Aortic valve calcification in echocardiography is a prognostic factor for short-term adverse outcomes in elderly patients with severe COVID-19: a retrospective cohort study

Introduction

Coronavirus disease 2019 (COVID-19) is highly infectious. In early 2020, there was a global outbreak of the disease, resulting in a rapid increase in the number of infections worldwide, adversely affecting both physical and mental health of the public (1). Due to weakened immunity and the presence of chronic diseases, the elderly population is a high-risk group (2,3). According to research, in some regions affected by the COVID-19 epidemic, elderly people aged over 65 years account for more than half of the cases, and most deaths occur in those aged over 60 years (4,5).

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19, mainly affects the respiratory system and the symptoms of SARS-CoV-2 range from mild upper respiratory symptoms to acute respiratory distress syndrome (6). However, there is also substantial evidence indicating that the cardiovascular system is also affected by this disease (7). Studies have shown that pre-existing cardiovascular comorbidities (e.g., hypertension, coronary artery disease) and acute cardiac complications (e.g., myocardial injury, arrhythmias) are strongly associated with poor prognosis in COVID-19 patients (7,8).

The prognosis of COVID-19 patients is primarily assessed based on baseline conditions or laboratory biomarkers (9). While these parameters reflect systemic inflammation and organ dysfunction, they fail to capture specific structural or functional cardiac abnormalities that may drive adverse outcomes. Advanced imaging modalities, such as cardiac magnetic resonance imaging or myocardial perfusion imaging, are often impractical in the context of the COVID-19 pandemic, particularly for critically ill patients.

Most elderly individuals have varying degrees of heart problems, such as hypertension, arrhythmia, coronary heart disease (CHD), and heart failure, along with changes in the structure or function of the heart. Echocardiography is a non-invasive and radiation-free examination method that can comprehensively detect abnormalities in the structure, function, and blood flow of the heart, and thus is a commonly used method for screening heart disease. Recent studies suggest its potential in COVID-19 risk stratification. For instance, left and right ventricular (RV) dysfunction on echocardiography were associated with increased mortality in hospitalized patients (10-13). However, these studies paid insufficient attention to elderly patients with severe COVID-19. Therefore, we designed this study to explore the relationship between echocardiographic parameters and short-term adverse outcomes in these patients, aiming to facilitate early identification of high-risk individuals. We present this article in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-830/rc).

Methods

Patients

This retrospective observational study collected data from COVID-19 patients who were admitted to The Second Affiliated Hospital of Soochow University between December 2022 and January 2023 and met the inclusion criteria. The “good prognosis group” comprised patients whose symptoms improved progressively during the treatment course, culminating in successful discharge within two weeks. The “poor prognosis group” comprised patients whose condition deteriorated despite active treatment, including progression to critical illness or death. The diagnosis, treatment plan, and severity assessment were based on the Coronavirus Disease-19 Prevention and Control Consensus: Diagnosis and Treatment of Coronavirus Disease-19 (9th Trial Edition), issued by the National Health Commission of the People’s Republic of China. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Informed consent was obtained from all participants. This study was approved by the Ethics Committee of The Second Affiliated Hospital of Soochow University (No. JD-HG-2024-020).

To be eligible for inclusion in the study, the patients had to meet the following inclusion criteria: (I) were aged ≥60 years; (II) met the diagnostic criteria for severe COVID-19; (III) had received standard treatment for COVID-19 following exclusion of absolute contraindications; and (IV) had a left ventricular ejection fraction (LVEF) ≥50%. Patients were excluded from the study if they met any of the following exclusion criteria: (I) were aged <60 years; (II) had mild, moderate, or critical COVID-19; (III) had complications such as decreased LVEF, heart valve stenosis, heart valve prolapse, prosthetic valve replacement, and congenital heart disease; (IV) had a physical condition that was too poor to withstand standard treatment; and/or (V) had incomplete data.

For the adult patients, COVID-19 severity was classified as follows: (I) mild: limited clinical manifestations with no pneumonia evident in imaging; (II) moderate: clinical symptoms coupled with evidence of pneumonia in imaging; or (III) severe: (i) respiratory distress with respiratory rate ≥30 breaths/min; (ii) resting oxygen saturation ≤93%; (iii) arterial partial pressure of oxygen (PaO₂)/fraction of inspired oxygen (FiO₂) ≤300 mmHg; and/or (iv) progression of clinical symptoms accompanied by >50% increase in lung lesions within 24–48 hours; or (IV) critical: (i) respiratory failure development necessitating mechanical ventilation; (ii) manifestation of shock; and/or (iii) multi-organ failure requiring intensive care unit management.

