Association between eosinophil counts and outcomes of severe coronavirus disease 2019 in elderly asthma patients: a prospective cohort study

Introduction

Prevalence of asthma among patients with coronavirus disease 2019 (COVID-19) varies significantly, ranging from 0.9% to 17%, depending on the patient population (1-3). Given the association between respiratory viral diseases and acute exacerbations of asthma, and the fact that an association between asthma and COVID-19 outcomes is not fully understood, asthma patients should be closely monitored during the COVID-19 pandemic. This is especially pertinent to elderly patients with asthma since older age, along with comorbid conditions, such as diabetes, cardiovascular disease, and hypertension, are associated with more serious complications of COVID-19 and higher mortality (4).

Given effects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection on the respiratory system and epidemiological burden of asthma worldwide, COVID-19 in asthma patients caused reasonable concern to be a “dangerous” combination in early pandemic. However, subsequently published studies on outcomes of COVID-19 in asthma patients were controversial. Moreover, factors related to susceptibility to SARS-CoV-2 infection and severity of COVID-19 in asthma patients were investigated including severity of asthma, asthma phenotype, asthma treatment including systemic steroids, comorbidity, and age (5).

An association between eosinophil level and clinical course of COVID-19 is widely discussed now. According to recent publications, changes in routine peripheral blood parameters, including eosinophil count, could be predictive of the disease outcome and treatment efficacy in COVID-19 patients (6). Being potent proinflammatory regulatory cells, eosinophils are also involved in antiviral immunity. The role of eosinophils in fighting against respiratory viruses has been investigated in numerous studies (7). Eosinophils migrate to lymph nodes, present antigens to T-cells, and express multiple toll-like receptors (TLR), such as TLR-3, TLR-7, and TLR-9, which are involved in virus recognition (8,9). Moreover, eosinophils produce nitric oxide (NO) by inducible NO-synthase which inhibits virus replication through different mechanisms (10,11).

Severe COVID-19 is typically associated with lower lymphocyte counts, higher leukocyte counts, higher neutrophil/lymphocyte ratio (NLR), and lower percentages of monocytes, eosinophils, and basophils (12). Patients with COVID-19 generally have blood eosinophil counts close to the lower limit of normal; in most cases, these changes are seen early in the course of the disease, irrespective of its severity (13).

A prospective cohort study showed that asthma patients might be at lower risk of poor outcomes from COVID-19, and eosinophilia in COVID-19 patients with or without asthma could be associated with reduced risk of death (14). The study results showed that pre-existing blood eosinophil levels ≥150 cells/µL were protective against hospitalization due to COVID-19 and the same blood eosinophil levels reported during hospitalization were associated with lower mortality (15).

Patients with Th2 asthma phenotype are known to be at higher risk of virus-induced acute exacerbations of asthma and may have decreased innate immunity response to respiratory viruses. On the other hand, eosinophils could enhance the defense against respiratory viruses (7,15). It is not completely clear whether eosinophilic inflammation is protective or increases the risk of poor outcomes in COVID-19 patients with asthma (14). Several authors published evidence demonstrating that Th2 asthma phenotype could be an important predictor for lower morbidity and mortality in COVID-19 patients (15).

Given a complex interaction between multiple confounding factors, there is a need for large studies on effects of asthma on susceptibility to SARS-CoV-2 infection and outcomes of COVID-19 (4). We failed to find recent publications on possible poor outcomes in early post-discharge period, including death, re-hospitalization or a need for emergency care, in elderly patients with asthma (16). However, asthma in elderly is known to have some peculiarities, including multiple comorbidities, polypharmacy, individual response to treatment, individual perception of the disease and higher mortality (17).

Therefore, there is a need to evaluate COVID-19 outcomes in different populations of asthma patients to better understand the association between asthma and outcome of COVID-19. We hypothesized that higher eosinophil count is associated with reduced mortality in elderly asthma patients with severe COVID-19. To address this, we conducted a prospective cohort study to investigate an association between eosinophil levels and the clinical course and outcomes of severe COVID-19 in elderly patients with asthma. We present this article in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-338/rc).

Methods

Study design

This single-center prospective non-interventional cohort study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Local Ethics Committee of Sechenov University (protocol #10-20). Written informed consent was obtained from all subjects.

