Quantification of hs-Troponin Levels and Global Longitudinal Strain among Critical COVID-19 Patients with Myocardial Involvement
by
, , , and
Mochamad Yusuf Alsagaff
1,*,
Louisa Fadjri Kusuma Wardhani
1,
Ricardo Adrian Nugraha
1,
Tony Santoso Putra
1,
Bagus Putra Dharma Khrisna
1,
Makhyan Jibril Al-Farabi
1,
Ruth Irena Gunadi
1,
Yusuf Azmi
1,
Christian Pramudita Budianto
1,
Rosi Amrilla Fagi
1,
Nadya Luthfah
1,
Agus Subagjo
1,
Yudi Her Oktaviono
1,
Achmad Lefi
1,
Budi Baktijasa Dharmadjati
1,
Firas Farisi Alkaff
2 and
Budi Susetyo Pikir
1 1
Department of Cardiology and Vascular Medicine, Faculty of Medicine Universitas Airlangga—Dr. Soetomo General Hospital, Jalan Mayjend Prof. Dr. Moestopo 6-8, Surabaya 60286, Indonesia
2
University Medical Center Groningen, 9713 GZ Groningen, The Netherlands
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2024, 13(2), 352; https://doi.org/10.3390/jcm13020352
Submission received: 20 October 2023
/
Revised: 29 December 2023
/
Accepted: 30 December 2023
/
Published: 8 January 2024
(This article belongs to the Section Cardiovascular Medicine)
Abstract
:Background. Myocardial involvement among critically ill patients with coronavirus disease 2019 (COVID-19) often has worse outcomes. An imbalance in the oxygen supply causes the excessive release of pro-inflammatory cytokines, which results in increased ventilation requirements and the risk of death in COVID-19 patients. Purpose. We evaluated the association between the hs-troponin I levels and global longitudinal strain (GLS) as evidence of myocardial involvement among critical COVID-19 patients. Methods. We conducted a prospective cohort study from 1 February to 31 July 2021 at RSUD Dr. Soetomo, Surabaya, as a COVID-19 referral center. Of the 65 critical COVID-19 patients included, 41 (63.1%) were men, with a median age (interquartile range) of 51.0 years (20.0–75.0). Subjects were recruited based on WHO criteria for severe COVID-19, and myocardial involvement in the form of myocarditis was assessed using CDC criteria. Subjects were examined using echocardiography to measure the GLS, and blood samples were taken to measure the hs-troponin. Subjects were then followed for their need for mechanical ventilation and in-hospital mortality. Results. Severe COVID-19 patients with cardiac injury were associated with an increased need for intubation (78.5%) and an increased incidence of myocarditis (50.8%). There was a relationship between the use of intubation and the risk of death in patients (66.7% vs. 33.3%, p-value < 0.001). Decreased GLS and increased hs-troponin were associated with increased myocarditis (p values < 0.001 and 0.004, respectively). Decreased GLS was associated with a higher need for mechanical ventilation (12.17 + 4.79 vs. 15.65 + 4.90, p-value = 0.02) and higher mortality (11.36 + 4.64 vs. 14.74 + 4.82; p-value = 0.005). Elevated hs-troponin was associated with a higher need for mechanical ventilation (25.33% vs. 3.56%, p-value = 0.002) and higher mortality (34.57% vs. 5.76%, p-value = 0.002). Conclusions. Critically ill COVID-19 patients with myocardial involvement and elevated cardiac troponin levels are associated with a higher need for mechanical ventilation and higher mortality.
1. Introduction
COVID-19 is a worldwide pandemic with a mortality rate approaching ten percent in Indonesia [1]. Fever, cough, and dyspnea are common symptoms of COVID-19, which are exacerbated in the elderly and other comorbidities, such as chronic obstructive pulmonary disease or heart disease [2]. Cardiac injury, as one of the cardiac manifestations that exacerbate the systolic and diastolic functions, has potentially worsened the condition of COVID-19 patients and caused mortality [3].
Acute myocarditis in COVID-19 patients, which is associated with immunologic responses and cytokine storms, destroys the myocardium, which is characterized by an increase in a specific marker for cardiac enzymes: high-sensitive troponin I (hs c-TnI) [3,4,5].
Hue et al. reported that acute myocarditis also causes heart failure accompanied by an increase in NT-proBNP associated with a fatal outcome, a high risk of death for COVID-19 patients [6]. The adaptive response appears due to the cardiac burden in patients with COVID-19 infection. Echocardiography is an imaging modality used in patients with suspected cardiac injury [7,8].
Examination of several cardiac-marker enzymes are used to establish a diagnosis and predict the prognosis of patients with COVID-19 [9,10]. This study aims to determine the ability of hs c-TnI and the NT-proBNP level as non-invasive prognostic tools, and to predict the outcome of heart injury in COVID-19 patients without a prior history of heart disease.
2. Methods
2.1. Data Source
Primary data in this study were obtained through laboratory examinations measuring cardiac markers and echocardiography to assess the GLS in adult COVID-19 patients, which was performed by a certified cardiology specialist at Dr. Soetomo General Hospital, Surabaya.
2.2. Study Population and Design
An observational analytic study with a single-center prospective cohort design was used. Research and treatment of this patient was carried out at Dr. Soetomo General Hospital, Surabaya, from 1 July to 31 December 2020. The population in this study were all COVID-19-positive patients aged 18–90 years with clinical manifestations. WHO diagnostic criteria were used in the laboratory results.
2.3. Data Collection
Data were collected by total sampling from all included patients in a prospective cohort. All patient data were examined clinically apart from the research team independently. Primary data were obtained from interviews, physical examinations, laboratory tests, and echocardiography.
2.4. Statistical Analysis
Data were analyzed using inferential statistical analysis and were tested for normality using the one-sample Kolmogorov–Smirnov and Shapiro–Wilk tests. The independent t-test followed by linear regression statistical tests were used if the data were normally distributed, and the Wilcoxon Mann–Whitney test followed by polynomial regression statistical tests were used if the data were not normally distributed. The gender, blood type, ethnicity, and type of financing were analyzed using the χ2 and Fisher exact tests, followed by logistic regression. Multivariate analysis was carried out if there were significant variables. All analysis results were presented as narratives, tables, and graphs. Statistical analysis was performed using SPSS software version 25.0.0 for Windows, 2017 (Armonk, NY, USA: IBM Corp.).
3. Results
This research was conducted in the Intensive Care Unit (ICU) of Dr. Soetomo General Hospital, Surabaya, from 1 February to 31 July 2021.
3.1. Baseline Characteristics of Subjects
A total of 65 severe COVID-19 patients with HFpEF who met the inclusion and exclusion criteria were included in this study. Based on the clinical, echocardiographic, and laboratory criteria, 33 subjects were diagnosed with myocarditis. The length-of-stay, GLS, and LAVI values were normally distributed. Subjects had a median age of 56 years, and 63.1% were male. Most subjects had histories of hypertension (69.2%) and diabetes (60%). Significant increases in mortality were associated with increased hs-troponin levels (p = 0.011), increased procalcitonin levels (p = 0.001), increased serum creatinine (p < 0.001), a decreased mean GLS (p = 0.005), an increased degree of diastolic dysfunction (p = 0.003), the need for mechanical ventilation (p < 0.001), and the incidence of myocarditis (p = 0.002). Increased body mass index and NT-proBNP were also associated with increased mortality (p = 0.044 and p = 0.002, respectively). The baseline characteristics are presented in Table 1.
3.2. Subanalysis of Myocardial Injury and Severe COVID-19 Outcome
The bivariate analysis showed an association between increased NT-proBNP and hs-troponin-I levels and mortality (Figure 1).
Myocardial injury was characterized via cardiac-marker increases, particularly the NT-proBNP and hs-troponin. Bivariate analysis was conducted to evaluate the association of the two laboratory parameters with the outcomes of severe COVID-19 patients. There was a significant relationship between increased NT-proBNP and the degree of diastolic dysfunction (p < 0.001). Elevated hs-troponin was also associated with the need for mechanical ventilation and a higher degree of diastolic dysfunction (p = 0.044 and p = 0.001, respectively) (Table 2 and Figure 2 and Figure 3).
