Respiratory Tract Pathogen Profiles of COVID-19 Pneumonia Patients and the Mortality Prediction

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Exploring the pathogen profiles of COVID-19 patients-related facilitated the clinical management and decisions to pursue better prognosis. Methods This study systematically compared the sputum culture results and death events of 170 non-COVID-19 and 197 COVID-19 patients. Statistical analysis was carried out to find the pathogen profile difference between the two populations. The death risk model was constructed for the infected COVID-19. Results It was found that co-infection with bacteria and fungi increased the mortality of COVID-19 pneumonia patients. The isolation rate of Acinetobacter baumannii in COVID-19 patients was significantly higher than that in non-COVID-19 patients and often showed multi-drug resistant phenotypes. The COVID-19 pneumonia patients showed a higher incidence of intensive care unit admission, ventilator-assisted ventilation and death with fungal infection. The serum levels of interleukin-1, interleukin-6, interleukin-8, TNF, lymphocytes, neutrophils and white blood cells in patients with COVID-19 pneumonia decreased. A death prediction model was constructed based on machine learning methods, achieving a prediction accuracy of 90.0%. The main factors affecting the survival rate of COVID-19 pneumonia patients co-infected with other pathogens were admission to the intensive care unit, days of hospital stay, ventilator-aided treatment, carbapenems administration, lymphocyte, serum aspartate aminotransferase level, Acinetobacter baumannii infection, and Candida infection. Conclusions This study provided necessary clinical indicators for timely and precise intervention of COVID-19 pneumonia patients when they were infected by other pathogens. The COVID-19-related secondary infection microorganisms were different compared with the pathogens isolated from non-COVID-19 patients. Biological sciences/Microbiology/Infectious disease diagnostics Biological sciences/Microbiology/Pathogens COVID-19 Pneumonia Infection Mortality prediction Machine learning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first reported in Wuhan in 2019 and rapidly developed to be a global pandemic coined as coronavirus infection disease − 19 (COVID-19) [ 1 , 2 ]. This virus usually attacks the respiratory system and causes COVID-19 pneumonia. Some patients are readily to develop acute respiratory distress syndrome, including pulmonary fibrosis and severe pneumonia [ 3 , 4 ]. Due to the damage to the lower respiratory tract mucosa and the immune escape ability of the virus, SARS-CoV-2 infection is easily complicated with other microorganism infections, which aggravates the severity of the disease and increases medical expenses [ 5 ]. COVID-19 pneumonia combined with bacterial and/or fungal infections were common complications in hospitalized patients [ 6 , 7 ]. Secondary bacterial infections (SBI) were significantly associated with the degree of poor prognosis [ 6 ]. The situation might be even worse for the patients admitted to intensive care units (ICU). Among the COVID-ve19 pneumonia patients with SBI, 55% developed septic shock, 29% developed refractory respiratory failure, and the total mortality reached 54% [ 8 ]. Therefore, establishing a prediction model for the prognosis of COVID-19 pneumonia patients might provide valuable information for avoiding or alleviating poor prognosis [ 9 , 10 ]. To this end, many prediction models have been developed. Kim et al. constructed a model to predict the possibility of ICU admission for COVID-19 patients. Although the model addressed using the easily accessible parameters, performed well and was thought to be superior to CURB-65 score, it did not predict patient mortality in the ICU [ 10 ]. Wan et al. identified age, lifestyle, illness, income, and family disease history were key parameters to predict COVID-19 mortality. The area under the receiver operating characteristic curve (AUC) was 0.86 (95% CI 0.84–0.88), but it did not consider the presence of SBI [ 11 ]. COVID-19 sporadically outbreaks in certain countries currently. With the worldwide vaccination projects and the decrease of pathogenicity of SARS-CoV-2 variants, most of the affected patients showed mild symptoms and good prognosis [ 12 , 13 ]. More attention should be paid to patients with severe illness, e.g., those co-infected with bacteria and/or fungi [ 14 ]. In this light, this study considered the infection parameters to construct a mortality prediction model. It was found that the inclusion of those parameters improved the prediction ability and the prediction accuracy was up to 90%. Methods Subjects 211 bacterial culture-positive and 192 fungal culture-positive results were collected from 170 non-SARS-CoV-2 infected patients and 197 SARS-CoV-2 infected patients. The patients were hospitalized at the 2 nd Hospital of Dalian Medical University from Dec.1, 2022 to Feb.28, 2023. All the patients were diagnosed with pneumonia through chest CT and clinical laboratory results and subjected to reverse transcription quantitative real-time polymerase chain reaction using nasopharyngeal swab samples. SARS-CoV-2 infected patient was diagnosed with a positive amplification result. Subjects with the same sputum culture results within 7 days were kept only once. Antibiotic susceptibility tests were performed as recommended by the K-B method or dilution method [15]. Statistical analysis The quantitation data qualified to normal distribution were expressed as mean±s.d. and the comparison between independent samples was analyzed by two independent sample t-test. The paired data were analyzed by paired t-tests. Data of non-normal distribution were expressed as median and compared by rank sum test. Count data were analyzed using the chi-square test. A p-value <0.05 was considered statistically significant. All the statistical analysis was carried out using SPSS 26.0((Developed by IBM Co., Chicago, USA).). Machine learning methods and the model construction A total of 49 indicators including demographic data, laboratory examination results, species of pathogens isolated from the respiratory tract and treatment strategies were collected. The dataset was divided into five panels according to feature attributes as shown in Table 1. For prediction model construction the "outcome" was defined as patient death that occurred within 30 days after being admitted into the wards. Each model was built based on the sequential accumulation of the 5 panels’ data (Table 1). All the patients were randomly divided into two parts, in which 80% of them were used to train each model and 20% were used to validate the performance of each model. The considered modeling algorithms included Decision Tree (DT), Categorical Boosting (CatBoost), Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM). The predictive abilities of the models were analyzed and compared according to their areas under the receiver operating characteristic curve (AUC) values. The good performance models were subjected to the Tree-structured Parzen Estimator (TPE) analysis by Bayesian optimization (BO) to find the optimal parameter combination and improve the prediction accuracy. The most optimized model was validated by the validation set data. The kept parameters in the best model were subjected to analysis by the Permutation Feature Importance (PFI) method to calculate the importance of each variable, and the Shapley Additive exPlanations (SHAP) method to analyze the contribution of different parameters to the model output. A flow chart of the modeling process was shown in Fig. 1. Results Demographic data A total of 367 patients were included in this study. The median age was 74 years (11-100 years). 247 patients were male (67.5%). Compared with patients without COVID-19, patients with COVID-19 were older (79 vs. 70, P<0.001) and had more days of hospital stay (13 vs. 11, P50%), including hypertension (50.5%), diabetes (35.2%), cardiovascular and cerebrovascular diseases (21.6%), chronic kidney disease (12.6%), and chronic lung disease (8.5%). Patients with COVID-19 were more likely to have diabetes (39.9% vs.29.8%, P=0.032). All the pneumonia patients had the experience of receiving antibiotics, corticosteroids, antiviral agents, antifungal agents, and mechanical ventilation alone or in combination throughout their hospitalization. Patients with COVID-19 were more likely to be treated with antiviral drugs (47.7% vs 11.2%) and corticosteroids (74.1% vs 36.5%) (P<0.001). Detailed information was given in Table 2. Outcomes In this study, the mortality rate of COVID-19 combined with SBI was 40.0% (n = 80) as shown in Table 2. Among them, 40 cases (50.0%) died of bacterial infection, 27 cases (33.8%) died of fungal infection, and 13 cases (16.2%) died of bacterial and fungal coinfection. Mortality was increased in patients with COVID-19 than in patients without COVID-19 (40.0% vs. 29.9%, P=0.046). Pathogen profiles Candida spp . (175 cases, 91.4%) and Aspergillus spp . (16 cases, 8.3%) were the most common fungi isolated from sputum samples of 192 patients. There was no significant difference in the incidence of Candida and Aspergillus infection between patients with or without COVID-19 (Table 3). Bacterial pathogens were isolated from 211 patients. The main strains were A. baumannii (93 cases, 44.1%), K. pneumoniae (83 cases, 39.3%), P. aeruginosa (42 cases, 19.9%), S. aureus (14 cases, 6.6%). The isolation rate of A. baumannii was higher in patients with COVID-19 than in those without COVID-19 (51.4% vs. 36.3%, P=0.027). There was no significant difference in the incidence of S. aureus infection between patients with or without COVID-19 (6.4% vs. 6.8%, P=0.898). The isolation rate of multi-drug resistant bacteria increased in COVID-19 patients. The detection rate of carbapenem-resistant A. baumannii (CRAB) in COVID-19 patients was higher than in those without COVID-19 (94.6% vs. 86.5%, P=0.011). For the COVID-19 patients, the resistance rates of CRAB to ceftazidime, ciprofloxacin, gentamicin, piperacillin-tazobactam were all more than 90%. Fortunately, most of the isolated CRAB were sensitive to tigecycline (90.2%). The isolation rates of carbapenem-resistant K. pneumoniae (CRKP) and carbapenem-resistant P. aeruginosa (CRPA) were similar (26.8% vs 26.2% and 28.6 vs 38.0%, respectively) between the two groups of patients. The detailed information was shown in Tables 4-6. Clinical laboratory test results In Table 7, it was clear that white blood cells, neutrophils, lymphocytes, interleukin-1, interleukin-6, interleukin-8, and TNF were lower (P<0.05) in the patients with COVID-19 compared to in the patients without COVID-19. Construction of the prediction models Because the DT and CatBoost models were not accurate and stable, they were excluded initially. After every 10 iterations for each model, boxplots (Fig.. 2) were used to comprehensively compare the stability and performance of the left five models. The AUC values of the five models were all above 0.6, and the median values were all above 0.7. Specifically, the XGBoost model exhibited the shortest variation distribution range in the boxplot (excluding the outlier with an AUC value of 0.6310). Its