Lung microbiota of ARDS patients due to COVID-19 receiving ECMO

Lung microbiota of ARDS patients due to COVID-19 receiving ECMO | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Lung microbiota of ARDS patients due to COVID-19 receiving ECMO Yumi Mitsuyama, Kentaro Shimizu, Daisuke Motooka, Hiroshi Ogura, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4225435/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Diversity of the microbiota, which is essential for lower airway homeostasis, is greatly altered in acute respiratory distress syndrome (ARDS). Extracorporeal membrane oxygenation (ECMO) is the ultimate protective treatment for the lungs of patients with severe ARDS, but little is known about its effect on the lung microbiota of these patients. The aim of this study was to evaluate the effect of ECMO on the lung microbiota of ARDS patients. Methods This was a prospective, observational clinical study of ARDS patients with COVID-19. We performed 16S rRNA and fungal ITS1 profiling and shotgun sequencing on bronchoalveolar lavage fluid (BALF) samples collected from patients with ARDS due to COVID-19. Results BALF was collected from 13 patients, five of whom underwent ECMO. The median age of the patients with ECMO was significantly younger than that of those without ECMO (44 [IQR: 36–48] years vs. 64 [IQR: 53–74] years, p < 0.007). The median APACHE II score was significantly higher in the patients with ECMO versus those without ECMO (20 [IQR: 17–22] vs. 15 [IQR: 12–18], p = 0.018). In all ARDS patients, Pseudomonas was the most abundant of the bacteria. The patients with ECMO had more Pseudomonas and more Klebsiella than those without ECMO. The most abundant fungi were unspecified fungi in the patients with ECMO and Emmia lacerata in the patients without ECMO. Alpha diversity of bacteria and fungi did not differ significantly between the two groups. Human betaherpesvirus 5 and human alphaherpesvirus 1 were predominant in all patients, with human betaherpesvirus 5 decreasing over time in the ECMO patients. Conclusion The patients with ARDS due to COVID-19 who received ECMO had a different lung microbiota than those who did not receive ECMO. Microbiota ECMO ARDS Mycobiota Virome COVID-19 Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The lungs of healthy adults contain a unique microbial community that is involved in the maintenance of respiratory physiology and immune homeostasis by forming a network of ecological interaction between the microbiota and the host [ 1 – 3 ]. Microorganisms abundant in the lower respiratory tract include the Firmicutes ( Streptococcus and Veillonella ) and Bacteroidetes ( Prevotella ) among bacteria; Aspergillus , Candida , Cladosporium , Malassezia , and Saccharomyces among fungi; and Anelloviridae and Redondoviridae among the viruses [ 4 – 7 ]. Dysbiosis, defined as deviation from the normal microbial composition, is involved in the development and progression of respiratory disease [ 8 , 9 ]. In patients with acute respiratory distress syndrome (ARDS), lung diversity decreases over time and dysbiosis progresses due to both the progression of the disease itself and the effects of positive pressure ventilation. Extracorporeal membrane oxygenation (ECMO) is a treatment for patients with severe ARDS in which gas-exchanged oxygenated blood is delivered by an extracorporeal artificial lung to replace inadequate tissue oxygen supply for days to weeks and to prevent the development of ventilator-induced lung injury due to exposure to high concentrations of oxygen and hyperventilation associated with ventilation. Thus, the lungs are temporarily rested to prevent irreversible damage to the injured lung while it is being treated and restored [ 10 ]. To our knowledge, there are no reports to date on the lung microbiota of ARDS patients treated with ECMO. The purpose of this study was to determine the effect of ECMO on the lung microbiota of patients with ARDS. Materials and methods Study design and participants This single-center, prospective, observational clinical study was conducted in patients with ARDS