Targeted next-generation sequencing of pathogens reveals the profile of secondary infections in COVID-19 patients

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METHODS: Patients with infections secondary to severe and critical COVID-19 and hospitalised at the First Affiliated Hospital of Shandong First Medical University between 1 December 2022 and 30 June 2023 were included in the study. The characteristics and etiological distribution of secondary infections in these patients were analysed using tNGS. RESULTS: A total of 95 patients with COVID-19 secondary infections were included in the study, of whom 87.37% had one or more underlying diseases. Forty-eight pathogens were detected, the most common being HSV-4, Candida albicans, Klebsiella pneumoniae, Enterococcus faecium, HSV-1, Staphylococcus aureus, Aspergillus fumigatus, Acinetobacter baumannii, HSV-5, and Stenotrophomonas maltophilia, with Pneumocystis jirovecii being detected in 14.29% of cases. The majority (76.84%) of COVID-19 secondary infections were mixed infections, with mixed viral-bacterial-fungal infections being the most common (28.42%). CONCLUSION: Most secondary infections in severe and critical COVID-19 patients are mixed, with high rates of viral and fungal infections. In clinical settings, monitoring for reactivation or secondary infections by Herpesviridae viruses is crucial; additionally, these patients have a significantly higher rate of P. jirovecii infection. tNGS testing on bronchoalveolar lavage fluid can help determine the aetiology of secondary infections early in COVID-19 patients and assist in choosing appropriate antibiotics. COVID-19 targeted next-generation sequencing bronchoalveolar lavage fluid secondary infection aetiological studies Figures Figure 1 Introduction Novel coronavirus pneumonia (COVID-19) has continued to spread globally since the end of 2019 and has become a global public health event that has yet to be effectively controlled. A small percentage of COVID-19 patients progress to severe or critical disease with worse clinical outcomes. Previous correlation studies have shown that critical COVID-19 disease is associated with intensive care unit admission, increased rates of secondary infections, and heightened risk of invasive procedures [ 1 ]. In particular, secondary respiratory infections by other viral, bacterial, and fungal pathogens are major contributors to increased disease severity. These patients tend to have a worse prognosis, as evidenced by higher rates of hospitalisation, severe illness, and mortality [ 2 ]. Knowledge of microbial epidemiological data on secondary infections in patients with COVID-19 in a particular region, as well as early determination of the aetiology of secondary infections in patients with COVID-19, is critical for clinicians to select and initiate targeted antibiotic therapy at early stages to shorten the duration of hospitalisation and improve prognosis. Clinical practice currently relies heavily on routine microbiological tests of lower respiratory tract specimens, such as microbial culture, serological testing, and nucleic acid polymerase chain reaction (PCR) testing for specific pathogens. These often require long turnaround times, have low sensitivity and specificity, and are capable of detecting only a small variety of pathogens, which increases the likelihood of missing certain atypical pathogens. Metagenomic next-generation sequencing (mNGS) detects a broad range of pathogens [ 3 ] and has advantages over conventional culture methods but is prone to contamination during experimental operation, leading to false-positive results. In addition, mNGS is expensive, time-consuming, and unreliable for complex clinical specimens containing many different microorganisms, limiting its broader clinical application. Targeted next-generation sequencing (tNGS) [ 4 ] is a technology utilising PCR or probe hybridisation to capture and enrich genomic regions of interest for high-throughput sequencing. tNGS is less time-consuming and more accurate than conventional microbial identification techniques and less expensive than mNGS. Currently, no domestic or international studies have investigated the use of tNGS to evaluate the characteristics of the aetiological distribution of secondary infections in COVID-19 patients. In this study, tNGS was used to analyse data of infectious microbes in clinical bronchoalveolar lavage fluid (BALF) specimens from patients with severe and critical COVID-19 infections to provide a reference for early empirical selection of antibiotics in such patients, with the aim of accurately guiding rational antibiotic use in these patients. Materials and methods Patients This was a retrospective observational study that included patients with severe and critical COVID-19 who were hospitalised at the First Affiliated Hospital of Shandong First Medical University and underwent tNGS between 1 December 2022 and 30 June 2023. All patients tested positive for SARS-CoV-2 nucleic acid. Patients with severe COVID-19 were defined as patients meeting any of the following conditions that could not be explained by reasons other than COVID-19 infection: 1) Shortness of breath, respiratory rate ≥ 30 breaths/min; 2) Resting oxygen saturation ≤ 93% on inhalation; 3) Arterial partial pressure of oxygen (PaO2)/fraction of inspired oxygen (FiO2) ≤ 300 mmHg (1 mmHg = 0.133 kPa), with correction for high altitude (> 1000 m) (PaO2/FiO2 × [760 / atmospheric pressure (mmHg)]); 4) Progressive exacerbation of clinical symptoms and lung imaging indicating marked progression of lesions > 50% within 24–48 hours. Patients with critical COVID-19 were defined as those meeting any of the following conditions: 1) Respiratory failure requiring mechanical ventilation; 2) Shock; 3) Concomitant failure of other organs requiring intensive care unit (ICU) monitoring and treatment. Secondary infection was diagnosed if the patient presented with clinical symptoms or positive imaging evidence suggestive of a new lung infection 48 hours after admission and a positive laboratory-confirmed aetiological result (positive tNGS result). Sample procedures Patients who met the criteria for severe or critical COVID-19 and were suspected of having a secondary infection underwent tNGS testing 48 h after admission. tNGS testing of BALF was performed at the Medical Laboratory Diagnostic Centre of the First Affiliated Hospital of Shandong First Medical University. Statistics SPSS 26.0 and GraphPad Prism 8.0 software were used for data analysis and visualisation. Count data was compared between groups using the χ2 test or Fisher's exact test. Continuous data conforming to the normal distribution was expressed as mean ± standard deviation. One-way analysis of variance was used to compare between multiple groups of samples, and the t-test was used to compare data between two groups of samples. Differences with p < 0.05 were considered statistically significant. Results Characteristics of patients Ninety-five patients with COVID-19 were included (Table 1 ), of whom 54 cases had severe disease and 41 cases had critical disease. The age of the patients was in the range 24–94 (mean: 69.87 ± 14.80) years. Sixty-six patients (69.47%) were male, and 29 (30.53%) were female. Eighty-three patients (87.37%) suffered from one or more underlying diseases, which included hypertension in 51 cases (53.68%), diabetes in 33 cases (34.74%), cardiovascular disease in 37 cases (38.95%), cerebrovascular disease in 25 cases (26.32%), rheumatic-autoimmune disease in two cases (2.11%), cancer in 20 cases (21.05%), chronic kidney disease in six cases (6.32%), and chronic lung disease in five cases (5.26%). Thirteen patients (13.68%), all of whom had critical disease, died during the study period. Comparing the clinical data of patients with severe and critical COVID-19 showed that patients with critical COVID-19 had higher rates of hypertension, cardiovascular disease, and mortality than did patients with severe COVID-19; the difference was statistically significant (p < 0.05). The differences in age, sex, and rates of diabetes, cerebrovascular disease, rheumatic-autoimmune disease, cancer, chronic kidney disease, and chronic lung disease between the two groups were not statistically significant. Table 1 Comparison of clinical data of patients with severe and critical COVID-19 Clinical data Total (n = 95) Severe (n = 54) Critical (n = 41) p Age (years) 69.87 ± 14.80 68.65 ± 15.08 71.49 ± 14.44 0.357 Sex 0.828 Male 66 38 28 Female 29 16 13 Underlying disease Hypertension 51 23 28 0.013 Diabetes 33 17 16 0.444 Cardiovascular disease 37 14 23 0.003 Cerebrovascular disease 25 15 10 0.710 Rheumatic-autoimmune disease 2 1 1 1 Cancer 20 13 7 0.407 Chronic kidney disease 6 2 4 0.438 Chronic lung disease 5 4 1 0.542 Outcome 0.000 Recovered 82 54 28 Died 13 0 13 Characterisation of the aetiological distribution of secondary infections tNGS was performed on BALF from the 95 patients, and one or more pathogens other than severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were detected in all cases (Fig. 1 ). Forty-eight pathogens were identified, of which 10 (20.83%) were viruses, 25 (52.08%) were bacteria, 11 (22.92%) were fungi, and two (4.17%) were other pathogens ( Mycoplasma and Chlamydia ). The top 10 pathogens detected and their corresponding rates of detection were HSV-4 (43 cases, 45.26%), Candida albicans (30 cases, 31.58%), Klebsiella pneumoniae (28 cases, 29.47%), Enterococcus faecium (23 cases, 24.21%), HSV-1 (23 cases, 24.21%), Staphylococcus aureus (19 cases, 20.00%), Aspergillus fumigatus (17 cases, 17.89%), Acinetobacter baumannii (15 cases, 15.79%), HSV-5 (14 cases, 14.74%), and S. maltophilia (12 cases, 12.63%). Among the 95 COVID-19 patients, viral infection was detected in seven (7.37%), bacterial infection in 12 (12.63%), fungal infection in three (3.16%), mixed viral-bacterial infection in 22 (23.19%), mixed viral-fungal infection in 12 (12.63%), mixed bacterial-fungal infections in 12 (12.63%), and mixed viral-bacterial-fungal infections in 27 (28.42%) (Table 2 ). Comparing the aetiological distribution of secondary infections in patients with severe and critical COVID-19, the detection rate of bacterial infections was lower, and the detection rate of mixed viral-bacterial infections was higher in patients with critical COVID-19 than in patients with severe COVID-19; the difference was statistically significant ( p < 0.05). Table 2 Characteristics of secondary infections in patients with severe and critical COVID-19 Characteristic Total (n = 95) Severe (n = 54) Critical (n = 41) p Viral infection 7 5 2 0.680 Bacterial infection 12 10 2 0.047 Fungal infection 3 1 2 0.808 Mixed viral-bacterial infection 22 8 14 0.027 Mixed viral-fungal infection 12 6 6 0.609 Mixed bacterial-fungal infection 12 9 3 0.174 Mixed viral-bacterial-fungal infection 27 15 12 0.873 Distribution of secondary viral infections One hundred strains of viruses were detected in patients with severe and critical COVID-19, with the top five strains being HSV-4 (43 cases, 43.00%), HSV-1 (23 cases, 23.00%), HSV-5 (14 cases, 14.00%), HSV-7 (12 cases, 12.00%), and HSV-6 (4 cases, 4.00%); there were four cases (4.40%) involving other viruses (Table 3 ). HSV-1 was detected at a higher rate, and HSV-7 was identified at a lower rate in patients with critical COVID-19 compared to that in patients with severe COVID-19 disease; the difference was statistically significant ( p < 0.05). Table 3 Viruses detected in patients with severe and critical COVID-19 Characteristic Total (n = 100) Severe (n = 48) Critical (n = 52) p HSV-4 43 21 22 0.884 HSV-1 23 6 17 0.017 HSV-5 14 7 7 0.872 HSV-7 12 9 3 0.046 HSV-6 4 2 2 1.000 Others 4 3 1 0.554 Distribution of secondary bacterial infections A total of 169 bacterial strains were detected in patients with severe and critical COVID-19, with the top five being K. pneumoniae (28 cases, 16.57%), E. faecium (23 cases, 13.61%), S. aureus (19 cases, 11.24%), A. baumannii (15 cases, 8.88%), and S. maltophilia (12, 7.10%); there were 82 cases involving other bacteria (48.52%) (Table 4 ). The detection rate of E. faecium was higher, and the detection rate of S. aureus was lower in patients with critical COVID-19 compared to that of patients with severe COVID-19; the difference was statistically significant ( p < 0.05). Table 4 Secondary bacterial infections in patients with severe and critical COVID-19 Characteristic Total (n = 169) Severe (n = 93) Critical (n = 76) p Klebsiella pneumoniae 28 16 12 0.806 Enterococcus faecium 23 8 15 0.036 Staphylococcus aureus 19 15 4 0.026 Acinetobacter baumannii 15 6 9 0.220 Stenotrophomonas maltophilia 12 7 5 0.811 Others 82 41 41 0.202 Distribution of secondary fungal infections Seventy-seven fungal strains were detected in patients with severe and critical COVID-19, with the top five being C. albicans (30, 38.96%), A. fumigatus (17, 22.08%), Pneumocystis jirovecii (11, 14.29%), Candida tropicalis (5, 6.49%), and Aspergillus flavus (4, 5.19%); there were 10 cases involving other fungi (12.99%) (Table 5 ). None of the differences in the detection rates of the different fungi between the patients with critical and severe COVID-19 were statistically significant. Table 5 Secondary fungal infections in patients with severe and critical COVID-19 Characteristic Total (n = 77) Severe (n = 45) Critical (n = 32) p Candida albicans 30 16 14 0.467 Aspergillus fumigatus 17 11 6 0.553 Pneumocystis jirovecii 11 5 6 0.539 Candida tropicalis 5 3 2 1.000 Aspergillus flavus 4 4 0 0.226 Others 10 6 4 1.000 Discussion COVID-19 is an emergent infectious disease with a high risk of mortality that represents a major threat to human life and health and is transmitted primarily through respiratory droplets and close contact [ 5 ]. The overall incidence of secondary infections in patients with COVID-19 is low; Wu et al. [ 6 ] reported that the overall incidence of secondary infections in all patients hospitalised with COVID-19 was only 3.7%. However, the incidence of secondary infections is directly correlated with disease severity; Grasselli et al. [ 7 ] found that the incidence of secondary infections reached 46% among patients in the ICU. This is consistent with the findings of Zamora-Cintas et al. [ 8 ], who reported an incidence of secondary infections in standard ward patients of 9% but a significant increase in secondary infections in critical care patients, at 31.5%. Secondary infections are also a major cause of increased mortality in patients with COVID-19. Notably, a study [ 9 ] found that the rate of vasopressor use, glucocorticoid use, continuous renal replacement therapy, mechanical ventilation, and extracorporeal membrane oxygenation during hospitalisation was higher in patients with secondary infections than in those without, and the mortality rate of patients with secondary infections was approximately twice as high as in those without. Ninety-five patients with positive reverse transcription polymerase chain reaction results for SARS-CoV-2 were included in the present study, including 54 with severe COVID-19 and 41 with critical COVID-19. Moreover, a multi-centre study in Korea reported that the 28-day mortality rate of secondary infection in patients with COVID-19 was 17.3% [ 9 ], which was consistent with that of 13.68% in the present study. A multifactorial analysis by Taysi et al. [ 10 ] showed that the use of mechanical ventilation for over 48 hours, central venous catheterisation for over 72 hours, duration of ICU stay of over 10 days, and duration of hospital stay of over 48 hours were all risk factors for secondary infections. A recent study [ 11 ] showed that factors associated with secondary infection include age > 64 years, duration of ICU stay > 7 days, type 2 diabetes, cardiovascular disease, central venous catheter placement, intubation, Acute Physiology and Chronic Health Evaluation II score > 25, mechanical ventilation > 48 hours, and urinary catheter placement. The results of the present study showed that 87.37% of the patients suffered from one or more underlying diseases, which included hypertension (53.68%), diabetes (34.74%), cardiovascular disease (38.95%), cerebrovascular disease (26.32%), cancer (21.05%), chronic kidney disease (6.32%), chronic lung disease (5.26%), and rheumatic-autoimmune disease (2.11%), and that patients with critical COVID-19 had significantly higher rates of hypertension, cardiovascular disease, and mortality than did patients with severe COVID-19. In another study [ 12 ], independent risk factors associated with mortality were found to include advanced age, male sex, inhalation of high concentrations of oxygen, high positive end-expiratory pressure or low oxygenation index, as well as history of chronic obstructive pulmonary disease, hypercholesterolemia, and type 1 diabetes. In contrast, a study of COVID-19 patients without prior chronic underlying disease [ 13 ] showed that age ≥ 47 years, oxygen saturation < 95%, elevated lactate dehydrogenase, neutrophil count, direct bilirubin, creatine phosphokinase, blood urea nitrogen, dyspnoea, elevated blood glucose, and prothrombin time were associated with mortality in patients with COVID-19. The aetiological profile of secondary infections has become a common concern in critical care medicine. Sang et al. [ 14 ] assessed the epidemiology of secondary infections in patients with severe and critical COVID-19 and found that the most abundant microorganisms detected in sputum/endotracheal aspirate cultures of the patients were bacteria, followed by fungi, with the five most common microorganisms being K. pneumoniae , A. baumannii , S. maltophilia , C. albicans , and Pseudomonas spp. Another retrospective study [ 15 ] showed that the most common secondary infections in patients with COVID-19 were coagulase-negative staphylococci, A. baumannii , and Escherichia coli . However, conventional culture has the disadvantages of being time-consuming, having low sensitivity, and covering few species of pathogens. Therefore, tNGS was used in the present study to test and analyse the BALF of COVID-19 patients. The results indicated that 48 pathogens, including bacteria, fungi, viruses, Mycoplasma , and Chlamydia , were detected in 95 patients, and the 10 most common pathogens were HSV-4, C. albicans , K. pneumoniae , E. faecium , HSV-1, S. aureus , A. fumigatus , A. baumannii , HSV-5, and S. maltophilia. The majority of infections were mixed (76.84%), with mixed viral-bacterial-fungal infections being the most common (28.42%), followed by mixed viral-bacterial infections (23.19%). In addition, the rate of bacterial infection was lower, and that of mixed viral-bacterial infection was higher in critical COVID-19 patients than in severe COVID-19 patients; however, these differences