Virtual Cardiology: Past, Present, Future Directions, and Considerations.

Purpose of Review: Through this review, we attempt to explore the role of telemedicine and virtual visits in the field of cardiology pre-COVID-19 and during COVID-19 pandemic, their limitations and their future scope for delivery of care. Recent Findings: Telemedicine, which rose to prominence during COVID-19 pandemic, helped not only in reducing the burden on the healthcare system during a time of crisis but also in improving patient outcomes. Patients and physicians also favored virtual visits when feasible. Virtual visits were found to have the potential to be continued beyond the pandemic and play a significant role in patient care alongside conventional face-to-face visits. Summary: Although tele-cardiology has proven beneficial in terms of patient care, convenience, and access, it comes with its fair share of limitations-both logistical and medical. Whilst there remains a great scope for improvement in the quality of patient care provided through telemedicine, it has shown the potential to become an integral part of medical practice in the future. Supplementary Information: The online version contains supplementary material available at 10.1007/s12170-023-00719-0.

Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework.

BACKGROUND AND MOTIVATION: Lung computed tomography (CT) techniques are high-resolution and are well adopted in the intensive care unit (ICU) for COVID-19 disease control classification. Most artificial intelligence (AI) systems do not undergo generalization and are typically overfitted. Such trained AI systems are not practical for clinical settings and therefore do not give accurate results when executed on unseen data sets. We hypothesize that ensemble deep learning (EDL) is superior to deep transfer learning (TL) in both non-augmented and augmented frameworks. METHODOLOGY: The system consists of a cascade of quality control, ResNet-UNet-based hybrid deep learning for lung segmentation, and seven models using TL-based classification followed by five types of EDL's. To prove our hypothesis, five different kinds of data combinations (DC) were designed using a combination of two multicenter cohorts-Croatia (80 COVID) and Italy (72 COVID and 30 controls)-leading to 12,000 CT slices. As part of generalization, the system was tested on unseen data and statistically tested for reliability/stability. RESULTS: Using the K5 (80:20) cross-validation protocol on the balanced and augmented dataset, the five DC datasets improved TL mean accuracy by 3.32%, 6.56%, 12.96%, 47.1%, and 2.78%, respectively. The five EDL systems showed improvements in accuracy of 2.12%, 5.78%, 6.72%, 32.05%, and 2.40%, thus validating our hypothesis. All statistical tests proved positive for reliability and stability. CONCLUSION: EDL showed superior performance to TL systems for both (a) unbalanced and unaugmented and (b) balanced and augmented datasets for both (i) seen and (ii) unseen paradigms, validating both our hypotheses.

Oral Nirmatrelvir and Ritonavir in Non-hospitalized Vaccinated Patients with Covid-19.

BACKGROUND: Treatment of coronavirus disease-2019 (Covid-19) with nirmatrelvir plus ritonavir (NMV-r) in high-risk non-hospitalized unvaccinated patients reduced the risk of progression to severe disease. However, the potential benefits of NMV-r among vaccinated patients are unclear. METHODS: We conducted a comparative retrospective cohort study using the TriNetX research network. Patients ≥18 years of age who were vaccinated and subsequently developed Covid-19 between December 1, 2021, and April 18, 2022, were included. Cohorts were developed based on the use of NMV-r within five days of diagnosis. The primary composite outcome was all-cause emergency room (ER) visit, hospitalization, or death at a 30-days follow-up. Secondary outcomes included individual components of primary outcomes, multisystem symptoms, Covid-19 associated complications, and diagnostic test utilization. RESULTS: After propensity score matching, 1,130 patients remained in each cohort. A primary composite outcome of all-cause ER visits, hospitalization, or death in 30 days occurred in 89 (7.87%) patients in the NMV-r cohort as compared to 163 (14.4%) patients in the non-NMV-r cohort (OR 0.5, CI 0.39-0.67; p<0.005) consistent with 45% relative risk reduction. A significant reduction in multisystem symptom burden and subsequent complications such as lower respiratory tract infection, cardiac arrhythmia, and diagnostic radiology testing were noted in NMV-r treated patients. There was no apparent increase serious complications between days 10 to 30. CONCLUSION: Treatment with NMV-r in non-hospitalized vaccinated patients with Covid-19 was associated with a reduced likelihood of emergency room visits, hospitalization, or death. Complications and overall resource utilization were also decreased.