The treatment plans for the patients included: (I) general measures (i.e., bed rest, supportive care, ensuring adequate energy and nutritional intake, and maintaining water and electrolyte balance and stabilizing the internal environment); (II) standardized oxygen therapy (including the use of nasal cannula, oxygen mask, and high-flow nasal oxygen therapy); (III) antiviral therapy; (IV) immunotherapy (mainly comprising glucocorticoids such as dexamethasone at a daily dose of 5 mg) (14); (V) anticoagulant therapy (i.e., low molecular weight heparin at a dose of 4,000 units per day); (VI) prone ventilation therapy; and (VII) psychological interventions.

Data collection

General information

At admission, demographic data including age, gender, height, weight, heart rate (HR), and blood pressure (BP), and medical history [e.g., underlying lung diseases, such as emphysema, bullae, chronic obstructive pulmonary disease, and interstitial lung changes; hypertension/high blood pressure (HBP); diabetes mellitus (DM); CHD; atrial fibrillation (AF); history of cancer, and messenger RNA COVID-19 vaccination] were collected.

Hematological indicators

The following hematological indicators were recorded: PaO₂, oxygenation index (OI), lactate (Lac), white blood cell count (WBC), hemoglobin (Hb), platelet (PLT), neutrophil ratio (NE%), lymphocyte count (Lym), C-reactive protein (CRP), procalcitonin (PCT), interleukin 6 (IL-6), cardiac troponin T (cTnT), N-terminal pro-brain natriuretic peptide (NT-proBNP), serum creatinine, estimated glomerular filtration rate (eGFR), albumin, and D-dimer (D-d).

Echocardiographic parameters

All the echocardiographic examinations were conducted by specialized physicians in the Cardiac Function Department. The following echocardiographic data were collected: left atrial diameter (LAD), left ventricular internal diameter at end-diastole (LVIDD), left ventricular internal diameter at end-systole (LVIDS), LVEF, interventricular septum thickness (IVST), left ventricular posterior wall thickness (LVPWT), tricuspid regurgitation peak velocity (TRv), aortic valve flow peak velocity (AV Vmax), and pulmonary artery systolic pressure (PASP). Right atrial (RA) enlargement was defined as a maximum upper and lower diameter >53 mm; while RV enlargement was defined as a basal segment >42 mm. Additionally, a moderate or greater amount of pericardial effusion (PE), mitral and tricuspid valve regurgitation severity (MR, TR), and the presence of moderate or severe aortic valve regurgitation (AVR) and aortic valve calcification (AVC) were recorded. AVC was defined as an aortic valve leaflet thickness ≥1 mm with overall or partial thickening and enhancement, with or without reduced leaflet mobility (15). The early diastolic peak velocity of the MR (E) and the early diastolic peak velocity of the lateral mitral annulus (e’) were measured, and E/e’ was calculated. Body surface area (BSA) was used to correct for the left atrial diameter index (LADI and LAD/BSA) calculation. Left ventricular (LV) mass was calculated using the following formula: LV mass (g) = 0.8 × 1.04 × [(LVIDD + LVPWT + IVST)³ − LVIDD³] + 0.6. The left ventricular mass index (LVMI, g/m^2.7) was calculated as follows: LVMI = LV mass/height^2.7 (16). As per the guideline, LA size, e’, E/e’, and TRv were used to evaluate LV diastolic function (17).

Statistical analysis

The statistical analysis was performed using R software (version 4.0.3; R Foundation for Statistical Computing) with the “tableone” and “glmnet” R packages. Continuous variables were expressed as medians with interquartile ranges, and between-group comparisons were analyzed using the non-parametric Mann-Whitney test. Categorical data were presented as frequencies and percentages, and the between-group comparisons were analyzed using Fisher’s exact test. Univariate logistic regression identified features associated with poor outcomes (P<0.05 threshold for selection). Selected features underwent the least absolute shrinkage and selection operator (LASSO) logistic regression with cross-validation to determine the optimal λ parameter. Final multivariate logistic regression was conducted on LASSO-derived features, with two-tailed P<0.05 considered statistically significant.

Results

Comparison of basic information

In total, 75 patients were included in the study, of whom 56 were treated successfully, 19 progressed to critical illness, and 9 died during hospitalization. The study comprised 43 male and 32 female patients, who had a median age of 79 years. As Table 1 shows, the patients in the poor prognosis group had a higher age, a lower body mass index, and a greater prevalence of cardiovascular-related basic diseases, such as DM, CHD, and AF; however, these differences were not statistically significant. Thus, no statistically significant differences were observed in baseline clinical characteristics between the two groups. In terms of the hematological data, the poor prognosis group had lower PaO₂, OI values, and Lym levels, and higher CRP, PCT, IL-6, cTnT, NT-proBNP and D-d levels (P<0.05).

