Investigational Treatments for COVID-19 may Increase Ventricular Arrhythmia Risk Through Drug Interactions

HPC Framework for Performing in Silico Trials Using a 3D Virtual Human Cardiac Population as Means to Assess Drug-Induced Arrhythmic Risk

Following the 3 R's principles of animal research-replacement, reduction, and refinement-a high-performance computational framework was produced to generate a platform to perform human cardiac in-silico clinical trials as means to assess the pro-arrhythmic risk after the administrations of one or combination of two potentially arrhythmic drugs. The drugs assessed in this study were hydroxychloroquine and azithromycin. The framework employs electrophysiology simulations on high-resolution three-dimensional, biventricular human heart anatomies including phenotypic variabilities, so as to determine if differential QT-prolongation responds to drugs as observed clinically. These simulations also reproduce sex-specific ionic channel characteristics. The derived changes in the pseudo-electrocardiograms, calcium concentrations, as well as activation patterns within 3D geometries were evaluated for signs of induced arrhythmia. The virtual subjects could be evaluated at two different cycle lengths: at a normal heart rate and at a heart rate associated with stress as means to analyze the proarrhythmic risks after the administrations of hydroxychloroquine and azithromycin. Additionally, a series of experiments performed on reanimated swine hearts utilizing Visible Heart® methodologies in a four-chamber working heart model were performed to verify the arrhythmic behaviors observed in the in silico trials.The obtained results indicated similar pro-arrhythmic risk assessments within the virtual population as compared to published clinical trials (21% clinical risk vs 21.8% in silico trial risk). Evidence of transmurally heterogeneous action potential prolongations after providing a large dose of hydroxychloroquine was found as the observed mechanisms for elicited arrhythmias, both in the in vitro and the in silico models. The proposed workflow for in silico clinical drug cardiotoxicity trials allows for reproducing the complex behavior of cardiac electrophysiology in a varied population, in a matter of a few days as compared to the months or years it requires for most in vivo human clinical trials. Importantly, our results provided evidence of the common phenotype variants that produce distinct drug-induced arrhythmogenic outcomes.

Combining pharmacokinetic and electrophysiological models for early prediction of drug-induced arrhythmogenicity

BACKGROUND AND OBJECTIVE

In silico methods are gaining attention for predicting drug-induced Torsade de Pointes (TdP) in different stages of drug development. However, many computational models tended not to account for inter-individual response variability due to demographic covariates, such as sex, or physiologic covariates, such as renal function, which may be crucial when predicting TdP. This study aims to compare the effects of drugs in male and female populations with normal and impaired renal function using in silico methods.

METHODS

Pharmacokinetic models considering sex and renal function as covariates were implemented from data published in pharmacokinetic studies. Drug effects were simulated using an electrophysiologically calibrated population of cellular models of 300 males and 300 females. The population of models was built by modifying the endocardial action potential model published by O'Hara et al. (2011) according to the experimentally measured gene expression levels of 12 ion channels.

RESULTS

Fifteen pharmacokinetic models for CiPA drugs were implemented and validated in this study. Eight pharmacokinetic models included the effect of renal function and four the effect of sex. The mean difference in action potential duration (APD) between male and female populations was 24.9 ms (p<0.05). Our simulations indicated that women with impaired renal function were particularly susceptible to drug-induced arrhythmias, whereas healthy men were less prone to TdP. Differences between patient groups were more pronounced for high TdP-risk drugs. The proposed in silico tool also revealed that individuals with impaired renal function, electrophysiologically simulated with hyperkalemia (extracellular potassium concentration [K+]o = 7 mM) exhibited less pronounced APD prolongation than individuals with normal potassium levels. The pharmacokinetic/electrophysiological framework was used to determine the maximum safe dose of dofetilide in different patient groups. As a proof of concept, 3D simulations were also run for dofetilide obtaining QT prolongation in accordance with previously reported clinical values.

CONCLUSIONS

This study presents a novel methodology that combines pharmacokinetic and electrophysiological models to incorporate the effects of sex and renal function into in silico drug simulations and highlights their impact on TdP-risk assessment. Furthermore, it may also help inform maximum dose regimens that ensure TdP-related safety in a specific sub-population of patients.