median AUC value was 0.7761, indicating that this model was more stable compared to others. The stabilities of LR model and RF model were ranked as second and third. Although SVM had the highest AUC value (0.9464), its deviation distribution was wide, indicating that the stability was relatively poor. Although the difference in AUC values amid the models was not significant, XGBoost model and LightGBM model had superior advantages when considering computational efficiency, dealing with complex relationships, providing feature importance information and parameter tunable range (data not shown). Therefore, we further selected the XGBoost model and LightGBM model for BO to ensure better prediction performance in practice. Given that LightGBM and XGBoost had a large space for parameter optimization, we further combined each model with the TPE method for hyperparameter optimization. For both LightGBM and XGBoost models, the accuracy of the BO-optimized models was higher than that before optimization (Fig. 3). The AUC value of XGBoost increased from 0.8960 to 0.9493 (Fig. 4a), and the AUC value of LightGBM increased from 0.8972 to 0.9699 (Fig. 4b). Finally, the prediction accuracy of both models reached up to 90% tested by the validation data (Fig. 3). The SHAP summary plots based on the optimal parameter combinations identified the key risk factors dominating the predictive model (Fig. 5). Each point represented a sample, and the color of the point represented the relative significance of the eigenvalues, with red indicating high eigenvalues and blue indicating low eigenvalues. Taking whether to use ventilator treatment as an example, a large number of red samples were clustered in the area with negative SHAP values, which meant that if the patient received ventilator treatment (marked as 1), SHAP values would be low. The magnitude of SHAP values indicated the degree of influence on the prediction results. The greater the absolute values of SHAP, the greater the impact of this variable on the outcome of the patient’s survival. Fig. 6 showed the variable importance changes for each established model before and after optimization using the 49 indicators (variables). The greater the importance value of the variable, the greater the impact on the survival rate of the patients. In this light, intensive care unit admission, days of hospital stay, ventilator use, carbapenem use, lymphocyte count, AST value, A. baumannii infection, and Candida infection had important effects on patient survival. Discussion Many studies had indicated that the dominant pathogenic strains isolated in COVID-19 pneumonia patients were different compared to the common nosocomial or community-acquired pneumonia patients [16-18]. For our studied non-COVID-19 patients the major isolated strains were K. pneumoniae (Table 4). A. baumannii is an opportunistic pathogen that often causes nosocomial infections, especially in intensive care units [19]. Patients with COVID-19 were more susceptible to A. baumannii infection and death due to their high proportion of ICU admissions, longer hospital stays, carbapenem antibiotics administration, and mechanical ventilation [1, 2]. Isolation rates of A. baumannii in COVID-19 patients who were admitted to ICU always ranked the first and 50% to 85.7% of the victims would die [20-22]. The isolated A. baumannii often showed carbapenem-resistance phenotypes due to the production of acquired β-lactamase [19]. Not limited to that, multi-drug resistant strains were more prevalent in those patients [23]. Resistance of A. baumannii to carbapenem was an important concern because this type of antibiotic is the last line of defense in the treatment of infections caused by multidrug-resistant Gram-negative bacteria [19]. Empirical use of antibiotics for COVID-19 patients increased the possibility of antibiotic-resistant A. baumannii [19]. Facilitates used in the mechanical ventilation treatment were favorable to A. baumannii colonization and the formation of biofilm [24]. Bacterial biofilm prompted the emergence of antibiotic resistance and multi-drug resistance strains [25]. Collectively, admission to the ICU, days of hospital stay, ventilator-aided treatment, carbapenems administration, and A. baumannii infection all added extra insult to COVID-19 pneumonia. Thus, it was rational that the prediction models kept all the above-mentioned parameters (Fig. 5), and they all played key roles in the death prediction (Fig. 6). The isolation rates of CRAB and K. pneumoniae were comparable to the previous reports [26]. Whereas the isolation of CRKP in this study was lower than in the other studies [26]. This decline might be attributed to improved hand hygiene during the pandemic, extensive disinfection of the environment, use of masks, keeping social distance, and reduced empirical use of antibiotics in the hospital during the period of the Chinese pandemic [19]. According to Table 5, if timely sputum culture results were not available the choice of empirical antimicrobial regimen might focus on the treatment of A. baumannii and combined recipes might be more feasible. Co-infection with SARS-CoV-2 had no significant effect on the isolated species of fungal pathogens in our studied populations. We did not find the infection rate difference between the COVID-19 and non-COVID-19 patients (Table 2). This study reconfirmed the findings that C. albicans were the predominant isolations in fungal infections [27]. Different from the analysis carried out before the SARS-CoV-2 pandemic, except A. fumigatus , a large proportion of other Aspergillus species infection and mixed infection of Aspergillus and Candida were found in our study. We did not find a difference in antifungal therapies compared to the previous report [28]. Whereas, the auxiliary measures were adopted more in our studied patients than in the pre-pandemic patients [28]. These operations might contribute to the fungal infection chance. Intubation e.g., placement of a central venous catheter, was reported to increase the susceptibility to Candida invasion in patients with COVID-19 [29]. Death rates of Candida -affected COVID-19 patients were about 35.0% to 76.3%[30]. For severe COVID-19 pneumonia patients, the prognosis might be even worse when the patients were affected by fungal infection [31]. Thus, it was imaginable that Candida infection was kept as a key risk factor for patient death (Figs 5,6). In our model, the only left parameter about antibiotics was the administration of Carbapenems. It was positively related to the death risk. In theory, broad spectrum antibiotic application should facilitate pathogenic bacteria eradication. Considering the antibiotic susceptibility test results of the major isolated bacteria, the Carbapenems administration did not work well (Table 5). Additionally, this kind of antibiotic administration might facilitate the emergence of multi-drug resistant strains of other species. Thus, precise prescription of antibiotics aiming at special bacteria was crucial to avoid a worse prognosis. Conclusions The death risks were closely linked to the secondary infection for COVID-19 patients. Thus, in this study, we took the relative parameters into consideration to predict the death event. Clearly, over half of the relevant parameters were kept in the prediction model. Validated by another set of data, both of the models performed well. Compared to the previous studies, our model was more applicable to COVID-19 patients with SBI. It also should be acknowledged that our study only included a limited scale of patients. All the included patients were hospitalized during the pandemic. Currently, many clinical guidelines have been modified. The applicability of the model was warranted to be tested and improved based on a broad range of future patients. Declarations Acknowledgments Thanks to Mr. Hao Wang of Dongbei University of Finance and Economics for his help. Author’s Contributions Conceptualization, Ruihua Li and Peng Gao; methodology, Jiaxin Liu ; software, Yaolin Wen; validation, Jiaxin Liu, Yaolin Wen and Yiming Gao; formal analysis, Pengchao Fan; investigation, Rihong Huang; resources, Rihong Huang; data curation, Ruihua Li; writing—original draft preparation, Jiaxin Liu; writing—review and editing, Ruihua Li; visualization, Yaolin Wen; supervision, Peng Gao; project administration, Ruihua Li; funding acquisition, Peng Gao, Wenzhi Liu. All authors have read and approved the final version of the manuscript. Funding This research was funded by "Touching China" Infection Prevention and Control Research Project, Institute of Hospital Administration, National Health Commission, grant number GY2023043. Availability of data and materials The datasets that support the findings of this study are available from the corresponding authors upon reasonable request. Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the Second Hospital of Dalian Medical University (KY2024-046-01). Informed consent was obtained from all subjects involved in the study. Consent to publication Not applicable. Competing interests The authors declare no competing interest. References Mahase E: Coronavirus covid-19 has killed more people than SARS and MERS combined, despite lower case fatality rate . BMJ (Clinical research ed) 2020, 368 :m641. Shereen MA, Khan S, Kazmi A, Bashir N, Siddique R: COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses . Journal of advanced research 2020, 24 :91-98. Li X, Wang L, Yan S, Yang F, Xiang L, Zhu J, Shen B, Gong Z: Clinical characteristics of 25 death cases with COVID-19: A retrospective review of medical records in a single medical center, Wuhan, China . 