Characterization and Prognostic Role of Myocardial Injury in Patients With COVID-19. The CardioCOVID Gemelli Study.
Conditions: Myocardial Injury, COVID-19
Sponsor: Fondazione Policlinico Universitario Agostino Gemelli IRCCS

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Characterization and prognostic relevance of myocardial injury in patients with 
coronavirus disease-2019 (COVID-19): the “CardioCovid” study 
 
 
Acronym: CardioCovid 
 
 
 
Principal Investigator (PI): Rocco Antonio Montone 
 
 
 
Sub-investigators: Riccardo Rinaldi, Giulia La Vecchia 
 
 
 
Promoter: Fondazione Policlinico Universitario Agostino Gemelli (FPG-IRCCS) 
 
 
 
Funding: No profit (Research Fund of Institute of Cardiology) 
 
 
 
Version: 1.0 
 
Date: March 24, 2022 
 
 
 
Firma 
 
 

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Background 
 
Coronavirus disease-2019 (COVID-19) is a global pandemic caused by the novel severe 
acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that is resulting in substantial 
morbidity and mortality (1). Of interest, a significant proportion of patients presenting with 
COVID-19 infection and requiring hospitalization demonstrate biomarker evidence of 
myocardial injury (defined as elevation in circulating cardiac troponin levels), which has 
been shown to be associated with increased risk of in-hospital morbidity (i.e.: rate of non- 
invasive and invasive ventilation, access to intensive care unit, length of hospitalization, use 
of glucocorticoids or antibiotics) and mortality (2–6). The pathogenesis of myocardial injury 
in COVID-19 is still unclear, with proposed mechanisms that include cytokine-mediated 
damage, oxygen supply-demand imbalance, ischemic injury from microvascular thrombi 
formation and direct viral invasion of the myocardium (7–9). Notably, the clinical predictors 
of myocardial injury in COVID-19 infection are still unknown, and, therefore, there is lack 
of therapeutic opportunities to prevent or reduce myocardial injury and the related- 
unfavourable outcomes. 
Moreover, the post-acute sequelae of COVID-19 could potentially involve the pulmonary 
and several extrapulmonary organs, including the cardiovascular system (10). A few studies 
to date have investigated cardiovascular outcomes in the post-acute phase of the COVID- 
19, and most with a short duration of follow-up and a narrow selection of cardiovascular 
outcomes (11–13). Therefore, a comprehensive assessment of post-acute COVID-19 sequelae 
on the cardiovascular system is still missing and, of interest, studies addressing the post- 
acute COVID-19 sequelae in patients who developed myocardial injury during COVID-19 

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infection are lacking (14). 
 
Given these shortcomings in this evolving area of clinical research, a comprehensive 
characterization of myocardial injury in patients with COVID-19 infection and the 
identification of clinical predictors for its occurrence could allow the development of 
predictive models, thus helping clinicians in the disease phenotyping and the early 
assessment of the risk of unfavourable outcome and improving a fast determination of most 
appropriate treatments and clinical paths, efficient planning and use of clinical resources in 
terms of treatment type and units (such as intensive care or coronary intensive care). In 
addition, the identification of clinical predictors and/or protective factors for myocardial 
injury in COVID-19 infection may pave the way for the development of specific therapeutic 
strategies aiming to reduce the impact of myocardial injury on COVID-19-related morbidity 
and mortality. Finally, assessing the long-term cardiovascular impact of myocardial injury 
in COVID-19 infection could help clinicians in the prognostic stratification and the choice of 
therapeutic strategies in the post-acute COVID-19 phases, possibly identifying those 
patients that may need a more aggressive therapy and a closer follow-up. 
A comprehensive characterization of myocardial injury in COVID-19 infection is still 
lacking, and no clinical predictors of myocardial injury has been identified yet. Moreover, 
the long-term cardiovascular outcome of myocardial injury in the post-acute COVID-19 
phase is largely unknown. We thus hypothesize that: 
• that clinical predictors and/or protective factors (including vaccination status) for 
myocardial injury in COVID-19 infection could be identified, thus allowing the 
development of predictive models that could help clinicians in the disease phenotyping, 

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the early assessment of the risk of unfavourable outcome and improving a fast 
determination of most appropriate management; 
• that myocardial injury in COVID-19 patients could be associated with a worse clinical 
course characterized by higher incidence of in-hospital complications and need for 
escalation of therapies; 
• that myocardial injury in COVID-19 patients could be associated with a worse angina 
status and quality of life as well as with a higher rate of adverse cardiovascular events in 
the post-acute COVID-19 phase, thus helping in the prognostic stratification of COVID- 
19 patients and identifying those that may need a more aggressive therapy and a closer 
follow-up. 