An analysis of the laboratory parameters using the receiver operating characteristic (ROC) showed that NT-pro-BNP, hs-troponin, and procalcitonin were reliable in predicting the mortality outcomes in patients with severe COVID-19 (NT pro-BNP: AUC = 0.720; p = 0.002; 95% CI = 0.595–0.844; hs-troponin: AUC = 0.722; p = 0.002; 95% CI = 0.599–0.846; procalcitonin: AUC = 0.731; p = 0.001; 95% CI = 0.607–0.856) (Figure 4).
A correlative analysis of the laboratory parameters using the Spearman test showed a moderate correlation between the NT-proBNP and hs-troponin (r = 0.412; p-value < 0.001) (Table 3). There was a weak correlation between increased hs-troponin and a shorter length of stay (r = 0.287, p = 0.02), but the correlation between the increased NT-proBNP and length of stay was not significant (r = 0.230, p = 0.065).
3.3. Subanalysis of Echocardiography Parameters and Severe COVID-19 Outcome
The echocardiographic assessment showed that the mean GLS value and degree of diastolic dysfunction were associated with mortality in COVID-19 patients. A subanalysis showed that a decrease in the mean GLS was associated with an increased need for mechanical ventilation (Table 3 and Figure 5). In addition, the receiver operating characteristic (ROC) analysis showed that the GLS might be able to predict mortality in severe COVID-19 patients (AUC = 0.700; p-value = 0.006; 95% CI = 0.568–0.833) (Figure 6).
3.4. Subanalysis of Clinical Characteristics and Myocarditis in Severe COVID-19
The diagnosis of myocarditis was based on clinical, echocardiographic, and laboratory criteria. Subjects suffering from myocarditis were associated with male gender, older age, a history of hypertension, and the incidence of acute kidney injury (AKI) during treatment. The occurrence of myocarditis was associated with increased creatinine serum (p < 0.001), increased NT-proBNP (p < 0.001), increased hs-troponin (p < 0.001), a decreased mean GLS (p = 0.004), and the need for mechanical ventilation (p < 0.001) (Table 4). Additionally, patients with diastolic dysfunction were associated with higher mortality (p = 0.032), increased incidence of myocarditis, including increased mitral velocity (MV grade, p = 0.047), and the occurrence of tricuspid regurgitation reflecting increased left atrial pressure (TR Vmax, p = 0.029, and TR maxPG, p = 0.026).
3.5. Subanalyses of Myocarditis Clinical Characteristics and Mortality
Subanalyses were performed to assess the clinical characteristics of the myocarditis subjects and mortality (Table 5). Myocarditis patients with diastolic dysfunction were associated with increased mortality (p = 0.005).
4. Discussion
4.1. Clinical Characteristics of the Research Subjects
COVID-19 patients are differentiated into five degrees of severity by the WHO based on complaints and clinical signs. There are two host responses to COVID-19 infection: the immune defense-based protective phase, and the inflammation-driven damaging phase. In the first phase, the focus is on improving the immune response. In contrast, in the second phase, efforts are made to suppress the excess immune response [11]. Increased inflammatory markers are obtained to moderate, severe, and critical degrees. The increase in such inflammatory markers occurs as a host immune response to the virus, which may be accompanied by acute myocardial injury [11,12,13].
Our research data show that patients with higher body mass indexes (BMIs) have better survival abilities. This condition is called the “obesity survival paradox (OSP).” This tendency toward bad outcomes is based on the fact that obesity is associated with various cardiovascular comorbidities. However, further studies are needed on the relationship between obesity and mortality in severe COVID-19 patients. The OSP in pneumonia was generally elaborated in a meta-analysis study conducted by Nie et al. in 2014 [14]. There are three explanations for the inverse correlation of obesity with mortality in pneumonia. First, obese patients tend to be aware of cardiovascular risks and perform adequate therapy. Second, tumor necrosis factor-alpha (TNF-a) is a pro-inflammatory cytokine that is important in inflammatory and immune responses. Fatty tissue produces TNF-a receptors, indicated by a decrease in the pneumonia severity in the obese group. Third, pneumonia patients with normal weights may not have good metabolic abilities to cope with increased catabolic stress [12,14].
The coronavirus binds to target cells through the ACE2 receptor, a homolog of angiotensin-converting enzymes, and converts angiotensin II into angiotensin I. It also reduces vasoconstriction mediated by the RAAS and plays a pro-inflammatory role in angiotensin II. The binding of SARS-CoV-2 to the ACE1 receptor results in a series of post-receptor signal changes that result in vasoconstriction, pro-inflammatory response, and endothelial dysfunction and affect myocardial injury and pro-thrombotic processes. The mechanisms mediated by the ACE2 receptor are related to the effects of hypertension, diabetes mellitus, and a history of CHD on more severe manifestations of COVID-19 [14,15].
Hypertension and DM are the most common comorbidities found in COVID-19 patients [16]. This is aligned with the clinical demographic of our research subjects. The presence of hypertension in the population of our study subjects was higher compared to multicenter studies conducted in China and the United States of America, which were 24% and 35% [16,17,18]. The percentage of DM patients was also higher in our study than in a study conducted in China (9.7%, 95% CI: 7.2–12.2%) [18]. The difference in the percentage of comorbidities in our study could be due to the inclusion of patients with severe COVID-19. Wang, et al. stated that COVID-19 patients admitted to the ICU had a higher comorbidity rate than those who were not admitted to the ICU (72.2% vs. 37.3%) [19]. Patients needing intubation support were associated with an increase in mortality (p = < 0.001) [19], similar to another study that demonstrated that severe pneumonia is independently associated with ICU care, the use of mechanical ventilation, and mortality [12,20].
4.2. Subanalysis of Myocardial Injury and the Outcomes of the Patients
Increased concentrations of cardiac biomarkers (NT-proBNP and hs-troponin) are correlated with the severity of COVID-19 infection [9,10]. Improvements in other inflammatory responses, such as procalcitonin and CRP, have also been associated with increased mortality [12,20]. Based on the description of the basic data on the laboratory parameters, higher levels of NT-proBNP and hs-troponin were obtained in subjects with mortality output (NT-proBNP: 1380.50 vs. 238.10, p = 0.002; hs-troponin: 34.57 vs. 5.76, p-value = 0.001). Nevertheless, only an increase in hs-troponin exceeding the normal values is associated with increased mortality. Several clinical factors strongly influence some of the underlying factors, including the adjustment of the normal value of NT-proBNP. In addition, there was also an association of increased procalcitonin with mortality (p = 0.001).
There is an association between hs-troponin and an increased need for intubation and diastolic dysfunction (p = 0.002 and 0.001, respectively). Hs-troponin levels in circulation indicate a systemic inflammatory impact caused by COVID-19 infection; thus, hs-troponin can be used to predict major adverse cardiovascular events (MACEs) [12,20,21]. The mechanism of myocardial injury can also be seen from an imbalance in the oxygen demand, increased ventricular strains, direct myocyte trauma, and the response to increased catecholamines [12,20,21].
In sepsis conditions, increased hs-troponin is associated with LV dysfunction, as seen from the transthoracic echocardiography examination. Ventricular dilatation and stress to the LV wall are other causes of increased hs-troponin in sepsis conditions [22]. Studies conducted by Mehta et al. in 2004 showed that patients with increased hs-troponin were associated with regional wall motion abnormalities (RWMAs) (56% vs. 6%; p = 0.002), decreases in EF (46% vs. 62%; p = 0.04), and increased mortality (56% vs. 24%; p = 0.04). This also indicates the linkage of the inflammatory process underlying the increase in hs-troponin, which is also associated with alterations in the picture of diastolic dysfunction in patients [23].
The hs-troponin level can predict 30-day death, namely, an AUC of 0.81 (95% CI: 0.73–0.88). Other laboratory parameters also support this capability in predicting mortality (NT-proBNP: AUC = 0.80; 95% CI = 0.74–0.86; procalcitonin: AUC = 0.77; 95% CI = 0.70–0.84). The results of the AUC analysis on our study samples also showed the fairly good abilities of hs-troponin, NT-proBNP, and procalcitonin in predicting mortality. In addition, it supports its usefulness in predicting the need for mechanical ventilation.