due to COVID-19 admitted to the Division of Trauma and Surgical Critical Care, Osaka General Medical Center between April 2021 and March 2022. The diagnosis of COVID-19 was confirmed by polymerase chain reaction testing of nasal swabs on admission. The diagnosis of ARDS followed the Berlin definition [ 11 ]. Clinical and biological parameters such as patient demographic characteristics, duration of mechanical ventilation and hospitalization, and comorbidities were collected from the electronic medical record. Severity scores were recorded using the Acute Physiology and Chronic Health Evaluation (APACHE) II score (range 0–71) and Sequential Organ Failure Assessment (SOFA) score (range 0–24), with ARDS severity rated as mild, moderate, or severe [ 12 , 13 ]. This study was approved by the institutional review board of Osaka General Medical Center (approval number: 2021-002). Written informed consent was obtained from all patients. The study was conducted in accordance with the Declaration of Helsinki. Sample collection Bronchoalveolar lavage fluid (BALF) was collected using a bronchial fiberscope within 24 hours after the diagnosis of ARDS. The bronchoalveolar lavage procedure was performed under aseptic conditions using a disposable AMBU®ASCOPE TM 4 (Ambu A/S, Ballerup, Denmark). In the patients with ECMO, BALF was collected as needed after the initial BALF collection. Bronchoalveolar lavage was performed according to standardized procedures, specifically by injecting 3 × 20 mL of sterile saline solution into the bronchi. After each injection, the largest volume of fluid in the bronchioles (nearly 10 mL total) was collected in 50 mL sterile plastic tubes and centrifuged to separate the supernatant from the sediment. The tubes were stored at -80°C until use. Amplicon library construction and sequencing Bacterial and fungal DNA were extracted from the precipitate fraction of BALF using a PI-1200 nucleic acid extraction system (Kurabo, Japan). For bacterial metagenome analysis, the V1-V2 variable region of the 16S rRNA gene was sequenced in 251-bp paired-end mode on an Illumina MiSeq platform (Illumina, San Diego, CA, USA). For fungal metagenome analysis, the fungal ITS1 region was sequenced in 301-bp paired-end mode on the Illumina MiSeq. The resulting paired-end sequences were merged, filtered, and denoised using DADA2 software ( https://benjjneb.github.io/dada2/ ). Taxonomic assignments were made using the QIIME2 feature-classifier plug-in in the Greengenes database (release 13_8) for bacteria and the ntF-ITS1 database for fungi. The QIIME2 pipeline, version 2020.2, was used as the bioinformatics environment for processing all relevant raw sequence data. Metagenomic shotgun sequencing Viral RNA was extracted from the precipitate fraction using a QIAamp MinElute Virus Spin Kit (Qiagen, Hilden, Germany). The extracted RNA was then used to synthesize double-stranded DNA using a ProtoScript II First Strand cDNA Synthesis Kit (New England Biolabs, Ipswich, MA, USA), NEBNext Ultra II Non-Directional RNA Second Strand Synthesis Module (New England Biolabs), and Random Primer 6 (random hexanucleotides; New England Biolabs). Next, viral metagenome shotgun libraries were prepared for each sample using a Twist Library Preparation Enzymatic Fragmentation Kit (Twist Bioscience, South San Francisco, CA, USA) and the Twist Comprehensive Viral Research Panel (Twist Bioscience). All libraries were converted to libraries for DNBSEQ using a MGIEasy Universal Library Conversion Kit (App-A). Sequencing was performed using a DNBSEQ-G400RS High-throughput Sequencing Kit (MGI Tech, Tokyo, Japan) in 100-bp paired-end mode. Each read was subjected to Kraken2 analysis against the PlusPFP database, which includes archaeal, bacterial, viral, plasmid, human, UniVec_core, protozoan, fungal, and plant sequences. Statical analysis Continuous variables are shown as the median and interquartile range (IQR), and categorical variables are shown as frequencies and percentages. The Wilcoxon rank-sum test was used to test continuous variables, and Fisher’s exact test was used to test