Using Shapes of COVID-19 Positive Patient-Specific Trajectories for Mortality Prediction.

Machine learning can be used to identify relevant trajectory shape features for improved predictive risk modeling, which can help inform decisions for individualized patient management in intensive care during COVID-19 outbreaks. We present explainable random forests to dynamically predict next day mortality risk in COVID -19 positive and negative patients admitted to the Mount Sinai Health System between March 1st and June 8th, 2020 using patient time-series data of vitals, blood and other laboratory measurements from the previous 7 days. Three different models were assessed by using time series with: 1) most recent patient measurements, 2) summary statistics of trajectories (min/max/median/first/last/count), and 3) coefficients of fitted cubic splines to trajectories. AUROC and AUPRC with cross-validation were used to compare models. We found that the second and third models performed statistically significantly better than the first model. Model interpretations are provided at patient-specific level to inform resource allocation and patient care.

BEHRTDAY: Dynamic Mortality Risk Prediction using Time-Variant COVID-19 Patient Specific Trajectories.

Incorporating repeated measurements of vitals and laboratory measurements can improve mortality risk-prediction and identify key risk factors in individualized treatment of COVID-19 hospitalized patients. In this observational study, demographic and laboratory data of all admitted patients to 5 hospitals of Mount Sinai Health System, New York, with COVID-19 positive tests between March 1st and June 8th, 2020, were extracted from electronic medical records and compared between survivors and non-survivors. Next day mortality risk of patients was assessed using a transformer-based model BEHRTDAY fitted to patient time series data of vital signs, blood and other laboratory measurements given the entire patients' hospital stay. The study population includes 3699 COVID-19 positive (57% male, median age: 67) patients. This model had a very high average precision score (0.96) and area under receiver operator curve (0.92) for next-day mortality prediction given entire patients' trajectories, and through masking, it learnt each variable's context.

Part 2: The Clinical Applications of Left Ventricular Myocardial Strain.

There is an increasing prevalence of cardiovascular disease and heart failure. Indices of left ventricular (LV) systolic function such as LV ejection fraction used to identify those at risk of adverse cardiac events such as heart failure may not be truly representative of LV systolic function in certain cardiac diseases. Given that LV ejection fraction reduction may represent more advanced irreversible stages of disease, measures of myocardial strain have emerged as a feasible and robust instrument for the early identification of heart disease and subtle LV systolic dysfunction. The purpose of this review was to provide an overview of emerging clinical applications of LV global longitudinal strain in valvular and cardiomyopathic diseases and coronavirus disease 2019.

Impact of Vaccination on Major Adverse Cardiovascular Events in Patients With COVID-19 Infection.

Taken from the largest U.S. cohort of patients with SARS-CoV2, our results demonstrate the association of even partial vaccination with lower risk of MACE after SARS-CoV-2 infection.

Using Shapes of COVID-19 Positive Patient-Specific Trajectories for Mortality Prediction

Machine learning can be used to identify relevant trajectory shape features for improved predictive risk modeling, which can help inform decisions for individualized patient management in intensive care during COVID-19 outbreaks. We present explainable random forests to dynamically predict next day mortality risk in COVID -19 positive and negative patients admitted to the Mount Sinai Health System between March 1st and June 8th, 2020 using patient time-series data of vitals, blood and other laboratory measurements from the previous 7 days. Three different models were assessed by using time series with: 1) most recent patient measurements, 2) summary statistics of trajectories (min/max/median/first/last/count), and 3) coefficients of fitted cubic splines to trajectories. AUROC and AUPRC with cross-validation were used to compare models. We found that the second and third models performed statistically significantly better than the first model. Model interpretations are provided at patient-specific level to inform resource allocation and patient care.