Study population

The study involved 131 patients >60 years of age with asthma who were consecutively admitted to the Sechenov University clinic for COVID-associated lung injury between October 1, 2020, and September 30, 2021. Patients were enrolled after signing the informed consent. After the discharge, patients were followed up for 90 days using weekly phone calls to assess clinical outcomes. Patients lost to follow-up were excluded from the analysis. Other exclusion criteria were hematological malignancies, parasitic disease, and intake of steroids or cytotoxic agents within previous 6 months according to electronic medical records and the patients’ interviews.

The diagnosis of COVID-19 was confirmed by PCR test of the upper respiratory tract samples, typical clinical symptoms of COVID-19 and multifocal viral pneumonia seen in computed tomography (CT) of the lungs (18).

All patients were previously diagnosed with asthma according to the Global Initiative for Asthma (GINA) 2020 criteria (19).

Only those patients who completed their hospital treatment (i.e., were discharged alive or died) at the time of analysis (December 30, 2021) were included in the analysis.

Data collection

The primary outcomes were in-hospital mortality and mortality within 90 days following discharge. We analyzed two groups of variables:

• Patient-related variables: demographic data, body mass index (BMI), clinical course, laboratory test results [blood cell count, blood chemistry, C-reactive protein (CRP), D-dimer, and coagulation profile], and chest CT findings;
• Outcome-related variables: discharged or died.

Comorbidity was assessed using the Charlson Comorbidity Index (20).

To ensure data accuracy, all variables were independently recorded by two researchers. Discrepancies were resolved through consensus or third-party adjudication. Missing laboratory or imaging data (<5% of cases) were imputed using median values for continuous variables and the mode for categorical variables, as validated in the sensitivity analysis.

Laboratory and radiological assessments

The complete blood count (red blood cells, white blood cells, absolute and relative number of neutrophils, lymphocytes, monocytes, eosinophils, and basophils) was done using the XE 2100 hematology automated analyzer (Sysmex Corporation, Japan). Tests were done on whole blood samples obtained at admission.

Lung CT scans were performed at admission using the Aquilion TSX-101A CT scanner (Toshiba Medical Systems, Japan). Scans were obtained with the slice thickness of 1 mm and the pitch factor of 1.5. To evaluate an area of the lung injury in patients with COVID-19, we used an empirical scale based on visual assessment of the attenuation area in more injured lung: no typical findings were referred to as CT-0; a minimal area of injury (<25% of the total lung area) was referred to as CT-1; a large area of the injury (50–75% of the total lung area) was referred to as CT-3, and a subtotal lung injury (>75% of the lung area) was referred to as CT-4.

Blood CRP was measured on days 1 and 5 by the latex immunoturbidimetric assay (Beckman Coulter, USA) using CRP latex reagents, Russia.

Statistical analysis

A statistical analysis was done using the GraphPad Prism 9 software (GraphPad Software, Inc., San Diego, CA) and the R (v.3.6, license GNU GPL2) environment, free software for statistical computing and graphics.

The required sample size was calculated for the Cox regression model. The method proposed by Hsieh and Lavori [2000] was used as a basis, which is an extension of the Schoenfeld [1983] formula for continuous predictors (21,22). The initial calculation parameters were: alpha =0.05 (two-tailed tests), power =0.80, effect size [hazard ratio (HR) =0.13; 95% confidence interval (CI): 0.06–0.27], and standard deviation (SD) =0.22×10⁹ cells/L (from Salai et al. [2023]) (23). The upper limit of the 95% CI (0.27) was used as a conservative estimate for the calculation, which ensures more reliable sample size planning. In accordance with the specified parameters and the applied methodology, the required total sample size was 95 patients to achieve the given statistical power and significance level.

The normality of distribution of the study parameters was tested using the Shapiro-Wilk’s normality test; most parameters had non-normal distribution. Quantitative variables were analyzed using the descriptive statistics method; median, interquartile range (IQR) and 95% CI were calculated. Multiple comparisons were done using the Kruskal-Wallis test; paired comparisons were made using Dunn’s test. Comparisons between two groups were done using the Mann-Whitney test. Categorical variables were analyzed using absolute and relative frequencies (percentages), 95% CI was calculated for relative frequencies using the Wilson method. Multiple comparisons were done using 2×2 Chi-squared test. Paired comparisons were done using Fisher’s exact test with the Holm-Bonferroni correction. Comparisons between two groups were done using the chi-square test or Fisher’s exact test if the expected frequency was <5%.