Assessment of the proarrhythmic effects of repurposed antimalarials for COVID-19 treatment using a comprehensive in vitro proarrhythmia assay (CiPA)

Due to the outbreak of the SARS-CoV-2 virus, drug repurposing and Emergency Use Authorization have been proposed to treat the coronavirus disease 2019 (COVID-19) during the pandemic. While the efficiency of the drugs has been discussed, it was identified that certain compounds, such as chloroquine and hydroxychloroquine, cause QT interval prolongation and potential cardiotoxic effects. Drug-induced cardiotoxicity and QT prolongation may lead to life-threatening arrhythmias such as torsades de pointes (TdP), a potentially fatal arrhythmic symptom. Here, we evaluated the risk of repurposed pyronaridine or artesunate-mediated cardiac arrhythmias alone and in combination for COVID-19 treatment through in vitro and in silico investigations using the Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative. The potential effects of each drug or in combinations on cardiac action potential (AP) and ion channels were explored using human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) and Chinese hamster ovary (CHO) cells transiently expressing cardiac ion channels (Nav1.5, Cav1.2, and hERG). We also performed in silico computer simulation using the optimized O'Hara-Rudy human ventricular myocyte model (ORd model) to classify TdP risk. Artesunate and dihydroartemisinin (DHA), the active metabolite of artesunate, are classified as a low risk of inducing TdP based on the torsade metric score (TMS). Moreover, artesunate does not significantly affect the cardiac APs of hiPSC-CMs even at concentrations up to 100 times the maximum serum concentration (Cmax). DHA modestly prolonged at APD90 (10.16%) at 100 times the Cmax. When considering Cmax, pyronaridine, and the combination of both drugs (pyronaridine and artesunate) are classified as having an intermediate risk of inducing TdP. However, when considering the unbound concentration (the free fraction not bound to carrier proteins or other tissues inducing pharmacological activity), both drugs are classified as having a low risk of inducing TdP. In summary, pyronaridine, artesunate, and a combination of both drugs have been confirmed to pose a low proarrhythmogenic risk at therapeutic and supratherapeutic (up to 4 times) free Cmax. Additionally, the CiPA initiative may be suitable for regulatory use and provide novel insights for evaluating drug-induced cardiotoxicity.

Diversity of cells and signals in the cardiovascular system

This white paper is the outcome of the seventh UC Davis Cardiovascular Research Symposium on Systems Approach to Understanding Cardiovascular Disease and Arrhythmia. This biannual meeting aims to bring together leading experts in subfields of cardiovascular biomedicine to focus on topics of importance to the field. The theme of the 2022 Symposium was 'Cell Diversity in the Cardiovascular System, cell-autonomous and cell-cell signalling'. Experts in the field contributed their experimental and mathematical modelling perspectives and discussed emerging questions, controversies, and challenges in examining cell and signal diversity, co-ordination and interrelationships involved in cardiovascular function. This paper originates from the topics of formal presentations and informal discussions from the Symposium, which aimed to develop a holistic view of how the multiple cell types in the cardiovascular system integrate to influence cardiovascular function, disease progression and therapeutic strategies. The first section describes the major cell types (e.g. cardiomyocytes, vascular smooth muscle and endothelial cells, fibroblasts, neurons, immune cells, etc.) and the signals involved in cardiovascular function. The second section emphasizes the complexity at the subcellular, cellular and system levels in the context of cardiovascular development, ageing and disease. Finally, the third section surveys the technological innovations that allow the interrogation of this diversity and advancing our understanding of the integrated cardiovascular function and dysfunction.

The potential role of SARS‐CoV‐2 infection in acute coronary syndrome and type 2 myocardial infarction (T2MI): Intertwining spread

Coronavirus disease 2019 (COVID‐19) is a novel pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). It has been shown that SARS‐CoV‐2 infection‐induced inflammatory and oxidative stress and associated endothelial dysfunction may lead to the development of acute coronary syndrome (ACS). Therefore, this review aimed to ascertain the link between severe SARS‐CoV‐2 infection and ACS. ACS is a spectrum of acute myocardial ischemia due to a sudden decrease in coronary blood flow, ranging from unstable angina to myocardial infarction (MI). Primary or type 1 MI (T1MI) is mainly caused by coronary plaque rupture and/or erosion with subsequent occlusive thrombosis. Secondary or type 2 MI (T2MI) is due to cardiac and systemic disorders without acute coronary atherothrombotic disruption. Acute SARS‐CoV‐2 infection is linked with the development of nonobstructive coronary disorders such as coronary vasospasm, dilated cardiomyopathy, myocardial fibrosis, and myocarditis. Furthermore, SARS‐CoV‐2 infection is associated with systemic inflammation that might affect coronary atherosclerotic plaque stability through augmentation of cardiac preload and afterload. Nevertheless, major coronary vessels with atherosclerotic plaques develop minor inflammation during COVID‐19 since coronary arteries are not initially and primarily targeted by SARS‐CoV‐2 due to low expression of angiotensin‐converting enzyme 2 in coronary vessels. In conclusion, SARS‐CoV‐2 infection through hypercytokinemia, direct cardiomyocyte injury, and dysregulation of the renin‐angiotensin system may aggravate underlying ACS or cause new‐onset T2MI. As well, arrhythmias induced by anti‐COVID‐19 medications could worsen underlying ACS.

