Characterization and Prognostic Role of Myocardial Injury in Patients With COVID-19. The CardioCOVID Gemelli Study.

COVID-19 Clinical Trial

— CardioCOVID  

Official title:

Characterization and Prognostic Role of Myocardial Injury in Patients With COVID-19. The CardioCOVID Gemelli Study.

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05392088
• Other study ID #: 4923
• Status: Recruiting
• Start date: May 6, 2022
• Est. completion date: August 2024

Study information

• Verified date: February 2024
• Source: Fondazione Policlinico Universitario Agostino Gemelli IRCCS
• Contact: Rocco A Montone, MD, PhD
• Phone: +39-0630154187
• Email: roccoantonio.montone[@]policlinicogemelli.it
• Is FDA regulated: No
• Study type: Observational [Patient Registry]

Clinical Trial Summary

The present retrospective and prospective observational study aims at evaluate the clinical predictors of myocardial injury in patients hospitalized for COVID-19 infection since the introduction of vaccines 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.

Description:

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 <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.

Sample size calculation:

A comprehensive characterization of myocardial injury in COVID-19 infection is still lacking, and no clinical predictors of myocardial injury have 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, the investigators expect to include in the study around 2000 patients hospitalized with COVID-19. Such a sample size is largely consistent with 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 1⁄4, 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.

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. 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. The investigators 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).

A pre-specified analysis to assess the preditors of myocardial injury across different age groups (< or = 60-year, 61-75-year, and > or = 75-year) will be performed.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 1500
• Est. completion date: August 2024
• Est. primary completion date: August 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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

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.

Related Conditions & MeSH terms

• COVID-19
• Myocardial Injury
• Wounds and Injuries

Intervention

Other:
• DATA EXTRACTION
For all patients enrolled Data Mart COVID-19 developed within Generator Real World will be used to automatically extract both structured and unstructured data from hospital databases of patients infected from SARS-CoV-2.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); Blood gas exchange information; Radiological reports; Medications at the time of admission, during hospitalization and at the time of discharge; Vaccination status.
• 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.

Locations

Country	Name	City	State
Italy	Fondazione Policlinico Universitario A. Gemelli IRCCS	Rome

Sponsors (1)

Lead Sponsor	Collaborator
Fondazione Policlinico Universitario Agostino Gemelli IRCCS

Country where clinical trial is conducted

Italy, 

References & Publications (17)

Al-Aly Z, Xie Y, Bowe B. High-dimensional characterization of post-acute sequelae of COVID-19. Nature. 2021 Jun;594(7862):259-264. doi: 10.1038/s41586-021-03553-9. Epub 2021 Apr 22. — View Citation

Bavishi C, Bonow RO, Trivedi V, Abbott JD, Messerli FH, Bhatt DL. Special Article - Acute myocardial injury in patients hospitalized with COVID-19 infection: A review. Prog Cardiovasc Dis. 2020 Sep-Oct;63(5):682-689. doi: 10.1016/j.pcad.2020.05.013. Epub 2020 Jun 6. — View Citation

Carfi A, Bernabei R, Landi F; Gemelli Against COVID-19 Post-Acute Care Study Group. Persistent Symptoms in Patients After Acute COVID-19. JAMA. 2020 Aug 11;324(6):603-605. doi: 10.1001/jama.2020.12603. — View Citation

Chan PS, Jones PG, Arnold SA, Spertus JA. Development and validation of a short version of the Seattle angina questionnaire. Circ Cardiovasc Qual Outcomes. 2014 Sep;7(5):640-7. doi: 10.1161/CIRCOUTCOMES.114.000967. Epub 2014 Sep 2. — View Citation

Daugherty SE, Guo Y, Heath K, Dasmarinas MC, Jubilo KG, Samranvedhya J, Lipsitch M, Cohen K. Risk of clinical sequelae after the acute phase of SARS-CoV-2 infection: retrospective cohort study. BMJ. 2021 May 19;373:n1098. doi: 10.1136/bmj.n1098. — View Citation

Fauci AS, Lane HC, Redfield RR. Covid-19 - Navigating the Uncharted. N Engl J Med. 2020 Mar 26;382(13):1268-1269. doi: 10.1056/NEJMe2002387. Epub 2020 Feb 28. No abstract available. — View Citation

Guo T, Fan Y, Chen M, Wu X, Zhang L, He T, Wang H, Wan J, Wang X, Lu Z. Cardiovascular Implications of Fatal Outcomes of Patients With Coronavirus Disease 2019 (COVID-19). JAMA Cardiol. 2020 Jul 1;5(7):811-818. doi: 10.1001/jamacardio.2020.1017. Erratum In: JAMA Cardiol. 2020 Jul 1;5(7):848. — View Citation