Comparison of echocardiography data

As Table 2 shows, there were no statistically significant differences between the two groups in terms of most of the echocardiographic data. However, the LVEF of the poor prognosis group was slightly lower than that of the good prognosis group, but both still fell within the normal range, and the absolute difference between them was not significant. The poor prognosis group also had a prevalence of both AVR and AVC compared to the good prognosis group (P<0.05).

Univariate logistic regression analysis

All the variables detailed in Tables 1,2 were analyzed using a univariate logistic regression model. The results revealed that 10 variables were significantly associated with a poor prognosis (all P values <0.05). Table 3 sets out these 10 variables and their respective P values.

LASSO regression analysis

The 10 variables identified in the univariate logistic regression and the patients’ basic demographic data, including age and gender, were included in the LASSO regression, resulting in a total of 12 variables (Figure 1A). The optimal penalty coefficient λ (0.0416) was determined by a five-fold cross-validation of the LASSO regression model. Based on λ + 1se (0.0875), the following seven variables with non-zero coefficients were selected: PaO₂, CRP, cTnT, D-d, LVEF, AVR, and AVC (Figure 1B).

Figure 1 Feature selection using LASSO logistic regression. (A) Selection of the tuning parameter (λ) for the LASSO regression using five-fold cross-validation via the minimum criteria. The partial likelihood binomial deviance is plotted against the log(λ). (B) LASSO coefficient profiles for clinical features. Each coefficient profile plot is produced against a log(λ) sequence. The optimal values of log(λ) where features are selected are indicated by dotted vertical lines using the minimum criteria and one standard error of the minimum criteria. The right dotted vertical line is set at the non-zero coefficients selected via five-fold cross-validation, where seven non-zero coefficients are included. LASSO, least absolute shrinkage and selection operator.

Multivariate logistic regression analysis

The seven variables identified by LASSO regression were included in the multivariate logistic regression analysis (Table 4). The results showed that PaO₂, CRP, and AVC were independently associated with poor outcomes among elderly patients with severe COVID-19. Furthermore, in Model 2, adjusting for age, gender, hypertension, diabetes, PaO2, and CRP using logistic regression, AVC was independently associated with poor prognosis [odds ratio (OR) =4.915, 95% confidence interval (CI): 1.266–19.085, P=0.02].

Discussion

The COVID-19 pandemic caused by the 2019 novel coronavirus is a catastrophic event that has swept the world over the past few years, affecting most people worldwide and resulting in many deaths. According to the latest research (18), cardiovascular symptoms after COVID-19 infection are more common in patients with post-acute COVID-19 syndrome. COVID-19 infection can not only cause damage to the heart in the acute phase, but also lead to long-term cardiovascular problems. Circulating biomarkers including IL-6, CRP, PCT, and D-d have been widely associated with COVID-19 severity and prognosis, all of which align with our findings (9). In our cohort, these biomarkers were significantly elevated in the poor prognosis group. However, despite the well-documented efficacy of COVID-19 vaccines in reducing severe outcomes and mortality across multiple studies, the vaccination rates between the two groups were comparable. This observation may be attributable to lower vaccination rates among elderly populations with comorbidities, as well as potential variability in vaccine dosage regimens and administration schedules.

In this study, echocardiographic analysis revealed that elderly patients with severe COVID-19 and AVC had significantly poorer short-term prognosis compared to those without AVC. Furthermore, a significant association was observed between AVC and adverse outcomes in this population. Compared with the patients without AVC, the elderly patients with severe COVID-19 and AVC had an increased risk of death and progression to critical illness. These findings indicate that AVC could serve as a prognostic factor for such patients.