Through the AUC test in our study, we obtained almost similar mortality prediction capabilities between hs-troponin, NT-proBNP, and procalcitonin. Our research shows that the AUC of NT pro-BNP is 0.720 (p = 0.002; 95% CI = 0.595–0.844) and that of hs-troponin is 0.722 (p = 0.002; 95% CI = 0.599–0.846). These values are similar to the result in the meta-analysis study conducted by Wibowo et al. in 2021: namely, the ability of hs-troponin to predict mortality with an AUC of 0.73 (0.69–0.77) [23,24].
Through Spearman correlation analysis, a correlation of the hs-troponin increase with the NT-proBNP increase was obtained in our study subjects with a moderate correlation. This supports that serial examination in conjunction with NT-proBNP examination can help analyze the prognoses of the existing biomarkers.
NT-proBNP is secreted in response to increased myocardial wall stress. The MMR process regulates this condition through pro-inflammatory molecules, such as lipopolysaccharides, interleukin 1, CRP, and cardiac troponin I, independent of the ventricular function. Elevated circulating natriuretic levels are associated with myocardial injury, inflammatory processes, and interaction with ACE2. In addition, its increase can also result from acute heart failure conditions [25,26].
Increased mortality was also observed to be significantly associated with an increase in procalcitonin (p-value = 0.001). An increase in procalcitonin is observed particularly in severe COVID-19 patients [26,27]. This condition can be observed in COVID-19 infection and sepsis. This was demonstrated in a study conducted by Firani and Priscilla (2022), which evaluated the significance of procalcitonin in predicting mortality (OR = 17.78, p = 0.001). The relationship between the increase in procalcitonin and the increase in hs-troponin could be related to the condition of increasing troponin, which can also be observed in conditions of sepsis [28].
A study conducted by Lippi and Plebani in 2020 showed an increase in procalcitonin occurring in 50% of severe COVID-19 infections and 80% of critical patients. Meanwhile, bacterial co-infection was only obtained in 20% of severe COVID-19 patients and in 50% of the critical degrees [29].
Our research data also show the levels of CRP (15.60 vs. 14.60), NLR (11.78 vs.9.86), and D-dimer (5240 vs. 4620), which are higher in the event group. Increased inflammatory markers in severe COVID-19 patients may be associated with cytokine storms in the second week of infection. In this condition, leukocytes become over-activated, which results in the release of cytokines. The relationship between increased neutrophils and decreased lymphocytes in COVID-19 infection is unclear. However, it is often associated with the involvement of neutrophil extracellular traps (NETs). This causes neutrophil infiltration into the pulmonary capillaries and results in organ damage and acute respiratory distress syndrome (ARDS). In comparison, the decrease in lymphocytes is caused by the expression of ACE2 receptors by lymphocytes, which results in apoptosis [27].
4.3. Subanalysis of the Echocardiographic Profile of Severe COVID-19 Patient Outcomes
GLS examination shows cardiac abnormalities in the early stages, with normal values of from −17% to −18% [8,23,24]. Systemic inflammation induced by COVID-19 can cause LV and RV disorders and result in heart failure [8]. The cardiovascular comorbidities result in a series of cellular changes, including changes in endocardial fibers that result in decreased GLS [8,30]. This also explains that while a decrease in GLS can describe myo-pericardial injury due to an acute process in COVID-19, other chronic conditions can also affect the decrease in GLS [8,30].
Our study excluded patients with a previous history of coronary heart disease or heart failure. We excluded echocardiographic findings indicating chronic LV function impairment characterized by regional wall motion abnormalities (RWMAs) and LV dilatation. Based on research conducted by Huang et al. (2022) [31], the pattern of acute LV dysfunction found in 22% of COVID-19 patients is mostly characterized by the absence of LV dilatation and a hypokinetic global pattern. Most of them show a picture that resembles septic cardiomyopathy: global hypokinetic without an increase in the E/A ratio.
Our findings showed that most patients had preserved LVEFs, non-dilated LVs, and normal E/A ratios. However, LVEF assessment is less able to show subtle myocardial dysfunction [32]. Therefore, in the assessment of the LV function, we included the GLS assessment, which was indicated by a decrease in the LV function in most of the samples, with a mean of 12.92 + 4.99. The E/A ratio value was low in 20% of the samples, and only 3% showed an increase (E/A ratio > 1.5). The finding of non-dilated LVs with the normal estimated LV filling pressure indicates acute secondary injury [33].
The study by Lairez et al. in 2021 showed no correlation between the decrease in GLS and the increase in the hs-troponin level [34]. The correlation analysis of GLS with hs-troponin using the Spearman method that we performed also showed no relationship between the GLS decrease and hs-troponin increase (r = 0.245; p = 0.05). However, the decrease in the GLS was associated with an increase in mortality (11.36 ± 4.64 vs. 14.74 ± 4.82; p-value = 0.005). The GLS examination also showed a fairly good ability to predict mortality (AUC = 0.700; p-value = 0.006; 95% CI = 0.568–0.833).
In addition, studies conducted by Bevilacqua et al. in 2021 state that a decrease in the LV GLS (cutoff −16.1%) can predict a decrease in the PaO2/FiO2 (p/F) ratio of <100 mmHg during maintenance, which marks the need for mechanical ventilation use (HR = 4.0; 95% CI = 1.4–11.1; p = 0.008) [35].
Our study shows a correlation between GLS decrease and increased intubation needs (12.17 ± 4.79 vs. 15.65 ± 4.90, p = 0.02). The AUC analysis also showed the ability of decreased GLS to predict intubation needs quite well (AUC = 0.707, p = 0.018, 95% CI = 0.514–0.873).
The cytokine storms in COVID-19 can induce distributive shock and cause acute respiratory distress syndrome (ARDS) due to COVID-19 pneumonia. This results in a decreased RV systolic function. Shortening measurements of longitudinal fibers as obtained via Tricuspid Annular Plane Systolic Excursion (TAPSE) are not sensitive enough to assess the RV involvement. Several factors influence changes in the RV systolic function, including the use of mechanical ventilation. Therefore, assessment with the RV GLS, the measurement of the RV dilatation, and a comparison of the RV end-diastolic area to the LV end-diastolic area (RVEDA/LVEDA) can be used as a reference [31,36]. Our study did not perform thorough measurements of the RV systolic function, so we did not include them in the discussion. The assessment we carried out was based on a decrease in the TAPSE scores found in 17% of the sample.
4.4. Analysis of Relationship of Clinical Characteristics to Incidence of Myocarditis
COVID-19 infection is associated with the incidences of myocardial injury, one of which is caused by myocarditis [5]. Myocarditis is an inflammatory process involving the myocardium [7]. The US Centers for Disease Control and Prevention (CDC) categorize myocarditis into probable and confirmed cases based on clinical manifestations and diagnostic features.
Making a diagnosis of myocarditis is not easy considering the heterogeneity of the clinical presentation and nonspecific echocardiographic findings. Echocardiographic findings of patients with myocarditis are indicated by global ventricular dysfunction, RWMAs, or diastolic dysfunction. Endomyocardial biopsy (EMB) examination is still the gold standard in confirming myocarditis. However, the invasiveness and low availability of EMB make its use less feasible in clinical practice. A systematic review study conducted by Urban et al., 2022 [37], showed that the application of EMB in the diagnosis of COVID-19 myocarditis was only carried out in 33% of cases. This makes cardiac magnetic resonance (CMR) examination the reference standard in diagnosing acute myocarditis. Its application is demonstrated by CMR’s ability to identify myocardial edema and inflammation non-invasively [32,37]. The establishment of the diagnosis of myocarditis uses MRI and/or biopsy; however, due to limitations, the use of additional diagnoses using CMR and biopsy could not be performed in our study. The classification of myocarditis in our study is based on the presence of one or more new clinical presentations or worsening (chest pain/suppression/discomfort, tightness, palpitations, or the occurrence of syncope), with ≥ 1 new findings from increased hs-troponin, an abnormal ECG picture, and abnormal heart function indicated by regional wall motion abnormality (RWMA) disorders through echocardiography, or, if CMR findings that support myocarditis are obtained without being accompanied by other causes of complaints, according to the Advisory Committee on Immunization Practices (ACIP).
