Myocardial Injury and Prognosis in Hospitalized COVID-19 Patients in Brazil: Results From The Brazilian COVID-19 Registry

Abstract

Background

Cardiovascular complications of COVID-19 are important aspects of the disease’s pathogenesis and prognosis. Evidence on the prognostic role of troponin and myocardial injury in Latin American hospitalized COVID-19 patients is still scarce.

Objectives

To evaluate myocardial injury as independent predictor of in-hospital mortality and invasive mechanical ventilation support in hospitalized patients, from the Brazilian COVID-19 Registry.

Methods

This cohort study is a substudy of the Brazilian COVID-19 Registry, conducted in 31 Brazilian hospitals of 17 cities, March-September 2020. Primary outcomes included in-hospital mortality and invasive mechanical ventilation support. Models for the primary outcomes were estimated by Poisson regression with robust variance, with statistical significance of p<0.05.

Results

Of 2,925 patients (median age of 60 years [48-71], 57.1% men), 27.3% presented myocardial injury. The proportion of patients with comorbidities was higher among patients with cardiac injury (median 2 [1-2] vs. 1 [0-2]). Patients with myocardial injury had higher median levels of brain natriuretic peptide, lactate dehydrogenase, creatine phosphokinase, N-terminal pro-brain natriuretic peptide, and C-reactive protein than patients without myocardial injury. As independent predictors, C-reactive protein and platelet counts were related to the risk of death, and neutrophils and platelet counts were related to the risk of invasive mechanical ventilation support. Patients with high troponin levels presented a higher risk of death (RR 2.03, 95% CI 1.60-2.58) and invasive mechanical ventilation support (RR 1.87, 95% CI 1.57-2.23), when compared to those with normal troponin levels.

Conclusion

Cardiac injury was an independent predictor of in-hospital mortality and the need for invasive mechanical ventilation support in hospitalized COVID-19 patients.

COVID-19; Coronavirus Infections; Troponin; Artificial Respiration; Mortality

Resumo

Fundamento

As complicações cardiovasculares da COVID-19 são aspectos importantes da patogênese e do prognóstico da doença. Evidências do papel prognóstico da troponina e da lesão miocárdica em pacientes hospitalizados com COVID-19 na América Latina são ainda escassos.

Objetivos

Avaliar a lesão miocárdica como preditor independente de mortalidade hospitalar e suporte ventilatório mecânico em pacientes hospitalizados, do registro brasileiro de COVID-19.

Métodos

Este estudo coorte é um subestudo do registro brasileiro de COVID-19, conduzido em 31 hospitais brasileiros de 17 cidades, de março a setembro de 2020. Os desfechos primários incluíram mortalidade hospitalar e suporte ventilatório mecânico invasivo. Os modelos para os desfechos primários foram estimados por regressão de Poisson com variância robusta, com significância estatística de p<0,05.

Resultados

Dos 2925 pacientes [idade mediana de 60 anos (48-71), 57,1%], 27,3% apresentaram lesão miocárdica. A proporção de pacientes com comorbidades foi maior nos pacientes com lesão miocárdica [mediana 2 (1-2) vs. 1 (0-20)]. Os pacientes com lesão miocárdica apresentaram maiores valores medianos de peptídeo natriurético cerebral, lactato desidrogenase, creatina fosfoquinase, N-terminal do pró-peptídeo natriurético tipo B e proteína C reativa em comparação a pacientes sem lesão miocárdica. Como fatores independentes, proteína C reativa e contagem de plaquetas foram relacionados com o risco de morte, e neutrófilos e contagem de plaquetas foram relacionados ao risco de suporte ventilatório mecânico invasivo. Os pacientes com níveis elevados de troponina apresentaram um maior risco de morte (RR 2,03, IC95% 1,60-2,58) e suporte ventilatório mecânico (RR 1,87;IC95% 1,57-2,23), em comparação àqueles com níveis de troponina normais.

Conclusão

Lesão cardíaca foi um preditor independente de mortalidade hospitalar e necessidade de suporte ventilatório mecânico em pacientes hospitalizados com COVID-19.

COVID-19; Infecções por Coronavírus; Troponina; Respiração artificial; Mortalidade

Introduction

Cardiovascular complications of the coronavirus disease 19 (COVID-19)¹ , ² represent an important aspect of the disease’s pathogenesis and prognosis. Myocardial injury is common in hospitalized patients with COVID-19 and has been reported in 7.2% to 36% of all patients.³ - ⁶ This disease has proven to be associated with a poorer prognosis;⁷ however, evidence concerning its prognostic role in hospitalized COVID-19 patients in Latin America is still scarce.

In Brazil, many people are still affected by Chagas heart disease and rheumatic valvulopathy.⁸ Furthermore, numerous polymorphisms, heterogeneity, and miscegenation exist among the population, which may influence rates of myocardial injury and levels of biomarkers in COVID-19 patients.⁹ In a previous analysis performed by our research group, we developed and validated a score with high discriminatory ability to predict mortality among Brazilian patients using data that are easily available on hospital admission, the ABC₂-SPH score.¹⁰

The present study aimed to evaluate myocardial injury as an independent predictor of in-hospital mortality and invasive mechanical ventilation support in COVID-19 hospitalized patients, from the Brazilian COVID-19 Registry.