In silico assessment on TdP risks of drug combinations under CiPA paradigm

Researchers have recently proposed the Comprehensive In-vitro Proarrhythmia Assay (CiPA) to analyze medicines' TdP risks. Using the TdP metric known as qNet, numerous single-drug effects have been studied to classify the medications as low, intermediate, and high-risk. Furthermore, multiple medication therapies are recognized as a potential method for curing patients, mainly when limited drugs are available. This work expands the TdP risk assessment of drugs by introducing a CiPA-based in silico analysis of the TdP risk of combined drugs. The cardiac cell model was simulated using the population of models approach incorporating drug-drug interactions (DDIs) models on several ion channels for various drug pairs. Action potential duration (APD90), qNet, and calcium duration (CaD90) were computed and analyzed as biomarker features. The drug combination maps were also used to illustrate combined medicines' TdP risk. We found that the combined drugs alter cell responses in terms of biomarkers such as APD90, qNet, and CaD90 in a highly nonlinear manner. The results also revealed that combinations of high-risk with low-risk and intermediate-risk with low-risk drugs could result in compounds with varying TdP risks depending on the drug concentrations.

Aspergillus-SARS-CoV-2 Coinfection: What Is Known?

COVID-19-associated pulmonary aspergillosis (CAPA) has had a high incidence. In addition, it has been associated with prolonged hospital stays, as well as several predisposing risk factors, such as fungal factors (nosocomial organism, the size of the conidia, and the ability of the Aspergillus spp. of colonizing the respiratory tract), environmental factors (remodeling in hospitals, use of air conditioning and negative pressure in intensive care units), comorbidities, and immunosuppressive therapies. In addition to these factors, SARS-CoV-2 per se is associated with significant dysfunction of the patient's immune system, involving both innate and acquired immunity, with reduced CD4+ and CD8+ T cell counts and cytokine storm. Therefore, this review aims to identify the factors influencing the fungus so that coinfection with SARS-CoV-2 can occur. In addition, we analyze the predisposing factors in the fungus, host, and the immune response alteration due to the pathogenicity of SARS-CoV-2 that causes the development of CAPA.

Considering population variability of electrophysiological models improves the in silico assessment of drug-induced torsadogenic risk

BACKGROUND AND OBJECTIVE

In silico tools are known to aid in drug cardiotoxicity assessment. However, computational models do not usually consider electrophysiological variability, which may be crucial when predicting rare adverse events such as drug-induced Torsade de Pointes (TdP). In addition, classification tools are usually binary and are not validated using an external data set. Here we analyze the role of incorporating electrophysiological variability in the prediction of drug-induced arrhythmogenic-risk, using a ternary classification and two external validation datasets.

METHODS

The effects of the 12 training CiPA drugs were simulated at three different concentrations using a single baseline model and an electrophysiologically calibrated population of models. 9 biomarkers related with action potential (AP), calcium dynamics and net charge were measured for each simulated concentration. These biomarkers were used to build ternary classifiers based on Support Vector Machines (SVM) methodology. Classifiers were validated using two external drug sets: the 16 validation CiPA drugs and 81 drugs from CredibleMeds database.

RESULTS

Population of models allowed to obtain different AP responses under the same pharmacological intervention and improve the prediction of drug-induced TdP with respect to the baseline model. The classification tools based on population of models achieve an accuracy higher than 0.8 and a mean classification error (MCE) lower than 0.3 for both validation drug sets and for the two electrophysiological action potential models studied (Tomek et al. 2020 and a modified version of O'Hara et al. 2011). In addition, simulations with population of models allowed the identification of individuals with lower conductances of I_Kr, I_Ks, and I_NaK and higher conductances of I_CaL, I_NaL, and I_NCX, which are more prone to develop TdP.

CONCLUSIONS

The methodology presented here provides new opportunities to assess drug-induced TdP-risk, taking into account electrophysiological variability and may be helpful to improve current cardiac safety screening methods.