A study by Maino et. al. in 2021 performed a cardiovascular magnetic resonance (CMR) examination on symptomatic COVID-19 patients. The examination showed cardiac involvement, edema, and myocardial scars in 58% of the patients through late gadolinium enhancement (LGE) [21]. In 2020, Knight et al. also conducted a study using CMR examination during early recovery to assess the presence, type, and extent of myocardial injury in troponin-positive COVID-19 patients (during treatment) [38]. Their study showed that abnormalities in CMR are common even though the heart function is generally normal. CMR indicates ischemic heart disease (17%) and suspicion of the myocarditis picture through LGE (38%), and it sometimes indicates multiple pathologies (ischemic and non-ischemic, 14%). In addition, the lack of edema in this group indicates that scars, such as those due to myocarditis, may be permanent [38].
An increase in hs-troponin alone cannot describe the incidence of myocarditis; therefore, its increase can also result from microangiopathy and myocardial infarction [39]. Biopsy examinations show diffuse monocular infiltration or high viral loads by the SARS-CoV-2 virus [40,41]. However, limited infrastructure does not allow biopsies or CMR examinations to be carried out. In our study, myocardial injury assessment was performed based on CDC criteria. This statement is supported by various studies that show hs-troponin as a biomarker that is very useful in assessing myocardial injury, as well as studies that show hs-troponin as a gold standard for the early diagnosis of cardiac complications with good clinical relevance [40,41]. Hs-troponin is even able to assess subclinical myocardial inflammation, which helps determine early treatment and assess the prognostics of cardiac complications in patients with COVID-19 [40].
Myocardial injury, as shown by an increase in the hs-troponin level, was obtained in 36% of patients who were hospitalized due to COVID-19 infection. However, in the case of fulminant myocarditis, normal hs-troponin is often obtained. Hence, there are restrictions on the use of hs-troponin as a single examination in diagnosing myocarditis. Subclinical myocardial dysfunction was reported in 79% of COVID-19 patients who underwent a strain-imaging examination using speckle-tracking echocardiography (GLS). Therefore, the use of these two parameters simultaneously needs to be analyzed in diagnosing myocarditis [41].
The pattern shown by the GLS examination can be a differentiating tool in patients with suspected acute myocardial infarction and myocarditis. Both conditions are often encountered in patients with COVID-19. Ischemic necrosis shows an endocardial strain that can extend to the epicardium that shows a transmural character. Meanwhile, abnormalities in myocarditis show a global or regional ischemic pattern that is limited to the epicardial or mid-wall area [32].
We conducted a follow-up analysis on the myocarditis group and found that there was an association between an increase in the hs-troponin level, a decrease in the GLS, and an increase in the degree of diastolic dysfunction and the incidence of myocarditis (p < 0.001, 0.004, and 0.032, respectively). The increased risk of death in the myocarditis group is supported by the impact of the myocardial injury on the cardiovascular system. Myocardial injury and decreased GLS can recognize both subclinical and acute myocardial dysfunction, which are associated with mortality events. Meanwhile, the evaluation of the two simultaneously supports the diagnosis of myocarditis. The myocardial injury underlying myocarditis also increases the filling pressure of the LV and may lead to diastolic dysfunction. Diastolic dysfunction contributes to an increase in the degree of aggravation and mortality in COVID-19 patients. An analysis of the myocarditis group showed that increased diastolic dysfunction was associated with increased mortality (p < 0.005).
The degree of diastolic dysfunction is an evaluation to assess the therapeutic response and predictors of HF-related hospitalization and mortality outcomes in patients with HFpEF according to the American Society of Echocardiography (ASE). However, no study has analyzed the degree of diastolic dysfunction in COVID-19 patients. Research conducted by AlJaroudi et al. in 2012 in the general population explains that diastolic dysfunction is an independent predictor of mortality. Improvements in diastolic dysfunction ≥ 2 showed better mortality outcomes. The degree of diastolic dysfunction illustrates the impact of the correlation between cardiovascular comorbidities and the cardiac response to COVID-19 infection. Stretching of the myocardium will occur due to a persistent increase in the LV filling pressure and result in LA dilatation, which, at an advanced stage, increases the LAVI. This explains that higher degrees of diastolic dysfunction are associated with the anatomical and functional impacts of the myocardium in compensating for changes in the LV charging pressure that occur as a result of myocardial injury in COVID-19 patients [42].
4.5. Study Limitation
This study is a single-center study; thus, further research is needed with a larger number of subjects and a multicenter study approach to provide an overview of the clinical characteristics of cardiac injury in patients with COVID-19 infection. The diagnosis of myocarditis using CMR and biopsy could not be performed in our study. However, CMR and biopsy examination may help to determine the definitive cause of the myocardial injury that occurs in severe COVID-19 patients.
5. Conclusions
Our study reveals a positive correlation between high hs-troponin levels and the need for intubation and diastolic dysfunction in patients with severe COVID-19. A higher NT-proBNP level was found with an increased degree of diastolic dysfunction in patients with severe COVID-19. Patients with a severe degree of COVID-19 with cardiac injury are associated with the need for intubation and the incidence of myocarditis. There was a correlation between the incidence of myocarditis and the risk of mortality in patients. High hs-troponin and NT-proBNP levels were found to increase the mortality in patients with severe degrees of COVID-19. This study also showed that hs-troponin and NT-proBNP have good abilities to predict the mortality of patients with severe COVID-19. However, this study showed no correlation between the increase in the NT-proBNP level and the duration of hospitalization. In addition, there was a weak correlation between the increase in the hs-troponin level and a short duration of hospitalization.
Author Contributions
Conceptualization, M.Y.A., L.F.K.W. and Y.H.O.; methodology, R.A.N. and T.S.P.; software, B.P.D.K.; validation, M.Y.A.; formal analysis, M.J.A.-F.; investigation, R.I.G. and R.A.N.; resources, A.S.; data curation, L.F.K.W., R.A.N. and T.S.P.; writing—original draft preparation, L.F.K.W. and Y.A.; writing—review and editing, R.A.N. and F.F.A.; visualization, R.A.F.; supervision, B.S.P.; project administration, C.P.B. and N.L.; funding acquisition, Y.H.O., B.B.D., A.L. and A.S. All authors have read and agreed to the published version of the manuscript.
Funding
The research leading to these results has received funding from Dr. Soetomo General Hospital Research Grant under the Grant agreement number 420/UN3.1.1/IPJ/Sp1/2020. The hs-troponin and NT-proBNP examinations received support from Abbott Indonesia.
Institutional Review Board Statement
We have obtained ethical clearance from Health Research Ethics Committee in the Dr. Soetomo General Academic Hospital, Surabaya, Indonesia (Ref Number: 0133/KEPK/I/2021) on 29 January 2021 under the name of Mochamad Yusuf Alsagaff as the Principical Investigator..This research conformed to all the applicable national rules, institutional policies, and the Declaration of Helsinki principles (as revised in 2013).
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from all patients to publish this paper.
Data Availability Statement
Data are available upon reasonable request.
Acknowledgments
The authors would like to express the biggest gratitude for Joni Wahyuhadi and Cita Rosita Sigit Prakoeswa for their financial support from Dr. Soetomo General Hospital, all staff and residents from Center for Integrated Cardiac Services at Soetomo General Academic Hospital, Surabaya, Indonesia for their support. The authors would like to dedicate this manuscript to honor the effort of brave health-care workers who sacrificed their lives to fight against COVID-19.
Conflicts of Interest
The authors declare no conflicts of interest.
References
- Kemenkes, R.I. Situasi Terkini COVID19. 2020. Internet. Available online: https://covid19.kemkes.go.id/situasi-infeksi-emerging/situasi-terkini-perkembangan-coronavirus-disease-covid-19-5-mei-2020 (accessed on 1 June 2020).