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Indicators used in different panels for model construction Panels Indicators 1 Gender, age, days of hospital stay, whether admitted to the ICU, whether smoking, whether drinking, whether to use the ventilator, whether to use corticosteroids therapy, whether to use antifungal drugs, whether to use antiviral drugs, whether to use carbapenem antibiotics, whether to use penicillin plus enzyme inhibitor antibiotics, whether to use penicillin antibiotics, whether to use tigecycline, whether to use glycopeptide antibiotics, whether to use quinolone antibiotics, whether to use cephalosporin antibiotics, whether to use nitroimidazole antibiotics, whether to use aminoglycoside antibiotics 2 Procalcitonin (PCT), alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin 3 White blood cells, neutrophils, lymphocytes 4 Background diseases (diabetes, hypertension, chronic kidney disease, cardiovascular disease, chronic lung disease) 5 Types of pathogens ( Acinetobacter baumanni, Aspergillus spp., Klebsiella aerogenes, Klebsiella oxytoca, Escherichia coli, Klebsiella pneumoniae, Enterobacter cloacae, Candida spp., Staphylococcus spp., Pandora spp., Stenotrophomonas maltophilia, Citrobacter spp., Pseudomonas aeruginosa, Burkholderia cepacian, Serratia marcescens, Victoria grisea, Penicillium spp., Alternaria spp. ) Table 2. The basic information of the included patients. All patients (n=367) Patients with COVID-19 coinfection (n=197) Patients without COVID-19 coinfection (n=170) P value Features Age (years) 74 (11-100) 79 (11-100) 70 (17-98) <0.001 Gender <0.001 Male 247 (67.5%) 138 (69.7%) 109 (64.9%) Female 119 (32.5%) 60 (30.3%) 59 (35.1%) Days of hospital stay (days) 12.5 (1-154) 13 (2-154) 11 (1-149) <0.001 Background diseases Diabetes 129 (35.2%) 79 (39.9%) 50 (29.8%) 0.032 Hypertension Chronic kidney disease Cardiovascular diseases Chronic lung disease 185 (50.5%) 46 (12.6%) 79 (21.6%) 31 (8.5%) 103 (52.0%) 26 (13.1%) 39 (19.7%) 13 (6.6%) 83 (49.4%) 20 (11.9%) 40 (23.8%) 18 (10.7%) 0.508 0.679 0.386 0.171 Treatments Mechanical ventilation Corticosteroids Antifungal therapy Antiviral drugs Outcomes Total mortality Bacterial infection Fungal infection Bacterial and fungal coinfection 146 (39.8%) 208 (56.7%) 112 (30.5%) 113 (30.8%) 132 (35.3%) 72 (19.6%) 45 (12.2%) 15 (4.1%) 82 (41.6 %) 146 (74.1%) 63 (32.0%) 94 (47.7%) 80 (40.0%) 40 (20.3%) 27 (13.7%) 13 (6.6%) 64 (37.6%) 62 (36.5%) 49 (28.8%) 19 (11.2%) 52 (29.9%) 32 (18.9%) 18(10.6%) 2 (1.2%) 0.438 <0.001 0.513 <0.001 0.046 Table 3. Isolated fungi from the patient sputum samples. Pathogenic fungi All patients (n=192) Patients With COVID-19 coinfection (n=111) Patients without COVID-19 coinfection (n=81) P value Candida Aspergillus 175 (91.4%) 16 (8.3%) 99 (89.2%) 11 (9.9%) 76 (93.8%) 5 (6.2%) 0.264 0.355 Bacterial pathogens were isolated from 211 patients. The main strains were A. baumannii (93 cases, 44.1%), K. pneumoniae (83 cases, 39.3%), P. aeruginosa (42 cases, 19.9%), S. aureus (14 cases, 6.6%). The isolation rate of A. baumannii was higher in patients with COVID-19 than in those without COVID-19 (51.4% vs. 36.3%, P=0.027). There was no significant difference in the incidence of S. aureus infection between patients with or without COVID-19 (6.4% vs. 6.8%, P=0.898). The isolation rate of multi-drug resistant bacteria increased in COVID-19 patients. The detection rate of carbapenem-resistant A. baumannii (CRAB) in COVID-19 patients was higher than in those without COVID-19 (94.6% vs. 86.5%, P=0.011). For the COVID-19 patients, the resistance rates of CRAB to ceftazidime, ciprofloxacin, gentamicin, piperacillin-tazobactam were all more than 90%. Fortunately, most of the isolated CRAB were sensitive to tigecycline (90.2%). The isolation rates of carbapenem-resistant K. pneumoniae (CRKP) and carbapenem-resistant P. aeruginosa (CRPA) were similar (26.8% vs 26.2% and 28.6 vs 38.0%, respectively) between the two groups of patients. The detailed information was shown in Tables 4-6. Table 4. The isolated bacterial pathogens in the two groups of patients. Pathogenic bacteria All patients (n=211) Patients with COVID-19 (n=109) Patients without COVID-19 (n=102) P value A. baumannii 93 (44.1%) 56 (51.4%) 37 (36.3%) 0.027 K. pneumoniae 83 (39.3%) 41 (37.6%) 42 (41.2%) 0.597 P.s aeruginosa 42 (19.9%) 21 (19.3%) 21 (20.6%) 0.810 S. aureus 14 (6.6%) 7 (6.4%) 7 (6.8%) 0.898 CRAB 85 (40.3%) 53 (48.6%) 32 (31.4%) 0.011 CRKP 22 (10.4%) 11 (10.1%) 11 (10.8%) 0.869 CRPA 14 (6.6%) 6 (5.5%) 8 (7.8%) 0.495 Table 5. The antibiotic resistance rates of the major Gram-negative bacteria isolated in COVID-19 patients. Antibiotics Major isolated Gram-negative bacteria A. baumannii (n=56) K. pneumoniae (n=41) P. aeruginosa (n=21) E. coli (n=20) Cefepime 53 (94.6) 14 (34.1) 4 (19.0) 4 (20) Ceftazidime 53 (94.6) 16 (39.0) 5 (23.8) 5 (25) Amikacin 47 (83.8) 6 (14.6) 1 (4.8) 0 Levofloxacin 28 (50) 14 (34.1) 6 (28.6) 7 (35) Ciprofloxacin 53 (94.6) 17 (41.5) 6 (28.6) 8 (40) Gentamycin 53 (94.6) 12 (29.2) 3 (14.3) 5 (25) Piperacillin and tazobactam 53 (94.6) 15 (36.6) 2 (9.5) 3 (15) Meropenem 53 (94.6) 11 (26.7） 7 (33.3) 0 Imipenem 53 (94.6) 11 (26.7） 7 (33.3) 0 Tigecycline 5 (8.9) 1 (2.4) 0 1 (5) Table 6. The antibiotic resistance rates of the major Gram-positive bacteria isolated in COVID-19 patients Antibiotics Major Gram-positive bacteria, Staphylococcus aureus (n=7) Levofloxacin 2 (28.6) Ciprofloxacin 2 (28.6) Gentamycin 1 (14.3) Penicillin 5 (71.4) Oxacillin 1 (14.3) Erythromycin 5 (71.4) Clindamycin 5 (71.4) Table 7. Some important clinical laboratory test results. All patients (n=367) Patients with COVID-19 (n=197) Patients without COVID-19 (n=170) P value Leucocyte 8.8 (0.39-39.39) 8.67 (0.39-39.39) 8.785 (0.93-10) <0.001 Neutrophilic granulocyte 7.27 (0.24-78) 7.19 (0.24-36.3) 7.255 (0.6-8.3) <0.001 Lymphocyte 0.72 (0.1-4.36) 0.7 (0.11-3.24) 0.72 (0.1-0.85) 0.046 Procalcitonin 0.39 (0.04-100) 0.4 (0.04-100) 0.385 (0.04-100) 0.086 Interleukin-1 7.41 (5-641) 5.29 (5-115) 9.24 (5-641) 0.001 Interleukin -2 1023 (12.4-11044) 1064 (12.4-11044) 984 (110-3979) 0.783 Interleukin -6 29 (2-1000) 22.75 (2-1000) 42 (2-1000) 0.014 Interleukin -8 52 (5-7305) 42 (5-3521) 67 (8-7305) 0.003 Interleukin -10 6 (2.34-288) 5.44 (5-199) 7 (2.34-288) 0.629 TNF 18 (4-249) 16 (4-249) 24 (5-176) 0.001 Additional Declarations No competing interests reported. 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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-4632591","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":327652245,"identity":"103dbd48-5f36-420d-96fb-ff73bb975858","order_by":0,"name":"Jiaxin Liu","email":"","orcid":"","institution":"Second Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiaxin","middleName":"","lastName":"Liu","suffix":""},{"id":327652246,"identity":"dfbf4a1f-31a0-4513-8dc2-464741a8816a","order_by":1,"name":"Wenzhi Liu","email":"","orcid":"","institution":"Second Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenzhi","middleName":"","lastName":"Liu","suffix":""},{"id":327652247,"identity":"3e255a8f-4498-48af-8acd-58cf50674533","order_by":2,"name":"Pengchao Fan","email":"","orcid":"","institution":"Second Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Pengchao","middleName":"","lastName":"Fan","suffix":""},{"id":327652248,"identity":"62734850-0de9-41ce-b57f-49bb153a51fd","order_by":3,"name":"Rihong Huang","email":"","orcid":"","institution":"First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rihong","middleName":"","lastName":"Huang","suffix":""},{"id":327652249,"identity":"c9d3e530-8df6-4099-9be6-27ea0135647b","order_by":4,"name":"Yaolin Wen","email":"","orcid":"","institution":"Dongbei University of Finance and Economics","correspondingAuthor":false,"prefix":"","firstName":"Yaolin","middleName":"","lastName":"Wen","suffix":""},{"id":327652250,"identity":"566569a0-2a9d-4517-b85a-40046946268d","order_by":5,"name":"Yiming Gao","email":"","orcid":"","institution":"Dongbei University of Finance and Economics","correspondingAuthor":false,"prefix":"","firstName":"Yiming","middleName":"","lastName":"Gao","suffix":""},{"id":327652251,"identity":"cd529526-7efd-409c-881b-8b1e86c18051","order_by":6,"name":"Ruihua Li","email":"","orcid":"","institution":"Second Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ruihua","middleName":"","lastName":"Li","suffix":""},{"id":327652252,"identity":"9cb91a77-996f-4093-a6af-0f9956f58417","order_by":7,"name":"Peng Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArUlEQVRIiWNgGAWjYLCCD2xgyoB4HYwzSNbCzEOSFnn3w8ce25RtS2xgb94mwVBzh7AWwzNp6cY5524nNvAcK5NgOPaMCC0NOWbSuW1ALRI5ZhKMDYeJ0NL/xkzaEqRF/g2RWuSBhkszgm3hIVKLgcSzdMOec7eN23jSii0SjhFjS3/ysQc/ym7L9rMf3njjQw0xthxggEQKmEwgrAFoSwNUyygYBaNgFIwCnAAALoo4Iwi1EEEAAAAASUVORK5CYII=","orcid":"","institution":"Second Affiliated Hospital of Dalian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Peng","middleName":"","lastName":"Gao","suffix":""}],"badges":[],"createdAt":"2024-06-25 00:21:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4632591/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4632591/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60811058,"identity":"d22e1ee3-76e4-4d31-b986-a9f53d9b8503","added_by":"auto","created_at":"2024-07-22 10:51:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":372547,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart summary of the model construction\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4632591/v1/9017d247fc78721f15fb41b0.png"},{"id":60809627,"identity":"1df6dfbc-7a0c-467b-bb7b-45553ad3392e","added_by":"auto","created_at":"2024-07-22 10:43:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":91847,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the 5 models in view of their deviation distribution and AUCs. The individual median and interquartile range for each model were also indicated.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4632591/v1/26ccffe82ad8044e9c4e532c.png"},{"id":60809626,"identity":"d4c6c768-3f80-40e0-a705-a9b5169b3f16","added_by":"auto","created_at":"2024-07-22 10:43:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":127451,"visible":true,"origin":"","legend":"\u003cp\u003eAccuracy comparison before and after optimization: (a) XGBoost, (b) LightGBM.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4632591/v1/9c72de445b86f93d620b0dac.png"},{"id":60809625,"identity":"e4386e19-b3da-4951-8a20-33729c132a1b","added_by":"auto","created_at":"2024-07-22 10:43:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":112861,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the AUCs before and after optimization (a) XGBoost, (b) LightGBM. The individual median and interquartile range for each model were also indicated.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4632591/v1/146afd8b90708ac065e90589.png"},{"id":60809628,"identity":"0fd0be43-082f-4428-9e0a-892412922392","added_by":"auto","created_at":"2024-07-22 10:43:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":233980,"visible":true,"origin":"","legend":"\u003cp\u003eSHAP summary plots of the two models: (a) XGBoost, (b) LightGBM.\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4632591/v1/b86e7d1e9665f16a046ddd76.png"},{"id":60809629,"identity":"5f158365-5042-4a8a-95b1-e5638b580544","added_by":"auto","created_at":"2024-07-22 10:43:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":333465,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap representing the importance of each variable in every machine learning model.\u003c/p\u003e","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4632591/v1/c5dd0fe0e8ea6a562824fc01.png"},{"id":64458981,"identity":"0db218ae-bdfe-4ffd-bb9f-c249232f3ed8","added_by":"auto","created_at":"2024-09-13 12:20:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2902429,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4632591/v1/72491fac-66d0-4d65-b6a5-c0b8d5b1bc47.