the nominal variables. A p-value of < 0.05 was considered to indicate statistical significance. All analyses were performed using JMP Pro17 (SAS Institute Inc., Cary, NC, USA) and Prism 9 (GraphPad Software, Boston, MA, USA). Results Patient characteristics In total, 13 patients were included in the study: 5 patients with ECMO and 8 patients without ECMO. Patient characteristics are shown in Table 1 . The median age (IQR) of the patients with ECMO was significantly younger than that of those without ECMO (44 [36–48] years vs. 64 [53–74] years, p < 0.007). The median APACHE II score was significantly higher in the patients with ECMO versus that in the patients without ECMO (20 [ 17 – 22 ] vs. 15 [ 12 – 18 ], p = 0.018). The median SOFA score of the patients with ECMO was also significantly higher than that of those without ECMO (10 [ 9 – 13 ] vs. 8 [ 4 – 9 ], p = 0.022). As adjunctive therapy for ARDS, 80% of the patients without ECMO were treated in the prone position, whereas all of the patients with ECMO were treated in the lateral position. The median length of ECMO was 11 (10–22) days. There was no significant difference in the duration of mechanical ventilation between the patients with ECMO and those without ECMO. The median length of stay in the intensive care unit (ICU) was significantly longer in the patients with ECMO (13 [ 11 – 29 ] days vs. 9 [ 6 – 12 ] days, p = 0.045). There was no significant difference in mortality between the two groups. Lung bacterial microbiota In all samples, Proteobacteria and Firmicutes were predominant in the composition of flora at the phylum level (Fig. 1 A). Figure 1 B shows the top bacterial phyla with relative abundance greater than 1% in the ECMO and non-ECMO groups. The top three of these bacterial phyla were the same in both groups, but their frequencies differed: the relative frequencies of Proteobacteria , Firmicutes , and Actinobacteria in the non-ECMO group were 51.3%, 29.3%, and 6.3%, whereas in the ECMO group they were 76.6%, 8.0%, and 6.5%. The composition of the bacterial flora at the genus level in all samples is shown in Fig. 1 C. The top bacterial genera averaging over 1% relative abundance in the ECMO and non-ECMO groups are shown in Fig. 1 D. Pseudomonas was the most predominant in both groups. The top three Gram-negative rods ( Pseudomonas , Curvibacter , and Sphingomonas ) tended to be more common in the ECMO group, whereas Streptococcus tended to be higher in the non-ECMO group (Fig. 1 E). The relative changes in abundance of the top six bacterial genera in patients with ECMO are shown in Fig. 1 F. Pseudomonas , Curvibacter , and Sphingomonas showed similar trends within the same patient. In two patients, Klebsiella tended to increase through day 21. There was little similarity in beta diversity between the patients with and without ECMO and in terms of the number of days on mechanical ventilation (Fig. 2 ). The patients with ECMO did not differ in alpha diversity from those without ECMO ( Fig. S1 ). Lung mycobiota The composition of the fungal flora at the species level in all samples is shown in Fig. 3 A. Each sample was dominated by only a few fungal species. The top fungal species averaging over 1% relative abundance are shown in Fig. 3 B. In the ECMO group, unclassified fungi was the most predominant, followed in abundance by Malassezia restricta . In the non-ECMO group, Emmia lacerata was the most predominant, followed in abundance by M. restricta . The patients with ECMO tended to have less diverse lung mycobiota than those without ECMO in terms of the Shannon and Simpson diversity indexes ( Fig. S2 ). There were no differences in beta diversity based on ECMO status or number of days on mechanical ventilation (Fig. 3 C). Lung virome The composition of viral flora in all samples is shown in Fig. 4 A. A bar graph of the viruses averaged by relative abundance greater than 1% in each patient group with and without ECMO is shown in Fig. 4 B. All patients had COVID-19, but human betaherpesvirus 5 and human alphaherpesvirus 1 , not severe acute respiratory syndrome coronavirus , were