Pathogen distribution and prognostic risk factors in respiratory intensive care unit (RICU) patients of a large general hospital before and after COVID-19 pandemic

Introduction

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory coronavirus 2 (SARS-CoV-2), has resulted in over 7 million deaths worldwide (1). Among the numerous variants of SARS-CoV-2, the Omicron variant is the most transmissible (2,3). In China, the Omicron wave of COVID-19 triggered a dramatic surge in hospitalizations following the regular prevention and control of COVID-19 (4,5). Before December 2022, China implemented a centralized management system for COVID-19 patients, whereby all confirmed patients were admitted to designated hospitals and isolated wards under standardized protocols. This approach facilitated coordinated resource allocation, minimized nosocomial transmission through prevention and control, and enabled the implementation of uniform treatment guidelines. The subsequent transition to routine hospital operations after strategy discontinuation created a unique natural experiment to study the epidemiological impact of routine hospital operations.

The post-strategy surge from the regular prevention and control of COVID-19 has likely altered the pathogen landscape of hospital-acquired pulmonary infections (6). the previously non-existent SARS-CoV-2 infections have increased significantly, and at the same time, the detection frequency of Gram-positive (G+) bacteria in COVID-19 patients has risen. Severe pulmonary infections are a major cause of morbidity and mortality in intensive care unit (ICU) patients (7,8), further complicated by the presence of SARS-CoV-2. The changed pathogen distribution may lead to changes in infection patterns, and mixed infections with different pathogenic bacteria may result in different prognoses for patients. COVID-19 patients face an elevated risk of developing secondary bacterial or fungal infections, including candidiasis and Aspergillus (9-13), which are associated with worse outcomes (14,15). The transition from the centralized to decentralized management of COVID-19 patients might have further influenced infection patterns, as shifts in infection control practices and patient distribution can alter microbial epidemiology. To date, there have been no systematic studies both domestically and internationally investigating the characteristics of pathogen distribution and prognosis of patients in the respiratory intensive care unit (RICU) during this specific period. Thus, further research needs to be conducted into the pathogen spectrum and clinical outcomes of patients with severe pulmonary infections in the context of the COVID-19 pandemic.

In this study, we retrospectively included RICU patients with pulmonary infections before and after the regular prevention and control of COVID-19 patients. Based on data such as demographic factors, clinical characteristics, underlying diseases, and laboratory test indicators, we compared the prognostic outcomes of patients in the two periods and analyzed the association between potential prognostic factors and prognostic outcomes. By comparing these two phases, the effect of the COVID-19 pandemic on pathogen distribution and patient outcomes was evaluated. Additionally, the risk factors associated with adverse outcomes were identified. Although the COVID-19 pandemic has now subsided, this study aims to provide certain references for the treatment and prognosis of patients with COVID-19. We present this article in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1296/rc).

Methods

Study design and participants

This retrospective cohort study was conducted at the RICU of the First Affiliated Hospital of Anhui Medical University. Patients admitted between January 2022 and March 2023 were enrolled in the study to compare the following two distinct epidemiological periods: the pre-strategy period (January to November 2022) in which COVID-19 patients underwent centralized management at designated facilities, and the post-strategy period (December 2022 to March 2023) in which regular prevention and control measures were employed. After excluding 39 patients with incomplete data or alternative diagnoses, 132 patients with pulmonary infections were included in the study (65 from the pre-strategy period and 67 from the post-strategy period).

Assessment of potential prognostic factors includes the following aspects: (I) demographic factors: including age, gender, etc.; (II) clinical features: symptoms such as fever, cough, fatigue, dyspnea, etc.; (III) underlying diseases: including hypertension, diabetes mellitus, coronary heart disease, chronic obstructive pulmonary disease, etc.; (IV) laboratory test indicators: such as blood routine (white blood cell count, lymphocyte count, etc.), inflammatory indicators (C-reactive protein, procalcitonin, etc.), liver and kidney function indicators, etc. The differences in these indicators between patients in the two periods will be compared to explore their association with prognosis.

Assessment of prognostic outcomes: the mortality rates of patients in the two periods will be calculated to evaluate the overall prognosis. Assessment methods: a retrospective cohort study will be adopted to follow up the patient cohorts in the two periods and collect information related to prognostic outcomes. Statistical methods such as t-test, chi-square test, and logistic regression analysis will be used to compare the differences in prognostic outcomes between patients in the two periods and analyze the association between potential prognostic factors and prognostic outcomes.