Materials and methods

This multicenter retrospective cohort study is a substudy of the Brazilian COVID-19 Registry, conducted in 31 Brazilian hospitals in 17 cities from five states (Minas Gerais, Pernambuco, Rio Grande do Sul, Santa Catarina, and São Paulo), detailed in a previous report.¹¹ The study was approved by the Brazilian National Ethics Committee in Research (CAAE: 30350820.5.1001.0008). Individual informed consent was waived due to the severity of the situation imposed by the pandemic and to the retrospective nature of the study.

This study included consecutive adult patients (aged ≥18 years) with laboratory-confirmed COVID-19,¹² who were admitted to the participating hospitals between March and September 2020 and who had at least one troponin value. Patients with an underlying diagnosis of chronic heart failure (HF) in the medical records, with glomerular filtration rate (GFR) of lower than 30 mL/min/1.73m² (estimated by the Chronic Kidney Disease Epidemiology Collaboration [CKD-EPI], set forth by Levey et al.¹³ ), those who were discharged in less than 24 hours, and those diagnosed with COVID during hospitalization were excluded from the study. Sample size calculation was not performed, all eligible patients were included.

Data collection

Demographic and clinical characteristics, exams (laboratory, electrocardiogram, and echocardiogram), treatments, and outcome data were collected by trained healthcare professionals and interns in each health center, using the Research Electronic Data Capture (REDCap) tool (version 7.3.1),¹⁴ - ¹⁶ at the Telehealth Center of the Federal University of Minas Gerais General Hospital. Comprehensive checks were undertaken to ensure a high-quality data collection. A code was developed in R software to identify non-conforming and inconsistency values, as previously described, based on expert-guided rules, and each study center was contacted to check and correct data, if needed.¹⁰

Myocardial injury, troponin assay and study groups

Myocardial injury was defined as an elevation of cardiac troponin (cTnI or cTnT) above the upper reference limit (URL) of the 99^thpercentile, according to the Fourth Universal Definition of Myocardial Infarction.¹⁷ Troponin was measured at the discretion of the treating physician. Troponin values at hospital admission, as well as minimum and maximum values after 24 hours of hospitalization were collected. Any abnormal value among these measures was considered for analysis in the present study. The reference group was composed of patients with troponin values within the normal range.

Troponin assay varied among different centers. Due to the shortage of crucial chemicals at the beginning of the pandemic, some centers used more than one assay during the study period. Therefore, the analysis considered the URL of the 99^thpercentile for each test for men and women, as reported in the Supplemental Material ( Table S1 ).

Outcomes

The primary outcomes were in-hospital mortality and invasive mechanical ventilation support. Secondary outcomes included cardiovascular complications (acute HF, acute myocardial infarction, and myocarditis), bleeding, thromboembolic events, septic shock, disseminated intravascular coagulation, nosocomial infection, admission to the intensive care unit (ICU), ICU length of stay, hospital length of stay, and need for renal replacement therapy.

Statistical analysis

Statistical analyses were performed in three main steps: (i) descriptive, (ii) bivariate (evaluation of the association of the outcome with each variable of interest), and (iii) multivariate analyses.

Descriptive analyses were run to describe all variables, stratified into control and study groups (patients with myocardial injury). Categorical variables were described as absolute and relative frequencies. The Shapiro-Wilk normality test was performed to determine whether the continuous variables were normally distributed. As all variables were found to have a non-normal distribution, they were described as medians and interquartile ranges (IQR). The number of comorbidities was defined based on eight comorbidities that had been proved to have a prognostic impact on COVID-19 (hypertension, diabetes mellitus, obesity, coronary artery disease, atrial or flutter fibrillation, cirrhosis, cancer, and previous stroke).¹⁰

In the bivariate analysis, demographic and clinical variables, and outcomes were assessed using the Fisher’s exact test or the chi-square test to compare proportions, as appropriate. The Kruskal-Wallis test was used to compare medians of continuous variables, while the Dunn´s test was used as a post-hoc test. For the multivariate analyses, two predictive models were estimated to evaluate the role of elevated troponin on the primary outcomes: in-hospital mortality and invasive mechanical ventilation support. All variables included in the models were obtained at hospital admission. A set of potential predictive variables for the primary outcomes was selected a priori (supplemental material - Figure S3 ) based on previous scientific evidence of variables associated with a worse prognosis of COVID-19.¹⁰ Laboratory tests were performed at the discretion of the attending physician. Imaging test results were not included, since they are not always performed on hospital admission, and their interpretation involves the examiner’s judgment.

Models for the primary outcomes were estimated by Poisson regression with robust variance. In the model to predict invasive mechanical ventilation support, patients who were on invasive mechanical ventilation at admission were not included (n=72). Poisson regression was chosen due to the ability to estimate the relative risk (RR), which is the parameter of primary interest, since an elevated event rate was expected.¹⁸ , ¹⁹

The shaping of the prediction models divided the variables into five blocks by a stepwise-forward approach,²⁰ mutually inserted in the regression models one to five. As the main goal of the analysis was to identify the association of myocardial injury with the study outcomes, this variable was tested in all five models. The first one included only myocardial injury. The second added age and sex, the third added the number of comorbidities, and the fourth added clinical characteristics on hospital admission. The fifth multivariate model contained only the variables with a 5% significance level after adjusting for the other variables added to the previous multivariate models. The variables were included from the largest to the least significance, to test which associations between the explicative variables and the outcomes would remain significant throughout the process.