must be confirmed in further studies with larger sample sizes. The detection rate of pathogenic microorganisms in the lower respiratory tract was increased using tNGS, but these microorganisms may not always be associated with infections, and differentiating between colonisation and infection is difficult in the clinical setting. As COVID-19 leads to impaired cell-mediated immunity [ 16 ], the use of glucocorticoids and immunosuppressants also leads to decreased immunity in patients with COVID-19 [ 17 , 18 ], which provides an opportunity for viral reactivation and secondary infections. The results of the present study showed that the five most commonly detected viruses in the BALF of COVID-19 patients were HSV-4 (EBV), HSV-1, HSV-5 (CMV), HSV-7 and HSV-6, all of which are in the herpesvirus family. HSV-1 was also detected at a higher rate, and HSV-7 was identified at a lower rate in critical COVID-19 patients compared to severe COVID-19 patients. Bernal et al. [ 19 ] reported EBV reactivation in 27.1% of patients with COVID-19, which was significantly higher than the EBV reactivation rate of 12.5% in patients without COVID-19. In China, Xie et al. [ 20 ] reported a rate of EBV reactivation of 13.3% in patients with COVID-19 and that the mortality rate of the EBV reactivation group was significantly higher than that of the non-EBV reactivation group at both 14 and 28 days. A study of 70 patients with COVID-19 by Franceschini et al. [ 21 ] found that HSV-1 viremia was detectable in 30.0% of patients and that steroid therapy, invasive mechanical ventilation, and high lactate dehydrogenase were significantly associated with an increased risk of HSV-1 reactivation. Similarly, a retrospective analysis of EBV, CMV, and HSV replication in 100 patients with severe COVID-19 by Saade et al. [ 22 ] found 63 patients with viral reactivation (12% HSV, 58% EBV, and 19% CMV). Moreover, both HSV-1 [ 23 ] and CMV reactivation [ 24 ] have been shown to increase mortality in patients with COVID-19. Overall, patients with COVID-19 are at potential risk for latent virus reactivation or secondary infection, which contributes to increased mortality, but standardised diagnostic and therapeutic protocols for herpesvirus reactivation or secondary infection in patients with COVID-19 remain lacking. Bacteria are the primary pathogens that cause secondary infections in patients with COVID-19. In the present study, 169 bacterial strains were detected in the BALF of COVID-19 patients, with the five most abundant bacteria being K. pneumoniae , E. faecium , S. aureus , A. baumannii , and S. maltophilia . Therefore, empirical antibiotic treatment of COVID-19 patients with suspected secondary infections should cover these pathogens. Torrego et al. [ 25 ] analysed the presence of Pseudomonas aeruginosa , S. aureus , Klebsiella aerogenes , Enterobacter cloacae , Enterobacter faecalis , and Escherichia coli in the BALF of COVID-19 patients and achieved results similar to the microbiota usually detected in ventilator-associated pneumonia. Moreover, Sang et al. [ 14 ] analysed the bacteria detected in patients with COVID-19 after admission to the ICU for over 72 hours. Their results were similar to those of the present study, and they further found that most of the K. pneumoniae and A. baumannii strains were resistant to carbapenem. This is consistent with the findings of Gomez-Simmonds et al. [ 26 ] that the majority of K. pneumoniae strains (16/17) were drug-resistant. A recent multi-centre study [ 9 ] found that the most common pathogens associated with secondary lung infections of hospitalised COVID-19 patients included A. baumannii , Klebsiella spp., Streptococcus spp., Haemophilus influenzae , and P. aeruginosa , and the most common pathogens associated with secondary bloodstream infections were coagulase-negative Staphylococcus and A. baumannii . A study of critical COVID-19 patients by Meawed et al. [ 27 ] showed that the common bacterial pathogens in ventilator-associated pneumonia included pandrug-resistant K. pneumoniae (41.1%) and multidrug-resistant A. baumannii (27.4%). Therefore, the possibility of multidrug-resistant organisms should be considered in the selection of antibiotics for patients with secondary infections from COVID-19, especially those undergoing invasive mechanical ventilation. Zhu et al. [ 28 ] reported that 23.3% of COVID-19 patients had comorbid fungal infections, and the incidence of fungal infections increased with the severity of COVID-19. There have been widespread reports of respiratory infections by invasive fungi, with common fungi including C. albicans , Aspergillus spp., and Mucor spp., as well as atypical fungal pathogens such as P. jirovecii , Cryptococcus spp., Candida auris , and Fusarium spp. [ 29 ]. The results of the present study showed that 77 fungal strains were detected in the BALF of COVID-19 patients, of which the five most abundant were C. albicans , A. fumigatus , P. jirovecii , C. tropicalis , and A. flavus . Lu et al. [ 30 ] reported a Candida detection rate of 77.8% in an early coinfection group (duration of hospitalisation 7 d) among COVID-19 patients in Taiwan, and that Candida spp. fungal coinfections were more common than Aspergillus spp. coinfections. However, the incidence of secondary Candida infections varies significantly between 0.4–23.5% across geographic regions [ 31 ], with studies from Europe reporting incidences of 0.4%, 8%, and 12.6% in Spain, Italy, and the United Kingdom, respectively, while those from Asia find incidences of 2.5%, 5%, and 23.5% in India, Iran, and China, respectively. Immunodeficiency, use of antivirals or immunosuppressants, direct injury from COVID-19, and dysbiosis may contribute to the development of pathogenic Candida infections in COVID-19 patients [ 32 ]. The incidence of Aspergillus infection was significantly higher in patients with COVID-19, and the present study found detection rates of A. fumigatus and A. flavus of 22.08% and 5.19%, respectively. One hospital in China reported an incidence of invasive pulmonary aspergillosis of 29.3% among patients with COVID-19 [ 33 ]. Chong et al. [ 34 ] reviewed 1421 COVID-19 patients and found an overall incidence of pulmonary aspergillosis of 13.5% (range: 2.5–35.0%), with the time to diagnosis of COVID-19-associated pulmonary aspergillosis from ICU admission and initiation of invasive mechanical ventilation ranging between 4.0–15.0 days and 3.0–8.0 days, respectively, and a mortality rate of 48.4%. Furthermore, a meta-analysis of 3,148 patients from 28 observational studies [ 35 ] estimated the morbidity and mortality of COVID-19-associated pulmonary aspergillosis in the ICU to be 10.2% and 54.9%, respectively. P. jirovecii is an opportunistic infectious agent that occurs mainly in immunocompromised patients, particularly human immunodeficiency virus (HIV)-positive patients, solid organ transplant recipients, those with hematologic malignancies, and rheumatologic patients undergoing prolonged steroid treatment [ 36 ]. Jeican et al. [ 37 ] reported the first autopsy-confirmed case of COVID-19 combined with P. jirovecii infection. The results of the present study showed a detection rate of P. jirovecii in patients with COVID-19 of 14.29%, which was significantly higher than that in patients without severe COVID-19. Furthermore, P. jirovecii was one of the pathogens severely underestimated for secondary infections in patients with COVID-19. Kang [ 38 ] analysed the incidence of P. jirovecii pneumonia in non-HIV patients between 2016 and 2022, and the results confirmed that the incidence of P. jirovecii pneumonia was significantly higher during the COVID-19 epidemic than before the epidemic. Significantly decreased lymphocyte count and glucocorticoid use are the principal risk factors for P. jirovecii infection, and the risk of P. jirovecii infection increases with higher doses of glucocorticoid treatment [ 39 ]. P. jirovecii pneumonia can be distinguished from the clinical symptoms and imaging features of COVID-19; thus, when the condition of a COVID-19 patient deteriorates, the possibility of P. jirovecii infection should also be considered, and early identification and treatment of secondary fungal infections is essential for improving patient outcomes. Conclusion COVID-19 remains a major infectious disease that threatens human life and health. Secondary viral, bacterial, and fungal respiratory infections are a major cause of disease exacerbation in patients with COVID-19. tNGS was used to detect and analyse the BALF of COVID-19 patients, and bacteria, fungi, viruses, Mycoplasma , and Chlamydia were detected, with the majority of cases (76.84%) being mixed infections. tNGS of BALF enables rapid and accurate analysis of the pathogen composition of secondary infections and lower respiratory tract diseases in patients with COVID-19 and lays a foundation for accurate diagnosis and individualised treatment. In particular, tNGS has significant advantages with respect to the precision of identifying specific pathogens such as fungi and viruses. Declarations Author contributions Feng-qin Ren, Feng Ji: original draft preparation; Zhao-qi Liu, Li-ru Yan, Zhi-wei Gao, Meng-zhen Liu, Xin-guang Teng: methodology, software, validation, investigation, resources, data curation, writing-review and editing, visualization, supervision; Guang-sheng Gao: conceptualization, writing-review and editing, supervision. All authors read and approved the final manuscript. Funding This work was supported by Projects of medical and health technology development program in Shandong province (NO.202203020343) and by Critical care medicine special fund of Shandong Pathophysiological Society (NO.2021BS007). Data availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Conflict of interest The authors declare no conflict of interest. Ethics approval This study was performed in line with the principles of the Declaration of Helsinki. 