A risk of death related to the eosinophil count was assessed using the Cox proportional hazards model; the proportional hazard assumption was tested with the Grambsch-Therneau non-proportionality test. Univariate analysis including eosinophil count, and multivariate analysis including eosinophil count and other risk factors for death, were performed. The relative risk was estimated and multivariate models were created separately for the in-hospital and 90-day post-discharge periods.

The survival rate related to the eosinophil count and a length of hospital stay in survivors was analyzed with Kaplan-Meier curves and the log-rank test.

The statistically significant difference (P value) was defined as P<0.05.

Results

Patients

One hundred and thirty-one elderly asthma patients (59 males) admitted to a hospital for COVID-associated lung injury were enrolled in the study; of them, 86 patients (66%) survived, 30 patients (22.9%) died in the hospital, and 15 patients (14.9%) died during 90 days post-discharge. The median age of patients was 74 (67; 80) years; and the median BMI was 27.5 (25; 30.2) kg/m²; 21 patients were smokers with the smoking history of 45 (32; 50) pack-years. The Charlson Comorbidity Index score was 5 (3; 6). Most patients (79%) had non-atopic asthma phenotype; 21% of the patients had atopic asthma phenotype; 22 patients (17%) were frequent exacerbators. Co-existing allergic diseases (chronic rhinosinusitis, seasonal pollinosis, or urticaria) were diagnosed in 21% of the patients. Mild asthma was diagnosed in 39 patients (30%), moderate asthma in 66 patients (50%), and severe asthma in 26 patients (20%). Controller therapy for asthma was administered to 109 patients (83%); 22 patients (17%) used short-acting bronchodilators only; 4 patients (3%) used biological agents (omalizumab and mepolizumab, n=2 for each). In the subgroups of non-survivors, 16 patients (53%) and 9 patients (60%), respectively, had controller therapy for asthma.

The most common comorbidity was ischemic heart disease (IHD) (51 patients, 39%). Among these IHD patients, 19 patients (15%) had atrial fibrillation; 44 patients (34%) had hypertension; and 33 patients (25%) had chronic heart failure (CHF). Diabetes and chronic obstructive pulmonary disease (COPD) were present in 26 IHD patients (20%) each.

All the patients were admitted to the hospital due to COVID-associated lung injury. Most patients (n=74; 57%) had moderate lung injury (25–49% of the lung area as assessed by lung CT scans). Severe lung injury (50–75% of the lung area) was found in 33 patients (25%); and very severe lung injury (>75% of the lung area) was diagnosed in 5 patients (4%).

The most common cause of in-hospital death was acute respiratory failure. Most common causes of death within 90 days post-discharge were acute cardiovascular events and pulmonary embolism.

Three subgroups of patients (survivors, patients who died in the hospital, and patients who died within 90 days post-discharge) did not differ significantly in age (P=0.37), BMI (P=0.18), and most other parameters (Table 1). Compared to the survivors, the non-survivors had higher Charlson Comorbidity Index (P<0.001 for patients who died in the hospital and P=0.05 for those who died post-discharge), higher respiratory rate (RR) (P=0.04 for both groups), larger area of COVID-associated lung injury (P=0.02 and P=0.05, respectively), higher neutrophil count (P=0.01 and P<0.001, respectively), higher NLR (P=0.02 and P<0,001, respectively), and lower eosinophil count (P=0.001 and P=0.04, respectively).

Eosinophils and COVID-19 outcomes

We analyzed the association between eosinophil count and outcomes of COVID-19. Distribution of eosinophil counts according to the disease outcome is demonstrated in Figure 1. Absolute eosinophil counts significantly differed depending on the disease outcome (P<0.001) and were significantly higher in survivors (40 [10; 100] cells/µL) compared to 14 [9; 30] cells/µL in the patients who died in the hospital (P=0.001) and 17 [9; 27] cells/µL in the patients who died post-discharge (P=0.04).

Figure 1 Distribution of absolute eosinophil count in study patients. *, P≤0.05 (Dunn’s test); ***, P<0.001.

The eosinophil count >100 cells/µL was reported in 21 survivors (24%) and in none of non-survivors (the Fisher’s exact test: P=0.002 for the comparison between survivors and those who died in the hospital; P=0.04 for the comparison between survivors and those who died post-discharge).