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OBJECTIVES 
 
 
 
Primary objective 
 
To identify clinical predictors of myocardial injury in patients hospitalized for COVID-19 
infection since the introduction of vaccines (i.e., March 1, 2021). 
 
 
Secondary objectives 
 
1. To evaluate if the presence of myocardial injury during the index hospitalization for 
COVID-19 is associated with a worse in-hospital clinical course in terms of in-hospital 
complications and need for aggressive therapies. 
2. To evaluate if the presence of myocardial injury during the index hospitalization for 
COVID-19 are associated with a worse angina status and quality of life (evaluated using 
the Seattle Angina Questionnaire [SAQ] summary score (15)) at 12-months follow-up. 
3. To evaluate if the presence of myocardial injury during the index hospitalization for 
COVID-19 predicts a worse clinical outcome in terms of major adverse cardiovascular 
events (MACE) (defined as the composite of cardiovascular death, ischemic heart disease 
[acute or chronic coronary syndromes], stroke/transient ischemic attack [TIA] and 
hospitalization for heart failure), dysrhythmias (defined as the composite of new-onset 
atrial fibrillation and/or ventricular arrhythmias), inflammatory heart disease 
(pericarditis and/or myocarditis) or thrombotic disorders (pulmonary embolism, deep 
vein or superficial vein thrombosis) at 12-months follow-up. 

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METHODS 
 
Study design 
 
Retrospective and prospective observational study. 
 
 
 
Population 
 
All consecutive patients hospitalized at Policlinico Universitario A. Gemelli IRCSS since 
March 1, 2021, with a diagnosis of SARS-CoV-2 infection (≥1 positive nasopharyngeal swab) 
and at least one value of high-sensitivity cardiac troponin I (hs-cTnI) measured during the 
index hospitalization will be enrolled. Given that the patients are unable to physically sign 
the informed consent form due to epidemiological reasons (contamination of papers by 
SARS-CoV-2), each patient will verbally declare the consent to “Consenso al trattamento dei 
dati personali e dei campioni biologici a scopo di ricerca – no profit”, “modulo di 
dichiarazione della volontà espresso ai fini del consenso informato generale alle cure” and 
“modulo dossier sanitario – richiesta di oscuramento/deoscuramento di eventi/episodi” at 
the presence of at least two witnesses. 
 
 
Inclusion criteria 
 
• Age ≥18 years; 
 
• Overt COVID-19 infection (molecular nasopharyngeal swab positive for SARS-CoV-2 ≥1); 
 
• Patient with at least a high sensitivity Troponin I measured during hospitalization course. 
 
• Available data on vaccination. 
 
• Verbal informed consent 

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Exclusion criteria 
 
• Age <18 years; 
 
• Patient in whom at least one high sensitivity Troponin I value measured during the 
course of hospitalization is not available. 
• No data available on vaccination status. 
 
 
 
Variables and procedures 
 
This study will make use of Data Mart COVID-19 developed within Generator Real World 
Data Unit. The Data Mart COVID-19 belongs to the family of the diagnostic and prognostic 
mini-bots developed within the Generator Real World Data Unit project in which artificial 
intelligence solutions (such as text mining) are adopted to automatically extract both 
structured and unstructured data from hospital databases of patients infected from SARS- 
CoV-2. Feature selection and machine learning algorithms can be applied to develop 
diagnostic and prognostic support systems for clinicians. 
 