Metabolic and electrolyte abnormalities as risk factors in drug-induced long QT syndrome

Drug-induced long QT syndrome (diLQTS) is the phenomenon by which the administration of drugs causes prolongation of cardiac repolarisation and leads to an increased risk of the ventricular tachycardia known as torsades de pointes (TdP). In most cases of diLQTS, the primary molecular target is the human ether-à-go-go-related gene protein (hERG) potassium channel, which carries the rapid delayed rectifier current (I_Kr) in the heart. However, the proarrhythmic risk associated with drugs that block hERG can be modified in patients by a range of environmental- and disease-related factors, such as febrile temperatures, alterations in pH, dyselectrolytaemias such as hypokalaemia and hypomagnesemia and coadministration with other drugs. In this review, we will discuss the clinical occurrence of drug-induced LQTS in the context of these modifying factors as well as the mechanisms by which they contribute to altered hERG potency and proarrhythmic risk.

Pathophysiological metabolic changes associated with disease modify the proarrhythmic risk profile of drugs with potential to prolong repolarisation

BACKGROUND AND PURPOSE

Hydroxychloroquine, chloroquine and azithromycin are three drugs that were proposed to treat COVID-19. While concern already existed around their proarrhythmic potential there is little data regarding how altered physiological states encountered in patients such as febrile state, electrolyte imbalances or acidosis might change their risk profiles.

EXPERIMENTAL APPROACH

Potency of hERG block was measured using high-throughput electrophysiology in the presence of variable environmental factors. These potencies informed simulations to predict population risk profiles. Effects on cardiac repolarisation were verified in human induced pluripotent stem cell-derived cardiomyocytes from multiple individuals.

KEY RESULTS

Chloroquine and hydroxychloroquine blocked hERG with IC₅₀ of 1.47±0.07 μM and 3.78±0.17 μM respectively, indicating proarrhythmic risk at concentrations effective against SARS-CoV-2 in vitro. Hypokalaemia and hypermagnesemia increased potency of chloroquine and hydroxychloroquine, indicating increased proarrhythmic risk. Acidosis significantly reduced potency of all drugs, whereas increased temperature decreased potency of chloroquine and hydroxychloroquine against hERG but increased potency for azithromycin. In silico simulations demonstrated that proarrhythmic risk was increased by female sex, hypokalaemia and heart failure, and identified specific genetic backgrounds associated with emergence of arrhythmia.

CONCLUSION AND IMPLICATIONS

Our study demonstrates how proarrhythmic risk can be exacerbated by metabolic changes and pre-existing disease. More broadly, the study acts as a blueprint for how high-throughput in vitro screening, combined with in silico simulations can help guide both preclinical screening and clinical management of patients in relation to drugs with potential to prolong repolarisation.

Air Pollution and Cardiac Arrhythmias: From Epidemiological and Clinical Evidences to Cellular Electrophysiological Mechanisms

Cardiovascular disease is the leading cause of death worldwide and kills over 17 million people per year. In the recent decade, growing epidemiological evidence links air pollution and cardiac arrhythmias, suggesting a detrimental influence of air pollution on cardiac electrophysiological functionality. However, the proarrhythmic mechanisms underlying the air pollution-induced cardiac arrhythmias are not fully understood. The purpose of this work is to provide recent advances in air pollution-induced arrhythmias with a comprehensive review of the literature on the common air pollutants and arrhythmias. Six common air pollutants of widespread concern are discussed, namely particulate matter, carbon monoxide, hydrogen sulfide, sulfur dioxide, nitrogen dioxide, and ozone. The epidemiological and clinical reports in recent years are reviewed by pollutant type, and the recently identified mechanisms including both the general pathways and the direct influences of air pollutants on the cellular electrophysiology are summarized. Particularly, this review focuses on the impaired ion channel functionality underlying the air pollution-induced arrhythmias. Alterations of ionic currents directly by the air pollutants, as well as the alterations mediated by intracellular signaling or other more general pathways are reviewed in this work. Finally, areas for future research are suggested to address several remaining scientific questions.