predominant. ECMO patients tended to have a lower relative abundance of human betaherpesvirus 5 and a greater relative abundance of severe acute respiratory syndrome coronavirus . The changes in relative abundance of the top five viruses in the patients with ECMO are shown in Fig. 4 C. The relative abundance of human betaherpesvirus 5 tended to decrease over the 21 days, but it increased in one patient after this time. Discussion We profiled the bacterial, fungal, and viral flora of the lower respiratory tract of patients with ARDS due to COVID-19. The microbiota of the lower respiratory tract of patients with ECMO was rich in Pseudomonas among the bacteria, unclassified fungi among the fungi, and human betaherpesvirus 5 ( Cytomegalovirus : CMV ) among the viruses. The Proteobacteria phylum is predominant in the lower respiratory tract of patients with ARDS due to COVID-19, and an increase in the abundance of the Pseudomonas genus and Enterobacter has been reported as a characteristic of the lower respiratory tract of patients with pneumonia due to COVID-19, which is consistent with the results of the present study [ 14 , 15 ]. Notably, patients with ECMO had a higher relative abundances of Pseudomonas and Klebsiella than those without ECMO, whereas patients without ECMO had a higher abundances of Streptococcus and Staphylococcus aureus . Viral infections damage tissues of the respiratory tract pathway, leading to dysbiosis and the promotion of bacterial colony formation [ 16 ]. In COVID-19, Pseudomonas aeruginosa has been reported to promote colony formation, and enrichment of P. aeruginosa is associated with poor prognosis [ 17 , 18 ]. Enrichment of Pseudomonas may have been greater in ECMO patients with severe ARDS, who have more severe lung injury. The oral microbiota of the elderly is rich in staphylococci and streptococci, and an increased relative abundance of Enterobacter in the gut has been reported in patients with severe ARDS [ 19 – 21 ]. Aspiration in patients with ARDS also affects the microbiota of the lower respiratory tract [ 22 , 23 ]. Thus, there may be an increase in streptococci in patients without ECMO and a more confirmed presence of Enterobacter predominantly in severely ill ECMO patients. Despite the enrichment and severity of highly pathogenic microorganisms in the ECMO patients, clinical outcomes and diversity were not inferior to those of the non-ECMO patients. ECMO replaces oxygenation and carbon dioxide removal that the lungs would normally do and allows for protective ventilation that significantly reduces plateau and driving pressures [ 24 ]. This results in a significant reduction in the concentrations of plasma sRAGE, interleukin-6, and monocyte chemotaxis protein-1, thus limiting pulmonary biotrauma caused by mechanical ventilation [ 25 ]. ECMO has also been reported to promote recovery of alveolar epithelial function in rat experiments [ 26 ]. The fact that changes in lung microbiota did not lead to a decrease in diversity in the ECMO patients may have contributed to the protective effect of ECMO on the lungs. The fungal flora of ARDS patients has been reported to be enriched with Candida albicans , and C. albicans is a risk factor for death [ 27 , 28 ]. This was similarly observed in ARDS with COVID-19, in which an increase in unidentified ascomycetes in ARDS patients who were not contaminated with Candida spp. was reported [ 29 ]. This is consistent with the results of the present study, which showed patients contaminated with C. albicans from an early stage and an increase in unidentified fungi in the uncontaminated cases. Notably, M. restricta was abundant in the present study. Malassezia restricta is endemic to the skin and intestinal tract [ 30 ], and in the intestinal fungal flora, M. restricta and C. albicans are prominent [ 31 ]. Similar to the bacterial flora, M. restricta may have been enriched through the gut-lung axis in ARDS patients. The respiratory virus flora are thought to play an important role in the pathogenesis of respiratory disease by interacting with the immune system [ 7 , 32 ]. Tobacco mosaic virus is reported to be abundant in COVID-19 and both Anelloviridae and Redondoviridae are abundant in severe cases, and the presence of these viruses is positively correlated with intubation during hospitalization [ 33 , 34 ]. In the present study, there was marked enrichment of CMV and Human alphaherpesvirus 1 (Herpes simplex virus 1 : HSV-1 ) , but not Anelloviridae and Redondoviridae . The patients with ECMO tended to have less Human betaherpesvirus 5 than those without ECMO. Human betaherpesvirus 5 and Human alphaherpesvirus are the most commonly identified viruses from patients on mechanical ventilation, and viral reactivation among ICU patients, especially among the herpes group, significantly changes the virome [ 35 ]. It was reported that CMV pulmonary infections, not HSV-1, were associated with increased length of mechanical ventilation and increased ICU length of stay and mortality in ventilated patients [ 36 ]. Given the decreasing trend in the relative abundance of CMV with the increasing duration of mechanical ventilation in ECMO patients, the lung rest provided by ECMO may contribute to the suppression of pathogenic viral enrichment. The subjects of the previously reported studies were early in the initiation of mechanical ventilation, and there are no reports on the progression of single-virus enrichment with increased duration of mechanical ventilation. Limitations This study has several limitations. First, although we detected a large number of microorganisms using metagenomic sequencing, contamination in the airways and of bronchoscopes should always be considered. We used disposable bronchoscopes to minimize contamination. Second, the present results are based on measurements from a single center and have not been validated in other cohorts. Third, we did not evaluate whether the microorganisms were truly pathogenic or only present in the airways. Further studies using animal models should provide important insights into the pathogenic role of microbiome alterations in patients with ECMO. Conclusion The ARDS patients treated with ECMO had a different lung microbiota than those without ECMO. It is speculated that critical illness, respiratory management, and a variety of other factors contribute to the lung microbiota of ARDS patients. Further studies are needed to determine how the unique respiratory management of ECMO affects the lung microbiota. Abbreviations ARDS Acute respiratory distress syndrome ECMO Extracorporeal membrane oxygenation COVID-19 Coronavirus disease 2019 APACHE II Acute Physiologic Assessment and Chronic Health Evaluation II SOFA Sequential Organ Failure Assessment IQR Interquartile range BALF Bronchoalveolar lavage fluid CMV Cytomegalovirus HSV-1 Herpes simplex virus 1 ICU Intensive care unit Declarations Ethics approval and consent to participate This study was approved by the institutional review board of Osaka General Medical Center (approval number: 2021-002). Written informed consent was obtained from all patients. The study was conducted in accordance with the Declaration of Helsinki. Consent for publication Not applicable. Availability of data and materials The datasets used and analyzed during the current study were submitted to DDBJ/EMBL/GenBank databases under accession number PRJDB17654. Competing interests None declared. Funding This work was supported by a Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science [grant number 22K0913]. Authors’ contributions Y.M. designed the study, analyzed the data, and wrote the manuscript; D.M. performed sequencing and helped analyze the data; K.S. supervised the conduction of the study. H.O., S.F., and J.O. critically revised the manuscript for intellectual content. All authors read and approved the final manuscript. Acknowledgements We appreciate the cooperation of the patients and families involved in this study. We also thank all of the medical staff for their cooperation. References Man WH, de Steenhuijsen Piters WAA, Bogaert D. The microbiota of the respiratory tract: gatekeeper to respiratory health. 