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Respiratory Tract Pathogen Profiles of COVID-19 Pneumonia Patients and the Mortality Prediction","fulltext":[{"header":"Background","content":"\u003cp\u003eSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first reported in Wuhan in 2019 and rapidly developed to be a global pandemic coined as coronavirus infection disease \u0026minus;\u0026thinsp;19 (COVID-19) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This virus usually attacks the respiratory system and causes COVID-19 pneumonia. Some patients are readily to develop acute respiratory distress syndrome, including pulmonary fibrosis and severe pneumonia [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Due to the damage to the lower respiratory tract mucosa and the immune escape ability of the virus, SARS-CoV-2 infection is easily complicated with other microorganism infections, which aggravates the severity of the disease and increases medical expenses [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCOVID-19 pneumonia combined with bacterial and/or fungal infections were common complications in hospitalized patients [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Secondary bacterial infections (SBI) were significantly associated with the degree of poor prognosis [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The situation might be even worse for the patients admitted to intensive care units (ICU). Among the COVID-ve19 pneumonia patients with SBI, 55% developed septic shock, 29% developed refractory respiratory failure, and the total mortality reached 54% [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, establishing a prediction model for the prognosis of COVID-19 pneumonia patients might provide valuable information for avoiding or alleviating poor prognosis [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. To this end, many prediction models have been developed. Kim et al. constructed a model to predict the possibility of ICU admission for COVID-19 patients. Although the model addressed using the easily accessible parameters, performed well and was thought to be superior to CURB-65 score, it did not predict patient mortality in the ICU [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Wan et al. identified age, lifestyle, illness, income, and family disease history were key parameters to predict COVID-19 mortality. The area under the receiver operating characteristic curve (AUC) was 0.86 (95% CI 0.84\u0026ndash;0.88), but it did not consider the presence of SBI [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCOVID-19 sporadically outbreaks in certain countries currently. With the worldwide vaccination projects and the decrease of pathogenicity of SARS-CoV-2 variants, most of the affected patients showed mild symptoms and good prognosis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. More attention should be paid to patients with severe illness, e.g., those co-infected with bacteria and/or fungi [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In this light, this study considered the infection parameters to construct a mortality prediction model. It was found that the inclusion of those parameters improved the prediction ability and the prediction accuracy was up to 90%.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eSubjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e211 bacterial culture-positive and 192 fungal culture-positive results were collected from 170 non-SARS-CoV-2 infected patients and 197 SARS-CoV-2 infected patients. The patients were hospitalized at the 2\u003csup\u003end\u003c/sup\u003e Hospital of Dalian Medical University from Dec.1, 2022 to Feb.28, 2023. All the patients were diagnosed with pneumonia through chest CT and clinical laboratory results and subjected to reverse transcription quantitative real-time polymerase chain reaction using nasopharyngeal swab samples. SARS-CoV-2 infected patient was diagnosed with a positive amplification result. Subjects with the same sputum culture results within 7 days were kept only once. Antibiotic susceptibility tests were performed as recommended by the K-B method or dilution method\u0026nbsp;[15].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cstrong\u003eanalysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe quantitation data qualified to normal distribution were expressed as mean\u0026plusmn;s.d. and the comparison between independent samples was analyzed by two independent sample t-test. The paired data were analyzed by paired t-tests. Data of non-normal distribution were expressed as median and compared by rank sum test. Count data were analyzed using the chi-square test. A p-value \u0026lt;0.05 was considered statistically significant. All the statistical analysis was carried out using SPSS 26.0((Developed by IBM Co., Chicago, USA).).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMachine\u003cem\u003e\u0026nbsp;learning methods and the model construction\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 49 indicators including demographic data, laboratory examination results, species of pathogens isolated from the respiratory tract and treatment strategies were collected. The dataset was divided into five panels according to feature attributes as shown in Table 1. For prediction model construction the \u0026quot;outcome\u0026quot; was defined as patient death that occurred within 30 days after being admitted into the wards. Each model was built based on the sequential accumulation of the 5 panels\u0026rsquo; data (Table 1).\u003c/p\u003e\n\u003cp\u003eAll the patients were randomly divided into two parts, in which 80% of them were used to train each model and 20% were used to validate the performance of each model. The considered modeling algorithms included Decision Tree (DT), Categorical Boosting (CatBoost), Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM). The predictive abilities of the models were analyzed and compared according to their areas under the receiver operating characteristic curve (AUC) values. The good performance models were subjected to the Tree-structured Parzen Estimator (TPE) analysis by Bayesian optimization (BO) to find the optimal parameter combination and improve the prediction accuracy. The most optimized model was validated by the validation set data. The kept parameters in the best model were subjected to analysis by the Permutation Feature Importance (PFI) method to calculate the importance of each variable, and the Shapley Additive exPlanations (SHAP) method to analyze the contribution of different parameters to the model output. A flow chart of the modeling process was shown in Fig. 1.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDemographic data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 367 patients were included in this study. The median age was 74 years (11-100 years). 247 patients were male (67.5%). Compared with patients without COVID-19, patients with COVID-19 were older (79 vs. 70, P\u0026lt;0.001) and had more days of hospital stay (13 vs. 11, P\u0026lt;0.001). Most of the patients had background diseases (\u0026gt;50%), including hypertension (50.5%), diabetes (35.2%), cardiovascular and cerebrovascular diseases (21.6%), chronic kidney disease (12.6%), and chronic lung disease (8.5%). Patients with COVID-19 were more likely to have diabetes (39.9% vs.29.8%, P=0.032). All the pneumonia patients had the experience of receiving antibiotics, corticosteroids, antiviral agents, antifungal agents, and mechanical ventilation alone or in combination throughout their hospitalization. Patients with COVID-19 were more likely to be treated with antiviral drugs (47.7% vs 11.2%) and corticosteroids (74.1% vs 36.5%) (P\u0026lt;0.001). Detailed information was given in Table 2.\u003c/p\u003e\n\u003cp\u003eOutcomes\u003c/p\u003e\n\u003cp\u003eIn this study, the mortality rate of COVID-19 combined with SBI was 40.0% (n = 80) as shown in Table 2. Among them, 40 cases (50.0%) died of bacterial infection, 27 cases (33.8%) died of fungal infection, and 13 cases (16.2%) died of bacterial and fungal coinfection. Mortality was increased in patients with COVID-19 than in patients without COVID-19 (40.0% vs. 29.9%, P=0.046).\u003c/p\u003e\n\u003cp\u003ePathogen profiles\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCandida\u003c/em\u003e \u003cem\u003espp\u003c/em\u003e. (175 cases, 91.4%) and \u003cem\u003eAspergillus\u003c/em\u003e \u003cem\u003espp\u003c/em\u003e. (16 cases, 8.3%) were the most common fungi isolated from sputum samples of 192 patients. There was no significant difference in the incidence of \u003cem\u003eCandida\u003c/em\u003e and \u003cem\u003eAspergillus\u003c/em\u003e infection between patients with or without COVID-19 (Table 3).\u003c/p\u003e\n\u003cp\u003eBacterial pathogens were isolated from 211 patients. The main strains were \u003cem\u003eA. baumannii\u003c/em\u003e (93 cases, 44.1%), \u003cem\u003eK. pneumoniae\u003c/em\u003e (83 cases, 39.3%), \u003cem\u003eP. aeruginosa\u003c/em\u003e (42 cases, 19.9%), \u003cem\u003eS. aureus\u003c/em\u003e (14 cases, 6.6%). The isolation rate of \u003cem\u003eA. baumannii\u003c/em\u003e was higher in patients with COVID-19 than in those without COVID-19 (51.4% vs. 36.3%, P=0.027). There was no significant difference in the incidence of \u003cem\u003eS. aureus\u003c/em\u003e infection between patients with or without COVID-19 (6.4% vs. 6.8%, P=0.898). The isolation rate of multi-drug resistant bacteria increased in COVID-19 patients. The detection rate of carbapenem-resistant \u003cem\u003eA. baumannii\u003c/em\u003e (CRAB) in COVID-19 patients was higher than in those without COVID-19 (94.6% vs. 86.5%, P=0.011). For the COVID-19 patients, the resistance rates of CRAB to ceftazidime, ciprofloxacin, gentamicin, piperacillin-tazobactam were all more than 90%. Fortunately, most of the isolated CRAB were sensitive to tigecycline (90.2%). The isolation rates of carbapenem-resistant \u003cem\u003eK. pneumoniae\u003c/em\u003e (CRKP) and carbapenem-resistant \u003cem\u003eP. aeruginosa\u003c/em\u003e (CRPA) were similar (26.8% vs 26.2% and 28.6 vs 38.0%, respectively) between the two groups of patients.\u0026nbsp;The detailed information was shown in Tables 4-6.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClinical laboratory test results\u003c/p\u003e\n\u003cp\u003eIn Table 7, it was clear that white blood cells, neutrophils, lymphocytes, interleukin-1, interleukin-6, interleukin-8, and TNF were lower (P\u0026lt;0.05) in the patients with COVID-19 compared to in the patients without COVID-19.\u003c/p\u003e\n\u003cp\u003eConstruction of the prediction models\u003c/p\u003e\n\u003cp\u003eBecause the DT and CatBoost models were not accurate and stable,\u0026nbsp;they\u0026nbsp;were excluded initially. After every 10 iterations for each model, boxplots (Fig.. 2) were used to comprehensively compare the stability and performance of the left five models. The AUC values of the five models were all above 0.6, and the median values were all above 0.7. Specifically, the XGBoost model exhibited the shortest variation distribution range in the boxplot (excluding the outlier with an AUC value of 0.6310). Its median AUC value was 0.7761, indicating that this model was more stable compared to others. The stabilities of LR model and RF model were ranked as second and third. Although SVM had the highest AUC value (0.9464), its deviation distribution was wide, indicating that the stability was relatively\u0026nbsp;poor. Although the difference in AUC values amid the models was not significant, XGBoost model and LightGBM model had superior advantages when considering computational efficiency, dealing with complex relationships, providing feature importance information and parameter tunable range (data not shown). Therefore, we further selected the XGBoost model and LightGBM model for BO to ensure better prediction performance in practice.\u003c/p\u003e\n\u003cp\u003eGiven that\u0026nbsp;LightGBM\u0026nbsp;and XGBoost had a large space for parameter optimization, we further combined each model with the TPE method for hyperparameter optimization. For both LightGBM and XGBoost models, the accuracy of the BO-optimized models was higher than that before optimization (Fig. 3). The AUC value of XGBoost increased from 0.8960 to 0.9493 (Fig. 4a), and the AUC value of LightGBM increased from 0.8972 to 0.9699 (Fig. 4b). Finally, the prediction accuracy of both models reached up to 90% tested by the validation data (Fig. 