Assessment of proarrhythmogenic risk for chloroquine and hydroxychloroquine using the CiPA concept

Chloroquine and hydroxychloroquine have been proposed recently as therapy for SARS-CoV-2-infected patients, but during 3 months of extensive use concerns were raised related to their clinical effectiveness and arrhythmogenic risk. Therefore, we estimated for these compounds several proarrhythmogenic risk predictors according to the Comprehensive in vitro Proarrhythmia Assay (CiPA) paradigm. Experiments were performed with either CytoPatch™2 automated or manual patch-clamp setups on HEK293T cells stably or transiently transfected with hERG1, hNav1.5, hKir2.1, hKv7.1+hMinK, and on Pluricyte® cardiomyocytes (Ncardia), using physiological solutions. Dose-response plots of hERG1 inhibition fitted with Hill functions yielded IC50 values in the low micromolar range for both compounds. We found hyperpolarizing shifts of tens of mV, larger for chloroquine, in the voltage-dependent activation but not inactivation, as well as a voltage-dependent block of hERG current, larger at positive potentials. We also found inhibitory effects on peak and late I_Na and on I_K1, with IC50 of tens of μM and larger for chloroquine. The two compounds, tested on Pluricyte® cardiomyocytes using the β-escin-perforated method, inhibited I_Kr, I_CaL, I_{Na peak}, but had no effect on I_f. In current-clamp they caused action potential prolongation. Our data and those from literature for I_to were used to compute proarrhythmogenic risk predictors B_net (Mistry HB, 2018) and Q_net (Dutta S et al., 2017), with hERG1 blocking/unblocking rates estimated from time constants of fractional block. Although the two antimalarials are successfully used in autoimmune diseases, and chloroquine may be effective in atrial fibrillation, assays place these drugs in the intermediate proarrhythmogenic risk group.

Recent applications of quantitative systems pharmacology and machine learning models across diseases

Quantitative systems pharmacology (QSP) is a quantitative and mechanistic platform describing the phenotypic interaction between drugs, biological networks, and disease conditions to predict optimal therapeutic response. In this meta-analysis study, we review the utility of the QSP platform in drug development and therapeutic strategies based on recent publications (2019-2021). We gathered recent original QSP models and described the diversity of their applications based on therapeutic areas, methodologies, software platforms, and functionalities. The collection and investigation of these publications can assist in providing a repository of recent QSP studies to facilitate the discovery and further reusability of QSP models. Our review shows that the largest number of QSP efforts in recent years is in Immuno-Oncology. We also addressed the benefits of integrative approaches in this field by presenting the applications of Machine Learning methods for drug discovery and QSP models. Based on this meta-analysis, we discuss the advantages and limitations of QSP models and propose fields where the QSP approach constitutes a valuable interface for more investigations to tackle complex diseases and improve drug development.

Inflammation as a Risk Factor in Cardiotoxicity: An Important Consideration for Screening During Drug Development

Numerous commonly prescribed drugs, including antiarrhythmics, antihistamines, and antibiotics, carry a proarrhythmic risk and may induce dangerous arrhythmias, including the potentially fatal Torsades de Pointes. For this reason, cardiotoxicity testing has become essential in drug development and a required step in the approval of any medication for use in humans. Blockade of the hERG K⁺ channel and the consequent prolongation of the QT interval on the ECG have been considered the gold standard to predict the arrhythmogenic risk of drugs. In recent years, however, preclinical safety pharmacology has begun to adopt a more integrative approach that incorporates mathematical modeling and considers the effects of drugs on multiple ion channels. Despite these advances, early stage drug screening research only evaluates QT prolongation in experimental and computational models that represent healthy individuals. We suggest here that integrating disease modeling with cardiotoxicity testing can improve drug risk stratification by predicting how disease processes and additional comorbidities may influence the risks posed by specific drugs. In particular, chronic systemic inflammation, a condition associated with many diseases, affects heart function and can exacerbate medications’ cardiotoxic effects. We discuss emerging research implicating the role of inflammation in cardiac electrophysiology, and we offer a perspective on how in silico modeling of inflammation may lead to improved evaluation of the proarrhythmic risk of drugs at their early stage of development.

In vitro and In silico Models to Study SARS-CoV-2 Infection: Integrating Experimental and Computational Tools to Mimic "COVID-19 Cardiomyocyte"