Risk factors of death

A relative risk adjusted for confounding factors associated with death (age, gender, smoking history, cardiovascular diseases, diabetes) was analyzed using the multivariate Cox proportional-hazards model (Figures 2,3). A relationship was found between the eosinophil count and the risk of death at the hospital stage: HR, 0.68 for every 10 cells/µL; 95% CI: 0.55–0.85; P<0.001; after adjustment for other risk factors: HR, 0.64; 95% CI: 0.49–0.84; P=0.002 (Figure 2).

Figure 2 Factors associated with a high risk of death during hospitalization. Adjusted relative risks (HR) and their 95% CI were calculated using a multivariate Cox proportional-hazards model. CCI, Charlson Comorbidity Index; CI, confidence interval; CRP, C-reactive protein; CT, computed tomography; EOS, eosinophils; FiO₂, fraction of inspired oxygen; GINA, Global Initiative for Asthma; HR, hazard ratio; LDH, lactate dehydrogenase; LYMPH, lymphocytes; NEU, neutrophils; RR, respiratory rate; SpO₂, peripheral oxygen saturation; WBC, white blood cell count.

Figure 3 Factors associated with a high risk of death after discharge. Adjusted relative risks (HR) and their 95% CI were calculated using a multivariate Cox proportional-hazards model. CCI, Charlson Comorbidity Index; CI, confidence interval; CT, computed tomography; EOS, eosinophils; HR, hazard ratio; LYMPH, lymphocytes; NEU, neutrophils; RR, respiratory rate; SpO₂, peripheral oxygen saturation; WBC, white blood cell count.

An association between eosinophil count and the risk of death was also observed in the post-discharge period [HR, 0.82 for every 10 cells/µL; 95% CI: 0.68–0.97; P=0.04; after adjustment for other risk factors: HR, 0.78; 95% CI: 0.65–0.95; P=0.01] (Figure 3).

Survival analysis

The survival rate during the hospital stay and 90 days post-discharge differed significantly depending on the eosinophil count; the cut-off value was 100 cells/µL.

The median in-hospital survival time in patients with the eosinophil count ≤100 cells/µL was 22 (95% CI: 18–25) days compared to ≥28 days in patients with the eosinophil count >100 cells/µL (no deaths were reported in this group during the hospitalization), log-rank, 12.2; P<0.001 (Figure 4A).

Figure 4 Survival curves for patients with COVID-19 and asthma depending on eosinophil count. (A) During the 28-day hospitalization period; (B) during the 90-day post-discharge period. COVID-19, coronavirus disease 2019; EOS, eosinophils.

The post-discharge survival time also differed significantly according to the eosinophil count (log-rank, 4.3; P=0.04); the median survival time was at least 90 days both in the patients with eosinophil count >100 cells/µL and <100 cells/µL, but mean post-discharge survival time was 76±3.3 days in the patients with eosinophil count <100 cells/µL compared to ≥90 days in the patients with eosinophil count >100 cells/µL (no deaths were reported in this group during 90 days post-discharge) (Figure 4B).

Discussion

Our study evaluated an association between eosinophil count, and clinical course and outcomes of COVID-associated lung injury in elderly patients with asthma, both during hospitalization and 90 days after discharge. Most patients had moderate non-atopic asthma and multiple comorbidities.

A statistically significant increase in Charlson Comorbidity Index score, RR, and area of COVID-associated lung injury was found in non-survivors. Non-survivors also had higher leukocyte and neutrophil counts, higher NLR, and significantly lower eosinophil counts.

Eosinophil count >100 cells/µL was found in 24% of survivors and in none of non-survivors. Moreover, the in-hospital and post-discharge survival time was shorter in the patients with eosinophil count ≤100 cells/µL. Therefore, the eosinophil count could be a useful predictor of death in asthma patients with COVID-19. Recent publications demonstrated that persistent eosinopenia in patients with COVID-19 after discharge was associated with more severe disease and slower recovery (6). Tan et al. showed that blood eosinophil count in patients with COVID-19 was more sensitive predictor than blood lymphocyte count (13). Blood eosinophil count was inversely related to COVID-19 severity; eosinophil level increased in survivors and critically decreased in non-survivors. The authors concluded that eosinophils in COVID-19 patients are closely related to human immune defense. Nevertheless, the actual mechanisms of antiviral effects of eosinophils in SARS-CoV-2 infection need to be thoroughly studied (6,23).