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe SHAP summary plots based on the optimal parameter combinations identified the key risk factors dominating the predictive model (Fig. 5). Each point represented a sample, and the color of the point represented the relative significance of the eigenvalues, with red indicating high eigenvalues and blue indicating low eigenvalues. Taking whether to use ventilator treatment as an example, a large number of red samples were clustered in the area with negative SHAP values, which meant that if the patient received ventilator treatment (marked as 1), SHAP values would be low. The magnitude of SHAP values indicated the degree of influence on the prediction results. The greater the absolute values of SHAP, the greater the impact of this variable on the outcome of the patient\u0026rsquo;s survival. Fig. 6 showed the variable importance changes for each established model before and after optimization using the 49 indicators (variables). The greater the importance value of the variable, the greater the impact on the survival rate of the patients. In this light, intensive care unit admission, days of hospital stay, ventilator use, carbapenem use, lymphocyte count, AST value, \u003cem\u003eA. baumannii\u003c/em\u003e infection, and \u003cem\u003eCandida\u003c/em\u003e infection had important effects on patient survival.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMany studies had indicated that the dominant pathogenic strains isolated in COVID-19 pneumonia patients were different compared to the common nosocomial or community-acquired pneumonia patients\u0026nbsp;[16-18]. For our studied non-COVID-19 patients the major isolated strains were \u003cem\u003eK. pneumoniae\u003c/em\u003e (Table 4). \u003cem\u003eA. baumannii\u003c/em\u003e is an opportunistic pathogen that often causes nosocomial infections, especially in intensive care units\u0026nbsp;[19]. Patients with COVID-19 were more susceptible to \u003cem\u003eA. baumannii\u0026nbsp;\u003c/em\u003einfection and death due to their high proportion of ICU admissions, longer hospital stays, carbapenem antibiotics administration, and mechanical ventilation\u0026nbsp;[1, 2].\u0026nbsp;Isolation rates of \u003cem\u003eA. baumannii\u003c/em\u003e in COVID-19 patients who were admitted to ICU always ranked the first and 50% to 85.7% of the victims would die\u0026nbsp;[20-22]. The isolated \u003cem\u003eA. baumannii\u003c/em\u003e often showed carbapenem-resistance phenotypes due to the production of acquired \u0026beta;-lactamase\u0026nbsp;[19]. Not limited to that, multi-drug resistant strains were more prevalent in those patients\u0026nbsp;[23]. Resistance of \u003cem\u003eA. baumannii\u003c/em\u003e to carbapenem was an important concern because this type of antibiotic is the last line of defense in the treatment of infections caused by multidrug-resistant Gram-negative bacteria\u0026nbsp;[19]. Empirical use of antibiotics for COVID-19 patients increased the possibility of antibiotic-resistant \u003cem\u003eA. baumannii\u003c/em\u003e [19]. Facilitates used in the mechanical ventilation treatment were favorable to \u003cem\u003eA. baumannii\u003c/em\u003e colonization and the formation of biofilm\u0026nbsp;[24]. Bacterial biofilm prompted the emergence of antibiotic resistance and multi-drug resistance strains\u0026nbsp;[25]. Collectively, admission to the ICU, days of hospital stay, ventilator-aided treatment, carbapenems administration, and \u003cem\u003eA. baumannii\u003c/em\u003e infection all added extra insult to COVID-19 pneumonia. Thus, it was rational that the prediction models kept all the above-mentioned parameters (Fig. 5), and they all played key roles in the death prediction (Fig. 6).\u003c/p\u003e\n\u003cp\u003eThe isolation rates of CRAB and \u003cem\u003eK. pneumoniae\u003c/em\u003e were comparable to the previous reports\u0026nbsp;[26]. Whereas the isolation of CRKP in this study was lower than in the other studies\u0026nbsp;[26].\u0026nbsp;This decline might be attributed to improved hand hygiene during the pandemic, extensive disinfection of the environment, use of masks, keeping social distance, and reduced empirical use of antibiotics in the hospital during the period of the Chinese pandemic\u0026nbsp;[19]. According to Table 5, if timely sputum culture results were not available the choice of empirical antimicrobial regimen might focus on the treatment of \u003cem\u003eA. baumannii\u003c/em\u003e and combined recipes might be more feasible.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCo-infection with SARS-CoV-2 had no significant effect on the isolated species of fungal pathogens in our studied populations. We did not find the infection rate difference between the COVID-19 and non-COVID-19 patients (Table 2). This study reconfirmed the findings that \u003cem\u003eC. albicans\u003c/em\u003e were the predominant isolations in fungal infections\u0026nbsp;[27]. Different from the analysis carried out before the SARS-CoV-2 pandemic, except \u003cem\u003eA. fumigatus\u003c/em\u003e, a large proportion of other \u003cem\u003eAspergillus\u003c/em\u003e species infection and mixed infection of \u003cem\u003eAspergillus\u003c/em\u003e and \u003cem\u003eCandida\u003c/em\u003e were found in our study. We did not find a difference in antifungal therapies compared to the previous report\u0026nbsp;[28].\u0026nbsp;Whereas, the auxiliary measures were adopted more in our studied patients than in the pre-pandemic patients\u0026nbsp;[28]. These operations might contribute to the fungal infection chance. Intubation e.g., placement of a central venous catheter, was reported to increase the susceptibility to \u003cem\u003eCandida\u003c/em\u003e invasion in patients with COVID-19\u0026nbsp;[29]. Death rates of \u003cem\u003eCandida\u003c/em\u003e-affected COVID-19 patients were about 35.0% to 76.3%[30]. For severe COVID-19 pneumonia patients, the prognosis might be even worse when the patients were affected by fungal infection\u0026nbsp;[31]. Thus, it was imaginable that \u003cem\u003eCandida\u0026nbsp;\u003c/em\u003einfection was kept as a key risk factor for patient death (Figs 5,6).\u003c/p\u003e\n\u003cp\u003eIn our model, the only left parameter about antibiotics was the administration of Carbapenems. It was positively related to the death risk. In theory, broad spectrum antibiotic application should facilitate pathogenic bacteria eradication. Considering the antibiotic susceptibility test results of the major isolated bacteria, the Carbapenems administration did not work well (Table 5). Additionally, this kind of antibiotic administration might facilitate the emergence of multi-drug resistant strains of other species. Thus, precise prescription of antibiotics aiming at special bacteria was crucial to avoid a worse prognosis.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe death risks were closely linked to the secondary infection for COVID-19 patients. Thus, in this study, we took the relative parameters into consideration to predict the death event. Clearly, over half of the relevant parameters were kept in the prediction model. Validated by another set of data, both of the models performed well. Compared to the previous studies, our model was more applicable to COVID-19 patients with SBI. It also should be acknowledged that our study only included a limited scale of patients. All the included patients were hospitalized during the pandemic. Currently, many clinical guidelines have been modified. The applicability of the model was warranted to be tested and improved based on a broad range of future patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e Thanks to Mr. Hao Wang of Dongbei University of Finance and Economics for his help.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s Contributions\u003c/strong\u003e Conceptualization, Ruihua Li and Peng Gao; methodology, Jiaxin Liu ; software, Yaolin Wen; validation, Jiaxin Liu, Yaolin Wen and Yiming Gao; formal analysis, Pengchao Fan; investigation, Rihong Huang; resources, Rihong Huang; data curation, Ruihua Li; writing\u0026mdash;original draft preparation, Jiaxin Liu; writing\u0026mdash;review and editing, Ruihua Li; visualization, Yaolin Wen; supervision, Peng Gao; project administration, Ruihua Li; funding acquisition, Peng Gao, Wenzhi Liu. All authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e This research was funded by \u0026quot;Touching China\u0026quot; Infection Prevention and Control Research Project, Institute of Hospital Administration, National Health Commission, grant number GY2023043.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003eThe datasets that support the findings of this study are available from the corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003eThe study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the Second Hospital of Dalian Medical University (KY2024-046-01).\u0026nbsp;Informed consent was obtained from all subjects involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e The authors declare no competing interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMahase E: \u003cstrong\u003eCoronavirus covid-19 has killed more people than SARS and MERS combined, despite lower case fatality rate\u003c/strong\u003e. \u003cem\u003eBMJ (Clinical research ed) \u003c/em\u003e2020, \u003cstrong\u003e368\u003c/strong\u003e:m641.\u003c/li\u003e\n\u003cli\u003eShereen MA, Khan S, Kazmi A, Bashir N, Siddique R: \u003cstrong\u003eCOVID-19 infection: Origin, transmission, and characteristics of human coronaviruses\u003c/strong\u003e. \u003cem\u003eJournal of advanced research \u003c/em\u003e2020, \u003cstrong\u003e24\u003c/strong\u003e:91-98.\u003c/li\u003e\n\u003cli\u003eLi X, Wang L, Yan S, Yang F, Xiang L, Zhu J, 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\u003cstrong\u003eCOVID-19 associated with pulmonary aspergillosis: A literature review\u003c/strong\u003e. \u003cem\u003eJournal of microbiology, immunology, and infection = Wei mian yu gan ran za zhi \u003c/em\u003e2021, \u003cstrong\u003e54\u003c/strong\u003e(1):46-53.\u003c/li\u003e\n\u003cli\u003eMina S, Yaakoub H, Annweiler C, Dub\u0026eacute;e V, Papon N: \u003cstrong\u003eCOVID-19 and Fungal infections: a double debacle\u003c/strong\u003e. \u003cem\u003eMicrobes and infection \u003c/em\u003e2022, \u003cstrong\u003e24\u003c/strong\u003e(8):105039.\u003c/li\u003e\n\u003cli\u003eNegm EM, Mohamed MS, Rabie RA, Fouad WS, Beniamen A, Mosallem A, Tawfik AE, Salama HM: \u003cstrong\u003eFungal infection profile in critically ill COVID-19 patients: a prospective study at a large teaching hospital in a middle-income country\u003c/strong\u003e. \u003cem\u003eBMC infectious diseases \u003c/em\u003e2023, \u003cstrong\u003e23\u003c/strong\u003e(1):246.\u003c/li\u003e\n\u003cli\u003eEzeokoli OT, Pohl CH: \u003cstrong\u003eOpportunistic pathogenic fungal co-infections are prevalent in critically ill COVID-19 patients: Are they risk factors for disease severity?