The rapid dissemination of SARS-CoV-2 has made COVID-19 a tremendous social, economic, and health burden. Despite the efforts to understand the virus and treat the disease, many questions remain unanswered about COVID-19 mechanisms of infection and progression. Severe Acute Respiratory Syndrome (SARS) infection can affect several organs in the body including the heart, which can result in thromboembolism, myocardial injury, acute coronary syndromes, and arrhythmias. Numerous cardiac adverse events, from cardiomyocyte death to secondary effects caused by exaggerated immunological response against the virus, have been clinically reported. In addition to the disease itself, repurposing of treatments by using "off label" drugs can also contribute to cardiotoxicity. Over the past several decades, animal models and more recently, stem cell-derived cardiomyocytes have been proposed for studying diseases and testing treatments in vitro. In addition, mechanistic in silico models have been widely used for disease and drug studies. In these models, several characteristics such as gender, electrolyte imbalance, and comorbidities can be implemented to study pathophysiology of cardiac diseases and to predict cardiotoxicity of drug treatments. In this Mini Review, we (1) present the state of the art of in vitro and in silico cardiomyocyte modeling currently in use to study COVID-19, (2) review in vitro and in silico models that can be adopted to mimic the effects of SARS-CoV-2 infection on cardiac function, and (3) provide a perspective on how to combine some of these models to mimic "COVID-19 cardiomyocytes environment.".

COVID-19 associated with pulmonary aspergillosis: A literature review

Bacterial or virus co-infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been reported in many studies, however, the knowledge on Aspergillus co-infection among patients with coronavirus disease 2019 (COVID-19) was limited. This literature review aims to explore and describe the updated information about COVID-19 associated with pulmonary aspergillosis. We found that Aspergillus spp. can cause co-infections in patients with COVID-19, especially in severe/critical illness. The incidence of IPA in COVID-19 ranged from 19.6% to 33.3%. Acute respiratory distress syndrome requiring mechanical ventilation was the common complications, and the overall mortality was high, which could be up to 64.7% (n = 22) in the pooled analysis of 34 reported cases. The conventional risk factors of invasive aspergillosis were not common among these specific populations. Fungus culture and galactomannan test, especially from respiratory specimens could help early diagnosis. Aspergillus fumigatus was the most common species causing co-infection in COVID-19 patients, followed by Aspergillus flavus. Although voriconazole is the recommended anti-Aspergillus agent and also the most commonly used antifungal agent, aspergillosis caused by azole-resistant Aspergillus is also possible. Additionally, voriconazole should be used carefully in the concern of complicated drug-drug interaction and enhancing cardiovascular toxicity on anti-SARS-CoV-2 agents. Finally, this review suggests that clinicians should keep alerting the possible occurrence of pulmonary aspergillosis in severe/critical COVID-19 patients, and aggressively microbiologic study in addition to SARS-CoV-2 via respiratory specimens should be indicated.

COVID-19 infection and cardiac arrhythmias

As the coronavirus 2019 (COVID-19) pandemic marches unrelentingly, more patients with cardiac arrhythmias are emerging due to the effects of the virus on the respiratory and cardiovascular (CV) systems and the systemic inflammation that it incurs, and also as a result of the proarrhythmic effects of COVID-19 pharmacotherapies and other drug interactions and the associated autonomic imbalance that enhance arrhythmogenicity. The most worrisome of all arrhythmogenic mechanisms is the QT prolonging effect of various anti-COVID pharmacotherapies that can lead to polymorphic ventricular tachycardia in the form of torsade des pointes and sudden cardiac death. It is therefore imperative to monitor the QT interval during treatment; however, conventional approaches to such monitoring increase the transmission risk for the staff and strain the health system. Hence, there is dire need for contactless monitoring and telemetry for inpatients, especially those admitted to the intensive care unit, as well as for outpatients needing continued management. In this context, recent technological advances have ushered in a new era in implementing digital health monitoring tools that circumvent these obstacles. All these issues are herein discussed and a large body of recent relevant data are reviewed.

Sex differences underlying preexisting cardiovascular disease and cardiovascular injury in COVID-19

The novel 2019 coronavirus disease (COVID-19), resulting from severe acute respiratory syndrome coronarvirus-2 (SARS-CoV-2) infection, typically leads to respiratory failure in severe cases; however, cardiovascular injury is reported to contribute to a substantial proportion of COVID-19 deaths. Preexisting cardiovascular disease (CVD) is among the most common risk factors for hospitalization and death in COVID-19 patients, and the pathogenic mechanisms of COVID-19 disease progression itself may promote the development of cardiovascular injury, increasing risk of in-hospital death. Sex differences in COVID-19 are becoming more apparent as mounting data indicate that males seem to be disproportionately at risk of severe COVID-19 outcome due to preexisting CVD and COVID-19-related cardiovascular injury. In this review, we will provide a basic science perspective on current clinical observations in this rapidly evolving field and discuss the interplay sex differences, preexisting CVD and COVID-19-related cardiac injury.