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Microorganisms. 2021;9(9):1896. https://doi.org/10.3390/microorganisms9091896 . Saade A, Moratelli G, Azoulay E, Darmon M. Herpesvirus reactivation during severe COVID-19 and high rate of immune defect. Infect Dis Now. 2021;51(8):676–9. https://doi.org/10.1016/j.idnow.2021.07.005 . Meyer A, Buetti N, Houhou-Fidouh N, et al. HSV-1 reactivation is associated with an increased risk of mortality and pneumonia in critically ill COVID-19 patients. Crit Care. 2021;25(1):417. https://doi.org/10.1186/s13054-021-03843-8 . Giacconi R, Cardelli M, Piacenza F, et al. Effect of Cytomegalovirus Reactivation on Inflammatory Status and Mortality of Older COVID-19 Patients. Int J Mol Sci. 2023;24(7):6832. https://doi.org/10.3390/ijms24076832 . Torrego A, Pajares V, Fernández-Arias C, Vera P, Mancebo J. Bronchoscopy in Patients with COVID-19 with Invasive Mechanical Ventilation: A Single-Center Experience. Am J Respir Crit Care Med. 2020;202(2):284–7. https://doi.org/10.1164/rccm.202004-0945LE . Gomez-Simmonds A, Annavajhala MK, McConville TH, et al. Carbapenemase-producing Enterobacterales causing secondary infections during the COVID-19 crisis at a New York City hospital. J Antimicrob Chemother. 2021;76(2):380–4. https://doi.org/10.1093/jac/dkaa466 . Meawed TE, Ahmed SM, Mowafy SMS, Samir GM, Anis RH. Bacterial and fungal ventilator associated pneumonia in critically ill COVID-19 patients during the second wave. J Infect Public Health. 2021;14(10):1375–80. https://doi.org/10.1016/j.jiph.2021.08.003 . Zhu X, Ge Y, Wu T, et al. Co-infection with respiratory pathogens among COVID-2019 cases. Virus Res. 2020;285:198005. https://doi.org/10.1016/j.virusres.2020.198005 . Chiurlo M, Mastrangelo A, Ripa M, Scarpellini P. Invasive fungal infections in patients with COVID-19: a review on pathogenesis, epidemiology, clinical features, treatment, and outcomes. New Microbiol. 2021;44(2):71–83. Lu DE, Hung SH, Su YS, Lee WS. Analysis of Fungal and Bacterial Co-Infections in Mortality Cases among Hospitalized Patients with COVID-19 in Taipei, Taiwan. J Fungi (Basel). 2022;8(1):91. https://doi.org/10.3390/jof8010091 . Mina S, Yaakoub H, Annweiler C, Dubée V, Papon N. COVID-19 and Fungal infections: a double debacle. Microbes Infect. 2022;24(8):105039. https://doi.org/10.1016/j.micinf.2022.105039 . Tsai CS, Lee SS, Chen WC, et al. COVID-19-associated candidiasis and the emerging concern of Candida auris infections. J Microbiol Immunol Infect. 2023;56(4):672–9. https://doi.org/10.1016/j.jmii.2022.12.002 . Zhou X, Wu X, Chen Z, et al. Risk factors and the value of microbiological examinations of COVID-19 associated pulmonary aspergillosis in critically ill patients in intensive care unit: the appropriate microbiological examinations are crucial for the timely diagnosis of CAPA. Front Cell Infect Microbiol. 2023;13:1287496. https://doi.org/10.3389/fcimb.2023.1287496 . Chong WH, Neu KP. Incidence, diagnosis and outcomes of COVID-19-associated pulmonary aspergillosis (CAPA): a systematic review. J Hosp Infect. 2021;113:115–29. https://doi.org/10.1016/j.jhin.2021.04.012 . Mitaka H, Kuno T, Takagi H, Patrawalla P. Incidence and mortality of COVID-19-associated pulmonary aspergillosis: A systematic review and meta-analysis. Mycoses. 2021;64(9):993–1001. https://doi.org/10.1111/myc.13292 . Buonomo AR, Viceconte G, Fusco L, et al. Prevalence of Pneumocystis jirovecii Colonization in Non-Critical Immunocompetent COVID-19 Patients: A Single-Center Prospective Study (JiroCOVID Study). Microorganisms. 2023;11(12):2839. https://doi.org/10.3390/microorganisms11122839 . Jeican II, Inișca P, Gheban D, et al. COVID-19 and Pneumocystis jirovecii Pulmonary Coinfection-The First Case Confirmed through Autopsy. Med (Kaunas). 2021;57(4):302. https://doi.org/10.3390/medicina57040302 . Kang JS. Changing Trends in the Incidence and Clinical Features of Pneumocystis jirovecii Pneumonia in Non-HIV Patients before and during the COVID-19 Era and Risk Factors for Mortality between 2016 and 2022. Life (Basel). 2023;13(6):1335. https://doi.org/10.3390/life13061335 . Viceconte G, Buonomo AR, D'Agostino A, et al. Risk Factors for Pneumocystis jirovecii Pneumonia in Non-HIV Patients Hospitalized for COVID-19: A Case-Control Study. J Fungi (Basel). 2023;9(8):838. https://doi.org/10.3390/jof9080838 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4113659","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":281060836,"identity":"6205f30e-6b13-4598-9bb4-c2a5dc34c494","order_by":0,"name":"Feng-qin Ren","email":"","orcid":"","institution":"Central Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Feng-qin","middleName":"","lastName":"Ren","suffix":""},{"id":281060837,"identity":"cc952dfb-6bd1-4100-ab8a-1f4eeccfdc54","order_by":1,"name":"Feng Ji","email":"","orcid":"","institution":"Central Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Ji","suffix":""},{"id":281060838,"identity":"5a1c6098-3518-456d-b2f6-f9519b26153a","order_by":2,"name":"Zhao-qi Liu","email":"","orcid":"","institution":"Central Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhao-qi","middleName":"","lastName":"Liu","suffix":""},{"id":281060839,"identity":"6c58c4f1-6cdd-432c-9c22-54f3d3765095","order_by":3,"name":"Li-ru Yan","email":"","orcid":"","institution":"Central Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Li-ru","middleName":"","lastName":"Yan","suffix":""},{"id":281060841,"identity":"152345a2-9897-4b13-b6bb-c2d702fee5c0","order_by":4,"name":"Zhi-wei Gao","email":"","orcid":"","institution":"Central Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhi-wei","middleName":"","lastName":"Gao","suffix":""},{"id":281060844,"identity":"a2f7de4c-0291-482c-91b8-89e3192e6fa8","order_by":5,"name":"Meng-zhen Liu","email":"","orcid":"","institution":"Central Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Meng-zhen","middleName":"","lastName":"Liu","suffix":""},{"id":281060846,"identity":"f9e4c05d-0d43-4816-80d6-26c2477b67ad","order_by":6,"name":"Xin-guang Teng","email":"","orcid":"","institution":"Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xin-guang","middleName":"","lastName":"Teng","suffix":""},{"id":281060851,"identity":"83c501da-79c0-4a13-9b38-9664d24022d6","order_by":7,"name":"Guang-sheng Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYDACCSjNxt5+8EFCRQ0JWvh4ziQbPDhzjAQtchIOZpIPW5gJ65Cf3Xzs4ZdfdnJsEgxpFYkNbAz87d0JeLUY3DmWbizbl2zMJt147EbiDhkGiTNnN+DXIpFjJi3Zw5zYJnMg7UbiGTagSC5+LfIz8r8BtdQntkkkmBUktjET1sJwI4dN8sOPw2AtDERpMbiRZibN2HDcmA0YyBIJZ47xEPSL/IzkZ5I//lTLybe3H/z4o6JGjr+9l4DDgICZtw3B4SGoHAQYf/whSt0oGAWjYBSMVAAAdFFJ0cVw3+0AAAAASUVORK5CYII=","orcid":"","institution":"Central Hospital Affiliated to Shandong First Medical University","correspondingAuthor":true,"prefix":"","firstName":"Guang-sheng","middleName":"","lastName":"Gao","suffix":""}],"badges":[],"createdAt":"2024-03-16 14:44:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4113659/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4113659/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53193379,"identity":"830dd13d-6e2b-4d5b-8cc8-0a8c5cc0e66e","added_by":"auto","created_at":"2024-03-21 18:00:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":48495,"visible":true,"origin":"","legend":"\u003cp\u003eAetiological distribution of secondary infections in patients with severe and critical COVID-19\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4113659/v1/ee52602a774b42f8a7f9f9d9.png"},{"id":54104315,"identity":"6df911b0-3b71-41c0-8e23-26aab8affe22","added_by":"auto","created_at":"2024-04-04 16:38:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":489129,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4113659/v1/a5594e29-cc20-4063-a90f-49df2fd5b11e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Targeted next-generation sequencing of pathogens reveals the profile of secondary infections in COVID-19 patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNovel coronavirus pneumonia (COVID-19) has continued to spread globally since the end of 2019 and has become a global public health event that has yet to be effectively controlled. A small percentage of COVID-19 patients progress to severe or critical disease with worse clinical outcomes. Previous correlation studies have shown that critical COVID-19 disease is associated with intensive care unit admission, increased rates of secondary infections, and heightened risk of invasive procedures [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In particular, secondary respiratory infections by other viral, bacterial, and fungal pathogens are major contributors to increased disease severity. These patients tend to have a worse prognosis, as evidenced by higher rates of hospitalisation, severe illness, and mortality [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Knowledge of microbial epidemiological data on secondary infections in patients with COVID-19 in a particular region, as well as early determination of the aetiology of secondary infections in patients with COVID-19, is critical for clinicians to select and initiate targeted antibiotic therapy at early stages to shorten the duration of hospitalisation and improve prognosis.