- Hageman, J.R. The Coronavirus Disease 2019 (COVID-19). Pediatr. Ann. 2020, 49, e99–e100. [Google Scholar] [CrossRef] [PubMed]
- Chen, C.; Yan, J.T.; Zhou, N.; Zhao, J.P.; Wang, D.W. Analysis of myocardial injury in patients with COVID-19 and association between concomitant cardiovascular diseases and severity of COVID-19. Zhonghua Xin Xue Guan Bing Za Zhi 2020, 48, e008. [Google Scholar]
- Clerkin, K.J.; Fried, J.A.; Raikhelkar, J.; Sayer, G.; Griffin, J.M.; Masoumi, A.; Jain, S.S.; Burkhoff, D.; Kumaraiah, D.; Rabbani, L.R.; et al. COVID-19 and Cardiovascular Disease. Circulation 2020, 141, 1648–1655. [Google Scholar] [CrossRef] [PubMed]
- Sagarad, S.V. NT-proBNP in Myocarditis after a Scorpion Sting Envenomation. J. Clin. Diagn. Res. 2013, 7, 118–121. [Google Scholar] [CrossRef] [PubMed]
- Hu, H.; Ma, F.; Wei, X.; Fang, Y. Coronavirus fulminant myocarditis treated with glucocorticoid and human immunoglobulin. Eur. Heart J. 2020, 42, 206. [Google Scholar] [CrossRef] [PubMed]
- Hékimian, G.; Combes, A. Myocardites. Rev. Méd. Interne 2017, 38, 531–538. [Google Scholar] [CrossRef] [PubMed]
- Hong, G.H.; Hays, A.G.; A Gilotra, N. The Evolving Role of Echocardiography During the Coronavirus Disease 2019 Pandemic. Heart Int. 2022, 16, 28–36. [Google Scholar] [CrossRef]
- Han, H.; Xie, L.; Liu, R.; Yang, J.; Liu, F.; Wu, K.; Chen, L.; Hou, W.; Feng, Y.; Zhu, C. Analysis of heart injury laboratory parameters in 273 COVID-19 patients in one hospital in Wuhan, China. J. Med. Virol. 2020, 92, 819–823. [Google Scholar] [CrossRef]
- Hu, L.; Chen, S.; Fu, Y.; Gao, Z.; Long, H.; Ren, H.-W.; Zuo, Y.; Wang, J.; Li, H.; Xu, Q.-B.; et al. Risk Factors Associated With Clinical Outcomes in 323 Coronavirus Disease 2019 (COVID-19) Hospitalized Patients in Wuhan, China. Clin. Infect. Dis. 2020, 71, 2089–2098. [Google Scholar] [CrossRef]
- Shi, Y.; Wang, Y.; Shao, C.; Huang, J.; Gan, J.; Huang, X.; Bucci, E.; Piacentini, M.; Ippolito, G.; Melino, G. COVID-19 infection: The perspectives on immune responses. Cell Death Differ. 2020, 27, 1451–1454. [Google Scholar] [CrossRef]
- Shi, S.; Qin, M.; Shen, B.; Cai, Y.; Liu, T.; Yang, F.; Gong, W.; Liu, X.; Liang, J.; Zhao, Q.; et al. Association of Cardiac Injury with Mortality in Hospitalized Patients With COVID-19 in Wuhan, China. JAMA Cardiol. 2020, 5, 802–810. [Google Scholar] [CrossRef] [PubMed]
- Bavishi, C.; Bonow, R.O.; Trivedi, V.; Abbott, J.D.; Messerli, F.H.; Bhatt, D.L. Special Article—Acute myocardial injury in patients hospitalized with COVID-19 infection: A review. Prog. Cardiovasc. Dis. 2020, 63, 682–689. [Google Scholar] [CrossRef] [PubMed]
- Nie, W.; Zhang, Y.; Jee, S.H.; Jung, K.J.; Li, B.; Xiu, Q. Obesity survival paradox in pneumonia: A meta-analysis. BMC Med. 2014, 12, 61. [Google Scholar] [CrossRef] [PubMed]
- de Almeida-Pititto, B.; Dualib, P.M.; Zajdenverg, L.; Dantas, J.R.; de Souza, F.D.; Rodacki, M.; Bertoluci, M.C.; Brazilian Diabetes Society Study Group (SBD). Severity and mortality of COVID 19 in patients with diabetes, hypertension and cardiovascular disease: A meta-analysis. Diabetol. Metab. Syndr. 2020, 12, 75. [Google Scholar] [CrossRef] [PubMed]
- Yang, J.; Zheng, Y.; Gou, X.; Pu, K.; Chen, Z.; Guo, Q.; Ji, R.; Wang, H.; Wang, Y.; Zhou, Y. Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: A systematic review and meta-analysis. Int. J. Infect. Dis. 2020, 94, 91–95. [Google Scholar] [CrossRef] [PubMed]
- Guan, W.J.; Ni, Z.Y.; Hu, Y.; Liang, W.H.; Ou, C.Q.; He, J.X.; Liu, L.; Shan, H.; Lei, C.L.; Hui, D.S.C.; et al. China Medical Treatment Expert Group for Covid-19. Clinical Characteristics of Coronavirus Disease 2019 in China. N. Engl. J. Med. 2020, 382, 1708–1720. [Google Scholar] [CrossRef] [PubMed]
- Reynolds, H.R.; Adhikari, S.; Pulgarin, C.; Troxel, A.B.; Iturrate, E.; Johnson, S.B.; Hausvater, A.; Newman, J.D.; Berger, J.S.; Bangalore, S.; et al. Renin–Angiotensin–Aldosterone System Inhibitors and Risk of Covid-19. N. Engl. J. Med. 2020, 382, 2441–2448. [Google Scholar] [CrossRef] [PubMed]
- Wang, D.; Hu, B.; Hu, C.; Zhu, F.; Liu, X.; Zhang, J.; Wang, B.; Xiang, H.; Cheng, Z.; Xiong, Y.; et al. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus–Infected Pneumonia in Wuhan, China. JAMA. 2020, 17, 1061–1069. [Google Scholar] [CrossRef]
- Jaffe, A.S.; Cleland, J.G.F.; Katus, H.A. Myocardial injury in severe COVID-19 infection. Eur. Heart J. 2020, 41, 2080–2082. [Google Scholar] [CrossRef]
- Maino, A.; Di Stasio, E.; Grimaldi, M.C.; Cappannoli, L.; Rocco, E.; Vergallo, R.; Biscetti, F.; Baroni, S.; Urbani, A.; Landolfi, R.; et al. Prevalence and characteristics of myocardial injury during COVID-19 pandemic: A new role for high-sensitive troponin. Int. J. Cardiol. 2021, 338, 278–285. [Google Scholar] [CrossRef]
- Ostermann, M.; Ayis, S.; Tuddenham, E.; Lo, J.; Lei, K.; Smith, J.; Sanderson, B.; Moran, C.; Collinson, P.; Peacock, J.; et al. Cardiac Troponin Release is Associated with Biomarkers of Inflammation and Ventricular Dilatation During Critical Illness. Shock 2017, 47, 702–708. [Google Scholar] [CrossRef] [PubMed]
- Mehta, N.J.; Khan, I.A.; Gupta, V.; Jani, K.; Gowda, R.M.; Smith, P.R. Cardiac troponin I predicts myocardial dysfunction and adverse outcome in septic shock. Int. J. Cardiol. 2004, 95, 13–17. [Google Scholar] [CrossRef]
- Wibowo, A.; Pranata, R.; Astuti, A.; Tiksnadi, B.B.; Martanto, E.; Martha, J.W.; Purnomowati, A.; Akbar, M.R. Left and right ventricular longitudinal strains are associated with poor outcome in COVID-19: A systematic review and meta-analysis. J. Intensiv. Care 2021, 9, 9. [Google Scholar] [CrossRef] [PubMed]
- Gao, L.; Jiang, D.; Wen, X.-S.; Cheng, X.-C.; Sun, M.; He, B.; You, L.-N.; Lei, P.; Tan, X.-W.; Qin, S.; et al. Prognostic value of NT-proBNP in patients with severe COVID-19. Respir. Res. 2020, 21, 83. [Google Scholar] [CrossRef] [PubMed]
- Kaufmann, C.C.; Ahmed, A.; Burger, A.L.; Muthspiel, M.; Jäger, B.; Wojta, J.; Huber, K. Biomarkers Associated with Cardiovascular Disease in COVID-19. Cells 2022, 11, 922. [Google Scholar] [CrossRef] [PubMed]
- Mudatsir, M.; Fajar, J.K.; Wulandari, L.; Soegiarto, G.; Ilmawan, M.; Purnamasari, Y.; Mahdi, B.A.; Jayanto, G.D.; Suhendra, S.; Setianingsih, Y.A.; et al. Predictors of COVID-19 severity: A systematic review and meta-analysis. F1000Research 2020, 9, 1107. [Google Scholar] [CrossRef]