The statistical significance of the variables that were part of the models was evaluated by analyzing the RR and their respective 95% confidence intervals (95% CI), as well as by the p-value of the tests, aimed at reducing the probability of type I error. A comparison of the models’ goodness-of-fit tests was performed using the Akaike Information Criterion.²⁰

For the regression models, the RR and their respective 95% CI were estimated. All analyses were performed in the STATA software (StataCorp. 2012. Stata Statistical, version 12) and R software (version 4.0.2), using the tidyverse, lubridate, stringi, rlang, jsonlite, Rcurl, writexl, openxlsx, readxl, and sandwich packages. A p-value <0.05 was considered statistically significant.

Results

Baseline characteristics

Of 7,760 patients, 2,925 were included in the present analyses ( Figure 1 ). Demographic and clinical characteristics are shown in Table 1 . Patients with myocardial injury had a median age of 10 years older than the controls, a higher number of comorbidities, and a higher prevalence of underlying hypertension, coronary artery disease, ischemic stroke, atrial fibrillation, diabetes, chronic obstructive pulmonary disease (COPD), cancer, and chronic kidney disease (CKD). Additionally, there was a higher proportion of individuals with myocardial injury with abnormal mental state and lower peripheral oxygen saturation (SpO₂)/fraction of inspired oxygen (FiO₂) ratio (SF ratio) at hospital admission. Regarding laboratory parameters, patients with myocardial injury had higher median levels of C-reactive protein (CRP), lactate dehydrogenase (LDH), and N-terminal pro-brain natriuretic peptide (NT-proBNP)/brain natriuretic peptide (BNP), when compared to the controls. Electrocardiogram and echocardiogram results are reported in the supplemental material ( Table S2 ).

Figure 1
– Flowchart of patients included in the study.

Table 1
– Demographic and clinical characteristics of the study population upon hospital admission

Variables	Study group (n = 832) N (%)	Controls (n = 2,093) N (%)	Total (n = 2,925) N (%)	p-value
Men	456 (27.3%)	1213 (72.7%)	1669 (57.1%)	0.121
Age ( median )	67 (57-77)	57 (45-67)	60 (48-71)	0.000
Comorbidities
Overall number ( median )	2 (1-2)	1 (0-2)	1 (0-2)	0.000
Cardiovascular diseases
Hypertension	533 (64.1%)	1.010 (48.3%)	1.543 (52.7%)	0.000
Coronary artery disease	62 (7.5%)	82 (3.9%)	144 (4.9%)	0.000
Ischemic stroke	42 (5.1%)	40 (1.9%)	82 (2.8%)	0.000
Atrial fibrillation/flutter	38 (4.6%)	26 (1.2%)	64 (2.2%)	0.000
Chagas disease	2 (0.2%)	2 (0.1%)	4 (0.1%)	0.321
Rheumatic valve disease	2 (0.2%)	0 (0.0%)	2 (0.1%)	0.081
Metabolic diseases
Diabetes mellitus	289 (34.7%)	528 (25.2%)	817 (27.9%)	0.000
Obesity	167 (20.1%)	425 (20.3%)	592 (20.2%)	0.887
Respiratory diseases
Asthma	149 (5.9%)	151 (7.2%)	200 (6.8%)	0.200
COPD	75 (9.0%)	95 (4.5%)	170 (5.8%)	0.000
Other conditions
Cancer	62 (7.5%)	78 (3.7%)	140 (4.8%)	0.000
Rheumatic disease	20 (2.4%)	32 (1.5%)	52 (1.8%)	0.106
Chronic renal disease	25 (3.0%)	14 (0.7%)	39 (1.3%)	0.000
HIV	5 (0.6%)	13 (0.6%)	18 (0.6%)	0.950
Cirrhosis	7 (0.8%)	4 (0.2%)	11 (0.4%)	0.016
Clinical characteristics	(n = 832)	(n = 2.093)	(n = 2.925)
Glasgow <15	170 (20.4%)	156 (7.5%)	326 (11.1%)	0.000
	(n = 810)	(n = 2.057)	(n = 2.867)
SF ratio (median)	362 (213-438)	433 (343-457)	424 (329-452)	0.000
Systolic blood pressure	(n = 787)	(n = 1995)	(n = 2782)
≥90 (mmHg)	770 (97.8%)	1.997 (99.1%)	2.747 (98.7%)	0.003
<90 (mmHg)	9 (1.1%)	15 (0.7%)	24 (0.9%)
Inotrope requirement	8 (1.0%)	3 (0.1%)	11 (0.4%)
Diastolic blood pressure	(n = 796)	(n = 1989)	(n = 2785)
> 60 (mmHg)	620 (77.9%)	1.737 (87.3%)	2.357 (84.6%)	0.000
≤ 60 (mmHg)	100 (12.6%)	210 (10.6%)	310 (11.1%)
Inotrope requirement	76 (9.5%)	42 (2.1%)	118 (4.2%)

• 22

Patients with myocardial injury had higher in-hospital mortality and a higher frequency of all secondary outcomes ( Table 2 ). Multivariate regression models for in-hospital mortality and invasive mechanical ventilation support have shown that myocardial injury was a significant predictor for both outcomes, even when adjusting for the other variables. A decrease was found in the impact of elevated troponin levels on the multivariate model for in-hospital mortality ( Table 3 ), with the addition of the variables to the model. In the fifth multivariate model, patients with elevated troponin levels presented a higher risk of death when compared to the controls (RR: 2.03 [1.60-2.58]). Age, number of comorbidities, respiratory rate, SF ratio, and CRP on admission were also associated with a higher risk of death. Myocardial injury also proved to be an independent predictor of invasive mechanical ventilation support (RR: 1.87 [1.57-2.23]) ( Table 4 ). The number of comorbidities, respiratory rate, CRP, and number of neutrophils were associated with an increased risk of invasive mechanical ventilation support. By contrast, high SF ratio and platelet count were associated with a reduced risk of invasive mechanical ventilation support. For information on models, see supplemental material S4. The central illustration of the main results of the article.

