Electrocardiograhic characteristics in patients with coronavirus infection: A single‐center observational study

Li, Yi; Liu, Tong; Tse, Gary; Wu, Mingxiang; Jiang, Jingjing; Liu, Ming; Tao, Liang

doi:10.1111/anec.12805

Cited by 39 publications

(44 citation statements)

References 21 publications

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“…For external validation, patients admitted to the Wuhan Asia General Hospital 38 , Wuhan, China, between 10 February and 10 March 2020, were included. Diagnosis of COVID-19 was based on positive PCR test and ground glass shadows in the lungs on computed tomography scan, with follow-up 2 weeks post-discharge.…”

Section: Methodsmentioning

confidence: 99%

Development of a multivariable prediction model for severe COVID-19 disease: a population-based study from Hong Kong

Zhou

Lee²,

Wang³

et al. 2021

npj Digit. Med.

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Recent studies have reported numerous predictors for adverse outcomes in COVID-19 disease. However, there have been few simple clinical risk scores available for prompt risk stratification. The objective is to develop a simple risk score for predicting severe COVID-19 disease using territory-wide data based on simple clinical and laboratory variables. Consecutive patients admitted to Hong Kong’s public hospitals between 1 January and 22 August 2020 and diagnosed with COVID-19, as confirmed by RT-PCR, were included. The primary outcome was composite intensive care unit admission, need for intubation or death with follow-up until 8 September 2020. An external independent cohort from Wuhan was used for model validation. COVID-19 testing was performed in 237,493 patients and 4442 patients (median age 44.8 years old, 95% confidence interval (CI): [28.9, 60.8]); 50% males) were tested positive. Of these, 209 patients (4.8%) met the primary outcome. A risk score including the following components was derived from Cox regression: gender, age, diabetes mellitus, hypertension, atrial fibrillation, heart failure, ischemic heart disease, peripheral vascular disease, stroke, dementia, liver diseases, gastrointestinal bleeding, cancer, increases in neutrophil count, potassium, urea, creatinine, aspartate transaminase, alanine transaminase, bilirubin, D-dimer, high sensitive troponin-I, lactate dehydrogenase, activated partial thromboplastin time, prothrombin time, and C-reactive protein, as well as decreases in lymphocyte count, platelet, hematocrit, albumin, sodium, low-density lipoprotein, high-density lipoprotein, cholesterol, glucose, and base excess. The model based on test results taken on the day of admission demonstrated an excellent predictive value. Incorporation of test results on successive time points did not further improve risk prediction. The derived score system was evaluated with out-of-sample five-cross-validation (AUC: 0.86, 95% CI: 0.82–0.91) and external validation (N = 202, AUC: 0.89, 95% CI: 0.85–0.93). A simple clinical score accurately predicted severe COVID-19 disease, even without including symptoms, blood pressure or oxygen status on presentation, or chest radiograph results.

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Section: Methodsmentioning

confidence: 99%

Development of a multivariable prediction model for severe COVID-19 disease: a population-based study from Hong Kong

Zhou

Lee²,

Wang³

et al. 2021

npj Digit. Med.

Self Cite

View full text Add to dashboard Cite

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“…COVID-19 infection associated with myocardial injury may demonstrate ST segment deviations (elevation or depression), T wave inversion, and pathologic Q waves [ [3] , [4] , [5] , 19 , 20 , 69 ]. One study found ST segment and T wave changes to be the most common abnormality in patients requiring ICU admission, occurring in 40% of patients [ 70 ]. Another investigation noted that nonspecific repolarization changes including ST segment and T wave abnormalities were encountered in 41% of patients; reportedly, these findings resulted from myocardial injury and are associated with poor outcomes, including increased need for ICU admission, more frequent mechanical ventilatory support, and increased mortality [ 32 , 40 ].…”

Section: Discussionmentioning

confidence: 99%

Electrocardiographic manifestations of COVID-19

Long

Brady

Bridwell

et al. 2021

The American Journal of Emergency Medicine

113

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Introduction Coronavirus disease of 2019 (COVID-19) is a lower respiratory tract infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This disease can impact the cardiovascular system and lead to abnormal electrocardiographic (ECG) findings. Emergency clinicians must be aware of the ECG manifestations of COVID-19. Objective This narrative review outlines the pathophysiology and electrocardiographic findings associated with COVID-19. Discussion COVID-19 is a potentially critical illness associated with a variety of ECG abnormalities, with up to 90% of critically ill patients demonstrating at least one abnormality. The ECG abnormalities in COVID-19 may be due to cytokine storm, hypoxic injury, electrolyte abnormalities, plaque rupture, coronary spasm, microthrombi, or direct endothelial or myocardial injury. While sinus tachycardia is the most common abnormality, others include supraventricular tachycardias such as atrial fibrillation or flutter, ventricular arrhythmias such as ventricular tachycardia or fibrillation, various bradycardias, interval and axis changes, and ST segment and T waves changes. Several ECG presentations are associated with poor outcome, including atrial fibrillation, QT interval prolongation, ST segment and T wave changes, and ventricular tachycardia/fibrillation. Conclusions This review summarizes the relevant ECG findings associated with COVID-19. Knowledge of these findings in COVID-19-related electrocardiographic presentations may assist emergency clinicians in the evaluation and management of potentially infected and infected patients.

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“…In this regard, electrocardiography (ECG) may be useful to discern acute myocardial injury or rhythm alterations in COVID-19 patients. ECG characteristics in COVID-19 pneumonia have been already described in previous studies, but their relations to outcomes are still debated [ 5 – 7 ]. The aim of the present study was, indeed, to explore ECG abnormalities at admission in all-comer COVID-19 patients admitted to the Emergency Department of Modena University Hospital.…”

Section: Introductionmentioning

confidence: 99%

Clinical and electrocardiographic characteristics at admission of COVID-19/SARS-CoV2 pneumonia infection

et al. 2021

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Electrocardiograhic characteristics in patients with coronavirus infection: A single‐center observational study

Abstract: This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Cited by 39 publications

References 21 publications

Development of a multivariable prediction model for severe COVID-19 disease: a population-based study from Hong Kong

Development of a multivariable prediction model for severe COVID-19 disease: a population-based study from Hong Kong

Electrocardiographic manifestations of COVID-19

Clinical and electrocardiographic characteristics at admission of COVID-19/SARS-CoV2 pneumonia infection

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