- Firani, N.K.; Prisilla, J. Procalcitonin and Troponin-I as Predictor of Mortality in Acute Myocardial Infarction Patients. Indones. J. Clin. Pathol. Med. Lab. 2022, 28, 170–174. [Google Scholar] [CrossRef]
- Lippi, G.; Plebani, M. Procalcitonin in patients with severe coronavirus disease 2019 (COVID-19): A meta-analysis. Clin. Chim. Acta 2020, 505, 190–191. [Google Scholar] [CrossRef]
- Verdonschot, J.A.; Henkens, M.T.; Wang, P.; Schummers, G.; Raafs, A.G.; Krapels, I.P.; van Empel, V.; Heymans, S.R.; Rocca, H.B.; Knackstedt, C. A global longitudinal strain cut-off value to predict adverse outcomes in individuals with a normal ejection fraction. ESC Heart Fail. 2021, 8, 4343–4345. [Google Scholar] [CrossRef]
- Huang, S.; Vignon, P.; Mekontso-Dessap, A.; Tran, S.; Prat, G.; Chew, M.; Balik, M.; Sanfilippo, F.; Banauch, G.; Clau-Terre, F.; et al. Echocardiography findings in COVID-19 patients admitted to intensive care units: A multi-national observational study (the ECHO-COVID study). Intensiv. Care Med. 2022, 48, 667–678. [Google Scholar] [CrossRef]
- Wieczorkiewicz, P.; Supel, K.; Przybylak, K.; Kacprzak, M.; Zielinska, M. Acute coronary syndrome versus acute myocarditis in young adults–value of speckle tracking echocardiography. PLoS ONE 2022, 17, e0271483. [Google Scholar] [CrossRef] [PubMed]
- Valenzuela, E.D.; Mercado, P.; Pairumani, R.; Medel, J.N.; Petruska, E.; Ugalde, D.; Morales, F.; Eisen, D.; Araya, C.; Montoya, J.; et al. Cardiac function in critically ill patients with severe COVID: A prospective cross-sectional study in mechanically ventilated patients. J. Crit. Care 2022, 72, 154166. [Google Scholar] [CrossRef] [PubMed]
- Lairez, O.; Blanchard, V.; Houard, V.; Vardon-Bounes, F.; Lemasle, M.; Cariou, E.; Lavie-Badie, Y.; Ruiz, S.; Cazalbou, S.; Delmas, C.; et al. Cardiac imaging phenotype in patients with coronavirus disease 2019 (COVID-19): Results of the cocarde study. Int. J. Cardiovasc. Imaging 2021, 37, 449–457. [Google Scholar] [CrossRef]
- Bevilacqua, M.; De Togni, P.; Cattazzo, F.; Dell’Atti, D.; Dalbeni, A.; Mazzaferri, F.; Tacconelli, E.; Farzaneh-Far, A.; Fava, C.; Minuz, P.; et al. Global Longitudinal Strain to Predict Respiratory Failure and Death in Patients Admitted for COVID-19–Related Disease. Am. J. Cardiol. 2022, 165, 109–115. [Google Scholar] [CrossRef]
- Huang, S.; Vieillard-Baron, A.; Evrard, B.; Prat, G.; Chew, M.S.; Balik, M.; Clau-Terré, F.; De Backer, D.; Mekontso Dessap, A.; Orde, S.; et al. Echocardiography phenotypes of right ventricular involvement in COVID-19 ARDS patients and ICU mortality: Post-hoc (exploratory) analysis of repeated data from the ECHO-COVID study. Intensive Care Med. 2023, 49, 946–956. [Google Scholar] [CrossRef] [PubMed]
- Urban, S.; Fułek, M.; Błaziak, M.; Iwanek, G.; Jura, M.; Fułek, K.; Guzik, M.; Garus, M.; Gajewski, P.; Lewandowski, Ł.; et al. COVID-19 Related Myocarditis in Adults: A Systematic Review of Case Reports. J. Clin. Med. 2022, 21, 5519. [Google Scholar] [CrossRef] [PubMed]
- Wibowo, A.; Pranata, R.; Akbar, M.R.; Purnomowati, A.; Martha, J.W. Prognostic performance of troponin in COVID-19: A diagnostic meta-analysis and meta-regression. Int. J. Infect. Dis. 2021, 105, 312–318. [Google Scholar] [CrossRef] [PubMed]
- Janardhanan, R. Myocarditis with very high troponins: Risk stratification by cardiac magnetic resonance. J. Thorac Dis. 2016, 8, E1333–E1336. [Google Scholar] [CrossRef]
- Kim, C.W.; Aronow, W.S. COVID-19, cardiovascular diseases and cardiac troponins. Futur. Cardiol. 2022, 18, 135–142. [Google Scholar] [CrossRef]
- Lovell, J.P.; Čiháková, D.; A Gilotra, N. COVID-19 and Myocarditis: Review of Clinical Presentations, Pathogenesis and Management. Heart Int. 2022, 16, 20–27. [Google Scholar] [CrossRef]
- AlJaroudi, W.; Alraies, M.C.; Halley, C.; Rodriguez, L.; Grimm, R.A.; Thomas, J.D.; Jaber, W.A. Impact of progression of diastolic dysfunction on mortality in patients with normal ejection fraction. Circulation 2012, 125, 782–788. [Google Scholar] [CrossRef] [PubMed]
Figure 1.
Boxplot analysis of relationship between increased (left side) NT-proBNP and (right side) hs-troponin and mortality.
Figure 1.
Boxplot analysis of relationship between increased (left side) NT-proBNP and (right side) hs-troponin and mortality.
Figure 2.
Boxplot analysis of increases in (left side) NT-proBNP and (right side) hs-troponin to intubation events.
Figure 2.
Boxplot analysis of increases in (left side) NT-proBNP and (right side) hs-troponin to intubation events.
Figure 3.
Boxplot analysis of relationship between increased (left side) NT-proBNP and (right side) hs-troponin and the degree of diastolic dysfunction.
Figure 3.
Boxplot analysis of relationship between increased (left side) NT-proBNP and (right side) hs-troponin and the degree of diastolic dysfunction.
Figure 4.
Analysis of receiver operating characteristic (ROC) of laboratory parameters on outcomes (mortality) of research subjects.
Figure 4.
Analysis of receiver operating characteristic (ROC) of laboratory parameters on outcomes (mortality) of research subjects.
Figure 5.
Boxplot analysis representing the following: (A) an increase in the LAVI is associated with an increase in the degree of diastolic dysfunction; (B) a decrease in the mean GLS is associated with an increase in the degree of diastolic dysfunction; and (C) a decrease in the mean GLS is associated with an increased need for intubation.
Figure 5.
Boxplot analysis representing the following: (A) an increase in the LAVI is associated with an increase in the degree of diastolic dysfunction; (B) a decrease in the mean GLS is associated with an increase in the degree of diastolic dysfunction; and (C) a decrease in the mean GLS is associated with an increased need for intubation.
Figure 6.
Receiver operating characteristic (ROC) analysis of GLS parameters on mortality. It is showed that GLS parameters could predict mortality (AUC 0.700, sensitivity 80%, specificity 64%).
Figure 6.
Receiver operating characteristic (ROC) analysis of GLS parameters on mortality. It is showed that GLS parameters could predict mortality (AUC 0.700, sensitivity 80%, specificity 64%).
Table 1.