Table 2
– Clinical outcomes of study patients

Variables	Study group (n = 832) N (%)	Controls (n = 2,093) N (%)	Total (n = 2,925) N (%)	p-value
Clinical assessment
Hospital length of stay ( median )	14 (7-25)	7 (4-13)	9 (5-16)	0.000
Admission to the ICU	584 (70.2%)	765 (36.6%)	1.349 (46.1%)	0.000
Length of stay in the ICU (days) ( median )	12 (6-22)	8 (4-16)	10 (5-19)	0.000
Invasive mechanical ventilation	509 (61.3%)	467 (22.3%)	976 (33.4%)	0.000
Acute respiratory distress syndrome	325 (39.1%)	428 (25.5%)	753 (25.7%)	0.000
Septic shock	258 (31.0%)	201 (9.6%)	459 (15.7%)	0.000
Nosocomial infection	186 (22.4%)	189 (9.0%)	375 (12.8%)	0.000
Hyperglycemia	148 (17.8%)	215 (10.3%)	363 (12.4%)	0.000
Vascular thrombosis	81 (9.7%)	125 (6.0%)	206 (7.1%)	0.000
Pulmonary thromboembolism	57 (6.9%)	95 (4.5%)	152 (5.2%)	0.011
Deep vein thrombosis	25 (3.0%)	31 (1.5%)	56 (1.9%)	0.007
Arterial thrombosis	6 (0.7%)	4 (0.2%)	10 (0.3%)	0.027
Acute or decompensated heart failure	33 (4.0%)	23 (1.1%)	56 (1.9%)	0.000
Acute myocardial infarction	25 (3.0%)	2 (0.1%)	27 (0.9%)	0.000
Myocarditis	7 (0.8%)	2 (0.1%)	9 (0.3%)	0.001
Bleeding	26 (53.1%)	24 (46.9%)	49 (1.7%)	0.000
Disseminated intravascular coagulation	7 (0.8%)	6 (0.3%)	13 (0.4%)	0.042
Death	376 (45.2%)	218 (10.4%)	594 (20.3%)	0.000

• ICU: intensive care unit.

Table 3
– Predictors (at hospital admission) of in-hospital mortality by Poisson regression model with robust variance

Variables	Multivariate model 1 RR (95% CI)	Multivariate model 2 RR (95% CI)	Multivariate model 3 RR (95% CI)	Multivariate model 4 RR (95% CI)	Multivariate model 5 RR (95% CI)
Myocardial injury	4.2482 (3.2820-5.4982)*	3.1956 (2.4335- 4.1962)*	3.1077 (2.4364- 3.9640)*	1.9057 (1.4843-2.4468)*	2.0323 (1.5995-2.5822)*
Age	---	1.0285 (1.0191-1.0379)*	1.0273 (1.0190- 1.0357)*	1.0262 (1.0176-1.0349)*	1.0269 (1.0192-1.0348)*
Female sex	---	0.8122 (0.6289-1.0489)***	0.7924 (0.6461-0.9718)**	0.9553 (0.7736-1.2621)***	---
Number of comorbidities****	---	---	1.1550 (1.0317- 1.2930)**	1.1089 (1.0187-1.2070)*	8.1862 (7.5153-8.9168)*
Respiratory rate	---	---	---	1.0213 (1.0018-1.0429)**	1.0223 (1.0086-1.0362)**
Heart rate	---	---	---	1.0039 (0.9994-1.0083)***	---
Systolic blood pressure <90 mmHg without inotropes	---	---	---	0.2508 (0.1062-0.5920)**	---
Systolic blood pressure <90 mmHg with inotropes	---	---	---	1.3022 (0.7688-2.2056)***	---
Glasgow <15	---	---	---	1.0723 (0.7372-1.5599)***	---
SF ratio	---	---	---	0.9971 (0.9959-0.9983)*	0.9969 (0.9963-0.9977)*
Invasive mechanical ventilation	---	---	---	0.9714 (0.5843-1.6148)***	---
C-reactive protein	---	---	---	1.0010 (1.0006-1.0027)*	1.0020 (1.0009-1.0026)*
Hemoglobin	---	---	---	1.0150 (0.9644-1.0682)***	---
Neutrophils	---	---	---	1.0000 (1.0000-1.0000)*	---
Platelet count	---	---	---	0.9994 (0.9993-0.9996)*	0.9994 (0.9993-0.9999)*
Urea	---	---	---	1.0046 (1.0001-1.0103)**	---
Lactate	---	---	---	0.9775 (0.9421-1.0143)***	---
Sodium	---	---	---	0.9948 (0.9763-1.0136)***	---
Bicarbonate	---	---	---	0.9716 (0.9443-0.9996)*	---
pH	---	---	---	0.9987 (0.9982-0.9993)*	---
pCO2	---	---	---	1.0034 (0.9939-1.0129)***	---
D-dimer	---	---	---	0.9999 (0.9998-1.0001)***	---

• 22

Table 4
– Predictors (at hospital admission) of invasive mechanical ventilation support by Poisson regression model with robust variance