An initial diagnosis of pulmonary infection required radiological evidence of new pulmonary infiltrates on chest computed tomography, accompanied by at least one clinical manifestation, including new or worsening respiratory symptoms (cough, sputum production, dyspnea, or chest pain), documented fever, or abnormal inflammatory markers. The definitive diagnosis of infection involved a comprehensive evaluation of various factors, including the patient’s clinical presentation, imaging results, and antibiotic treatment outcomes, conducted by attending physicians, microbiologists, and other experts. This study was approved by the Clinical Research Ethics Committee of the First Affiliated Hospital of Anhui Medical University (No. PJ2024-03-31). This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Informed consent was obtained from patients. If participants were unable to provide informed consent due to incapacity, informed consent was obtained from a legal guardian or family member on their behalf.

Microbiological evaluation

All patients underwent comprehensive microbiological assessment, including conventional methods and metagenomic next-generation sequencing (mNGS). The conventional microbiological tests included culture, (1,3)-the β-d-glucan test (G-test), the galactomannan test, and polymerase chain reaction (PCR) (for SARS-CoV-2). All patients underwent bronchoalveolar lavage fluid (BALF) collection for DNA-based mNGS testing (Hugobiotech, Beijing, China). For patients with prolonged infections or critical conditions, serial mNGS testing was performed. In cases with suspected viral infections, additional RNA-mNGS was performed to enhance viral pathogen detection. We deliberately restricted our pathogen spectrum analysis to the initial BALF-mNGS results. The RNA-mNGS results were incorporated into the viral pathogen analysis.

Clinical management and data collection

Treatment strategies were determined via a multidisciplinary evaluation of microbiological results with clinical correlation, such as cough, fever, consciousness disorders, respiratory failure, etc. and the therapeutic response of the patients was continuously monitored. In the antibacterial and antifungal treatment of patients with pulmonary infections, we determine the treatment based on the type of infection, characteristics of pathogenic bacteria, and severity of the condition. For severe bacterial pneumonia, the first choice is a combination of broad-spectrum β-lactam antibiotics with quinolones or aminoglycosides. The treatment of fungal pneumonia needs to focus on high-risk factors: for those on long-term hormone use, immunosuppressants, or with hematological diseases, fluconazole-based drugs are selected empirically. Voriconazole is the first choice for confirmed invasive aspergillosis, and liposomal amphotericin B is used instead for patients with drug resistance or intolerance. The demographic characteristics, medical history, clinical presentation, laboratory/radiological findings, treatment regimens, and 6-month follow-up data of the patients were collected from medical records. Poor prognosis is defined as death or lack of improvement after antibiotic treatment.

Statistical analysis

The data were analyzed using R software (version 4.1.3). The continuous variables are expressed as the median with the interquartile range (IQR), and were compared using the t-test. The categorical variables were analyzed using the Chi-square test (χ² test) or Fisher’s exact test. Univariate and multivariate logistic regression models were constructed using MASS package (version 7.3-54) to explore the risk factors for pulmonary infection and poor prognosis. A P value <0.05 was considered statistically significant. Two variables with the most significant P values were selected from both the clinical variables and the infection variables in the univariate analysis, and these four variables were subsequently used for the multivariate regression analysis. For infection-related variables, individual pathogens were specifically prioritized rather than broad categories (e.g., fungi or bacteria) to increase clinical specificity. Collinearity checks were performed to assess the linear correlation between variables using variance inflation factors (VIF). VIF value greater than 10 typically indicates the presence of severe multicollinearity. All statistical tests were two-sided, and a P value <0.05 was considered statistically significant.