AVC has been extensively studied as a prognostic marker in elderly populations. For example, Meyer et al. investigated the prognostic value of the computed tomography coronary artery calcium score (Weston score) in COVID-19, and found that the coronary artery calcium score was significantly associated with 30-day mortality in COVID-19 patients (OR =1.15; 95% CI: 1.06–1.25; P<0.001), with a strong positive correlation between coronary calcium score and age (19). This highlights the clinical significance of coronary artery calcification in elderly patients. Similarly, for AVC, both coronary artery calcification and AVC are part of the atherosclerotic process, which involves chronic inflammatory pathways, lipoprotein infiltration, and an osteogenic environment. These processes are typically associated with non-genetic risk factors such as an advanced age, male sex, obesity, hyperlipidemia, hypertension, diabetes, and smoking (20,21), factors that are also strongly associated with cardiac disease risk. Moreover, many studies have shown that AVC is associated with a higher mortality rate from cardiovascular disease, independent of age and gender (22,23). More than 16% of patients with COVID-19 suffer from concomitant LV diastolic dysfunction, which is exacerbated by the pathophysiological effects of AVC (13,24,25). This may lead to a higher incidence of cardiovascular events in critically ill patients with COVID-19. Given that vascular endothelial dysfunction, inflammatory responses, and myocardial injury are critical pathogenic mechanisms of SARS-CoV-2 (26,27), we hypothesize that elderly individuals with concomitant AVC may be at a higher risk of severe outcomes when infected with the virus. Therefore, elderly AVC patients, due to atherosclerosis-related inflammation, reduced cardiovascular reserve, and synergistic effects with COVID-19 pathogenesis, are more susceptible to severe disease progression following infection. This population requires particular clinical attention regarding cardiovascular protection and early intervention.

Due to the evident damage to the myocardium caused by SARS-CoV-2, echocardiography has become an important method for evaluating myocardial injury and has been extensively studied during the COVID-19 pandemic. Early on in the pandemic, several studies reported various manifestations of COVID-19 pneumonia in patients, including impaired LV and RV function, stress-induced cardiomyopathy, myocarditis, and myocardial suppression (10,11,28). Certain echocardiographic data, such as the first-phase LVEF, PASP, tricuspid annular plane systolic excursion, and RV dilatation, are thought to be associated with the poor prognosis of COVID-19 patients (12,29-31). However, the measurement of these indicators requires a certain level of expertise from the operator, and due to the unique nature of the virus, performing a comprehensive echocardiographic examination on patients during the early stages of the pandemic to prevent further transmission was challenging (32,33). Thus, the assessment of AVC is a more feasible and easier echocardiographic technique that may serve as an indicator for the short-term prognosis of COVID-19 patients and provide guidance for more proactive intervention measures.

There are several limitations in this study. First, it was a single-center study that was limited by the relative scarcity of medical resources during the outbreak of the COVID-19 pandemic, resulting in a small sample size. This limitation restricted our ability to perform other analyses, such as subgroup analyses, interaction analyses, and sensitivity analyses, and might have resulted in the underestimation of certain important factors, such as age and underlying cardiovascular-related diseases. Second, as a retrospective study, there was an inevitable selection bias in the patient recruitment. Third, this study only observed a phenomenon, and did not examine the possible underlying mechanisms. Fourth, many data points that could have been further analyzed, such as the mean transaortic gradient, aortic valve surface area, and permeability index, were not included in the routine echocardiography. Additionally, because patients were initially diagnosed at different hospitals, other pulmonary imaging data [e.g., computed tomography (CT) scans] were not fully integrated into a unified network, limiting our ability to utilize these valuable resources (34). Consequently, a more comprehensive interpretation of the results was challenging. Thus, future studies should aim to expand the sample size, strengthen the data collection and analysis, explore the interaction between AVC and other related diseases, and study effective treatment strategies to improve the prognosis of elderly patients with severe COVID-19.

The global COVID-19 pandemic appears to be subsiding; however, cases of reinfection with the virus are still being reported. Moreover, the potential challenges that healthcare systems may face during another global outbreak similar to COVID-19 remain uncertain, particularly given the persistent issue of limited medical resources, which may hinder the ability to provide adequate care and treatment for patients during such large-scale disease outbreaks (35). Thus, research into COVID-19 needs to be continued. We hope that our findings will highlight the importance of echocardiography, a simple yet valuable diagnostic tool, on helping physicians assess their patients’ conditions more accurately and serving as a reference for developing more effective treatment plans at an earlier stage in these conditions.

Conclusions

In this study, we analyzed the data of 75 severely ill COVID-19 patients who received standard treatment. Our research findings suggest that the presence of AVC on echocardiography may indicate a poor prognosis for such patients. Clinical physicians should pay close attention to this valuable indicator in such patients.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-830/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-830/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-830/prf

Funding: This study was supported by funding from the Science and Technology Development Plan of Suzhou City, China (contract grant number: SYS2020136).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-830/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Informed consent was obtained from all participants. This study was approved by the Ethics Committee of The Second Affiliated Hospital of Soochow University (No. JD-HG-2024-020).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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