\u003c/p\u003e \u003cp\u003eClinical practice currently relies heavily on routine microbiological tests of lower respiratory tract specimens, such as microbial culture, serological testing, and nucleic acid polymerase chain reaction (PCR) testing for specific pathogens. These often require long turnaround times, have low sensitivity and specificity, and are capable of detecting only a small variety of pathogens, which increases the likelihood of missing certain atypical pathogens. Metagenomic next-generation sequencing (mNGS) detects a broad range of pathogens [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and has advantages over conventional culture methods but is prone to contamination during experimental operation, leading to false-positive results. In addition, mNGS is expensive, time-consuming, and unreliable for complex clinical specimens containing many different microorganisms, limiting its broader clinical application. Targeted next-generation sequencing (tNGS) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] is a technology utilising PCR or probe hybridisation to capture and enrich genomic regions of interest for high-throughput sequencing. tNGS is less time-consuming and more accurate than conventional microbial identification techniques and less expensive than mNGS.\u003c/p\u003e \u003cp\u003eCurrently, no domestic or international studies have investigated the use of tNGS to evaluate the characteristics of the aetiological distribution of secondary infections in COVID-19 patients. In this study, tNGS was used to analyse data of infectious microbes in clinical bronchoalveolar lavage fluid (BALF) specimens from patients with severe and critical COVID-19 infections to provide a reference for early empirical selection of antibiotics in such patients, with the aim of accurately guiding rational antibiotic use in these patients.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eThis was a retrospective observational study that included patients with severe and critical COVID-19 who were hospitalised at the First Affiliated Hospital of Shandong First Medical University and underwent tNGS between 1 December 2022 and 30 June 2023. All patients tested positive for SARS-CoV-2 nucleic acid.\u003c/p\u003e \u003cp\u003ePatients with severe COVID-19 were defined as patients meeting any of the following conditions that could not be explained by reasons other than COVID-19 infection: 1) Shortness of breath, respiratory rate\u0026thinsp;\u0026ge;\u0026thinsp;30 breaths/min; 2) Resting oxygen saturation\u0026thinsp;\u0026le;\u0026thinsp;93% on inhalation; 3) Arterial partial pressure of oxygen (PaO2)/fraction of inspired oxygen (FiO2)\u0026thinsp;\u0026le;\u0026thinsp;300 mmHg (1 mmHg\u0026thinsp;=\u0026thinsp;0.133 kPa), with correction for high altitude (\u0026gt;\u0026thinsp;1000 m) (PaO2/FiO2 \u0026times; [760 / atmospheric pressure (mmHg)]); 4) Progressive exacerbation of clinical symptoms and lung imaging indicating marked progression of lesions\u0026thinsp;\u0026gt;\u0026thinsp;50% within 24\u0026ndash;48 hours.\u003c/p\u003e \u003cp\u003ePatients with critical COVID-19 were defined as those meeting any of the following conditions: 1) Respiratory failure requiring mechanical ventilation; 2) Shock; 3) Concomitant failure of other organs requiring intensive care unit (ICU) monitoring and treatment.\u003c/p\u003e \u003cp\u003eSecondary infection was diagnosed if the patient presented with clinical symptoms or positive imaging evidence suggestive of a new lung infection 48 hours after admission and a positive laboratory-confirmed aetiological result (positive tNGS result).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSample procedures\u003c/h2\u003e \u003cp\u003ePatients who met the criteria for severe or critical COVID-19 and were suspected of having a secondary infection underwent tNGS testing 48 h after admission. tNGS testing of BALF was performed at the Medical Laboratory Diagnostic Centre of the First Affiliated Hospital of Shandong First Medical University.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistics\u003c/h2\u003e \u003cp\u003eSPSS 26.0 and GraphPad Prism 8.0 software were used for data analysis and visualisation. Count data was compared between groups using the χ2 test or Fisher's exact test. Continuous data conforming to the normal distribution was expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. One-way analysis of variance was used to compare between multiple groups of samples, and the t-test was used to compare data between two groups of samples. Differences with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of patients\u003c/h2\u003e \u003cp\u003eNinety-five patients with COVID-19 were included (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), of whom 54 cases had severe disease and 41 cases had critical disease. The age of the patients was in the range 24\u0026ndash;94 (mean: 69.87\u0026thinsp;\u0026plusmn;\u0026thinsp;14.80) years. Sixty-six patients (69.47%) were male, and 29 (30.53%) were female. Eighty-three patients (87.37%) suffered from one or more underlying diseases, which included hypertension in 51 cases (53.68%), diabetes in 33 cases (34.74%), cardiovascular disease in 37 cases (38.95%), cerebrovascular disease in 25 cases (26.32%), rheumatic-autoimmune disease in two cases (2.11%), cancer in 20 cases (21.05%), chronic kidney disease in six cases (6.32%), and chronic lung disease in five cases (5.26%). Thirteen patients (13.68%), all of whom had critical disease, died during the study period. Comparing the clinical data of patients with severe and critical COVID-19 showed that patients with critical COVID-19 had higher rates of hypertension, cardiovascular disease, and mortality than did patients with severe COVID-19; the difference was statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The differences in age, sex, and rates of diabetes, cerebrovascular disease, rheumatic-autoimmune disease, cancer, chronic kidney disease, and chronic lung disease between the two groups were not statistically significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of clinical data of patients with severe and critical COVID-19\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;95)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSevere (n\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCritical (n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.87\u0026thinsp;\u0026plusmn;\u0026thinsp;14.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.65\u0026thinsp;\u0026plusmn;\u0026thinsp;15.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.49\u0026thinsp;\u0026plusmn;\u0026thinsp;14.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.357\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnderlying disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRheumatic-autoimmune disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.407\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic lung disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutcome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecovered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCharacterisation of the aetiological distribution of secondary infections\u003c/h2\u003e \u003cp\u003etNGS was performed on BALF from the 95 patients, and one or more pathogens other than severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were detected in all cases (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Forty-eight pathogens were identified, of which 10 (20.83%) were viruses, 25 (52.08%) were bacteria, 11 (22.92%) were fungi, and two (4.17%) were other pathogens (\u003cem\u003eMycoplasma\u003c/em\u003e and \u003cem\u003eChlamydia\u003c/em\u003e). The top 10 pathogens detected and their corresponding rates of detection were HSV-4 (43 cases, 45.26%), \u003cem\u003eCandida albicans\u003c/em\u003e (30 cases, 31.58%), \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e (28 cases, 29.47%), \u003cem\u003eEnterococcus faecium\u003c/em\u003e (23 cases, 24.21%), HSV-1 (23 cases, 24.21%), \u003cem\u003eStaphylococcus aureus\u003c/em\u003e (19 cases, 20.00%), \u003cem\u003eAspergillus fumigatus\u003c/em\u003e (17 cases, 17.89%), \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e (15 cases, 15.79%), HSV-5 (14 cases, 14.74%), and \u003cem\u003eS. maltophilia\u003c/em\u003e (12 cases, 12.63%).