BEHRTDAY: Dynamic Mortality Risk Prediction using Time-Variant COVID-19 Patient Specific Trajectories

Incorporating repeated measurements of vitals and laboratory measurements can improve mortality risk-prediction and identify key risk factors in individualized treatment of COVID-19 hospitalized patients. In this observational study, demographic and laboratory data of all admitted patients to 5 hospitals of Mount Sinai Health System, New York, with COVID-19 positive tests between March 1st and June 8th, 2020, were extracted from electronic medical records and compared between survivors and non-survivors. Next day mortality risk of patients was assessed using a transformer-based model BEHRTDAY fitted to patient time series data of vital signs, blood and other laboratory measurements given the entire patients’ hospital stay. The study population includes 3699 COVID-19 positive (57% male, median age: 67) patients. This model had a very high average precision score (0.96) and area under receiver operator curve (0.92) for next-day mortality prediction given entire patients’ trajectories, and through masking, it learnt each variable’s context.

Worldwide Disparities in Recovery of Cardiac Testing 1 Year Into COVID-19.

BACKGROUND: The extent to which health care systems have adapted to the COVID-19 pandemic to provide necessary cardiac diagnostic services is unknown. OBJECTIVES: The aim of this study was to determine the impact of the pandemic on cardiac testing practices, volumes and types of diagnostic services, and perceived psychological stress to health care providers worldwide. METHODS: The International Atomic Energy Agency conducted a worldwide survey assessing alterations from baseline in cardiovascular diagnostic care at the pandemic's onset and 1 year later. Multivariable regression was used to determine factors associated with procedure volume recovery. RESULTS: Surveys were submitted from 669 centers in 107 countries. Worldwide reduction in cardiac procedure volumes of 64% from March 2019 to April 2020 recovered by April 2021 in high- and upper middle-income countries (recovery rates of 108% and 99%) but remained depressed in lower middle- and low-income countries (46% and 30% recovery). Although stress testing was used 12% less frequently in 2021 than in 2019, coronary computed tomographic angiography was used 14% more, a trend also seen for other advanced cardiac imaging modalities (positron emission tomography and magnetic resonance; 22%-25% increases). Pandemic-related psychological stress was estimated to have affected nearly 40% of staff, impacting patient care at 78% of sites. In multivariable regression, only lower-income status and physicians' psychological stress were significant in predicting recovery of cardiac testing. CONCLUSIONS: Cardiac diagnostic testing has yet to recover to prepandemic levels in lower-income countries. Worldwide, the decrease in standard stress testing is offset by greater use of advanced cardiac imaging modalities. Pandemic-related psychological stress among providers is widespread and associated with poor recovery of cardiac testing.

Functional Effects of Cardiomyocyte Injury in COVID-19.

COVID-19 affects multiple organs. Clinical data from the Mount Sinai Health System show that substantial numbers of COVID-19 patients without prior heart disease develop cardiac dysfunction. How COVID-19 patients develop cardiac disease is not known. We integrated cell biological and physiological analyses of human cardiomyocytes differentiated from human induced pluripotent stem cells (hiPSCs) infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the presence of interleukins (ILs) with clinical findings related to laboratory values in COVID-19 patients to identify plausible mechanisms of cardiac disease in COVID-19 patients. We infected hiPSC-derived cardiomyocytes from healthy human subjects with SARS-CoV-2 in the absence and presence of IL-6 and IL-1ß. Infection resulted in increased numbers of multinucleated cells. Interleukin treatment and infection resulted in disorganization of myofibrils, extracellular release of troponin I, and reduced and erratic beating. Infection resulted in decreased expression of mRNA encoding key proteins of the cardiomyocyte contractile apparatus. Although interleukins did not increase the extent of infection, they increased the contractile dysfunction associated with viral infection of cardiomyocytes, resulting in cessation of beating. Clinical data from hospitalized patients from the Mount Sinai Health System show that a significant portion of COVID-19 patients without history of heart disease have elevated troponin and interleukin levels. A substantial subset of these patients showed reduced left ventricular function by echocardiography. Our laboratory observations, combined with the clinical data, indicate that direct effects on cardiomyocytes by interleukins and SARS-CoV-2 infection might underlie heart disease in COVID-19 patients. IMPORTANCE SARS-CoV-2 infects multiple organs, including the heart. Analyses of hospitalized patients show that a substantial number without prior indication of heart disease or comorbidities show significant injury to heart tissue, assessed by increased levels of troponin in blood. We studied the cell biological and physiological effects of virus infection of healthy human iPSC-derived cardiomyocytes in culture. Virus infection with interleukins disorganizes myofibrils, increases cell size and the numbers of multinucleated cells, and suppresses the expression of proteins of the contractile apparatus. Viral infection of cardiomyocytes in culture triggers release of troponin similar to elevation in levels of COVID-19 patients with heart disease. Viral infection in the presence of interleukins slows down and desynchronizes the beating of cardiomyocytes in culture. The cell-level physiological changes are similar to decreases in left ventricular ejection seen in imaging of patients' hearts. These observations suggest that direct injury to heart tissue by virus can be one underlying cause of heart disease in COVID-19.