 
The specific data that which will be exploited are: 
 
• Demographics data; 
 
• Patient comorbidities, vital signs and symptoms at the time of admission; 
 
• Laboratory analysis; 
 
• In-hospital complications (e.g.: need for non-invasive or invasive ventilation, access to 
intensive care unit, length of hospitalization, use of glucocorticoids or antibiotics, death); 

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• Blood gas exchange information; 
 
• Radiological reports; 
 
• Medications at the time of admission, during hospitalization and at the time of discharge; 
 
• Vaccination status. 
 
 
 
Clinical follow-up 
 
All patients will undergo a clinical follow-up by telephonic interview and/or clinical visit 
at 12 months from hospital discharge, during which the incidence of MACE, 
dysrhythmias, inflammatory heart disease and/or thrombotic disorders (both as incidence 
of the composite and as incidence of each individual components) in the past months will 
be investigated, the SAQ will be administered and the SAQ summary score will be 
collected. 

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ENDPOINTS 
 
Primary endpoint 
 
To assess for the presence of clinical predictors for the occurrence of myocardial injury, 
defined as at least one value of hs-cTnI >99th percentile upper reference limit (16), in patients 
hospitalized for COVID-19 infection. 
 
 
Secondary endpoints 
 
1. The occurrence of myocardial injury during the index hospitalization for COVID-19 
infection will be correlated with in-hospital complications (defined as the composite of 
the need for non-invasive or invasive ventilation, access to intensive care unit, length of 
hospitalization, use of glucocorticoids or antibiotics, and death). 
2. The occurrence of myocardial injury in patients during the index hospitalization for 
COVID-19 infection will be included in a prediction model and correlated with future 
cardiovascular events (MACE), dysrhythmias, inflammatory heart disease and/or 
thrombotic disorders at 12-months follow-up. 
3. The occurrence of myocardial injury during the index hospitalization for COVID-19 
infection will be correlated with angina status and quality of life, evaluated using the 
SAQ summary score, at 12-months follow-up. 

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STATISTICAL ANALYSIS PLAN 
 
 
 
Sample size calculation 
 
A comprehensive characterization of myocardial injury in COVID-19 infection is still 
lacking, and no clinical predictors of myocardial injury has been identified yet. Moreover, 
the long-term cardiovascular outcome of myocardial injury in the post-acute COVID-19 
phase is largely unknown. Thus, this configures as a pilot study on large-scale cohort. As 
such no formal sample size calculation is needed. However, based on the COVID-19 
datamart of our clinical institution and on inclusion criteria, we expect to include in the 
study around 2000 patients hospitalized with COVID-19. Such a sample size is largely 
consistentwith common rules of thumb for pilot studies. In addition, the estimated sample 
size is largely consistent with the van Smeden metamodels related to the new statistical 
approaches for the development of clinical prediction models (17). The square root of the 
mean squared prediction error (rMPSE) and mean absolute prediction error (MAPE) may 
be approximated via the results of these. Lower values for rMSPE and MAPE indicate better 
performance. For instance, at a sample size of N=400, with P=8 candidate predictors and an 
expected event fraction of ¼, the predicted out-of-sample rMPSE would be 0.065 when ML 
model (without variable selection) is applied, and 0.053 for Ridge regression; MAPE would 
instead be 0.045 for the ML model and 0.038 for the Ridge regression. 

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Statistical analysis 
 
All demographic, clinical and laboratory characteristics will be summarized by descriptive 
statistics techniques. In depth, qualitative variables will be expressed by absolute and 
relative percentage frequencies. Quantitative variables, indeed, will be reported either as 
mean and standard deviation (SD) or median and interquartile range (IQR), respectively in 
the case they were normally or not normally distributed. Their distribution will be 
previously assessed by the Shapiro Wilk test. Between groups differences in the 
demographic, clinical, laboratory and pathologic features will be assessed by the Chi Square 
or the Fisher’s exact test as for qualitative variables, with Yates correction, as appropriate, 
whilst quantitative variables will be evaluated either by the Student’s t test or the Mann- 
Whitney U test, according to their distribution. Univariable and multivariable logistic 
regression models will be performed to evaluate the presence of clinical predictors for the 
occurrence of myocardial injury in COVID-19 infection. Results will be expressed as odds 
ratio (OR) with 95% confidence interval (CI). Model calibration performances will be 
evaluated via the Hosmer-Lemeshow goodness of fit test with 10 groups. Model 
performance will be assessed through receiver-operator characteristic (ROC) curve analysis 
by reporting the area under the curve, sensitivity, specificity and +/− likelihood ratio (LR). 
Univariable and multivariable logistic regression analysis will be performed also to assess 
the association of myocardial injury in COVID-19 infection with in-hospital complications 
and need for aggressive therapies. 
Univariable and multivariable multiple linear regression will be instead used to assess 

