Press release | April 29, 2022
COVID-19 can cause a rare form of heart inflammation, study says
An international study co-authored by Professor Carlo Vittorio Cannistraci, Director of the Center for Complex Network Intelligence (CCNI) at the Tsinghua Laboratory of Brain and Intelligence, has investigated the extent to which COVID-19 can cause heart inflammation. The results of this study are published in Circulation, one of the most prestigious journals in cardiovascular science. The study concludes that COVID-19–associated acute myocarditis is rare and mainly occurs in the absence of pneumonia.
COVID-19 is known to majorly attack lungs causing pneumonia, and its impact on heart regardless of pneumonia has not been fully investigated. A form of heart tissue inflammation called Acute myocarditis (AM) is thought to be a rare cardiovascular complication of COVID-19 infections, although minimal data are available beyond case reports. Therefore, a reliable statistical estimation of COVID-19–associated acute myocarditis prevalence is crucial for tailoring diagnostic and therapeutic plans in COVID-19-associated cardiovascular diseases. The research team asked if acute myocarditis is a possible rare complication of COVID-19 infections and how rare it is it. These are fundamental questions that can advance scientific understanding of the consequences of COVID-19 infections and the related clinical planning.
Dr. Enrico Ammirati is a cardiologist at the Niguarda Hospital in Milano and a world-renowned expert on acute myocarditis and cardiomyopathies. He decided to design and lead a study to reply to the questions above. The study investigates the prevalence, baseline characteristics, in-hospital management, and outcomes for patients with COVID-19–associated acute myocarditis on the basis of a retrospective cohort from 23 hospitals in the United States and Europe. A total of 112 patients with suspected acute myocarditis from 56963 hospitalized patients with COVID-19 were evaluated between February 1, 2020, and April 30, 2021. Inclusion criteria were hospitalization for COVID-19 and a diagnosis of acute myocarditis on the basis of endomyocardial biopsy or increased troponin level plus typical signs of acute myocarditis on cardiac magnetic resonance imaging. The study identified 97 patients with possible acute myocarditis, and among them, 54 patients with definite/probable acute myocarditis supported by endomyocardial biopsy in 17 (31.5%) patients or magnetic resonance imaging in 50 (92.6%).
Dr Ammirati, who is now the first author and co-corresponding author of the published scientific article, says: “Replying to the question of how rare is COVID-19–associated acute myocarditis means to estimate its prevalence, for instance: 1 on 1000 COVID-19 cases present acute myocarditis. However, given the complexity of the data at hand, replying to this question and what is the uncertainty on prevalence estimation represented a serious data science obstacle in the study”. Hence, Dr. Ammirati contacted Dr. Cannistraci whose expertise ranges from complex network intelligence to data science for complex systems. The mission was clear: designing together an advanced statistical analysis able to estimate not only the prevalence of COVID-19–associated acute myocarditis but also the margin inside which this estimation could oscillate due to data uncertainly and complexity.
Dr Cannistraci notes: “The results of the analysis are surprising because of the rarity of the COVID-19–associated acute myocarditis disease and for this reason its diagnosis might need a special attention between clinicians. Although the disease is rare, a fast diagnosis and therapy can save many lives given the worldwide spread of COVID-19. Including only definite/probable cases (lower prevalence estimate) among hospitalized patients, a mean prevalence of 0.0024 COVID-19 Acute Myocarditis (2.4 cases among 1000 hospitalized patients with COVID-19) was estimated. And, also including possible cases (the most permissive prevalence, termed upper prevalence estimate), a mean prevalence of 0.0041 (4.1 cases among 1000 hospitalized patients with COVID-19 (Figure 1A). Then, using a second method that is based on a leave-1-out procedure, the boundaries inside which the estimation of the sample mean prevalence was expected to occur were assessed between 0.0012 and 0.0057 (Figure 1B).”
The results of this study are now published in Circulation which is one of the most prestigious journals in cardiovascular science. The study not only replied to the question about how rare is COVID-19–associated acute myocarditis but also assessed that the majority of acute myocarditis (57%) had no significant acute lung injury caused by COVID-19, but patients with concurrent pneumonia were more likely to develop hemodynamic instability, require temporary mechanical circulatory support, and die compared with those without pneumonia.