Impact of COVID-19 on Cardiovascular Testing in the United States Versus the Rest of the World.

OBJECTIVES: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-U.S. institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. BACKGROUND: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. METHODS: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. RESULTS: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. CONCLUSIONS: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection.

A media intervention applying debunking versus non-debunking content to combat vaccine misinformation in elderly in the Netherlands: A digital randomised trial.

BACKGROUND: As several COVID-19 vaccines are rolled-out globally, it has become important to develop an effective strategy for vaccine acceptance, especially in high-risk groups, such as elderly. Vaccine misconception was declared by WHO as one of the top 10 health issues in 2019. Here we test the effectiveness of applying debunking to combat vaccine misinformation, and reduce vaccine hesitancy. METHODS: Participants were recruited via a daily news show on Dutch Television, targeted to elderly viewers. The study was conducted in 980 elderly citizens during the October 2020 National Influenza Vaccination Campaign. Borrowing from the recent literature in behavioural science and psychology we conducted a two-arm randomized blinded parallel study, in which participants were allocated to exposure to a video containing social norms, vaccine information plus debunking of vaccination myths (intervention group, n = 505) or a video only containing vaccine information plus social norm (control group, n = 475). Participants who viewed either of the video's and completed both a pre- and post-intervention survey on vaccination trust and knowledge, were included in the analysis. The main outcomes of this study were improvement on vaccine knowledge and awareness. FINDINGS: Participants were recruited from the 13th of October 2020 till the 16th of October 2020 and could immediately participate in the pre-intervention survey. Subsequently, eligible participants were randomly assigned to an interventional video and the follow-up survey, distributed through email on the 18th of October 2020, and available for participation till the 24th of October 2020. We found that exposure to the video with addition of debunking strategies on top of social norm modelling and information resulted in substantially stronger rejection of vaccination misconceptions, including the belief that: (1) vaccinations can cause Autism Spectrum Disorders; (2) vaccinations weaken the immune system; (3) influenza vaccination would hamper the COVID-19 vaccine efficacy. Additionally, we observed that exposure to debunking in the intervention resulted in enhanced trust in government. INTERPRETATION: Utilizing debunking in media campaigns on top of vaccine information and social norm modeling is an effective means to combat misinformation and distrust associated with vaccination in elderly, and could help maximize grounds for the acceptance of vaccines, including the COVID-19 vaccines. FUNDING: Dutch Influenza Foundation.

Considerations for Patients With Peripheral Artery Disease During the COVID-19 Pandemic.

New York City was one of the epicenters of the COVID-19 pandemic. The management of peripheral artery disease (PAD) during this time has been a major challenge for health care systems and medical personnel. This document is based on the experiences of experts from various medical fields involved in the treatment of patients with PAD practicing in hospitals across New York City during the outbreak. The recommendations are based on certain aspects including the COVID-19 infection status as well as the clinical PAD presentation of the patient. Our case-based algorithm aims at guiding the treatment of patients with PAD during the pandemic in a safe and efficient way.