Variables	Multivariate model 1 RR (95% CI)	Multivariate model 2 RR (95% CI)	Multivariate model 3 RR (95% CI)	Multivariate model 4 RR (95% CI)	Multivariate model 5 RR (95% CI)
Myocardial injury	2.8607 (2.4221-3.3787)*	2.6906 (2.1353- 3.3901)*	2.5901 (2.1627-3.1021)*	1.9018 (1.5842-2.2830)*	1.8675 (1.5662-2.2268)*
Age	---	1.0057 (0.9983-1.0131)***	1.0034 (0.9976-1.0093)***	1.0068 (1.0002-1.0133)*	---
Female sex	---	0.8889 (0.7492-1.0546)***	0.8697 (0.7492-1.0546)***	0.9398 (0.7852-1.1249)***	---
Overall comorbidities****	---	---	1.1781 (1.0936-1.2691)*	1.1313 (1.0494-1.2196)**	1.1295 (1.0488-1.2164)*
Respiratory rate	---	---	---	1.0369 (1.0254-1.0485)*	1.0332 (1.0222-1.0443)*
Heart rate	---	---	---	1.0002 (0.9954-1.0051)***	---
Systolic blood pressure < 90 mmHg with no inotrope	---	---	---	0.4001 (0.1252-1.2781)***	---
Glasgow <15	---	---	---	0.8320 (0.6109-1.1332)***	---
SF ratio	---	---	---	0.9980 (0.9960-0.9990)*	0.9980 (0.9970-0.9990)*
C-reactive protein	---	---	---	1.0027 (1.0019-1.0034)*	1.0030 (1.0020-1.0040)*
Hemoglobin	---	---	---	1.0111 (0.9641-1.0605)***	---
Neutrophils	---	---	---	1.0005 (1.0003-1.0006)*	1.0004 (1.0002-1.0005)*
Platelet count	---	---	---	0.9980 (0.9970-0.9990)*	0.9995 (0.9994-0.9997)*
Urea	---	---	---	0.9954 (0.9912-0.9997)**	---
Lactate	---	---	---	0.9740 (0.9045-1.0487)***	---
Sodium	---	---	---	0.9869 (0.9688-1.0054)***	---
Bicarbonate	---	---	---	1.0439 (0.9752-1.1174)***	---
pH	---	---	---	0.0965 (0.0039-2.3727)***	---
pCO2	---	---	---	0.9743 (0.9413-1.0084)***	---
D-dimer	---	---	---	0.9999 (0.9998-1.0000)***	---

• 22

Central Illustration
: Myocardial Injury and Prognosis in Hospitalized COVID-19 Patients in Brazil: Results From The Brazilian COVID-19 Registry

Discussion

In this multicenter cohort study, with a large sample of 31 Brazilian hospitals, patients with myocardial injury were 10 years older (median) than the control, and had a higher prevalence of underlying comorbidities, worse clinical parameters at hospital admission, and higher levels of CRP, LDH, and NT-proBNP/BNP when compared to those without myocardial injury. Myocardial injury was an independent predictor of in-hospital mortality (RR: 2.03 [1.60-2.58]) and invasive mechanical ventilation support (RR: 1.87 [1.57-2.23]). Higher number of comorbidities, higher respiratory rate, and higher levels of CRP were associated with a higher risk of in-hospital mortality and invasive mechanical ventilation support. Additionally, age and lower SF ratio were also independently associated with in-hospital mortality, while the number of neutrophils was associated with an increased risk of invasive mechanical ventilation support. Patients with myocardial injury showed a higher frequency of cardiovascular complications, bleeding, thromboembolic events, septic shock, disseminated intravascular coagulation, nosocomial infection, admission to the ICU, ICU and hospital length of stay, and need for renal replacement therapy.

The present study’s findings are in line with results of studies from other countries, which reported remarkably similar characteristics of COVID-19 patients who developed myocardial injury, including advanced age and a high prevalence of underlying medical conditions.⁶ , ²¹ - ²³ There was no evidence of sex differences in the prevalence of the variables analyzed among patients with myocardial injury, which contrasts with data previously published in the literature. In the systematic review performed by Toraih et al.,²⁴ including 17,794 patients with cardiac injury assessed by troponin measurement, the authors found a significantly higher proportion of men with critical illnesses, defined as acute respiratory distress syndrome, invasive mechanical ventilation, and ICU admission. In the present analysis, older women with myocardial injury showed a higher risk for death and the need for invasive mechanical ventilation, but with no statistical significance in the latter.

In fact, troponin and other biomarkers are commonly abnormal in patients hospitalized with COVID‐19. There are different mechanisms for the development of cardiac damage in COVID-19 patients, such as increased cardiac effort in acute respiratory failure,²⁵ and SARS-CoV-2 interaction with angiotensin-converting enzyme 2 receptors.² , ²⁶ - ²⁸ Although the specific mechanisms of myocardial injury are uncertain, the mechanisms proposed include inflammatory response and immune system disorders during disease progression.²⁶ , ²⁹

Results of the tests used to assess cardiac function and injury have shown significantly higher values in COVID-19 patients who died than those who were discharged alive. Our results are in line with these findings, as BNP, creatine phosphokinase, LDH, NT-proBNP, and CRP, which is a biomarker of inflammatory response, were higher in patients with myocardial injury. A recent study which included 187 COVID-19 patients hospitalized in Rio de Janeiro has also reported troponin as an independent predictor for adverse events. Meanwhile, BNP was not an independent predictor for mortality or the need for invasive mechanical ventilation support.²⁹ This finding may be limited by the small sample size and high number of missing values.