Involvement of epigenetics in affecting host immunity during SARS-CoV-2 infection.

Coronavirus disease 19 (COVID-19) is caused by a highly contagious RNA virus Severe Acute Respiratory Syndrome coronavirus-2 (SARS-CoV-2), originated in December 2019 in Wuhan, China. Since then, it has become a global public health concern and leads the disease table with the highest mortality rate, highlighting the necessity for a thorough understanding of its biological properties. The intricate interaction between the virus and the host immune system gives rise to diverse implications of COVID-19. RNA viruses are known to hijack the host epigenetic mechanisms of immune cells to regulate antiviral defence. Epigenetics involves processes that alter gene expression without changing the DNA sequence, leading to heritable phenotypic changes. The epigenetic landscape consists of reversible modifications like chromatin remodelling, DNA/RNA methylation, and histone methylation/acetylation that regulates gene expression. The epigenetic machinery contributes to many aspects of SARS-CoV-2 pathogenesis, like global DNA methylation and receptor angiotensin-converting enzyme 2 (ACE2) methylation determines the viral entry inside the host, viral replication, and infection efficiency. Further, it is also reported to epigenetically regulate the expression of different host cytokines affecting antiviral response. The viral proteins of SARS-CoV-2 interact with various host epigenetic enzymes like histone deacetylases (HDACs) and bromodomain-containing proteins to antagonize cellular signalling. The central role of epigenetic factors in SARS-CoV-2 pathogenesis is now exploited as promising biomarkers and therapeutic targets against COVID-19. This review article highlights the ability of SARS-CoV-2 in regulating the host epigenetic landscape during infection leading to immune evasion. It also discusses the ongoing therapeutic approaches to curtail and control the viral outbreak.

Connectedness among major cryptocurrencies in standard times and during the COVID-19 outbreak

The cryptocurrency markets are perceived as being dominated by Bitcoin leading the overall system dynamics. Although the previous empirical evidence points towards strong connections among selected cryptocurrencies or, from the other side, weak dependence between Bitcoin and traditional financial assets, a focused study on the dynamics of return and volatility connectedness among a wider range of cryptocurrencies is lacking, and more so, one directed towards the very first actual critical period of the global economy coinciding with relevant crypto-markets. Using data for the 10 most capitalized cryptocurrencies between 1st October 2017 and 5th January 2021, we examine how cryptocurrencies interact and whether they have a clear leader, with a special focus on differences with respect to investment horizons and how the relationship structure evolves in time. We uncover a structural change in the connectedness evolving in 2020 as the market restructures in reaction to the unprecedented monetary injections as a counter to the COVID-19-induced economic standstill. The structural change is shown not only for cryptocurrencies considered separately but also when we jointly examine them with traditional assets.

Hospitality and tourism research during (COVID-19) pandemic: a bibliometric analysis

COVID-19 has changed the world forever in every imaginable aspect. Hospitality and Tourism has been one of the world's largest employers and key economic contributors. Hospitality and Tourism has been one of the worst-hit sectors due to the pandemic (COVID-19) worldwide. This has called upon the attention of many researchers worldwide. The main purpose of this study is to analyse the literature during 2019-2022, identify the most productive authors, most influential countries, most productive institution and journals also top-performing research articles and keyword analysis to know the research themes and trends focussing coronavirus in the fields of Hospitality and Tourism. The study also suggests the areas of future research to the researchers and policymakers and proposes solutions to contemporary issues. The study uses "biblioshiny" - an interface of R-package and VOSviewer for conducting bibliometric analysis that ameliorates the quality of review bereft of any subjective biasness.

Prognostic value of initial electrocardiography in predicting long-term all-cause mortality in COVID-19.