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Tables Table 1 Characteristics of the population ECMO (-) ECMO (+) Total n=8 n=5 N=13 sample n=8 sample n=13 sample N=21 P value Demographics Age (years), median (IQR) 64 (53-74) 44 (36-48) 51 (44-70) 0.007 Sex, male (%) 6 (75) 5 (100) 11 (84.6) 0.487 BMI, median (IQR) 28.6 (24.8-30.5) 33.7 (20.3-36.9) 28.7 (24.6-32.2) 0.558 Current smoker 0 (0) 2 (40) 2 (15.4) 0.128 Former smoker 3 (37.5) 1 (20) 4 (30.8) 0.506 Comorbidities, n (%) Hypertension 2 (25) 1 (20) 3 (23.1) 0.835 Diabetes 1 (12.5) 1 (20) 2 (15.4) 0.715 Immunocompromise 1 (12.5) 0 (0) 1 (7.7) 0.411 Cardiovascular compromise 0 (0) 0 (0) 0 (0) NA Chronic obstructive pulmonary disease 0 (0) 0 (0) 0 (0) NA Renal insufficiency 0 (0) 0 (0) 0 (0) NA Days after onset, days, median (IQR) 7 (4-8) 12 (7-17) 7 (6-12) 0.055 Severity of disease on admission APACHE II score, median (IQR) 15 (12-18) 20 (17-22) 17 (14-19) 0.018 SOFA score, median (IQR) 8 (4-9) 10 (9-13) 8 (6-10) 0.022 Severity of ARDS, n (%) 0.231 Severe 5 (62.5) 5 (100) 10 (76.9) Moderate 3 (37.5) 0 (0) 3 (23.1) Mild 0 (0) 0 (0) 0 (0) Treatment of disease, n (%) Antibiotics 2 (25) 2 (40) 4 (30.8) 0.571 Tazobactam and piperacillin 0 (0) 2 (40) 2 (15.4) 0.487 Ampicillin and sulbactam 1 (12.5) 0 (0) 1 (7.7) 0.385 Trimethoprim and sulfamethoxazole 1 (12.5) 0 (0) 1 (7.7) 0.385 Antivirals 3 (30) 3 (60) 6 (46.2) 0.592 Lopinavir 1 (12.5) 0 (0) 1 (7.7) 0.411 Favipiravir 1 (12.5) 0 (0) 1 (7.7) 0.411 Remdesivir 2 (25) 3 (60) 5 (38.5) 0.293 Glucocorticoid 8 (100) 5 (100) 13 (100) NA Adjunctive therapies for ARDS, n (%) Neuromuscular blockade 8 (100) 5 (100) 13 (100) NA Prone position 8 (80) 0 (0) 8 (61.5) <0.001 Lateral position 0 (0) 5 (100) 5 (38.5) <0.001 Disease course Length of ECMO, days, median (IQR) 0 (0) 11 (10-22) 11 (10-22) Length of mechanical ventilation, days, median (IQR) 12 (6-20) 12 (11-29) 12 (8-21) 0.27 Length of stay in ICU, days, median (IQR) 9 (6-12) 13 (11-29) 10 (7-16) 0.045 Length of stay in hospital, days, median (IQR) 23 (11-27) 30 (22-39) 25 (16-33) 0.107 ICU mortality 0 (0) 1 (20) 1 (7.7) 0.385 Hospital mortality 3 (37.5) 1 (20) 4 (30.8) 0.506 Number of days from admission date of sample collection Within 24 hours 8 5 2-7days 0 3 7-14days 0 2 >14dayes 0 3 ARDS, Acute Respiratory Distress Syndrome; IQR, interquartile range; BMI, body mass index APACHE, Acute Physiology and Chronic Health Evaluation; SOFA; Sequential Organ Failure Assessment ECMO, Extracorporeal membrane oxygenation; ICU, intensive care unit; NA, not available 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-4225435","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":290991949,"identity":"64ee2715-fc5d-4157-baf0-908be4404601","order_by":0,"name":"Yumi Mitsuyama","email":"data:image/png;base64,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","orcid":"","institution":"Osaka University Graduate School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Yumi","middleName":"","lastName":"Mitsuyama","suffix":""},{"id":290991950,"identity":"fc9ac06f-48f5-475c-a54e-c158fcff52ad","order_by":1,"name":"Kentaro Shimizu","email":"","orcid":"","institution":"Osaka University Graduate School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Kentaro","middleName":"","lastName":"Shimizu","suffix":""},{"id":290991951,"identity":"6c3a308a-85fa-481a-bfce-61cb39543d91","order_by":2,"name":"Daisuke Motooka","email":"","orcid":"","institution":"Osaka University","correspondingAuthor":false,"prefix":"","firstName":"Daisuke","middleName":"","lastName":"Motooka","suffix":""},{"id":290991952,"identity":"a997d239-0282-4562-947e-034223f1ca43","order_by":3,"name":"Hiroshi Ogura","email":"","orcid":"","institution":"Osaka University Graduate School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hiroshi","middleName":"","lastName":"Ogura","suffix":""},{"id":290991954,"identity":"6f7a84b3-dd29-4c41-8cd5-670f0f776aeb","order_by":4,"name":"Satoshi Fujimi","email":"","orcid":"","institution":"Osaka General Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Satoshi","middleName":"","lastName":"Fujimi","suffix":""},{"id":290991956,"identity":"99f351d5-1175-4cac-94b0-e3de4e0083aa","order_by":5,"name":"Jun