In another study, lower blood eosinophil count and lower eosinophil/lymphocyte ratio were associated with poor outcomes in COVID-19 patients; therefore, these markers could predict prolonged hospitalization in patients with moderate to severe disease (24).

In a retrospective study, Ferastraoaru et al. investigated risk factors associated with hospitalization and mortality in patients with asthma and COVID-19. Their study demonstrated that Th2 asthma phenotype, characterized by blood eosinophilia (≥150 cells/µL), was associated with decreased risk of hospitalization due to COVID-19. The same level of blood eosinophils during hospitalization was associated with lower in-hospital mortality from COVID-19 in asthma patients. Asthma patients with COVID-19 and blood eosinophilia ≥150 cells/µL on admission had lower probability of death compared to those with blood eosinophilia <150 cells/µL [9.6% vs. 25.8%; odds ratio (OR), 0.006; P=0.03]. Asthma patients with previously reported mean absolute eosinophil count ≥150 cells/µL had lower risk of hospitalization due to COVID-19 (OR, 0.46; 95% CI: 0.21–0.98; P=0.04), whereas such comorbidities as CHF, chronic kidney disease and COPD increased the risk of hospitalization (15-17).

А cut-off value for blood eosinophils of 150 cells/µL is often used in modern asthma studies (especially in pharmacological studies). However, a cut-off value of 100 cells/µL is also a justified criterion, as exemplified by a recent landmark study examining the course of asthma in relation to blood eosinophil levels (25). Recently published in vitro and in vivo studies have shown weak but very important correlation between angiotensin-converting enzyme (ACE) expression and Th2- and interleukin (IL)-17-dependent epithelial gene expression signatures. ACE expression within bronchial epithelium was lower in asthma patients with significant sensitization to allergens, whereas high ACE expression level was found in asthma patients with low blood eosinophil count (the cut-off value was 150 or 300 cells/µL) (26).

Moreover, IL-13 significantly decreased ACE expression in respiratory epithelium in patients with atopy with or without asthma (27). Taken together, these data demonstrate that patients with non-Th2-asthma could have higher risk of poor prognosis of COVID-19 (26), probably because of higher virus adhesion. Asthma patients with COVID-19 and blood eosinophilia ≥150 cells/µL during hospitalization also had higher eosinophil count before COVID-19 compared to the patients who had never had blood eosinophilia >150 cells/µL. These data suggest that hospitalized patients with the Th2 asthma phenotype could have better outcomes of COVID-19 (15).

Thus, in elderly asthma patients hospitalized due to COVID-19, the absolute eosinophil count measured both during hospitalization and after discharge could be a significant predictor of outcome both non-adjusted and adjusted for other risk factors. Poor outcome during hospitalization was also associated with higher Charlson Comorbidity Index, higher NLR, and lower total protein level. Poor prognosis after discharge was associated with higher Charlson Comorbidity Index, extended area of COVID-associated lung injury, and diabetes. Median in-hospital and post-discharge survival time was lower in patients with eosinophil counts ≤100 cells/µL.

Despite these promising findings, certain limitations should be acknowledged. At first, the sample size in this study was relatively small, limiting generalizability of the results. Secondly, the single-center design further restricts the ability to draw general conclusions about the relationship between eosinophil count and poor COVID-19 outcomes in elderly patients with asthma. Therefore, further studies in a larger cohort of patients are needed. Thirdly, we followed the patients after the discharge via weekly phone calls and assessed the post-discharge outcome relying on self-reported data; this could be a source of certain inaccuracies in symptom or outcome assessment. This is explained by the fact that at the time of the study, we were restricted in face-to-face communication with patients due to pandemic-related constraints.

Moreover, there is no clear understanding of the mechanism underlying this relationship; this is also required further fundamental investigations.

Conclusions

Our study suggests that blood eosinophil count could be used as a prognostic marker of outcome in elderly patients with asthma and COVID-associated lung injury. Eosinophil level ≤100 cells/µL is a threshold for predicting poor outcome, both in-hospital and 90-day post-discharge. The results also highlight the importance of comorbidity and inflammatory markers, such as NLR, in predicting poor outcomes from COVID-19 in elderly patients with asthma.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-338/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-338/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-338/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-338/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. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Local Ethics Committee of Sechenov University (protocol #10-20). Written informed consent was obtained from all subjects.

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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