BACKGROUND: The electrocardiography (ECG) has short-term prognostic value in coronavirus disease 2019 (COVID-19), yet its ability to predict long-term mortality is unknown. This study aimed to elucidate the predictive role of initial ECG on long-term all-cause mortality in patients diagnosed with COVID-19. METHODS: In this prospective cohort study, adults with COVID-19 who underwent ECG testing within a 17-hospital health system in Northeast Ohio and Florida between 03/2020-06/2020 were identified. An expert ECG reader analyzed all studies blinded to patient status. The associations of ECG characteristics with long-term all-cause mortality and intensive care unit (ICU) admission were assessed using Cox proportional hazards regression model and multivariable logistic regression models, respectively. Status of long-term mortality was adjudicated on 01/07/2022. RESULTS: Of 837 patients (median age 65 years, 51% female, 44% Black), 683 (81.6%) were hospitalized, 281 (33.6%) required ICU admission, 67 (8.0%) died in-hospital, and 206 (24.6%) died at final follow-up after a median (IQR) of 21 (9-103) days after ECG. Overall, 179 (20.7%) patients presented with sinus tachycardia, 12 (1.4%) with atrial flutter, and 45 (5.4%) with atrial fibrillation (AF). After multivariable adjustment, sinus tachycardia (E-value for HR=3.09, lower CI=2.2) and AF (E-value for HR=3.13, lower CI=2.03) each independently predicted all-cause mortality. At final follow-up, patients with AF had 64.5% probability of death compared with 20.5% for those with normal sinus rhythm (P<.0001). CONCLUSIONS: Sinus tachycardia and AF on initial ECG strongly predict long-term all-cause mortality in COVID-19. The ECG can serve as a powerful long-term prognostic tool in COVID-19.

Omicron BA.2 lineage predominance in severe acute respiratory syndrome coronavirus 2 positive cases during the third wave in North India.

Background: Recent studies on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reveal that Omicron variant BA.1 and sub-lineages have revived the concern over resistance to antiviral drugs and vaccine-induced immunity. The present study aims to analyze the clinical profile and genome characterization of the SARS-CoV-2 variant in eastern Uttar Pradesh (UP), North India. Methods: Whole-genome sequencing (WGS) was conducted for 146 SARS-CoV-2 samples obtained from individuals who tested coronavirus disease 2019 (COVID-19) positive between the period of 1 January 2022 and 24 February 2022, from three districts of eastern UP. The details regarding clinical and hospitalized status were captured through telephonic interviews after obtaining verbal informed consent. A maximum-likelihood phylogenetic tree was created for evolutionary analysis using MEGA7. Results: The mean age of study participants was 33.9 ± 13.1 years, with 73.5% accounting for male patients. Of the 98 cases contacted by telephone, 30 (30.6%) had a travel history (domestic/international), 16 (16.3%) reported having been infected with COVID-19 in past, 79 (80.6%) had symptoms, and seven had at least one comorbidity. Most of the sequences belonged to the Omicron variant, with BA.1 (6.2%), BA.1.1 (2.7%), BA.1.1.1 (0.7%), BA.1.1.7 (5.5%), BA.1.17.2 (0.7%), BA.1.18 (0.7%), BA.2 (30.8%), BA.2.10 (50.7%), BA.2.12 (0.7%), and B.1.617.2 (1.3%) lineages. BA.1 and BA.1.1 strains possess signature spike mutations S:A67V, S:T95I, S:R346K, S:S371L, S:G446S, S:G496S, S:T547K, S:N856K, and S:L981F, and BA.2 contains S:V213G, S:T376A, and S:D405N. Notably, ins214EPE (S1- N-Terminal domain) mutation was found in a significant number of Omicron BA.1 and sub-lineages. The overall Omicron BA.2 lineage was observed in 79.5% of women and 83.2% of men. Conclusion: The current study showed a predominance of the Omicron BA.2 variant outcompeting the BA.1 over a period in eastern UP. Most of the cases had a breakthrough infection following the recommended two doses of vaccine with four in five cases being symptomatic. There is a need to further explore the immune evasion properties of the Omicron variant.

Volatility spillovers during normal and high volatility states and their driving factors: A cross‐country and cross‐asset analysis

Understanding the transmission of volatility across markets is essential for managing risk and financial stability, especially under crisis periods during which an extreme event occurring in one market is easily transmitted to another market. To gain such an understanding and enrich the related literature, we examine in this article the system of volatility spillovers across various equity markets and asset classes using a quantile-based approach, allowing us to capture spillovers under normal and high volatility states. The sample period is 16 March 2011-10 November 2020 and the employed dataset comprises 12 implied volatility indices representing a forward-looking measure of uncertainty of global equities, strategic commodities and the US Treasury bond market. The results show that the identity of transmitters and receivers of volatility shocks differ between normal and high volatility states. The US stock market is at the centre of volatility spillovers in the normal volatility state. European and Chinese stock markets and strategic commodities (e.g. crude oil and gold) become major volatility transmitters in the high volatility state, after acting as volatility receivers during normal periods. Furthermore, we study the drivers of implied volatility spillovers using regression models and find that US Default spread contributes to the total volatility spillover index in both volatility states, whereas TED spread plays a significant role in the normal volatility state. As for the role of short rate and risk aversion, it is significant in the high volatility state. These findings matter to the decision-making process of risk managers and policymakers.

Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID-19 from Chest Radiographical Images: A Survey.

Artificial Intelligence (AI) has been applied successfully in many real-life domains for solving complex problems. With the invention of Machine Learning (ML) paradigms, it becomes convenient for researchers to predict the outcome based on past data. Nowadays, ML is acting as the biggest weapon against the COVID-19 pandemic by detecting symptomatic cases at an early stage and warning people about its futuristic effects. It is observed that COVID-19 has blown out globally so much in a short period because of the shortage of testing facilities and delays in test reports. To address this challenge, AI can be effectively applied to produce fast as well as cost-effective solutions. Plenty of researchers come up with AI-based solutions for preliminary diagnosis using chest CT Images, respiratory sound analysis, voice analysis of symptomatic persons with asymptomatic ones, and so forth. Some AI-based applications claim good accuracy in predicting the chances of being COVID-19-positive. Within a short period, plenty of research work is published regarding the identification of COVID-19. This paper has carefully examined and presented a comprehensive survey of more than 110 papers that came from various reputed sources, that is, Springer, IEEE, Elsevier, MDPI, arXiv, and medRxiv. Most of the papers selected for this survey presented candid work to detect and classify COVID-19, using deep-learning-based models from chest X-Rays and CT scan images. We hope that this survey covers most of the work and provides insights to the research community in proposing efficient as well as accurate solutions for fighting the pandemic.

Prognostic value of initial electrocardiography in predicting long-term all-cause mortality in COVID-19

Background The electrocardiography (ECG) has short-term prognostic value in coronavirus disease 2019 (COVID-19), yet its ability to predict long-term mortality is unknown. This study aimed to elucidate the predictive role of initial ECG on long-term all-cause mortality in patients diagnosed with COVID-19. Methods In this prospective cohort study, adults with COVID-19 who underwent ECG testing within a 17-hospital health system in Northeast Ohio and Florida between 03/2020-06/2020 were identified. An expert ECG reader analyzed all studies blinded to patient status. The associations of ECG characteristics with long-term all-cause mortality and intensive care unit (ICU) admission were assessed using Cox proportional hazards regression model and multivariable logistic regression models, respectively. Status of long-term mortality was adjudicated on 01/07/2022. Results Of 837 patients (median age 65 years, 51% female, 44% Black), 683 (81.6%) were hospitalized, 281 (33.6%) required ICU admission, 67 (8.0%) died in-hospital, and 206 (24.6%) died at final follow-up after a median (IQR) of 21 (9-103) days after ECG. Overall, 179 (20.7%) patients presented with sinus tachycardia, 12 (1.4%) with atrial flutter, and 45 (5.4%) with atrial fibrillation (AF). After multivariable adjustment, sinus tachycardia (E-value for HR=3.09, lower CI=2.2) and AF (E-value for HR=3.13, lower CI=2.03) each independently predicted all-cause mortality. At final follow-up, patients with AF had 64.5% probability of death compared with 20.5% for those with normal sinus rhythm (P<.0001). Conclusions Sinus tachycardia and AF on initial ECG strongly predict long-term all-cause mortality in COVID-19. The ECG can serve as a powerful long-term prognostic tool in COVID-19.

Disparities in Cardio-oncology: Effects On Outcomes and Opportunities for Improvement.

PURPOSE OF REVIEW: The purpose of this article is to provide a comprehensive review of available data on health disparities and the interconnected social determinants of health (SDOH) in cardio-oncology. We identify the gaps in the literature and suggest areas for future research. In addition, we propose strategies to address these disparities at various levels. RECENT FINDINGS: There has been increasing recognition of health disparities and the role of SODH on an individual's access to health care, quality of care, and outcomes of the illness. There is growing evidence of sex and race-based differences in cancer therapy-related cardiotoxicity. Recent studies have shown how access and quality of health care are affected by financial stability and rurality. Our recent study utilizing the social vulnerability index (SVI) and county-level patient data found graded increase in county-level cardio-oncology mortality with greater social vulnerability. The incremental impact of social vulnerability was higher for cardio-oncology mortality than for mortality related to either cancer or CVD alone. The mortality rates in these patients were higher in rural areas compared to urban areas regardless of social vulnerability. Additionally, for those within the counties within highest social vulnerability, Black individuals had significantly higher cardio-oncology mortality compared with White individuals. Disparities in the cardio-oncology population are deep-rooted and widespread, leading to poor quality of life and increased mortality. It is crucial to integrate SDOH, not only in clinical care delivery but also in future research, and registry data to improve our understanding and the outcomes in our unique subset of cardio-oncology patients.