Baseline characteristics of subjects.
| Variable | Total (n = 65) | Survived (n = 32) | Mortality (n = 33) | p-Value |
|---|---|---|---|---|
| Age | 51 (20, 75) | 46 (20, 75) | 56 (25, 75) | 0.085 |
| Gender Male Female | 41 (63.1%) 24 (36.9%) | 20 (48.8%) 10 (41.7%) | 21 (51.2%) 14 (58.3%) | 0.579 |
| Body Mass Index (kg/m2) | 27 (17, 45) | 28 (22, 37) | 26 (17, 45) | 0.044 |
| Past Medical History | ||||
| Hypertension | 45 (69.2%) | 18 (40%) | 27 (60%) | 0.135 |
| Diabetes Mellitus | 39 (60%) | 15 (38.5%) | 24 (61.5%) | 0.128 |
| Pregnancy | 8 (12.3%) | 5 (62.5%) | 3 (37.5%) | 0.270 |
| Acute Kidney Injury | 23 (35.4%) | 3 (13%) | 20 (87%) | <0.001 |
| Laboratory Parameters | ||||
| Serum Creatinine (mg/dL) | 1.10 (0.10, 10.00) | 0.80 (0.40, 3.70) | 1.70 (0.10, 10.00) | <0.001 |
| Increased NT-proBNP | 54 (83.1%) | 23 (42.6%) | 31 (57.4%) | 0.173 |
| NT-proBNP (pg/mL) | 581.90 (31, 10,000) | 238.10 (32, 10,000) | 1380.50 (82, 10,000) | 0.002 |
| Patients with Elevated Hs-Troponin-I | 37 (56.9%) | 12 (32.4%) | 25 (67.6%) | 0.011 |
| Mean Level of Hs-Troponin-I (ng/L) | 17.56 (1, 1407) | 5.76 (2, 162) | 34.57 (1, 1407) | 0.002 |
| Procalcitonin (ng/mL) | 1.41 (0.01, 100) | 0.65 (0.01, 100) | 2.32 (0.05, 100) | 0.001 |
| CRP (mg/dL) | 14.90 (0.36, 90.80) | 14.6 (0.36, 28.90) | 15.6 (0.50, 90.80) | 0.571 |
| NLR | 10.85 (2.10, 44.80) | 9.86 (3.87, 28.76) | 11.78 (2.10, 44.80) | 0.519 |
| D-dimer | 5030.00 (700, 25,230) | 4620 (940, 22,300) | 5240 (700, 25,230) | 0.146 |
| Echocardiography Parameters | ||||
| Biplane EF | 66.00 (51, 85) | 67.50 (53, 79) | 65.00 (51, 85) | 0.391 |
| Average GLS | 12.92 ± 4.99 | 14.74 ± 4.82 | 11.36 ± 4.64 | 0.005 |
| LAVI | 20.72 ± 7.31 | 20.05 ± 6.11 | 21.28 ± 8.24 | 0.503 |
| Velocity MV E | 0.59 (0.36, 0.92) | 0.59 (0.41, 0.85) | 0.58 (0.36, 0.92) | 0.818 |
| Average E/e’ | 6.99 (3.68, 14.00) | 6.39 (4.62, 10.65) | 6.47 (3.68, 14.00) | 0.927 |
| TR MaxPG | 00.00 (0.00, 83.18) | 0.00 (0.00, 56.66) | 0.00 (00.00, 83.18) | 0.018 |
| TR Vmax | 00.00 (00.00, 4.56) | 0.00 (0.00, 3.76) | 0.00 (0.00, 4.56) | 0.019 |
| Degree of Diastolic Dysfunction Grade 1 Grade 2 | 47 (72.3%) 18 (27.7%) | 27 (57.4%) 3 (16.7%) | 20 (42.6%) 15 (83.3%) | 0.003 |
| Other Clinical Parameters | ||||
| Length of Stay | 19 ± 9 | 21 ± 9 | 18 ± 10 | 0.115 |
| Intubation | 51 (78.5%) | 17 (33.3%) | 34 (66.7%) | <0.001 |
| ECMO | 13 (20%) | 7 (53.8%) | 6 (46.2%) | 0.534 |
| Myocarditis | 33 (50.8%) | 9 (27.3%) | 24 (72.7%) | 0.002 |
DM, diabetes mellitus; AKI, acute kidney injury; CRP, C-reactive protein; NLR, neutrophil-to-lymphocyte ratio; EF, ejection fraction; LAVI, left atrial volume index; GLS, global longitudinal strain; TR, tricuspid regurgitation; ECMO, extracorporeal membrane oxygenator.
Table 2.
Analysis of laboratory parameters in myocardial injury and the outcomes of patients with severe degrees of COVID-19.
Table 2.
Analysis of laboratory parameters in myocardial injury and the outcomes of patients with severe degrees of COVID-19.
| Variable | Event | Non-Event | p-Value |
|---|---|---|---|
| Intubation | Intubation | Non-Intubation | |
| NT-proBNP Hs-Troponin Procalcitonin Serum Creatinine | 742 (32–10,000) 25.33 (1–1407) 1.85 (0.05–100) 1.40 (0.10–10) | 246.30 (37–10,000) 3.56 (2–120) 0.59 (0.05–100) 0.75 (0.50–1.10) | 0.068 0.002 0.044 0.001 |
| ECMO | ECMO | Non-ECMO | |
| NT-proBNP Hs-Troponin Procalcitonin Serum Creatinine | 742 (32–10,000) 32.79 (2–1357) 2.01 (0.38–100) 0.80 (0.10–3.30) | 419.20 (37–10,000) 16.44 (1–1407) 1.38 (0.01–100) 1.10 (0.40–10.00) | 0.640 0.210 0.623 0.370 |
| Diastolic Dysfunction | Grade 1 | Grade 2 | |
| NT-proBNP Hs-Troponin Procalcitonin Serum Creatinine | 302.30 (32–10,000) 7.19 (1–1356) 1.20 (0.09–100) 1.00 (0.40–10.00) | 2290.05 (178–10,000) 91.47 (4–1407) 1.97 (0.01–100) 1.30 (0.10–6.80) | <0.001 0.001 0.655 0.454 |
Table 3.
Analysis of echocardiographic parameters on outcomes of patients with severe COVID-19.
| Variable | Event | Non-Event | p-Value |
|---|---|---|---|
| Intubation | Intubated | Not intubated | |
| Length of Stay | 19.71 ± 9.93 | 18.07 ± 7.05 | 0.567 |
| GLS Average | 12.17 ± 4.79 | 15.65 ± 4.90 | 0.02 |
| LAVI | 20.36 ± 7.74 | 22.02 ± 5.51 | 0.455 |
| ECMO | ECMO | Non- ECMO | |
| Length of Stay | 23.15 ± 9.88 | 18.40 ± 9.08 | 0.102 |
| GLS Average | 11.94 ± 4.71 | 13.17 ± 5.07 | 0.432 |
| LAVI | 22.21 ± 6.54 | 20.34 ± 7.50 | 0.415 |
| Diastolic Dysfunction | Grade 1 | Grade 2 | |
| Length of Stay | 20.49 ± 9.67 | 16.39 ± 7.99 | 0.115 |
| GLS Average | 13.35 ± 5.14 | 11.79 ± 4.49 | 0.261 |
| LAVI | 19.26 ± 6.77 | 24.52 ± 7.47 | 0.008 |
Table 4.
Correlation between clinical characteristics and event of myocarditis in severe COVID-19 patients.
Table 4.
