVSP) were measured at CRT implant and 6-month later (n=111).

Differences in Progression to Obstructive Lesions per High-Risk Plaque Features and Plaque Volumes With CCTA.

Objectives: This study explored whether the pattern of nonobstructive lesion progression into obstructive lesions would differ according to the presence of high-risk plaque (HRP).

Background: It is still debatable whether HRP simply represents a certain phase during the natural history of coronary atherosclerotic plaques or if disease progression would differ according to the presence of HRP.

Methods: Patients with nonobstructive coronary artery disease, defined as percent diameter stenosis (%DS) <50%, were enrolled from a prospective, multinational registry of consecutive patients who underwent serial coronary computed tomography angiography at an interscan interval of ≥2 years.

Evaluation of computed tomography myocardial perfusion in women with angina and no obstructive coronary artery disease.

Women with angina and no obstructive coronary artery disease (CAD) have worse cardiovascular prognosis than asymptomatic women. Limitation in myocardial perfusion caused by coronary microvascular dysfunction (CMD) is one of the proposed mechanisms contributing to the adverse prognosis. The aim of this study was to assess myocardial perfusion in symptomatic women with no obstructive CAD suspected for CMD compared with asymptomatic sex-matched controls using static CT perfusion (CTP).

Identification and Quantification of Cardiovascular Structures From CCTA: An End-to-End, Rapid, Pixel-Wise, Deep-Learning Method.

Objectives: This study designed and evaluated an end-to-end deep learning solution for cardiac segmentation and quantification.

Background: Segmentation of cardiac structures from coronary computed tomography angiography (CCTA) images is laborious. We designed an end-to-end deep-learning solution.

Risk Reclassification With Coronary Computed Tomography Angiography-Visualized Nonobstructive Coronary Artery Disease According to 2018 American College of Cardiology/American Heart Association Cholesterol Guidelines (from the Coronary Computed Tomography Angiography Evaluation for Clinical Outcomes : An International Multicenter Registry [CONFIRM]).

The 2018 American College of Cardiology (ACC)/American Heart Association (AHA) cholesterol management guideline recommends risk enhancers in the borderline-risk and statin recommended/intermediate-risk groups. We determined the risk reclassification by the presence and severity of coronary computed tomography angiography (CCTA)-visualized coronary artery disease (CAD) according to statin eligibility groups. Of 35,281 individuals who underwent CCTA, 1,303 asymptomatic patients (age 59, 65% male) were identified.

An augmented reality system for image guidance of transcatheter procedures for structural heart disease.

The primary mode of visualization during transcatheter procedures for structrural heart disease is fluoroscopy, which suffers from low contrast and lacks any depth perception, thus limiting the ability of an interventionalist to position a catheter accurately. This paper describes a new image guidance system by utilizing augmented reality to provide a 3D visual environment and quantitative feedback of the catheter's position within the heart of the patient. The real-time 3D position of the catheter is acquired via two fluoroscopic images taken at different angles, and a patient-specific 3D heart rendering is produced pre-operatively from a CT scan.

Non-invasive fractional flow reserve derived from coronary computed tomography angiography in patients with acute chest pain: Subgroup analysis of the ROMICAT II trial.

Background: Non-invasive fractional flow reserve (FFR) derived from coronary computed tomography angiography (CTA) permits hemodynamic evaluation of coronary stenosis and may improve efficiency of assessment in stable chest pain patients. We determined feasibility of FFR in the population of acute chest pain patients and assessed the relationship of FFR with outcomes of acute coronary syndrome (ACS) and revascularization and with plaque characteristics.

Methods: We included 68 patients (mean age 55.

Long-term prognostic utility of computed tomography coronary angiography in older populations.

Aims: The long-term prognostic value of coronary computed tomography angiography (CCTA)-identified coronary artery disease (CAD) has not been evaluated in elderly patients (≥70 years). We compared the ability of coronary CCTA to predict 5-year mortality in older vs. younger populations.

The Predictive Value of Coronary Artery Calcium Scoring for Major Adverse Cardiac Events According to Renal Function (from the Coronary Computed Tomography Angiography Evaluation for Clinical Outcomes: An International Multicenter [CONFIRM] Registry).

The prognostic performance of coronary artery calcium score (CACS) for predicting adverse outcomes in patients with decreased renal function remains unclear. We aimed to examine whether CACS improves risk stratification by demonstrating incremental value beyond a traditional risk score according to renal function status. 9,563 individuals without known coronary artery disease were enrolled.

Deep learning for cardiovascular medicine: a practical primer.

Deep learning (DL) is a branch of machine learning (ML) showing increasing promise in medicine, to assist in data classification, novel disease phenotyping and complex decision making. Deep learning is a form of ML typically implemented via multi-layered neural networks. Deep learning has accelerated by recent advances in computer hardware and algorithms and is increasingly applied in e-commerce, finance, and voice and image recognition to learn and classify complex datasets.

A cross-sectional survey of coronary plaque composition in individuals on non-statin lipid lowering drug therapies and undergoing coronary computed tomography angiography.

Introduction: Non-statin therapy (NST) is used as second-line treatment when statin monotherapy is inadequate or poorly tolerated.

Objective: To determine the association of NST with plaque composition, alone or in combination with statins, in patients undergoing coronary computed tomography angiography (coronary CTA).

Methods: From the multicenter CONFIRM registry, we analyzed individuals who underwent coronary CTA with known lipid-lowering therapy status and without prior coronary artery disease at baseline.

Selective Referral Using CCTA Versus Direct Referral for Individuals Referred to Invasive Coronary Angiography for Suspected CAD: A Randomized, Controlled, Open-Label Trial.

Objectives: This study compared the safety and diagnostic yield of a selective referral strategy using coronary computed tomographic angiography (CCTA) compared with a direct referral strategy using invasive coronary angiography (ICA) as the index procedure.

Background: Among patients presenting with signs and symptoms suggestive of coronary artery disease (CAD), a sizeable proportion who are referred to ICA do not have a significant, obstructive stenosis.

Methods: In a multinational, randomized clinical trial of patients referred to ICA for nonemergent indications, a selective referral strategy was compared with a direct referral strategy.