Essential role of Rnd1 in innate immunity during viral and bacterial infections.

Intracellular and cell surface pattern-recognition receptors (PRRs) are an essential part of innate immune recognition and host defense. Here, we have compared the innate immune responses between humans and bats to identify a novel membrane-associated protein, Rnd1, which defends against viral and bacterial infection in an interferon-independent manner. Rnd1 belongs to the Rho GTPase family, but unlike other small GTPase members, it is constitutively active. We show that Rnd1 is induced by pro-inflammatory cytokines during viral and bacterial infections and provides protection against these pathogens through two distinct mechanisms. Rnd1 counteracts intracellular calcium fluctuations by inhibiting RhoA activation, thereby inhibiting virus internalisation. On the other hand, Rnd1 also facilitates pro-inflammatory cytokines IL-6 and TNF-α through Plxnb1, which are highly effective against intracellular bacterial infections. These data provide a novel Rnd1-mediated innate defense against viral and bacterial infections.

Application of surfactants for better tomorrow

Surfactants are the important class of amphiphilic species, which consists of both hydrophilic as well as hydrophobic part. They are characterized by some important properties like critical micelle concentration (CMC), charge, hydrophile-lypophile balance (HLB), aggregation, and chemical structure, which make them good emulsifying, dispersing and foaming agents. Presently, the global demand of the surfactants is on the peak due to their increased applications in detergents, paints, food emulsion, biotechnological processes, biosciences, pharmaceuticals, cosmetic products, etc. In order to prevent Corona pandemic disease, WHO and other regulatory authorities have recommended frequent use of soaps and sanitizers that makes surfactants an important class of species to be explored more in terms of their applications.

Clinical Features of COVID-19 and Factors Associated with Severe Clinical Course: A Systematic Review and Meta-Analysis

BACKGROUND: COVID-19 is a new disease which has become a global pandemic, and is caused by a novel coronavirus, SARS-CoV-2. The disease is still not very well characterized, and factors associated with severe clinical course are not well known. METHODS: The main objectives were to determine the demographic, clinical and laboratory manifestations of COVID-19 and to identify the factors associated with severe clinical course. We searched the PubMed for studies published between Jan 1, 2020 and Mar 17, 2020, and included them if they were in English language, published in full, were retrospective or prospective observational or case control study with data on clinical, laboratory and imaging features of adult patients with COVID-19 disease from single or multiple centers. Studies that included exclusively pediatric patients were excluded. The demographic, clinical and laboratory data was displayed as n (%) or mean (SD). The meta-analysis on factors associated with severe clinical course was performed using the random effects model, and odds ratios (ORs) with 95% confidence intervals (CIs) were calculated as the effect sizes. FINDINGS: We included 58 studies (6892 patients) for the systematic review on clinical manifestations and 21 studies (3496 patients) for meta-analysis on factors associated with severe clinical course. The mean age of patients with COVID-19 is 49.7±16.3 years with a male to female ratio of 1.2:1. Common symptoms and their frequency are: fever (83.4%), cough (60.5%), fatigue (33.8%), sputum (28.9%), dyspnea (22.1%), myalgia (20.6%), chest tightness / pain (16.3%), sore throat (13.5%), headache (11.2%), diarhhea (7.5%), nasal congestion / rhinorrhea (6.7%), nausea / vomiting (5.6%), pain abdomen (4.6%), and hemoptysis (1.7%). The comorbidities associated with COVID-19 are: hypertension (18.4%), diabetes mellitus (9.8%), cardiovascular diseases (8.8%), endocrine diseases (5.8%), gastrointestinal diseases (5%), CLD (3%), and COPD (2.8%). Among the laboratory parameters WBC was low in 27%, high in 9%, platelets were low in 22.9%, creatinine was high in 6.5%, AST was high in 25.3%, ALT was high in 22.7%, bilirubin was high in 8.8%, albumin was low 60.1%, CT chest was abnormal in 89%, CRP was high in 67.5%, LDH was high in 52%, D-dimer was high in 34.8%, CK was high in 14.4%, and procalcitonin was high in 15.4%. Factors significantly associated severe clinical course (with their ORs) are as follows: High CRP (5.78), high procalcitonin (5.45), age >60 (4.82), dyspnea (4.66), high LDH (4.59), COPD (4.37), low albumin (4.34), high D-dimer (4.03), cardiac disease (3.88), low lymphocyte count (3.22), any associated comorbidity (3.16), diabetes mellitus (3.11), high WBC count (2.67), high bilirubin level (2.55), high creatinine (2.34), high AST (2.31), hypertension (2.30), low platelets (1.78), High ALT (1.69), high CK (1.66), fever spikes ≥39°C (1.59), diarrhea (1.55), male gender (1.47), and sputum (1.35). INTERPRETATION: Identification of these factors associated with severe COVID-19 will help the physicians working at all levels of healthcare (primary, secondary, tertiary and ICU) in determining which patients need home care, hospital care, HDU care, and ICU admission;and thus, prioritize the scarce healthcare resource use more judiciously. Many of these identified factors can also help the public at large in the current COVID-19 epidemic setting, to judge when they should seek immediate medical care. Funding Statement: None. Declaration of Interests: The authors declare no competing interests.