Huang C, Huang L, Wang Y, Li X, Ren L, Gu X, Kang L, Guo L, Liu M, Zhou X, Luo J, Huang Z, Tu S, Zhao Y, Chen L, Xu D, Li Y, Li C, Peng L, Li Y, Xie W, Cui D, Shang L, Fan G, Xu J, Wang G, Wang Y, Zhong J, Wang C, Wang J, Zhang D, Cao B. 6-month consequences of COVID-19 in patients discharged from hospital: a cohort study. Lancet. 2021 Jan 16;397(10270):220-232. doi: 10.1016/S0140-6736(20)32656-8. Epub 2021 Jan 8. — View Citation

Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, Cheng Z, Yu T, Xia J, Wei Y, Wu W, Xie X, Yin W, Li H, Liu M, Xiao Y, Gao H, Guo L, Xie J, Wang G, Jiang R, Gao Z, Jin Q, Wang J, Cao B. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020 Feb 15;395(10223):497-506. doi: 10.1016/S0140-6736(20)30183-5. Epub 2020 Jan 24. Erratum In: Lancet. 2020 Jan 30;: — View Citation

Li JW, Han TW, Woodward M, Anderson CS, Zhou H, Chen YD, Neal B. The impact of 2019 novel coronavirus on heart injury: A Systematic review and Meta-analysis. Prog Cardiovasc Dis. 2020 Jul-Aug;63(4):518-524. doi: 10.1016/j.pcad.2020.04.008. Epub 2020 Apr 16. — View Citation

Lippi G, Lavie CJ, Sanchis-Gomar F. Cardiac troponin I in patients with coronavirus disease 2019 (COVID-19): Evidence from a meta-analysis. Prog Cardiovasc Dis. 2020 May-Jun;63(3):390-391. doi: 10.1016/j.pcad.2020.03.001. Epub 2020 Mar 10. No abstract available. — View Citation

Shi S, Qin M, Shen B, Cai Y, Liu T, Yang F, Gong W, Liu X, Liang J, Zhao Q, Huang H, Yang B, Huang C. Association of Cardiac Injury With Mortality in Hospitalized Patients With COVID-19 in Wuhan, China. JAMA Cardiol. 2020 Jul 1;5(7):802-810. doi: 10.1001/jamacardio.2020.0950. — View Citation

Thygesen K, Alpert JS, Jaffe AS, Chaitman BR, Bax JJ, Morrow DA, White HD; Executive Group on behalf of the Joint European Society of Cardiology (ESC)/American College of Cardiology (ACC)/American Heart Association (AHA)/World Heart Federation (WHF) Task Force for the Universal Definition of Myocardial Infarction. Fourth Universal Definition of Myocardial Infarction (2018). J Am Coll Cardiol. 2018 Oct 30;72(18):2231-2264. doi: 10.1016/j.jacc.2018.08.1038. Epub 2018 Aug 25. No abstract available. — View Citation

van Smeden M, Moons KG, de Groot JA, Collins GS, Altman DG, Eijkemans MJ, Reitsma JB. Sample size for binary logistic prediction models: Beyond events per variable criteria. Stat Methods Med Res. 2019 Aug;28(8):2455-2474. doi: 10.1177/0962280218784726. Epub 2018 Jul 3. — View Citation

Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, Wang B, Xiang H, Cheng Z, Xiong Y, Zhao Y, Li Y, Wang X, Peng Z. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA. 2020 Mar 17;323(11):1061-1069. doi: 10.1001/jama.2020.1585. Erratum In: JAMA. 2021 Mar 16;325(11):1113. — View Citation

Xie Y, Xu E, Bowe B, Al-Aly Z. Long-term cardiovascular outcomes of COVID-19. Nat Med. 2022 Mar;28(3):583-590. doi: 10.1038/s41591-022-01689-3. Epub 2022 Feb 7. — View Citation

Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, Xiang J, Wang Y, Song B, Gu X, Guan L, Wei Y, Li H, Wu X, Xu J, Tu S, Zhang Y, Chen H, Cao B. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020 Mar 28;395(10229):1054-1062. doi: 10.1016/S0140-6736(20)30566-3. Epub 2020 Mar 11. Erratum In: Lancet. 2020 Mar 28;395(10229):1038. Lancet. 2020 Mar 28;395(10229):1038. — View Citation

* Note: There are 17 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	To identify the clinical predictors of myocardial injury	Th occurence of clinical predictors of myocardial injury during the index hospitalization (defined as at least one value of hs-cTnI >99th percentile upper reference limit) will be evaluated using univariable and multivariable logistic regression models. Results will be expressed as odds ratio (OR) with 95% confidence interval (CI).	Up to 30 days
Secondary	To assess the correlation between myocardial injury and in-hospital complications	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) using univariable and multivariable logistic regression analysis.	Up to 30 days
Secondary	To assess if myocardial injury can predict MACE at 1-year follow-up	The occurence of myocardial injury during the index hospitalization for COVID-19 infection will be included in a prediction model and correlated with 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, inflammatory heart disease and/or thrombotic disorders at 12-months follow-up using uni- and multivariable interaction proportional hazard Cox regression models.	1 year