\u003c/p\u003e \u003cp\u003eAmong the 95 COVID-19 patients, viral infection was detected in seven (7.37%), bacterial infection in 12 (12.63%), fungal infection in three (3.16%), mixed viral-bacterial infection in 22 (23.19%), mixed viral-fungal infection in 12 (12.63%), mixed bacterial-fungal infections in 12 (12.63%), and mixed viral-bacterial-fungal infections in 27 (28.42%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Comparing the aetiological distribution of secondary infections in patients with severe and critical COVID-19, the detection rate of bacterial infections was lower, and the detection rate of mixed viral-bacterial infections was higher in patients with critical COVID-19 than in patients with severe COVID-19; the difference was statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of secondary infections in patients with severe and critical COVID-19\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;95)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSevere (n\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCritical (n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eViral infection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBacterial infection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFungal infection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMixed viral-bacterial infection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMixed viral-fungal infection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMixed bacterial-fungal infection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMixed viral-bacterial-fungal infection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDistribution of secondary viral infections\u003c/h2\u003e \u003cp\u003eOne hundred strains of viruses were detected in patients with severe and critical COVID-19, with the top five strains being HSV-4 (43 cases, 43.00%), HSV-1 (23 cases, 23.00%), HSV-5 (14 cases, 14.00%), HSV-7 (12 cases, 12.00%), and HSV-6 (4 cases, 4.00%); there were four cases (4.40%) involving other viruses (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). HSV-1 was detected at a higher rate, and HSV-7 was identified at a lower rate in patients with critical COVID-19 compared to that in patients with severe COVID-19 disease; the difference was statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eViruses detected in patients with severe and critical COVID-19\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSevere (n\u0026thinsp;=\u0026thinsp;48)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCritical (n\u0026thinsp;=\u0026thinsp;52)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHSV-4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.884\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHSV-1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.017\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHSV-5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.872\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHSV-7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.046\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHSV-6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e1.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOthers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.554\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDistribution of secondary bacterial infections\u003c/h2\u003e \u003cp\u003eA total of 169 bacterial strains were detected in patients with severe and critical COVID-19, with the top five being \u003cem\u003eK. pneumoniae\u003c/em\u003e (28 cases, 16.57%), \u003cem\u003eE. faecium\u003c/em\u003e (23 cases, 13.61%), \u003cem\u003eS. aureus\u003c/em\u003e (19 cases, 11.24%), \u003cem\u003eA. baumannii\u003c/em\u003e (15 cases, 8.88%), and \u003cem\u003eS. maltophilia\u003c/em\u003e (12, 7.10%); there were 82 cases involving other bacteria (48.52%) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The detection rate of \u003cem\u003eE. faecium\u003c/em\u003e was higher, and the detection rate of \u003cem\u003eS. aureus\u003c/em\u003e was lower in patients with critical COVID-19 compared to that of patients with severe COVID-19; the difference was statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSecondary bacterial infections in patients with severe and critical COVID-19\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;169)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSevere (n\u0026thinsp;=\u0026thinsp;93)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCritical (n\u0026thinsp;=\u0026thinsp;76)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKlebsiella pneumoniae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEnterococcus faecium\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStaphylococcus aureus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAcinetobacter baumannii\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStenotrophomonas maltophilia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOthers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDistribution of secondary fungal infections\u003c/h2\u003e \u003cp\u003eSeventy-seven fungal strains were detected in patients with severe and critical COVID-19, with the top five being \u003cem\u003eC. albicans\u003c/em\u003e (30, 38.96%), \u003cem\u003eA. fumigatus\u003c/em\u003e (17, 22.08%), \u003cem\u003ePneumocystis jirovecii\u003c/em\u003e (11, 14.29%), \u003cem\u003eCandida tropicalis\u003c/em\u003e (5, 6.49%), and \u003cem\u003eAspergillus flavus\u003c/em\u003e (4, 5.19%); there were 10 cases involving other fungi (12.99%) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). None of the differences in the detection rates of the different fungi between the patients with critical and severe COVID-19 were statistically significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSecondary fungal infections in patients with severe and critical COVID-19\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSevere (n\u0026thinsp;=\u0026thinsp;45)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCritical (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCandida albicans\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAspergillus fumigatus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePneumocystis jirovecii\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCandida tropicalis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAspergillus flavus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOthers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCOVID-19 is an emergent infectious disease with a high risk of mortality that represents a major threat to human life and health and is transmitted primarily through respiratory droplets and close contact [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The overall incidence of secondary infections in patients with COVID-19 is low; Wu \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] reported that the overall incidence of secondary infections in all patients hospitalised with COVID-19 was only 3.7%. However, the incidence of secondary infections is directly correlated with disease severity; Grasselli \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] found that the incidence of secondary infections reached 46% among patients in the ICU. This is consistent with the findings of Zamora-Cintas \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], who reported an incidence of secondary infections in standard ward patients of 9% but a significant increase in secondary infections in critical care patients, at 31.5%. Secondary infections are also a major cause of increased mortality in patients with COVID-19. Notably, a study [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] found that the rate of vasopressor use, glucocorticoid use, continuous renal replacement therapy, mechanical ventilation, and extracorporeal membrane oxygenation during hospitalisation was higher in patients with secondary infections than in those without, and the mortality rate of patients with secondary infections was approximately twice as high as in those without. Ninety-five patients with positive reverse transcription polymerase chain reaction results for SARS-CoV-2 were included in the present study, including 54 with severe COVID-19 and 41 with critical COVID-19. Moreover, a multi-centre study in Korea reported that the 28-day mortality rate of secondary infection in patients with COVID-19 was 17.3% [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], which was consistent with that of 13.68% in the present study. A multifactorial analysis by Taysi \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] showed that the use of mechanical ventilation for over 48 hours, central venous catheterisation for over 72 hours, duration of ICU stay of over 10 days, and duration of hospital stay of over 48 hours were all risk factors for secondary infections. A recent study [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] showed that factors associated with secondary infection include age\u0026thinsp;\u0026gt;\u0026thinsp;64 years, duration of ICU stay\u0026thinsp;\u0026gt;\u0026thinsp;7 days, type 2 diabetes, cardiovascular disease, central venous catheter placement, intubation, Acute Physiology and Chronic Health Evaluation II score\u0026thinsp;\u0026gt;\u0026thinsp;25, mechanical ventilation\u0026thinsp;\u0026gt;\u0026thinsp;48 hours, and urinary catheter placement. The results of the present study showed that 87.37% of the patients suffered from one or more underlying diseases, which included hypertension (53.68%), diabetes (34.74%), cardiovascular disease (38.95%), cerebrovascular disease (26.32%), cancer (21.05%), chronic kidney disease (6.32%), chronic lung disease (5.26%), and rheumatic-autoimmune disease (2.11%), and that patients with critical COVID-19 had significantly higher rates of hypertension, cardiovascular disease, and mortality than did patients with severe COVID-19. In another study [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], independent risk factors associated with mortality were found to include advanced age, male sex, inhalation of high concentrations of oxygen, high positive end-expiratory pressure or low oxygenation index, as well as history of chronic obstructive pulmonary disease, hypercholesterolemia, and type 1 diabetes. In contrast, a study of COVID-19 patients without prior chronic underlying disease [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] showed that age\u0026thinsp;\u0026ge;\u0026thinsp;47 years, oxygen saturation\u0026thinsp;\u0026lt;\u0026thinsp;95%, elevated lactate dehydrogenase, neutrophil count, direct bilirubin, creatine phosphokinase, blood urea nitrogen, dyspnoea, elevated blood glucose, and prothrombin time were associated with mortality in patients with COVID-19.