key: cord-0051497-4d8i7hh0 authors: Sengupta, Partho P.; Chandrashekhar, Y.S. title: Cardiac Involvement in the COVID-19 Pandemic: Hazy Lessons from Cardiac Imaging? date: 2020-10-10 journal: JACC Cardiovasc Imaging DOI: 10.1016/j.jcmg.2020.10.001 sha: 58d7a8b0dfd6964c9354326968f0943c1eccff93 doc_id: 51497 cord_uid: 4d8i7hh0 nan , but there has been special interest in the presence of cardiac injury, seen in up to 12% of all hospitalized patients, with nearly one-half of the patients referred for cardiac imaging having abnormal studies (3, 4) . However, the reason why specific individuals may have a propensity for cardiac involvement after SARS-CoV-2 infection remains a mystery, and imaging might provide some lessons. JACC: Cardiovascular Imaging (iJACC) has seen an excess of COVID-19 papers over the last 6 months, and despite this deluge, only a few of those have contributed to our understanding in a new and novel manner there is so much we still do not know. In this month's issue of iJACC, we present a "COVID-19 collection" to share intriguing observations that highlight the multiple, complex diagnostic uncertainties associated with cardiac involvement in patients with COVID-19. The initial investigations from Wuhan alerted us to the high propensity of hospitalized patients with preexisting heart conditions to develop cardiac injury (5) . The presence of risk factors and pre-existing coronary atherosclerosis, as identified by imaging coronary artery calcification using computerized tomography, was associated with a worse prognosis in hospitalized patients with COVID-19 (6) . Myocardial dysfunction is common in many inflammatory syndromes, and both left ventricular and right ventricular function, especially measured with deformation imaging, has strong prognostic significance in the failing heart (7) . The right ventricle has been called the barometer of all that lies ahead (8) . Right-heart dilation and dysfunction seems to be a high risk marker for respiratory failure or hospital mortality (9) (10) (11) . Speckle tracking echocardiography to delineate latent right and left heart dysfunction has improved prognostication of hospitalized patients (11) (12) (13) . However, most of the prognostic models continue to wrestle with the uncertainties of the risk prediction because of the wide range of underlying -CMR can provide refined functional information (16) and could be an area of fertile research in these patients. CMR is now believed to be the gold standard for diagnosing myocarditis (17) because of its high diagnostic accuracy (18) . In addition, it can quantitate fibrosis as well as inflammation (19) and identify complex presentations like MINOCA Q4 or takotsubo that could affect management (20) . However, most studies in the literature Q5 , except for a few (21) , have been small, done at varying times, and are convenience samples. Two studies in this issue of iJACC report important findings from the use of CMR imaging for understanding myocarditis-like presentations (22, 23) . An initial report of 10 cases from Italy described 2 cases of stress cardiomyopathy with apical ballooning, whereas the remaining patients showed for high-level athletes (33, 34) . We desperately need more information regarding the clinical significance and long-term evolution of imaging findings. Until that time, careful delineation of individual risk with attention to symptoms, biomarkers, electrocardiography, and handheld cardiac ultrasound during clinical evaluations seem justified for rationalizing further use of cardiac imaging. One limitation of the imaging papers on this pandemic is that they often do not, a priori, help us get patient specific, clinically actionable information; most identify damage that has happened, derive prognostic models, or identify general etiologies for abnormal findings like the cause of troponin leak. These studies have not, as yet, helped with creating unique tailored treatment algorithms specific for COVID-19. Because of the uncertainties, it is time we strengthen the evidence. Retrospective cohort and single-center studies are highly unlikely to give us definitive answers to these unknowns. Effort and focus should be concentrated on collating multicenter experience into registries that assess diagnostic strategies while targeting patient-oriented outcomes. If traditional study methodologies fail, new data science approaches may be useful to assess causation and/or effectiveness and risk prediction. As a community, our responsibility is to ensure that our response to the pandemic is based on sound scientific foundations, and this is even more applicable to diagnostic modalities like imaging that can find a lot of incidental pathology in the midst of other relevant findings. 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