\u003c/strong\u003e \u003cem\u003eSouth African medical journal = Suid-Afrikaanse tydskrif vir geneeskunde \u003c/em\u003e2020, \u003cstrong\u003e110\u003c/strong\u003e(11):1081-1085.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;1.\u0026nbsp;\u003c/strong\u003eIndicators used in different panels for model construction\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePanels\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84.62929475587704%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndicators\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84.62929475587704%\"\u003e\n \u003cp\u003eGender, age, days of hospital stay, whether admitted to the ICU, whether smoking, whether drinking, whether to use the ventilator, whether to use corticosteroids therapy, whether to use antifungal drugs, whether to use antiviral drugs, whether to use carbapenem antibiotics, whether to use penicillin plus enzyme inhibitor antibiotics, whether to use penicillin antibiotics, whether to use tigecycline, whether to use glycopeptide antibiotics, whether to use quinolone antibiotics, whether to use cephalosporin antibiotics, whether to use nitroimidazole antibiotics, whether to use aminoglycoside antibiotics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84.62929475587704%\"\u003e\n \u003cp\u003eProcalcitonin (PCT), alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84.62929475587704%\"\u003e\n \u003cp\u003eWhite blood cells, neutrophils, lymphocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84.62929475587704%\"\u003e\n \u003cp\u003eBackground diseases (diabetes, hypertension, chronic kidney disease, cardiovascular disease, chronic lung disease)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.370705244122966%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84.62929475587704%\"\u003e\n \u003cp\u003eTypes of pathogens (\u003cem\u003eAcinetobacter baumanni, Aspergillus spp., Klebsiella aerogenes, Klebsiella oxytoca, Escherichia coli, Klebsiella pneumoniae, Enterobacter cloacae, Candida spp., Staphylococcus spp., Pandora spp., Stenotrophomonas maltophilia, Citrobacter spp., Pseudomonas aeruginosa, Burkholderia cepacian, Serratia marcescens, Victoria grisea, Penicillium spp., Alternaria spp.\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eThe basic information of the included patients.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"680\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.794117647058822%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.61764705882353%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;All patients\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(n=367)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.205882352941178%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003cstrong\u003ePatients with COVID-19 coinfection\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(n=197)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.205882352941178%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Patients without COVID-19 coinfection\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(n=170)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.176470588235293%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.794117647058822%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFeatures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.61764705882353%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.205882352941178%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.205882352941178%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.176470588235293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.794117647058822%\" valign=\"top\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.61764705882353%\" valign=\"top\"\u003e\n \u003cp\u003e74 (11-100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.205882352941178%\" valign=\"top\"\u003e\n \u003cp\u003e79 (11-100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.205882352941178%\" valign=\"top\"\u003e\n \u003cp\u003e70 (17-98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.176470588235293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.794117647058822%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.61764705882353%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.205882352941178%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.205882352941178%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.176470588235293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.794117647058822%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.61764705882353%\" valign=\"top\"\u003e\n \u003cp\u003e247 (67.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.205882352941178%\" valign=\"top\"\u003e\n \u003cp\u003e138 (69.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.205882352941178%\" valign=\"top\"\u003e\n \u003cp\u003e109 (64.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.176470588235293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.794117647058822%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.61764705882353%\" valign=\"top\"\u003e\n \u003cp\u003e119 (32.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.205882352941178%\" valign=\"top\"\u003e\n \u003cp\u003e60 (30.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.205882352941178%\" valign=\"top\"\u003e\n \u003cp\u003e59 (35.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.176470588235293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.794117647058822%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDays of hospital stay (days)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.61764705882353%\"\u003e\n \u003cp\u003e12.5 (1-154)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.205882352941178%\"\u003e\n \u003cp\u003e13 (2-154)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.205882352941178%\"\u003e\n \u003cp\u003e11 (1-149)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.176470588235293%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.794117647058822%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e \u003cstrong\u003ediseases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.61764705882353%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.205882352941178%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.205882352941178%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.176470588235293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.794117647058822%\"\u003e\n \u003cp\u003e\u0026nbsp;Diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.61764705882353%\"\u003e\n \u003cp\u003e129 (35.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.205882352941178%\"\u003e\n \u003cp\u003e79 (39.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.205882352941178%\"\u003e\n \u003cp\u003e50 (29.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.176470588235293%\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.794117647058822%\"\u003e\n \u003cp\u003e\u0026nbsp;Hypertension\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Chronic kidney disease\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Cardiovascular diseases\u003c/p\u003e\n \u003cp\u003eChronic lung disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.61764705882353%\"\u003e\n \u003cp\u003e185 (50.5%)\u003c/p\u003e\n \u003cp\u003e46 (12.6%)\u003c/p\u003e\n \u003cp\u003e79 (21.6%)\u003c/p\u003e\n \u003cp\u003e31 (8.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.205882352941178%\"\u003e\n \u003cp\u003e103 (52.0%)\u003c/p\u003e\n \u003cp\u003e26 (13.1%)\u003c/p\u003e\n \u003cp\u003e39 (19.7%)\u003c/p\u003e\n \u003cp\u003e13 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.205882352941178%\"\u003e\n \u003cp\u003e83 (49.4%)\u003c/p\u003e\n \u003cp\u003e20 (11.9%)\u003c/p\u003e\n \u003cp\u003e40 (23.8%)\u003c/p\u003e\n \u003cp\u003e18 (10.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.176470588235293%\"\u003e\n \u003cp\u003e0.508\u003c/p\u003e\n \u003cp\u003e0.679\u003c/p\u003e\n \u003cp\u003e0.386\u003c/p\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.794117647058822%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatments\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Mechanical ventilation\u003c/p\u003e\n \u003cp\u003eCorticosteroids\u003c/p\u003e\n \u003cp\u003eAntifungal therapy\u003c/p\u003e\n \u003cp\u003eAntiviral drugs\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOutcomes\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eTotal mortality\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Bacterial infection\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Fungal infection\u003c/p\u003e\n \u003cp\u003eBacterial and fungal coinfection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.61764705882353%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e146 (39.8%)\u003c/p\u003e\n \u003cp\u003e208 (56.7%)\u003c/p\u003e\n \u003cp\u003e112 (30.5%)\u003c/p\u003e\n \u003cp\u003e113 (30.8%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e132 (35.3%)\u003c/p\u003e\n \u003cp\u003e72 (19.6%)\u003c/p\u003e\n \u003cp\u003e45 (12.2%)\u003c/p\u003e\n \u003cp\u003e15 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.205882352941178%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e82 (41.6 %)\u003c/p\u003e\n \u003cp\u003e146 (74.1%)\u003c/p\u003e\n \u003cp\u003e63 (32.0%)\u003c/p\u003e\n \u003cp\u003e94 (47.7%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e80 (40.0%)\u003c/p\u003e\n \u003cp\u003e40 (20.3%)\u003c/p\u003e\n \u003cp\u003e27 (13.7%)\u003c/p\u003e\n \u003cp\u003e13 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.205882352941178%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e64 (37.6%)\u003c/p\u003e\n \u003cp\u003e62 (36.5%)\u003c/p\u003e\n \u003cp\u003e49 (28.8%)\u003c/p\u003e\n \u003cp\u003e19 (11.2%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e52 (29.9%)\u003c/p\u003e\n \u003cp\u003e32 (18.9%)\u003c/p\u003e\n \u003cp\u003e18(10.6%)\u003c/p\u003e\n \u003cp\u003e2 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.176470588235293%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e0.513\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Isolated fungi from the patient sputum samples.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"662\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.844175491679273%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Pathogenic fungi\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.667170953101362%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll patients\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(n=192)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.146747352496217%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients With COVID-19 coinfection\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(n=111)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.844175491679273%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients without COVID-19 coinfection\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(n=81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.497730711043873%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.844175491679273%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003cem\u003eCandida\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003cem\u003eAspergillus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.667170953101362%\" valign=\"top\"\u003e\n \u003cp\u003e175 (91.4%)\u003c/p\u003e\n \u003cp\u003e16 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.146747352496217%\" valign=\"top\"\u003e\n \u003cp\u003e99 (89.2%)\u003c/p\u003e\n \u003cp\u003e11 (9.