Results

Clinical characteristics

Our study included 132 patients, of whom, most (72%) were male. The median age of the patients was 68.5 years (IQR, 56.5–77 years). The most common clinical symptoms were cough (92 patients, 69.7%), fever (51, 38.6%), consciousness disorder (19, 14.4%), and respiratory failure (10, 7.6%). Procalcitonin and C-reactive protein levels were significantly elevated in almost all patients. Underlying diseases were present in 87.1% of the patients, of which, hypertension was the most frequent (25%), followed by malignancies (23.5%), diabetes (17.4%), and chronic obstructive pulmonary disease (9.8%). Of the patients, 24 were immunocompromised. Following targeted antibiotic treatment, 85 patients showed clinical improvement, 30 showed no improvement, and 17 died. After a 6-month follow-up period (during which, 5 patients were lost to follow up), 87 patients were alive, and 40 had died from infection-related complications. The detailed baseline characteristics of the patients are presented in Table 1.

We also compared the clinical characteristics of the patients before and after the COVID-19 pandemic (Table 1). The proportion of patients with fever was significantly higher post-strategy than pre-strategy (47.8% vs. 29.2%, P=0.045). The proportion of patients with SARS-CoV-2 infection was accounted for 74.6% (50/67) of post-strategy admissions but it was entirely absent pre-strategy due to the control measures. Notably, the COVID-19 patients exhibited a higher frequency of fever than the non-COVID patients (52.0% vs. 30.5%, P=0.01) (Table S1). The patients who were admitted post-strategy were also significantly older than those who were admitted pre-strategy (median IQR, 60–80 vs. 54–73 years). This difference may reflect both the slightly higher proportion of COVID-19 patients who were elderly, and the increased mobility in older populations after strategy changes.

Pathogen profiles shift with strategy changes

In total, 73 distinct pathogens, comprising 26 Gram-negative (G−) bacteria, 15 Gram-positive (G+) bacteria, 15 fungi, 9 DNA viruses, 3 RNA viruses, 1 Mycoplasma, 1 chlamydia, and 3 Mycobacterium, were identified in our cohort. Among these, 21 pathogens were detected using both mNGS and conventional methods, while mNGS alone detected 41 pathogens. The distribution of the pathogens is shown in Figure 1A.

Figure 1 Pathogen profiles. (A) Potential pathogens detected by mNGS and conventional methods (CTM) in patients before (left) and after (right) December 2022. The pathogens consisted of joint detection (the purple) of mNGS with CTM, and extra detection by mNGS (yellow) and CTM (blue). (B) Proportions of pathogens detected in COVID-19 and non-COVID-19 patients. Proportions are calculated as the case numbers of the pathogen out of the total number of patients in each group. Color intensity reflects proportion, with darker shades denoting higher values. CMV, cytomegalovirus; COVID-19, coronavirus disease 2019; CTM, conventional methods; EBV, Epstein-Barr virus; HHV, human herpes virus; HIV, human immunodeficiency virus; HSV, herpes simplex virus; mNGS, metagenomic next-generation sequencing.

We compared the differences in the overall pathogen profiles of the patients before and after the COVID-19 pandemic (Figure 1A,1B). The most striking change was the emergence of SARS-CoV-2 infections, which accounted for 74.6% of post-strategy infections compared to a complete absence (0%) pre-strategy. In addition to the significant increase in the incidence of COVID-19 after the strategy change, a certain change in the prevalence trends of other pathogens was also observed. Before December 2022, the most frequently detected bacteria were Klebsiella pneumoniae (K. pneumoniae), Acinetobacter baumannii (A. baumannii), and Pseudomonas aeruginosa (P. aeruginosa). Following the strategy change, these trends continued, with a notable increase in the incidence of Staphylococcus aureus (S. aureus) cases (7.46% vs. 1.54%). Conversely, the frequency of Streptococcus pneumoniae (S. pneumoniae) and Haemophilus influenza (H. influenza) appeared to decrease post-strategy change (from 13.85% to 7.46% for S. pneumoniae and from 12.31% to 2.99% for H. influenza). The fungi Candida albicans, Aspergillus flavus, and Aspergillus fumigatus (A. fumigatus) remained the most commonly identified species both before and after the strategy shift. In addition to the significant incidence of SARS-CoV-2, other viruses such as herpes simplex virus 1 (HSV-1), Epstein-Barr virus (EBV), and cytomegalovirus (CMV) were also found to have a high occurrence rate after the strategy change.