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whether SAQ summary score at 12-months follow-up is affected by myocardial injury in 
patients with COVID-19 infection. 
Finally, to assess whether the presence of myocardial injury during COVID-19 predicts a 
worse outcome, in term of both MACEs at 12 months and single components, uni- and 
multivariable interaction proportional hazard Cox regression models will be computed. We 
will consider, as for MACEs, time to first event, and for each component, time to each event. 
To 
evaluate 
combined 
effects 
between 
each 
clinical/laboratory 
predictor 
and 
presence/absence of Myocardial Injury (MI), multivariable interaction Cox models will be 
fitted, and the interaction hazard ratios (IHR) evaluated. In particular, for each predictor, 
one interaction Cox model will be fitted. In this framework, IHR = 1 indicates no synergy 
between predictor and MI, IHR <1 expresses a reduction of hazard due to the synergy, while 
IHR >1 an increased hazard. The interaction parameters (IHR) will be interpreted as 
difference (in HR terms) of the predictor and MI (absence of MI as reference category). 
Proportionality of the hazard functions will be assessed by visual inspection of hazard plots 
and Schoenfeld residuals. When proportionality was doubtful, weighted Cox regression 
models would be fitted. 
A two-tailed analysis will be performed and a p value <0.05 will be considered as statistically 
significant. Statistical analyses will be performed using R software version 4.2.1 (CRAN ®, 
R Core 2021) (and its packages survival, survminer, coxphw and Hmisc) (18). 

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Data privacy and data protection 
 
Privacy issues related to this project have been analyzed with Policlinico Gemelli Data 
Protection Officer (D.P.O.). As a GENERATOR RWD study, this project will be compliant 
with GDPR Italian and European directives and regulation (EU Directive 2016/679 and 
under Italian Laws: Decreto Legislativo 196/2003, Decreto Legislativo 101 2018, 
Autorizzazione Generale Garante 9/2016) 
The guidelines for data privacy are covered by two main documents prepared with the 
D.P.O.: 
• Generator main document “Linee Guida Utilizzo Real World Data” which prescribes the 
governance mechanisms for Generator through two main bodies (Comitato di 
Supervisione and Comitato Operativo) where representatives appointed from IRCSS and 
other key Units of Policlinico participate and have mandate to decide; 
• “Orizzonte Normativo Real World Data” prepared by the D.P.O., which provides the 
directives for the execution of any Generator study to assure its compliance with 
European and Italian GDPR directives. 
 
 
All RWD studies have research purposes and therefore consent-related procedures are 
consistent with European Directive 2016/679. The data architecture which supports RWD 
studies has been defined to provide the highest degree of protection, in accordance with all 
GDPR and security requirements. In this respect, RWD provides “protection by design” in 
each step of the process. 

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Significance and innovation 
 
This study will provide a detailed characterization of myocardial injury in patients with 
COVID-19 and its role in determining the different clinical courses of SARS-CoV-2 infection. 
Moreover, the aim of study is to identify, for the first time, the clinical predictors of the 
occurrence of myocardial injury in patients with COVID-19 infection that could allow the 
development of predictive models as well as help clinicians in the early assessment of the 
risk of myocardial injury and the prevention of the associated unfavourable outcomes. 
Furthermore, this study will characterize the cardiovascular outcomes in the post-acute 
COVID-19 phase, and it will evaluate for the first time the long-term clinical outcomes of 
patients who experienced myocardial injury, possibly paving the way for the 
implementation of specific therapies aiming to reduce the cardiovascular risk and the long- 
term sequelae of COVID-19. 

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