A systematic review and meta-analysis of Chinese cohort studies by Alzahrani and Al-Rabia² showed that 45.2% of the patients with COVID-19-induced myocardial injury have died.²⁶ In the present study, there was a 4.25-fold higher mortality rate in patients with myocardial injury when compared to the controls, as well as a higher median hospital length of stay, ICU admissions, and all other aforementioned secondary outcomes. Hence, COVID-19 patients with cardiac injury were more susceptible to disease complications and had poorer prognoses. These findings are consistent with results reported by studies performed in other countries.⁶ , ³¹ , ³²

Currently, several studies have proposed clinical variables, laboratory and chest X-ray findings for the prediction of the risk of severe COVID-19 progression and mortality.³³ In a previous analysis, our research group developed and validated a risk prediction score for in-hospital mortality in COVID-19 patients (available through the link https://abc2sph.com/pt/), which includes older age, blood urea nitrogen, number of comorbidities, CRP, peripheral SF ratio, platelet count, and heart rate as predictors. However, troponin was not included in those analyses.¹⁰ In the present study, cardiac injury was an independent risk factor for mortality and invasive mechanical ventilation support, in addition to the variables previously tested, which was the most important finding of the current analyses. These results play an important role in patient care, as they can help healthcare professionals identify patients who may have a worse prognosis, guiding interventions for the management of clinical conditions and improvements in health care. A logical next step would be to assess the inclusion of troponin to the ABC₂-SPH risk score, an important topic for future studies concerning ABC₂-SPH. It is our hope that this cohort can also be useful in further studies to create a predictive score for myocardial injury.

This study has limitations. First, we cannot assure national representativeness of participating hospitals. Since this is a multicentric analysis of COVID-19, troponin tests could have inconsistencies, given that multiple commercial laboratory kits were used in an inter- and intra-institutional manner, and different reference values were standardized in each study, leading to measurement bias. Another limitation was that the grouped analyses did not allow the recognition of the contribution of each center to the outcomes of death and invasive mechanical ventilation support, as well as institutional factors related to mortality.³⁴ It is important to mention that the pandemic period when the data were collected involved a population that had not been vaccinated yet,³⁵ and part of the patients in the study group might not have developed myocardial injury, but rather acute myocardial infarction. Due to the potential risk of transmissibility of COVID-19, there is a possibility that no differential diagnostic technique is performed in some cases.³⁶ Furthermore, since the present study was an observational study, other variables that may be confounding and unmeasurable or unrecognized may not have been collected or analyzed.

Regarding the study’s strengths, a strict methodological criterion was used to perform this study, which was based on a robust patient sample, with a confirmed COVID-19 diagnosis. The sample was obtained by the collaboration of researchers from 31 public, private, and mixed hospitals of different sizes and complexity levels, from different Brazilian regions, in order to guarantee a diversity of the studied population. However, they are not representative of the entire healthcare system of the country.

Conclusion

Cardiac injury, measured by elevated troponin levels, was an independent predictor of mortality and invasive mechanical ventilation support among hospitalized COVID-19 patients, as were the variables of a recently validated risk prediction score. Future strategies involving the frequent monitoring of troponin levels as risk biomarkers in patients with COVID-19 during their hospitalization should be tested to investigate their role in reducing the risk of complications and death, as well as in improving patient care.

Acknowledgements

We would like to thank the hospitals that participated in this collaboration, especially the hospitals that participated in this analysis: Hospitais da Rede Mater Dei, Hospital das Clínicas da UFMG, Hospital Santo Antônio, Hospital Márcio Cunha, Hospital Metropolitano Doutor Célio de Castro, Hospital Metropolitano Odilon Behrens, Hospital Risoleta Tolentino Neves, Hospital Santa Rosália, Hospital São João de Deus, Hospital Semper, Hospital UNIMED, Hospital Mãe de Deus, Hospital Universitário Canoas, Hospital Universitário de Santa Maria, Hospital Moinhos de Vento, Instituto Mário Penna/Hospital Luxemburgo, Hospital Nossa Senhora da Conceição, Hospital Regional Antônio Dias, Hospital Mãe de Deus, Hospital Regional do Oeste, Hospital das Clínicas da Faculdade de Medicina de Botucatu, Hospital das Clínicas da Universidade Federal de Pernambuco, Hospital Universitário Ciências Médicas, Hospital Bruno Born, Hospital SOS Cárdio, Hospital São Lucas da PUCRS. We would also like to thank all of the clinical staff from the hospitals and all of the undergraduate students who helped with data collection.

* Supplemental Materials

For additional information, please click here.