\u003c/p\u003e \u003cp\u003eThe aetiological profile of secondary infections has become a common concern in critical care medicine. Sang \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] assessed the epidemiology of secondary infections in patients with severe and critical COVID-19 and found that the most abundant microorganisms detected in sputum/endotracheal aspirate cultures of the patients were bacteria, followed by fungi, with the five most common microorganisms being \u003cem\u003eK. pneumoniae\u003c/em\u003e, \u003cem\u003eA. baumannii\u003c/em\u003e, \u003cem\u003eS. maltophilia\u003c/em\u003e, \u003cem\u003eC. albicans\u003c/em\u003e, and \u003cem\u003ePseudomonas\u003c/em\u003e spp. Another retrospective study [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] showed that the most common secondary infections in patients with COVID-19 were \u003cem\u003ecoagulase-negative staphylococci, A. baumannii\u003c/em\u003e, and \u003cem\u003eEscherichia coli\u003c/em\u003e. However, conventional culture has the disadvantages of being time-consuming, having low sensitivity, and covering few species of pathogens. Therefore, tNGS was used in the present study to test and analyse the BALF of COVID-19 patients. The results indicated that 48 pathogens, including bacteria, fungi, viruses, \u003cem\u003eMycoplasma\u003c/em\u003e, and \u003cem\u003eChlamydia\u003c/em\u003e, were detected in 95 patients, and the 10 most common pathogens were HSV-4, \u003cem\u003eC. albicans\u003c/em\u003e, \u003cem\u003eK. pneumoniae\u003c/em\u003e, \u003cem\u003eE. faecium\u003c/em\u003e, HSV-1, \u003cem\u003eS. aureus\u003c/em\u003e, \u003cem\u003eA. fumigatus\u003c/em\u003e, \u003cem\u003eA. baumannii\u003c/em\u003e, HSV-5, and \u003cem\u003eS. maltophilia.\u003c/em\u003e The majority of infections were mixed (76.84%), with mixed viral-bacterial-fungal infections being the most common (28.42%), followed by mixed viral-bacterial infections (23.19%). In addition, the rate of bacterial infection was lower, and that of mixed viral-bacterial infection was higher in critical COVID-19 patients than in severe COVID-19 patients; however, these differences must be confirmed in further studies with larger sample sizes. The detection rate of pathogenic microorganisms in the lower respiratory tract was increased using tNGS, but these microorganisms may not always be associated with infections, and differentiating between colonisation and infection is difficult in the clinical setting.\u003c/p\u003e \u003cp\u003eAs COVID-19 leads to impaired cell-mediated immunity [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], the use of glucocorticoids and immunosuppressants also leads to decreased immunity in patients with COVID-19 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], which provides an opportunity for viral reactivation and secondary infections. The results of the present study showed that the five most commonly detected viruses in the BALF of COVID-19 patients were HSV-4 (EBV), HSV-1, HSV-5 (CMV), HSV-7 and HSV-6, all of which are in the herpesvirus family. HSV-1 was also detected at a higher rate, and HSV-7 was identified at a lower rate in critical COVID-19 patients compared to severe COVID-19 patients. Bernal \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] reported EBV reactivation in 27.1% of patients with COVID-19, which was significantly higher than the EBV reactivation rate of 12.5% in patients without COVID-19. In China, Xie \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] reported a rate of EBV reactivation of 13.3% in patients with COVID-19 and that the mortality rate of the EBV reactivation group was significantly higher than that of the non-EBV reactivation group at both 14 and 28 days. A study of 70 patients with COVID-19 by Franceschini \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] found that HSV-1 viremia was detectable in 30.0% of patients and that steroid therapy, invasive mechanical ventilation, and high lactate dehydrogenase were significantly associated with an increased risk of HSV-1 reactivation. Similarly, a retrospective analysis of EBV, CMV, and HSV replication in 100 patients with severe COVID-19 by Saade \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] found 63 patients with viral reactivation (12% HSV, 58% EBV, and 19% CMV). Moreover, both HSV-1 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and CMV reactivation [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] have been shown to increase mortality in patients with COVID-19. Overall, patients with COVID-19 are at potential risk for latent virus reactivation or secondary infection, which contributes to increased mortality, but standardised diagnostic and therapeutic protocols for herpesvirus reactivation or secondary infection in patients with COVID-19 remain lacking.\u003c/p\u003e \u003cp\u003eBacteria are the primary pathogens that cause secondary infections in patients with COVID-19. In the present study, 169 bacterial strains were detected in the BALF of COVID-19 patients, with the five most abundant bacteria being \u003cem\u003eK. pneumoniae\u003c/em\u003e, \u003cem\u003eE. faecium\u003c/em\u003e, \u003cem\u003eS. aureus\u003c/em\u003e, \u003cem\u003eA. baumannii\u003c/em\u003e, and \u003cem\u003eS. maltophilia\u003c/em\u003e. Therefore, empirical antibiotic treatment of COVID-19 patients with suspected secondary infections should cover these pathogens. Torrego \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] analysed the presence of \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, \u003cem\u003eS. aureus\u003c/em\u003e, \u003cem\u003eKlebsiella aerogenes\u003c/em\u003e, \u003cem\u003eEnterobacter cloacae\u003c/em\u003e, \u003cem\u003eEnterobacter faecalis\u003c/em\u003e, and \u003cem\u003eEscherichia coli\u003c/em\u003e in the BALF of COVID-19 patients and achieved results similar to the microbiota usually detected in ventilator-associated pneumonia. Moreover, Sang \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] analysed the bacteria detected in patients with COVID-19 after admission to the ICU for over 72 hours. Their results were similar to those of the present study, and they further found that most of the \u003cem\u003eK. pneumoniae\u003c/em\u003e and \u003cem\u003eA. baumannii\u003c/em\u003e strains were resistant to carbapenem. This is consistent with the findings of Gomez-Simmonds \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] that the majority of \u003cem\u003eK. pneumoniae\u003c/em\u003e strains (16/17) were drug-resistant. A recent multi-centre study [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] found that the most common pathogens associated with secondary lung infections of hospitalised COVID-19 patients included \u003cem\u003eA. baumannii\u003c/em\u003e, \u003cem\u003eKlebsiella\u003c/em\u003e spp., \u003cem\u003eStreptococcus\u003c/em\u003e spp., \u003cem\u003eHaemophilus influenzae\u003c/em\u003e, and \u003cem\u003eP. aeruginosa\u003c/em\u003e, and the most common pathogens associated with secondary bloodstream infections were coagulase-negative \u003cem\u003eStaphylococcus\u003c/em\u003e and \u003cem\u003eA. baumannii\u003c/em\u003e. A study of critical COVID-19 patients by Meawed \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] showed that the common bacterial pathogens in ventilator-associated pneumonia included pandrug-resistant \u003cem\u003eK. pneumoniae\u003c/em\u003e (41.1%) and multidrug-resistant \u003cem\u003eA. baumannii\u003c/em\u003e (27.4%). Therefore, the possibility of multidrug-resistant organisms should be considered in the selection of antibiotics for patients with secondary infections from COVID-19, especially those undergoing invasive mechanical ventilation.\u003c/p\u003e \u003cp\u003eZhu \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] reported that 23.3% of COVID-19 patients had comorbid fungal infections, and the incidence of fungal infections increased with the severity of COVID-19. There have been widespread reports of respiratory infections by invasive fungi, with common fungi including \u003cem\u003eC. albicans\u003c/em\u003e, \u003cem\u003eAspergillus\u003c/em\u003e spp., and \u003cem\u003eMucor\u003c/em\u003e spp., as well as atypical fungal pathogens such as \u003cem\u003eP. jirovecii\u003c/em\u003e, \u003cem\u003eCryptococcus\u003c/em\u003e spp., \u003cem\u003eCandida auris\u003c/em\u003e, and \u003cem\u003eFusarium\u003c/em\u003e spp. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The results of the present study showed that 77 fungal strains were detected in the BALF of COVID-19 patients, of which the five most abundant were \u003cem\u003eC. albicans\u003c/em\u003e, \u003cem\u003eA. fumigatus\u003c/em\u003e, \u003cem\u003eP. jirovecii\u003c/em\u003e, \u003cem\u003eC. tropicalis\u003c/em\u003e, and \u003cem\u003eA. flavus\u003c/em\u003e. Lu \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] reported a \u003cem\u003eCandida\u003c/em\u003e detection rate of 77.8% in an early coinfection group (duration of hospitalisation\u0026thinsp;\u0026lt;\u0026thinsp;7 days) and 43.2% in a late coinfection group (duration of hospitalisation\u0026thinsp;\u0026gt;\u0026thinsp;7 d) among COVID-19 patients in Taiwan, and that \u003cem\u003eCandida\u003c/em\u003e spp. fungal coinfections were more common than \u003cem\u003eAspergillus\u003c/em\u003e spp. coinfections. However, the incidence of secondary \u003cem\u003eCandida\u003c/em\u003e infections varies significantly between 0.4\u0026ndash;23.5% across geographic regions [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], with studies from Europe reporting incidences of 0.4%, 8%, and 12.6% in Spain, Italy, and the United Kingdom, respectively, while those from Asia find incidences of 2.5%, 5%, and 23.5% in India, Iran, and China, respectively. Immunodeficiency, use of antivirals or immunosuppressants, direct injury from COVID-19, and dysbiosis may contribute to the development of pathogenic \u003cem\u003eCandida\u003c/em\u003e infections in COVID-19 patients [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The incidence of \u003cem\u003eAspergillus\u003c/em\u003e infection was significantly higher in patients with COVID-19, and the present study found detection rates of \u003cem\u003eA. fumigatus\u003c/em\u003e and \u003cem\u003eA. flavus\u003c/em\u003e of 22.08% and 5.19%, respectively. One hospital in China reported an incidence of invasive pulmonary aspergillosis of 29.3% among patients with COVID-19 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Chong \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] reviewed 1421 COVID-19 patients and found an overall incidence of pulmonary aspergillosis of 13.5% (range: 2.5\u0026ndash;35.0%), with the time to diagnosis of COVID-19-associated pulmonary aspergillosis from ICU admission and initiation of invasive mechanical ventilation ranging between 4.0\u0026ndash;15.0 days and 3.0\u0026ndash;8.0 days, respectively, and a mortality rate of 48.4%. Furthermore, a meta-analysis of 3,148 patients from 28 observational studies [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] estimated the morbidity and mortality of COVID-19-associated pulmonary aspergillosis in the ICU to be 10.2% and 54.9%, respectively. \u003cem\u003eP. jirovecii\u003c/em\u003e is an opportunistic infectious agent that occurs mainly in immunocompromised patients, particularly human immunodeficiency virus (HIV)-positive patients, solid organ transplant recipients, those with hematologic malignancies, and rheumatologic patients undergoing prolonged steroid treatment [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Jeican \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] reported the first autopsy-confirmed case of COVID-19 combined with \u003cem\u003eP. jirovecii\u003c/em\u003e infection. The results of the present study showed a detection rate of \u003cem\u003eP. jirovecii\u003c/em\u003e in patients with COVID-19 of 14.29%, which was significantly higher than that in patients without severe COVID-19. Furthermore, \u003cem\u003eP. jirovecii\u003c/em\u003e was one of the pathogens severely underestimated for secondary infections in patients with COVID-19. Kang [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] analysed the incidence of \u003cem\u003eP. jirovecii\u003c/em\u003e pneumonia in non-HIV patients between 2016 and 2022, and the results confirmed that the incidence of \u003cem\u003eP. jirovecii\u003c/em\u003e pneumonia was significantly higher during the COVID-19 epidemic than before the epidemic. Significantly decreased lymphocyte count and glucocorticoid use are the principal risk factors for \u003cem\u003eP. jirovecii\u003c/em\u003e infection, and the risk of \u003cem\u003eP. jirovecii\u003c/em\u003e infection increases with higher doses of glucocorticoid treatment [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. \u003cem\u003eP. jirovecii\u003c/em\u003e pneumonia can be distinguished from the clinical symptoms and imaging features of COVID-19; thus, when the condition of a COVID-19 patient deteriorates, the possibility of \u003cem\u003eP. jirovecii\u003c/em\u003e infection should also be considered, and early identification and treatment of secondary fungal infections is essential for improving patient outcomes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eCOVID-19 remains a major infectious disease that threatens human life and health. Secondary viral, bacterial, and fungal respiratory infections are a major cause of disease exacerbation in patients with COVID-19. tNGS was used to detect and analyse the BALF of COVID-19 patients, and bacteria, fungi, viruses, \u003cem\u003eMycoplasma\u003c/em\u003e, and \u003cem\u003eChlamydia\u003c/em\u003e were detected, with the majority of cases (76.84%) being mixed infections. tNGS of BALF enables rapid and accurate analysis of the pathogen composition of secondary infections and lower respiratory tract diseases in patients with COVID-19 and lays a foundation for accurate diagnosis and individualised treatment. In particular, tNGS has significant advantages with respect to the precision of identifying specific pathogens such as fungi and viruses.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFeng-qin Ren, Feng Ji: original draft preparation; Zhao-qi Liu, Li-ru Yan, Zhi-wei Gao, Meng-zhen Liu, Xin-guang Teng: methodology, software, validation, investigation, resources, data curation, writing-review and editing, visualization, supervision; Guang-sheng Gao: conceptualization, writing-review and editing, supervision.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis work was supported by\u0026nbsp;Projects of medical and health technology development program in Shandong province (NO.202203020343) and by Critical care medicine special fund of Shandong Pathophysiological Society (NO.2021BS007).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. 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J Fungi (Basel). 2023;9(8):838. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/jof9080838\u003c/span\u003e\u003cspan address=\"10.3390/jof9080838\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, targeted next-generation sequencing, bronchoalveolar lavage fluid, secondary infection, aetiological studies","lastPublishedDoi":"10.21203/rs.3.rs-4113659/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4113659/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePURPOSE: To use targeted next-generation sequencing (tNGS) of pathogens for analysing the etiological distribution of secondary infections in patients with severe and critical novel coronavirus pneumonia (COVID-19), to obtain microbial epidemiological data on secondary infections in patients with COVID-19, and to provide a reference for early empirical antibiotic treatment of such patients.\u003c/p\u003e\n\u003cp\u003eMETHODS: Patients with infections secondary to severe and critical COVID-19 and hospitalised at the First Affiliated Hospital of Shandong First Medical University between 1 December 2022 and 30 June 2023 were included in the study. The characteristics and etiological distribution of secondary infections in these patients were analysed using tNGS.\u003c/p\u003e\n\u003cp\u003eRESULTS: A total of 95 patients with COVID-19 secondary infections were included in the study, of whom 87.37% had one or more underlying diseases. Forty-eight pathogens were detected, the most common being HSV-4, Candida albicans, Klebsiella pneumoniae, Enterococcus faecium, HSV-1, Staphylococcus aureus, Aspergillus fumigatus, Acinetobacter baumannii, HSV-5, and Stenotrophomonas maltophilia, with Pneumocystis jirovecii being detected in 14.29% of cases. The majority (76.84%) of COVID-19 secondary infections were mixed infections, with mixed viral-bacterial-fungal infections being the most common (28.42%).\u003c/p\u003e\n\u003cp\u003eCONCLUSION: Most secondary infections in severe and critical COVID-19 patients are mixed, with high rates of viral and fungal infections. In clinical settings, monitoring for reactivation or secondary infections by Herpesviridae viruses is crucial; additionally, these patients have a significantly higher rate of P. jirovecii infection. tNGS testing on bronchoalveolar lavage fluid can help determine the aetiology of secondary infections early in COVID-19 patients and assist in choosing appropriate antibiotics.\u003c/p\u003e","manuscriptTitle":"Targeted next-generation sequencing of pathogens reveals the profile of secondary infections in COVID-19 patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-21 18:00:33","doi":"10.21203/rs.3.rs-4113659/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"791173dd-7fb6-4272-b326-df76e32c2323","owner":[],"postedDate":"March 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-26T06:13:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-21 18:00:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4113659","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4113659","identity":"rs-4113659","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}