We further compared the distribution of co-infecting pathogens in COVID-19 patients, as well as the differences in prevalent pathogens among the non-COVID-19 patients before and after the pandemic. The COVID-19 patients showed a higher prevalence of dominant G+ bacteria and a lower prevalence of dominant G− bacteria. Candida and HSV-1 were more frequently detected in the COVID-19 patients (32.00% for both) than the other two groups of non-COVID-19 patients (20.00% for Candida and 26.15% for HSV-1 in the pre-strategy change patients, and 11.76% for Candida and 5.88% for HSV-1 in the non-COVID-19 patients after the strategy change); however, the difference was not statistically significant. Aspergillus was commonly detected in both the pre-strategy change patients and COVID-19 patients (38.47% and 32%, respectively), but its occurrence was less frequent in the non-COVID-19 patients following the strategy change (17.64%). In the non-COVID-19 patients, the incidence of S. aureus (17.65% vs. 1.54%) and EBV (29.41% vs. 20.00%) was higher after the strategy change than before the strategy change, while the frequency of Stenotrophomonas maltophilia (S. maltophilia), H. influenza, S. pneumoniae, A. fumigatus, Pneumocystis jirovecii (P. jirovecii), HSV-1, and CMV decreased. Detailed information is provided in Figure 1B. Given the limited sample size of the non-COVID-19 patients enrolled after the strategy change, further research is warranted.

Risk factors associated with a poor prognosis

The univariate analysis identified several infection factors associated with a poor prognosis (n=47) compared to an improved prognosis (n=85). The COVID-19 patients with fungal co-infections had worse outcomes [36.2% vs. 16.5%, odds ratio (OR) =0.35, P=0.01], particularly those with Aspergillus co-infection (17.0% vs. 5.9%, OR =0.30, P=0.049). Viral co-infections were also significantly linked to a poor prognosis (34.0% vs. 17.6%, OR =0.42, P=0.04). The COVID-19 infection itself showed a borderline association with poor outcomes (48.9% vs. 31.8%, OR =0.49, P=0.053), but bacterial co-infections demonstrated a non-significant trend (36.2% vs. 21.2%, OR =0.47, P=0.06). These findings suggest that secondary fungal and viral infections, but not baseline comorbidities, are important determinants of prognosis in COVID-19 patients.

We compared the differences in the pathogen profiles between patients with an improved prognosis and those without any improvement (Figure 2A,2B). We observed that the prognosis of COVID-19 patients was marginally better than that of patients without COVID-19; however, this difference was not statistically significant (OR =0.49, P=0.053). Among the non-COVID-19 patients, the patients with a poor prognosis had higher incidence rates of A. baumannii, K. pneumoniae, P. aeruginosa, S. maltophilia, Candida, P. jirovecii, HSV-1, and CMV. In the COVID-19 patients, the incidence rates of Candida and HSV-1 were further increased in those with a poor prognosis, while the trends for other pathogens declined (Figure 2A). Additionally, the detection rates for Aspergillus, Enterococcus, and Staphylococcus were also higher in the poor prognosis group; however, these trends were not observed in the non-COVID-19 patients.

Figure 2 Co-infection with other pathogens and its effect on the poor prognosis of COVID-19 patients. (A) Proportions of pathogens identified in COVID-19 and non-COVID-19 patients with a poor prognosis and an improved prognosis. Proportions are calculated as the case numbers of the pathogen out of the total number of patients in each group. The intensity of the color corresponds to the proportion value, with darker shades indicating higher proportions. (B) Comparison of poor prognosis rates in patients with or without other pathogens. CMV, cytomegalovirus; COVID-19, coronavirus disease 2019; EBV, Epstein-Barr virus; HSV, herpes simplex virus; HHV, human herpes virus.

Logistic regression models were used to further investigate the correlation between patient age, comorbidities, immune function, infection, and the timing of pathogen detection in relation to a poor prognosis in this cohort. The univariate analysis results revealed that the main difference between the poor prognosis group and the improved prognosis group was the presence of SARS-CoV-2 infection, while the coexistence of fungi (particularly Candida) or viruses with SARS-CoV-2 showed significant differences (Table 2). In the multivariate analysis, the co-infection of Candida with SARS-CoV-2 was primarily associated with an increased risk of poor prognosis (Figure 3). VIFs for all variables were <2, indicating no significant multicollinearity. Univariate analysis of death after treatment revealed similar findings, coexistence with Candida with SARS-CoV-2 showed significantly differences between death and alive group (Table S1). In the non-COVID-19 patients with pulmonary infection, both the univariate and multivariate analysis indicated that mixed infections, especially bacterial and fungal co-infections, contributed significantly to a poor prognosis in the infected patients (Table S2).