Referências

• ¹
  World Health Organization. Health Emergency Dashboard [Internet]. Geneva: World Health Organization; 2022 [Cited 2022 Dec 29]. Available from: https://covid19.who.int/
  » https://covid19.who.int/
• ²
  Alzahrani SH, Al-Rabia MW. Cardiac Injury Biomarkers and the Risk of Death in Patients with COVID-19: A Systematic Review and Meta-Analysis. Cardiol Res Pract. 2021;2021:9363569. doi: 10.1155/2021/9363569.
• ³
  Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical Features of Patients Infected with 2019 Novel Coronavirus in Wuhan, China. Lancet. 2020;395(10223):497-506. doi: 10.1016/S0140-6736(20)30183-5.
• ⁴
  Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. Clinical Characteristics of 138 Hospitalized Patients with 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA. 2020;323(11):1061-9. doi: 10.1001/jama.2020.1585.
• ⁵
  Lala A, Johnson KW, Januzzi JL, Russak AJ, Paranjpe I, Richter F, et al. Prevalence and Impact of Myocardial Injury in Patients Hospitalized with COVID-19 Infection. J Am Coll Cardiol. 2020;76(5):533-46. doi: 10.1016/j.jacc.2020.06.007.
• ⁶
  Shi S, Qin M, Shen B, Cai Y, Liu T, Yang F, et al. Association of Cardiac Injury with Mortality in Hospitalized Patients with COVID-19 in Wuhan, China. JAMA Cardiol. 2020;5(7):802-810. doi: 10.1001/jamacardio.2020.0950.
• ⁷
  Egidio AN, Campos BN, Junqueira CF, Martins FR, Fabri J Jr, Rodrigues JF, et al. Cardiovascular Implications in Covid-19: A Systematic Review. Braz J Develop. 2020;6(10):82111-28. doi: 10.34117/bjdv6n10-588.
• ⁸
  Simões MV, Romano MD, Schmidt A, Martins KSM, Marin-Neto JA. Chagas Disease Cardiomyopathy. Int J Cardiovasc Sci. 2018;31(2):173-89. doi: 10.5935/2359-4802.20190019.
• ⁹
  Bonini-Domingos CR. The Hemoglobinopathies and Genetic Diversity of the Brazilian Population. Rev Bras Hematol Hemoter. 2009;31(6):401. doi: 10.1590/S1516-84842009000600001.
• ¹⁰
  Marcolino MS, Pires MC, Ramos LEF, Silva RT, Oliveira LM, Carvalho RLR, et al. ABC2-SPH Risk Score for In-Hospital Mortality in COVID-19 Patients: Development, External Validation and Comparison with Other Available Scores. Int J Infect Dis. 2021;110:281-308. doi: 10.1016/j.ijid.2021.07.049.
• ¹¹
  Marcolino MS, Ziegelmann PK, Souza-Silva MVR, Nascimento IJB, Oliveira LM, Monteiro LS, et al. Brazilian COVID-19 Registry Investigators. Clinical Characteristics and Outcomes of Patients Hospitalized with COVID-19 in Brazil: Results from the Brazilian COVID-19 registry. Int J Infect Dis. 2021;107:300-310. doi: 10.1016/j.ijid.2021.01.019.
• ¹²
  World Health Organization. Diagnostic testing for SARS-CoV-2: Interim guidance [Internet]. Geneva: World Health Organization; 2022 [Cited 2022 Dec 29]. Available from: World Health Organization; 2020. Available from: https://www.who.int/publications/i/item/diagnostic-testing-for-sars-cov-2
  » https://www.who.int/publications/i/item/diagnostic-testing-for-sars-cov-2
• ¹³
  Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, et al. A New Equation to Estimate Glomerular Filtration Rate. Ann Intern Med. 2009;150(9):604-12. doi: 10.7326/0003-4819-150-9-200905050-00006.
• ¹⁴
  Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O’Neal L, et al. The REDCap Consortium: Building an International Community of Software Platform Partners. J Biomed Inform. 2019;95:103208. doi: 10.1016/j.jbi.2019.103208.
• ¹⁵
  Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research Electronic Data Capture (REDCap) -- A Metadata-Driven Methodology and Workflow Process for Providing Translational Research Informatics Support. J Biomed Inform. 2009;42(2):377-81. doi: 10.1016/j.jbi.2008.08.010.
• ¹⁶
  Marcolino MS, Figueira RM, Santos JPA, Cardoso CS, Ribeiro ALP, Alkimin MB. The Experience of a Sustainable Large Scale Brazilian Telehealth Network. Telemed J E Health. 2016;22(11):899-908. doi: 10.1089/tmj.2015.0234.
• ¹⁷
  Thygesen K, Alpert JS, Jaffe AS, Chaitman BR, Bax JJ, Morrow DA, et al. Fourth Universal Definition of Myocardial Infarction (2018). Circulation. 2018;138(20):e618-e651. doi: 10.1161/CIR.0000000000000617.
• ¹⁸
  Zou G. A Modified Poisson Regression Approach to Prospective Studies with Binary Data. Am J Epidemiol. 2004;159(7):702-6. doi: 10.1093/aje/kwh090.
• ¹⁹
  Coutinho LM, Scazufca M, Menezes PR. Methods for Estimating Prevalence Ratios in Cross-Sectional Studies. Rev Saude Publica. 2008;42(6):992-8.
• ²⁰
  Hosmer DW, Lemeshow S. Applied logistic regression. New York: Wiley & Sons; 2022.
• ²¹
  Efros O, Barda N, Meisel E, Leibowitz A, Fardman A, Rahav G, et al. Myocardial Injury in Hospitalized Patients with COVID-19 Infection-Risk Factors and Outcomes. PLoS One. 2021;16(2):e0247800. doi: 10.1371/journal.pone.0247800.