Current Trends in Protein-Surfactant Interactions: A Review

The protein-surfactant interactions have been investigated for a long time due to their increased effectiveness in cleanliness and industrial processes. Moreover, Covid-19 outbreak has revolutionized the significance of protein-surfactant interactions, as soaps were proved the best cleansers and safe during this period. In this review article, an overview of physico-chemical aspects of protein-surfactant interactions along with various techniques has been discussed. In addition, the effect of different factors such as co-solvent, temperature, pH, chain length of surfactants, salt, and their impact on critical micelle concentration (CMC) of surfactant, structure, binding, thermodynamic parameters, and stability was also explained. This review will help the scientific community in understanding how to improve the effectiveness and applications of proteins and surfactants in pharmaceutical and other industries as protein-surfactant interactions can explain the significant phenomena of hydrophobic and electrostatic interactions.

Lessons Learned During the Pandemic: Correlation of QT Intervals Between Telemetry and 12-Lead Electrocardiogram.

Background and objective QT prolongation is associated with an increased risk of ventricular arrhythmias. Since some patients on contact or droplet precautions require QT-prolonging medications, monitoring the QT interval may become imperative to prevent fatal arrhythmias. To limit the exposure of staff to patients during and even after the coronavirus disease 2019 (COVID-19) pandemic and judiciously use personal protective equipment (PPE), it is important to find alternatives to frequent 12-lead electrocardiograms (ECG). The objective of this study was to compare QT intervals measured on telemetry to those measured on 12-lead ECG to determine whether telemetry QT interval measurements could be used in place of 12-lead measurements. Methods Simultaneous telemetry recordings via a Philips telemetry monitoring system (Philips Healthcare, Eindhoven, Netherlands) and 12-lead ECGs were obtained from 50 patients. Patients were from cardiac telemetry and cardiac intensive care units. QT interval from the telemetry system was compared to the QT interval on the 12-lead ECG. QT intervals on two telemetry strips were uninterpretable as the termination of the T-wave could not be defined appropriately; therefore, these patients were excluded. Results In 33 of 48 patients (69%), QT intervals from the telemetry studies matched the QT intervals measured by 12-lead ECG. The intraclass correlation coefficient (ICC) between telemetry QT and 12-lead ECG QT was 0.887 (95% CI: 0.809-0.934; p<0.001). In 15 of 48 patients (31%), the QT intervals measured from telemetry were different from those measured by 12-lead ECG. These patients either had an abnormal rhythm, conduction abnormalities, or repolarization abnormalities at baseline. Conclusion Telemetry is a suitable alternative for measuring QT intervals in the majority of patients. However, those with baseline ECG abnormalities should have serial 12-lead ECGs. This can reduce the risk of staff exposure to pathogens and prevent overuse of PPE during the COVID-19 pandemic and for other patients in isolation.