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.844175491679273%\" valign=\"top\"\u003e\n \u003cp\u003e76 (93.8%)\u003c/p\u003e\n \u003cp\u003e5 (6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.497730711043873%\" valign=\"top\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBacterial pathogens were isolated from 211 patients. The main strains were \u003cem\u003eA. baumannii\u003c/em\u003e (93 cases, 44.1%), \u003cem\u003eK. pneumoniae\u003c/em\u003e (83 cases, 39.3%), \u003cem\u003eP. aeruginosa\u003c/em\u003e (42 cases, 19.9%), \u003cem\u003eS. aureus\u003c/em\u003e (14 cases, 6.6%). The isolation rate of \u003cem\u003eA. baumannii\u003c/em\u003e was higher in patients with COVID-19 than in those without COVID-19 (51.4% vs. 36.3%, P=0.027). There was no significant difference in the incidence of \u003cem\u003eS. aureus\u003c/em\u003e infection between patients with or without COVID-19 (6.4% vs. 6.8%, P=0.898). The isolation rate of multi-drug resistant bacteria increased in COVID-19 patients. The detection rate of carbapenem-resistant \u003cem\u003eA. baumannii\u003c/em\u003e (CRAB) in COVID-19 patients was higher than in those without COVID-19 (94.6% vs. 86.5%, P=0.011). For the COVID-19 patients, the resistance rates of CRAB to ceftazidime, ciprofloxacin, gentamicin, piperacillin-tazobactam were all more than 90%. Fortunately, most of the isolated CRAB were sensitive to tigecycline (90.2%). The isolation rates of carbapenem-resistant \u003cem\u003eK. pneumoniae\u003c/em\u003e (CRKP) and carbapenem-resistant \u003cem\u003eP. aeruginosa\u003c/em\u003e (CRPA) were similar (26.8% vs 26.2% and 28.6 vs 38.0%, respectively) between the two groups of patients. The detailed information was shown in Tables 4-6.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eThe isolated bacterial pathogens in the two groups of patients.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"671\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.825633383010434%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Pathogenic bacteria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.84053651266766%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003cstrong\u003eAll patients\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(n=211)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.994038748137108%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients with COVID-19\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(n=109)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.503725782414307%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients without COVID-19\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(n=102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.836065573770492%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.825633383010434%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eA. baumannii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.84053651266766%\" valign=\"top\"\u003e\n \u003cp\u003e93 (44.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.994038748137108%\" valign=\"top\"\u003e\n \u003cp\u003e56 (51.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.503725782414307%\" valign=\"top\"\u003e\n \u003cp\u003e37 (36.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.836065573770492%\" valign=\"top\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.825633383010434%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;K. pneumoniae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.84053651266766%\" valign=\"top\"\u003e\n \u003cp\u003e83 (39.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.994038748137108%\" valign=\"top\"\u003e\n \u003cp\u003e41 (37.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.503725782414307%\" valign=\"top\"\u003e\n \u003cp\u003e42 (41.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.836065573770492%\" valign=\"top\"\u003e\n \u003cp\u003e0.597\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.825633383010434%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP.s aeruginosa\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.84053651266766%\" valign=\"top\"\u003e\n \u003cp\u003e42 (19.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.994038748137108%\" valign=\"top\"\u003e\n \u003cp\u003e21 (19.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.503725782414307%\" valign=\"top\"\u003e\n \u003cp\u003e21 (20.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.836065573770492%\" valign=\"top\"\u003e\n \u003cp\u003e0.810\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.825633383010434%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;S. aureus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.84053651266766%\" valign=\"top\"\u003e\n \u003cp\u003e14 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.994038748137108%\" valign=\"top\"\u003e\n \u003cp\u003e7 (6.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.503725782414307%\" valign=\"top\"\u003e\n \u003cp\u003e7 (6.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.836065573770492%\" valign=\"top\"\u003e\n \u003cp\u003e0.898\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.825633383010434%\" valign=\"top\"\u003e\n \u003cp\u003eCRAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.84053651266766%\" valign=\"top\"\u003e\n \u003cp\u003e85 (40.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.994038748137108%\" valign=\"top\"\u003e\n \u003cp\u003e53 (48.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.503725782414307%\" valign=\"top\"\u003e\n \u003cp\u003e32 (31.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.836065573770492%\" valign=\"top\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.825633383010434%\" valign=\"top\"\u003e\n \u003cp\u003eCRKP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.84053651266766%\" valign=\"top\"\u003e\n \u003cp\u003e22 (10.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.994038748137108%\" valign=\"top\"\u003e\n \u003cp\u003e11 (10.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.503725782414307%\" valign=\"top\"\u003e\n \u003cp\u003e11 (10.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.836065573770492%\" valign=\"top\"\u003e\n \u003cp\u003e0.869\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.825633383010434%\" valign=\"top\"\u003e\n \u003cp\u003eCRPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.84053651266766%\" valign=\"top\"\u003e\n \u003cp\u003e14 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.994038748137108%\" valign=\"top\"\u003e\n \u003cp\u003e6 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.503725782414307%\" valign=\"top\"\u003e\n \u003cp\u003e8 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.836065573770492%\" valign=\"top\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u0026nbsp;\u003c/strong\u003eThe antibiotic resistance rates of the major Gram-negative bacteria isolated in COVID-19 patients.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"709\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.232722143864597%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntibiotics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84.76727785613541%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eMajor isolated Gram-negative bacteria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.578073089700997%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eA. baumannii\u003c/em\u003e\u003c/strong\u003e (n=56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.07308970099668%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eK. pneumoniae\u003c/em\u003e\u003c/strong\u003e (n=41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.578073089700997%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP. aeruginosa\u003c/em\u003e\u003c/strong\u003e (n=21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.770764119601328%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eE. coli\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e(n=20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.211267605633802%\"\u003e\n \u003cp\u003eCefepime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003e53 (94.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.802816901408452%\"\u003e\n \u003cp\u003e14 (34.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003e4 (19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.915492957746478%\"\u003e\n \u003cp\u003e4 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.211267605633802%\"\u003e\n \u003cp\u003eCeftazidime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003e53 (94.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.802816901408452%\"\u003e\n \u003cp\u003e16 (39.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003e5 (23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.915492957746478%\"\u003e\n \u003cp\u003e5 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.211267605633802%\"\u003e\n \u003cp\u003eAmikacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003e47 (83.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.802816901408452%\"\u003e\n \u003cp\u003e6 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003e1 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.915492957746478%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.211267605633802%\"\u003e\n \u003cp\u003eLevofloxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003e28 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.802816901408452%\"\u003e\n \u003cp\u003e14 (34.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003e6 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.915492957746478%\"\u003e\n \u003cp\u003e7 (35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.211267605633802%\"\u003e\n \u003cp\u003eCiprofloxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003e53 (94.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.802816901408452%\"\u003e\n \u003cp\u003e17 (41.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003e6 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.915492957746478%\"\u003e\n \u003cp\u003e8 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.211267605633802%\"\u003e\n \u003cp\u003eGentamycin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003e53 (94.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.802816901408452%\"\u003e\n \u003cp\u003e12 (29.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003e3 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.915492957746478%\"\u003e\n \u003cp\u003e5 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.211267605633802%\"\u003e\n \u003cp\u003ePiperacillin and tazobactam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003e53 (94.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.802816901408452%\"\u003e\n \u003cp\u003e15 (36.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003e2 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.915492957746478%\"\u003e\n \u003cp\u003e3 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.211267605633802%\"\u003e\n \u003cp\u003eMeropenem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003e53 (94.