• ²²
  Guo T, Fan Y, Chen M, Wu X, Zhang L, He T, et al. Cardiovascular Implications of Fatal Outcomes of Patients with Coronavirus Disease 2019 (COVID-19). JAMA Cardiol. 2020;5(7):811-818. doi: 10.1001/jamacardio.2020.1017.
• ²³
  Lombardi CM, Carubelli V, Iorio A, Inciardi RM, Bellasi A, Canale C, et al. Association of Troponin Levels with Mortality in Italian Patients Hospitalized with Coronavirus Disease 2019: Results of a Multicenter Study. JAMA Cardiol. 2020;5(11):1274-1280. doi: 10.1001/jamacardio.2020.3538.
• ²⁴
  Toraih EA, Elshazli RM, Hussein MH, Elgaml A, Amin M, El-Mowafy M, et al. Association of Cardiac Biomarkers and Comorbidities with Increased Mortality, Severity, and Cardiac Injury in COVID-19 Patients: A Meta-Regression and Decision Tree Analysis. J Med Virol. 2020;92(11):2473-2488. doi: 10.1002/jmv.26166.
• ²⁵
  Davidson JA, Warren-Gash C. Cardiovascular Complications of Acute Respiratory Infections: Current Research and Future Directions. Expert Rev Anti Infect Ther. 2019;17(12):939-942. doi: 10.1080/14787210.2019.1689817.
• ²⁶
  Zheng YY, Ma YT, Zhang JY, Xie X. COVID-19 and the Cardiovascular System. Nat Rev Cardiol. 2020;17(5):259-260. doi: 10.1038/s41569-020-0360-5.
• ²⁷
  Chen T, Wu D, Chen H, Yan W, Yang D, Chen G, et al. Clinical Characteristics of 113 Deceased Patients with Coronavirus Disease 2019: Retrospective Study. BMJ. 2020;368:m1091. doi: 10.1136/bmj.m1091.
• ²⁸
  Nishiga M, Wang DW, Han Y, Lewis DB, Wu JC. COVID-19 and Cardiovascular Disease: From Basic Mechanisms to Clinical Perspectives. Nat Rev Cardiol. 2020;17(9):543-558. doi: 10.1038/s41569-020-0413-9.
• ²⁹
  Libby P. Mechanisms of Acute Coronary Syndromes and Their Implications for Therapy. N Engl J Med. 2013;368(21):2004-13. doi: 10.1056/NEJMra1216063.
• ³⁰
  Almeida GLG Jr, Braga F, Jorge JK, Nobre GF, Kalichsztein M, Faria PMP, et al. Prognostic Value of Troponin-T and B-Type Natriuretic Peptide in Patients Hospitalized for COVID-19. Arq Bras Cardiol. 2020;115(4):660-666. English, Portuguese. doi: 10.36660/abc.20200385.
• ³¹
  Toloui A, Moshrefiaraghi D, Madani Neishaboori A, Yousefifard M, Haji Aghajani M. Cardiac Complications and Pertaining Mortality Rate in COVID-19 Patients; a Systematic Review and Meta-Analysis. Arch Acad Emerg Med. 2021;9(1):e18. doi: 10.22037/aaem.v9i1.1071.
• ³²
  Li X, Guan B, Su T, Liu W, Chen M, Bin Waleed K, et al. Impact of Cardiovascular Disease and Cardiac Injury On In-Hospital Mortality in Patients with COVID-19: A Systematic Review and Meta-Analysis. Heart. 2020;106(15):1142-1147. doi: 10.1136/heartjnl-2020-317062.
• ³³
  Manocha KK, Kirzner J, Ying X, Yeo I, Peltzer B, Ang B, et al. Troponin and Other Biomarker Levels and Outcomes Among Patients Hospitalized with COVID-19: Derivation and Validation of the HA2T2 COVID-19 Mortality Risk Score. J Am Heart Assoc. 2021;10(6):e018477. doi: 10.1161/JAHA.120.018477.
• ³⁴
  Souza-Silva MVR, Ziegelmann PK, Nobre V, Gomes VMR, Etges APBDS, Schwarzbold AV, et al. Hospital Characteristics Associated with COVID-19 Mortality: Data From the Multicenter Cohort Brazilian Registry. Intern Emerg Med. 2022;17(8):2299-2313. doi: 10.1007/s11739-022-03092-9.
• ³⁵
  Lima EJF, Almeida AM, Kfouri RA. Vaccines for COVID-19 - state of the art. Rev Bras Saúde Mater Infant. 2021;21(supl.1):13-19. doi: 10.1590/1806-9304202100S100002.
• ³⁶
  Caldeira D, Pinto FJ. COVID-19 and Myocardial Infarction. The Lancet. 2021;398(10315):1963-4. doi: 10.1016/S0140-6736(21)02284-4.

• Study association
  This article is part of the thesis of Doctoral submitted by Hannah Cardoso Barbosa, from Universidade Federal de Minas Gerais.
• Ethics approval and consent to participate
  This study was approved by the Brazilian National Commission for Research Ethics under the protocol number (CAAE 30350820.5.1001.0008).

• Sources of funding: This study was supported in part by Minas Gerais State Agency for Research and Development (Fundação de Amparo à Pesquisa do Estado de Minas Gerais - FAPEMIG) [grant number APQ-00208-20], National Institute of Science and Technology for Health Technology Assessment (Instituto de Avaliação de Tecnologias em Saúde – IATS)/ National Councilfor Scientific and Technological Development (Conselho Nacional de De-senvolvimento Científico e Tecnológico - CNPq) [grant number 465518/2014–1].

Publication Dates

• Publication in this collection
  27 Feb 2023
• Date of issue
  2023

History

• Received
  04 Apr 2022
• Reviewed
  05 Oct 2022
• Accepted
  16 Nov 2022