Correlation between clinical characteristics and event of myocarditis in severe COVID-19 patients.
| Variable | Non-Myocarditis (n = 32) | Myocarditis (n = 33) | p-Value |
|---|---|---|---|
| Age | 49 (20, 75) | 55 (25, 73) | 0.753 |
| Gender Male Female | 21 (51.2%) 11 (45.8%) | 20 (48.8%) 13 (54.2%) | 0.675 |
| Body Mass Index | 27 (22.37) | 27 (17, 45) | 0.693 |
| Past Medical History | |||
| Hypertension | 20 (44.4%) | 25 (55.6%) | 0.247 |
| Diabetes Mellitus | 21 (53.8%) | 18 (46.2%) | 0.362 |
| Pregnancy | 4 (50%) | 4 (50%) | 0.628 |
| Acute Kidney Injury | 8 (34.8%) | 15 (65.2%) | 0.085 |
| Laboratory Parameters | |||
| Serum Creatinine | 0.80 (0.40, 10.00) | 1.60 (0.10, 8.30) | <0.001 |
| NT-proBNP | 238.10 (32, 7536) | 2524.40 (72, 10,000) | <0.001 |
| Increased NT-proBNP | 24 (44.4%) | 30 (55.6%) | 0.087 |
| Hs-Troponin | 3.56 (1, 36) | 78.32 (2, 1407) | <0.001 |
| Increased Hs-Troponin | 6 (16.2%) | 31 (83.8%) | <0.001 |
| Procalcitonin | 1.29 (0.01, 97.65) | 1.92 (0.05, 100) | 0.158 |
| CRP | 11.75 (0.36, 30.10) | 15.70 (0.50, 90.80) | 0.462 |
| NLR | 9.47 (3.21, 25.21) | 13.22 (2.10, 44.80) | 0.181 |
| D-dimer | 4440.00 (960, 25,230) | 5720 (700, 22,300) | 0.300 |
| Echocardiography Parameters | |||
| Biplane EF | 67 (60, 79) | 66 (51, 85) | 0.669 |
| MV Evel | 0.58 (0.36, 0.81) | 0.62 (0.42, 0.92) | 0.047 |
| Average E/E’ | 6.38 (3.77, 10.65) | 6.76 (3.68, 14.00) | 0.679 |
| TR maxPG | 0.00 (0.00, 56.66) | 7.01 (0.00, 83.18) | 0.026 |
| TR Vmax | 0.00 (0.00, 3.76) | 1.32 (0.00, 4.56) | 0.029 |
| LAVI | 19.99 ± 5.68 | 21.42 ± 8.63 | 0.436 |
| Average GLS | 14.70 ± 5.20 | 11.19 ± 4.16 | 0.004 |
| Degree of Diastolic Dysfunction Grade 1 Grade 2 | 27 (57.4%) 5 (27.8%) | 20 (42.6%) 13 (72.2%) | 0.032 |
| Other Clinical Parameters | |||
| Intubation | 19 (37.3%) | 32 (62.7%) | <0.001 |
| ECMO | 6 (46.2%) | 7 (53.8%) | 0.804 |
| Length of Stay | 21 ± 9 | 18 ± 10 | 0.309 |
| Mortality | 11 (31.4%) | 24 (68.6%) | 0.002 |
DM, diabetes mellitus; AKI, acute kidney injury; CRP, C-reactive protein; NLR, Neutrophil to Lymphocyte Ratio; EF, ejection fraction; LAVI, left atrial volume index; GLS, global longitudinal strain; TR, tricuspid regurgitation; ECMO, extracorporeal membrane oxygenator.
Table 5.
Relationship between clinical characteristics of myocarditis subjects and mortality in severe COVID-19 patients.
Table 5.
Relationship between clinical characteristics of myocarditis subjects and mortality in severe COVID-19 patients.
| Myocarditis Variables | Survived (%) | Mortality (%) | p-Value |
|---|---|---|---|
| Total | 9 (27.3%) | 24 (72.7%) | - |
| Length of Stay | 23 ± 11 | 16 ± 9 | 0.095 |
| Intubation | 8 (25%) | 24 (75%) | 0.273 |
| ECMO | 4 (57.1%) | 3 (42.9%) | 0.068 |
| Demographic Parameters | |||
| Age | 44.0 (29, 63) | 56.6 (25, 73) | 0.392 |
| Gender Male Female | 7 (35%) 2 (15.4%) | 13 (65%) 11 (84.6%) | 0.216 |
| Anthropometry | |||
| Body Mass Index | 28 (22, 36) | 26.5 (17, 45) | 0.766 |
| Past Medical History | |||
| Hypertension | 5 (20%) | 20 (80%) | 0.117 |
| Diabetes Mellitus | 4 (22.2%) | 14 (77.8%) | 0.475 |
| Pregnancy | 1 (25%) | 3 (75%) | 0.705 |
| Acute Kidney Injury | 2 (13.3%) | 13 (86.7%) | 0.101 |
| Laboratory Parameters | |||
| Increased NT-proBNP | 8 (26.7%) | 22 (73.3%) | 0.629 |
| Increased HS-Troponin | 8 (25.8%) | 23 (74.2%) | 0.477 |
| Serum Creatinine | 1.00 (0.70, 3.70) | 0.10 (0.10, 8.30) | 0.036 |
| NT-proBNP | 895.70 (72, 10,000) | 3180.10 (120, 10,000) | 0.179 |
| Hs-Troponin | 46.55 (2, 162) | 135.99 (5, 1407) | 0.079 |
| Procalcitonin | 0.70 (0.33, 100) | 2.80 (0.05, 100) | 0.437 |
| CRP | 14.30 (4.60, 23.30) | 17.85 (0.50, 90.80) | 0.538 |
| NLR | 13.22 (4.64, 28.76) | 13.24 (2.10, 44.80) | 0.890 |
| D-dimer | 5720 (940, 22,300) | 5580 (700, 12,870) | 0.984 |
| Echocardiography Parameters | |||
| Biplane EF | 57 (53, 77) | 65 (51, 85) | 0.592 |
| Average GLS | 10.90 (4, 16) | 10.40 (5, 22) | 0.981 |
| LAVI | 19.36 ± 7.00 | 22.19 ± 9.18 | 0.409 |
| MV E Vel | 0.62 (0.42, 0.85) | 0.63 (0.43, 0.92) | 0.238 |
| Average E/E’ | 5.87 (4.74, 10.53) | 7.09 (3.68, 14.00) | 0.619 |
| TR Vmax | 0.00 (0.00, 2.84) | 3.10 (0.00, 4.56) | 0.018 |
| TR MaxPG | 0.00 (00.00, 32.00) | 38.37 (00.00, 83.18) | 0.018 |
| Diastolic Dysfunction Grade 1 Grade 2 | 9 (45%) 0 (0%) | 11 (55%) 13 (100%) | 0.005 |
DM, diabetes mellitus; AKI, acute kidney injury; CRP, C-reactive protein; NLR, Neutrophil to Lymphocyte Ratio; EF, ejection fraction; LAVI, left atrial volume index; GLS, global longitudinal strain; TR, tricuspid regurgitation; ECMO, extracorporeal membrane oxygenator.
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MDPI and ACS Style
Alsagaff, M.Y.; Wardhani, L.F.K.; Nugraha, R.A.; Putra, T.S.; Khrisna, B.P.D.; Al-Farabi, M.J.; Gunadi, R.I.; Azmi, Y.; Budianto, C.P.; Fagi, R.A.; et al. Quantification of hs-Troponin Levels and Global Longitudinal Strain among Critical COVID-19 Patients with Myocardial Involvement. J. Clin. Med. 2024, 13, 352. https://doi.org/10.3390/jcm13020352
AMA Style
Alsagaff MY, Wardhani LFK, Nugraha RA, Putra TS, Khrisna BPD, Al-Farabi MJ, Gunadi RI, Azmi Y, Budianto CP, Fagi RA, et al. Quantification of hs-Troponin Levels and Global Longitudinal Strain among Critical COVID-19 Patients with Myocardial Involvement. Journal of Clinical Medicine. 2024; 13(2):352. https://doi.org/10.3390/jcm13020352
Chicago/Turabian StyleAlsagaff, Mochamad Yusuf, Louisa Fadjri Kusuma Wardhani, Ricardo Adrian Nugraha, Tony Santoso Putra, Bagus Putra Dharma Khrisna, Makhyan Jibril Al-Farabi, Ruth Irena Gunadi, Yusuf Azmi, Christian Pramudita Budianto, Rosi Amrilla Fagi, and et al. 2024. "Quantification of hs-Troponin Levels and Global Longitudinal Strain among Critical COVID-19 Patients with Myocardial Involvement" Journal of Clinical Medicine 13, no. 2: 352. https://doi.org/10.3390/jcm13020352
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