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.802816901408452%\"\u003e\n \u003cp\u003e11 (26.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003e7 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.915492957746478%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.211267605633802%\"\u003e\n \u003cp\u003eImipenem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003e53 (94.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.802816901408452%\"\u003e\n \u003cp\u003e11 (26.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003e7 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.915492957746478%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.211267605633802%\"\u003e\n \u003cp\u003eTigecycline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003e5 (8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.802816901408452%\"\u003e\n \u003cp\u003e1 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.915492957746478%\"\u003e\n \u003cp\u003e1 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6.\u0026nbsp;\u003c/strong\u003eThe antibiotic resistance rates of the major Gram-positive bacteria isolated in COVID-19 patients\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.262798634812285%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntibiotics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.737201365187715%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMajor Gram-positive bacteria,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e (n=7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.262798634812285%\"\u003e\n \u003cp\u003eLevofloxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.737201365187715%\"\u003e\n \u003cp\u003e2 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.262798634812285%\"\u003e\n \u003cp\u003eCiprofloxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.737201365187715%\"\u003e\n \u003cp\u003e2 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.262798634812285%\"\u003e\n \u003cp\u003eGentamycin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.737201365187715%\"\u003e\n \u003cp\u003e1 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.262798634812285%\"\u003e\n \u003cp\u003ePenicillin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.737201365187715%\"\u003e\n \u003cp\u003e5 (71.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.262798634812285%\"\u003e\n \u003cp\u003eOxacillin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.737201365187715%\"\u003e\n \u003cp\u003e1 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.262798634812285%\"\u003e\n \u003cp\u003eErythromycin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.737201365187715%\"\u003e\n \u003cp\u003e5 (71.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.262798634812285%\"\u003e\n \u003cp\u003eClindamycin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.737201365187715%\"\u003e\n \u003cp\u003e5 (71.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7.\u0026nbsp;\u003c/strong\u003eSome important\u0026nbsp;clinical laboratory test results.\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"709\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.61777150916784%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\"\u003e\n \u003cp\u003e\u0026nbsp;All patients\u003c/p\u003e\n \u003cp\u003e(n=367)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.24682651622003%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients with COVID-19\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(n=197)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.38787023977433%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients without COVID-19\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(n=170)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.61777150916784%\" valign=\"top\"\u003e\n \u003cp\u003eLeucocyte\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\" valign=\"top\"\u003e\n \u003cp\u003e8.8 (0.39-39.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.24682651622003%\" valign=\"top\"\u003e\n \u003cp\u003e8.67 (0.39-39.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.38787023977433%\" valign=\"top\"\u003e\n \u003cp\u003e8.785 (0.93-10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.61777150916784%\" valign=\"top\"\u003e\n \u003cp\u003eNeutrophilic granulocyte\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\"\u003e\n \u003cp\u003e7.27 (0.24-78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.24682651622003%\"\u003e\n \u003cp\u003e7.19 (0.24-36.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.38787023977433%\"\u003e\n \u003cp\u003e7.255 (0.6-8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.61777150916784%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Lymphocyte\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\" valign=\"top\"\u003e\n \u003cp\u003e0.72 (0.1-4.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.24682651622003%\" valign=\"top\"\u003e\n \u003cp\u003e0.7 (0.11-3.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.38787023977433%\" valign=\"top\"\u003e\n \u003cp\u003e0.72 (0.1-0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\" valign=\"top\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.61777150916784%\" valign=\"top\"\u003e\n \u003cp\u003eProcalcitonin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\" valign=\"top\"\u003e\n \u003cp\u003e0.39 (0.04-100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.24682651622003%\" valign=\"top\"\u003e\n \u003cp\u003e0.4 (0.04-100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.38787023977433%\" valign=\"top\"\u003e\n \u003cp\u003e0.385 (0.04-100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\" valign=\"top\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.61777150916784%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Interleukin-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\" valign=\"top\"\u003e\n \u003cp\u003e7.41 (5-641)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.24682651622003%\" valign=\"top\"\u003e\n \u003cp\u003e5.29 (5-115)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.38787023977433%\" valign=\"top\"\u003e\n \u003cp\u003e9.24 (5-641)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.61777150916784%\" valign=\"top\"\u003e\n \u003cp\u003eInterleukin -2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\" valign=\"top\"\u003e\n \u003cp\u003e1023 (12.4-11044)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.24682651622003%\" valign=\"top\"\u003e\n \u003cp\u003e1064 (12.4-11044)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.38787023977433%\" valign=\"top\"\u003e\n \u003cp\u003e984 (110-3979)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\" valign=\"top\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.61777150916784%\" valign=\"top\"\u003e\n \u003cp\u003eInterleukin -6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\" valign=\"top\"\u003e\n \u003cp\u003e29 (2-1000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.24682651622003%\" valign=\"top\"\u003e\n \u003cp\u003e22.75 (2-1000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.38787023977433%\" valign=\"top\"\u003e\n \u003cp\u003e42 (2-1000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\" valign=\"top\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.61777150916784%\" valign=\"top\"\u003e\n \u003cp\u003eInterleukin -8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\" valign=\"top\"\u003e\n \u003cp\u003e52 (5-7305)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.24682651622003%\" valign=\"top\"\u003e\n \u003cp\u003e42 (5-3521)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.38787023977433%\" valign=\"top\"\u003e\n \u003cp\u003e67 (8-7305)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n 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\u003c/td\u003e\n \u003ctd width=\"25.38787023977433%\" valign=\"top\"\u003e\n \u003cp\u003e24 (5-176)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\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, Pneumonia, Infection, Mortality prediction, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-4632591/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4632591/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCOVID-19 pneumonia is easily complicated with other respiratory pathogenic attacks, increasing the risk of death. Exploring the pathogen profiles of COVID-19 patients-related facilitated the clinical management and decisions to pursue better prognosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study systematically compared the sputum culture results and death events of 170 non-COVID-19 and 197 COVID-19 patients. Statistical analysis was carried out to find the pathogen profile difference between the two populations. The death risk model was constructed for the infected COVID-19.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt was found that co-infection with bacteria and fungi increased the mortality of COVID-19 pneumonia patients. The isolation rate of \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e in COVID-19 patients was significantly higher than that in non-COVID-19 patients and often showed multi-drug resistant phenotypes. The COVID-19 pneumonia patients showed a higher incidence of intensive care unit admission, ventilator-assisted ventilation and death with fungal infection. The serum levels of interleukin-1, interleukin-6, interleukin-8, TNF, lymphocytes, neutrophils and white blood cells in patients with COVID-19 pneumonia decreased. A death prediction model was constructed based on machine learning methods, achieving a prediction accuracy of 90.0%. The main factors affecting the survival rate of COVID-19 pneumonia patients co-infected with other pathogens were admission to the intensive care unit, days of hospital stay, ventilator-aided treatment, carbapenems administration, lymphocyte, serum aspartate aminotransferase level, \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e infection, and \u003cem\u003eCandida\u003c/em\u003e infection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study provided necessary clinical indicators for timely and precise intervention of COVID-19 pneumonia patients when they were infected by other pathogens. The COVID-19-related secondary infection microorganisms were different compared with the pathogens isolated from non-COVID-19 patients.\u003c/p\u003e","manuscriptTitle":"Respiratory Tract Pathogen Profiles of COVID-19 Pneumonia Patients and the Mortality Prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-22 10:43:46","doi":"10.21203/rs.3.rs-4632591/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":"67bfc4da-e271-48ad-b86d-b39c4951a3d1","owner":[],"postedDate":"July 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":34677607,"name":"Biological sciences/Microbiology/Infectious disease diagnostics"},{"id":34677608,"name":"Biological sciences/Microbiology/Pathogens"}],"tags":[],"updatedAt":"2024-09-13T12:12:51+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-22 10:43:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4632591","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4632591","identity":"rs-4632591","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}