Figure 3 Multivariate analysis of factors associated with poor prognosis in patients with pulmonary infection in this study. CI, confidence interval; COVID-19, coronavirus disease 2019; OR, odds ratio.

Discussion

We conducted an analysis of patients with pulmonary infections who were admitted to our RICU before and after the regular prevention and control of COVID-19 in December 2022. The high transmissibility of the Omicron variant of SARS-CoV-2 resulted in a significant influx of patients into our RICU, leading to a higher incidence of adverse outcomes in the COVID-19 patients compared to the non-COVID-19 patients. We observed that the prevalence of certain pathogens differed before and after the regular prevention and control of COVID-19.

In the COVID-19 patients, we noted a slightly decreased frequency of G− bacteria and an increased frequency of G+ bacteria. Candida and HSV-1 were also prevalent in the COVID-19 patients. Our findings indicated that 70% of the COVID-19 patients had bacterial co-infections, over 40% of whom had a poor prognosis. The predominant bacteria identified in COVID-19 patients were K. pneumoniae, A. baumannii, and S. maltophilia. These pathogens were also common before the strategy change, and their presence was not correlated with a poor prognosis in COVID-19 patients. Conversely, there was an increase in Staphylococcus and Enterococcus in the COVID-19 patients with a poor prognosis, which was rarely observed before the Omicron wave. Previous studies have also reported an abnormality in the occurrence of S. aureus in COVID-19 patients during this wave (16,17). The elevated incidence of Enterococcus may be related to changes in the gut microbiota caused by COVID-19 (18,19).

In the non-COVID-19 patients, a notable change in the frequency of certain pathogens before and after the regular prevention and control of COVID-19 was observed, especially in G+ bacteria (S. aureus and S. pneumoniae) and viruses that are not typically pathogenic (CMV, HSV-1, EBV, and HHV-7). However, due to the small number of non-COVID-19 patients post-strategy adjustment (27 only), further research is required to examine the effect of the Omicron wave on non-COVID-19 patients. Our analysis also identified the coexistence of bacteria and fungi as a primary factor associated with a poor prognosis in the non-COVID-19 patients. Understanding the interaction between these pathogens may help develop effective therapies for patients with pulmonary infections (20).

The Omicron variants pose a reduced threat to human life; however, certain factors are linked to increased severity and fatality rates, such as advanced patient age and the presence of underlying diseases (e.g., diabetes, hypertension, cardiovascular disease, and chronic lung disease) (5). Additionally, our research revealed an association between the presence of Aspergillus/Candida and a poor prognosis in COVID-19 patients. This is consistent with previous studies that have found fungal infections in COVID-19 patients are associated with higher morbidity and mortality rates (14,21-26). Recent research has revealed that disturbances in immune cell populations can increase the risk of developing COVID-19-associated pulmonary aspergillosis (CAPA), with hybrid neutrophils playing a crucial role in the specialized response to CAPA, and an adequate neutrophil response being a key determinant of survival (27). A neutrophil increase was commonly observed in the COVID-19 patients in this study (28). However, there was no significant difference in the neutrophil count between the patients with and without Aspergillus. There is no significant correlation between immunocompromise and poor prognosis in patients (29). A previous study has also shown that severe COVID-19 patients with elevated levels of Candida albicans immunoglobulin G antibodies may develop intestinal Candida overgrowth (30). This is believed to be linked to the disruption of the intestinal mucosal barrier caused by increased angiotensin-converting enzyme 2 (ACE2) receptor expression in enterocytes, facilitating Candida translocation from the gut into the bloodstream and potentially leading to candidemia (31). Additionally, the presence of Candida in critically ill COVID-19 patients could disrupt the immune response, increasing susceptibility to Candida infection (32,33). Notably, the presence of high Aspergillus in COVID-19 patients may not be solely due to COVID-19 itself; a variety of other factors could lead to this situation. For example, the critical condition of the patients, the use of broad-spectrum antibiotics, and the placement of catheters may all be factors that increase the risk of Aspergillus infection. Further research is needed to investigate the mechanisms that lead to a poorer prognosis in COVID-19 patients co-infected with Aspergillus and Candida.