The funding for this research in the Center for Complex Network Intelligence lead by Dr. Cannistraci was provided by Zhou Yahui Chair Professorship of Tsinghua University, the starting funding of the Tsinghua Laboratory of Brain and Intelligence, and the National High-level Talent Program of the Ministry of Science and Technology of China.
Biographic and Institution Information
Carlo Vittorio Cannistraci is a theoretical engineer and computational innovator. He is a Professor in the Tsinghua Laboratory of Brain and Intelligence (THBI) and an adjunct professor in the Department of Computer Science and in the Department of Biomedical Engineering at Tsinghua University. He directs the Center for Complex Network Intelligence (CCNI) in THBI, which seeks to create pioneering algorithms at the interface between information science, physics of complex systems, complex networks and machine intelligence, with a focus in brain/life-inspired computing for big data analysis. These computational methods are often applied to precision biomedicine, neuroscience, social and economic science. https://brain.tsinghua.edu.cn/en/info/1010/1003.htm
The Tsinghua Laboratory of Brain and Intelligence (THBI) is an interdisciplinary research institute established in 2017. The institute aims to answer foundational questions about brain-mind and intelligence through cutting edge, interdisciplinary research effort. To do so, THBI brings together domestic and foreign experts from the fields of brain science, computational neuroscience, engineering, artificial intelligence, and cognitive science. https://brain.tsinghua.edu.cn/en/index.htm
“Prevalence, Characteristics, and Outcomes of COVID-19–Associated Acute Myocarditis”.
Enrico Ammirati, Laura Lupi, Matteo Palazzini, Nicholas S. Hendren, Justin L. Grodin, Carlo V. Cannistraci, Matthieu Schmidt, Guillaume Hekimian, Giovanni Peretto, Thomas Bochaton, Ahmad Hayek, Nicolas Piriou, Sergio Leonardi, Stefania Guida, Annalisa Turco, Simone Sala, Aitor Uribarri, Caroline M. Van de Heyning, Massimo Mapelli, Jeness Campodonico, Patrizia Pedrotti, Maria Isabel Barrionuevo Sánchez, Albert Ariza Sole, Marco Marini, Maria Vittoria Matassini, Mickael Vourch, Antonio Cannatà, Daniel I. Bromage, Daniele Briguglia, Jorge Salamanca, Pablo Diez-Villanueva, Jukka Lehtonen, Florent Huang, Stéphanie Russel, Francesco Soriano, Fabrizio Turrini, Manlio Cipriani, Manuela Bramerio, Mattia Di Pasquale, Aurelia Grosu, Michele Senni, Davide Farina, Piergiuseppe Agostoni, Stefania Rizzo, Monica De Gaspari, Francesca Marzo, Jason M. Duran, Eric D. Adler, Cristina Giannattasio, Cristina Basso, Theresa McDonagh, Mathieu Kerneis, Alain Combes, Paolo G. Camici, James A. de Lemos and Marco Metra
Circulation 2022; 145:1123–1139
Originally published 11 Apr 2022
Research Highlight on NATURE
“How rare is heart inflammation in COVID-19 patients?”
A large international study has shed new light on the frequency of acute myocarditis as a complication of SARS-CoV-2 infection.
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Figure 1. Prof. Carlo Vittorio Cannistraci, CCNI at THBI, Tsinghua University
Figure 2. Estimation of lower prevalence estimate (LPE) and upper prevalence estimate (UPE) of acute myocarditis among hospitalized patients with COVID-19. (Source: https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.121.056817 )
(A) Mean LPE and mean UPE computed with the respective CIs on the 23 centers. (B) Mean LPE and mean UPE with the respective CIs iteratively computed on 22 centers (by means of leave-1-out procedure). The red dashed line at the top (which is the maximum level reached by the CIs) and the black dashed line at the bottom (which is the minimum level reached by the CIs) represent the boundaries inside which the estimation of the sample mean prevalence is expected to occur.