Respiratory viruses, such as influenza virus, rhinovirus, adenovirus, and respiratory syncytial virus, are commonly found in adults with pulmonary infection (34,35). However, in our study, these viruses were rarely detected in the COVID-19 patients who underwent RNA-mNGS testing (n=38). Consistent with our findings, Fan et al. also reported infrequent respiratory viral co-infection with SARS-CoV-2 during the Omicron wave (16). Conversely, the occurrence of HSV-1 was relatively high in this patient population. The elevated detection rate of HSV-1 may suggest the possibility of HSV-1 reactivation in COVID-19 patients (36-39), which could potentially be linked to a higher risk of poor prognosis and hospital-acquired pneumonia (40). However, the specific clinical significance of these HSV-1 detections requires further exploration, and the management of HSV-1 reactivation in these patients also merits additional investigation. Nevertheless, critically ill COVID-19 patients should be regularly monitored for these viral co-infections, and treatment should be initiated as necessary.

Limitations

This study had several limitations. First, it was a single-center study with a small sample size, and potential confounders between different populations might have introduced bias into the outcome analysis. Second, due to the limited sample size and the lack of relevant multiplex quantitative PCR kits, we were unable to verify the numerous potential pathogens detected by mNGS. Similarly, due to funding constraints, we did not carry out RNA-mNGS testing in all samples to examine the prevalence of RNA viruses. Third, our study detected a large number of potential pathogenic microorganisms, including Candida, Enterococcus faecalis (E. faecalis), and Corynebacterium. In patients with normal immunity, these microorganisms are often not considered pathogens. However, since all the patients in this study were critically ill, and given that their immune status might have been compromised, as well as their critical condition, the presence of these potential pathogens were not directly excluded (41,42). Clinicians further determined the presence of these potential pathogens based on the patients’ clinical symptoms, and antibiotics were used specifically for those that could not be ruled out. For example, for Candida, our patients were treated with fluconazole or caspofungin, while for E. faecium or methicillin-resistant S. aureus, our patients were treated with linezolid. When patients’ symptoms improved after medication, these potential pathogens were considered pathogenic microorganisms. We attempted to determine whether the microorganisms were simple colonizers or contributing to the infection; however, the possibility of misjudgment cannot be completely ruled out. Furthermore, other variables unrelated to routine preventive measures may also interfere with the study results. For instance, seasonal changes can indirectly affect the efficacy of disease diagnosis and treatment by altering the epidemiological characteristics of pathogens; meanwhile, advancements in medical practices such as the optimization of mechanical ventilation strategies and the improvement of infection control procedures in intensive care units (ICUs) may likewise exert a significant impact on patient prognosis. And as this is a retrospective study, relevant indicators of clinical severity scores were not collected during data collection, which may affect outcomes and hinder the optimization of model adjustment. Finally, the lack of COVID-19 patients before the strategy changes and the small sample size of the non-COVID-19 patients after the strategy change prevented further epidemiology analysis of the effect of COVID-19. Accordingly, future studies should include a larger sample size across multiple centers, more comprehensive enrollment data, and longer-term follow-up periods to enable more in-depth epidemiological analyses of COVID-19’s impact.

Conclusions

Our study found significant differences in the pathogen landscape of pulmonary infections among ICU patients before and after the regular prevention and control of COVID-19 in December 2022, underscoring not only the complexity of pulmonary infectious diseases in COVID-19 patients but also the potential risks associated with such infections. In addition to the risks posed by co-infections of fungi and viruses in COVID-19 patients, our findings suggest that attention should also be paid to the increased incidence of G+ bacteria, especially Staphylococcus and Enterococcus, in COVID-19 patients.

Acknowledgments

The authors would like to thank all patients who participated in this study.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1296/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1296/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1296/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-2025-1296/coif). H.X. is from Hugobiotech Co., Ltd., Beijing, China. The other 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 approved by the Clinical Research Ethics Committee of the First Affiliated Hospital of Anhui Medical University (No. PJ2024-03-31). This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. All participants provided their informed consent. If participants were unable to provide informed consent due to incapacity, informed consent was obtained from a legal guardian or family member on their behalf.

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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