Oda","email":"","orcid":"","institution":"Osaka University Graduate School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Oda","suffix":""}],"badges":[],"createdAt":"2024-04-06 03:14:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4225435/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4225435/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54997221,"identity":"05fc982d-0a97-46b8-a5fc-a984970f0bf2","added_by":"auto","created_at":"2024-04-19 18:09:20","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":965941,"visible":true,"origin":"","legend":"\u003cp\u003eLung bacterial microbiota. (A) Composition of the lung microbiota at the phylum level for all samples. The stacked bars indicate the mean relative abundance at the portal level of all samples. (B) Bar graph shows the top bacterial phyla in the groups with and without ECMO by averaging the bacterial phyla with a relative abundance of 1% or greater. (C) Composition of lung microbiota at the genus level for all samples. The legend indicates the top 20 families. (D) Bar graph shows the top bacterial genera in the groups with and without ECMO by averaging the bacterial genera with a relative abundance of 1% or greater. (E) Box-and-whisker diagrams show the relative abundance of the top four genera in the patients with and without ECMO. (F) The relative changes in abundance of the top six bacterial genera in the patients with ECMO. \u003cem\u003eECMO\u003c/em\u003eextracorporeal membrane oxygenation\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4225435/v1/f71ae9f4f908abf411021c5c.jpg"},{"id":54997220,"identity":"db6e5a75-4a56-4560-9a50-d6a07ecc511b","added_by":"auto","created_at":"2024-04-19 18:09:20","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":272268,"visible":true,"origin":"","legend":"\u003cp\u003ePCoA2D plots of β diversity analysis of lung bacterial flora. (A) PCoA2D plots of β diversity analysis for BALF in patients with and without ECMO. Dissimilarity between samples was measured by unweighted UniFrac distance. (B) PCoA2D plots of β diversity analysis for BALF by days of mechanical ventilation in patients. Dissimilarity between samples was measured by unweighted UniFrac distance. \u0026lt;1 day indicates within the first day of mechanical ventilation. 2 days-7 days indicates from the second day to the seventh day of mechanical ventilation. \u0026gt; 7 days indicates from day 7 onward of mechanical ventilation. (C) PCoA2D plots of β diversity analysis for BALF in patients with and without ECMO. Dissimilarity between samples was measured by weighted UniFrac distance. (D) PCoA2D plots of β diversity analysis for BALF by days of mechanical ventilation in the patients. Dissimilarity between samples was measured by weighted UniFrac distance. \u0026lt;1 day indicates within the first day of mechanical ventilation. 2 days-7 days indicates from the second day to the seventh day of mechanical ventilation. \u0026gt;7 days indicates from day 7 onward of mechanical ventilation. \u003cem\u003eBALF\u003c/em\u003e bronchoalveolar lavage fluid, \u003cem\u003eECMO\u003c/em\u003e extracorporeal membrane oxygenation, \u003cem\u003ePCoA2D\u003c/em\u003e principle coordinate analysis 2-dimensional, \u003cem\u003eANOSIM\u003c/em\u003e analysis of similarities\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4225435/v1/fcc0b65c7bdcdae24e3a8c77.jpg"},{"id":54997920,"identity":"ebf055d8-7120-4336-a43a-87b71e661c47","added_by":"auto","created_at":"2024-04-19 18:17:20","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":623170,"visible":true,"origin":"","legend":"\u003cp\u003eLung mycobiota at the species level. (A) Composition of lung mycobiota at the species level for all samples. The legend indicates the 27 species. (B) Bar graph shows top fungal species in the groups with and without ECMO by averaging the fungal species with a relative abundance of 1% or greater. (C) PCoA2D plots of β diversity analysis of lung fungal flora. (a) PCoA2D plots of β diversity analysis for BALF in patients with and without ECMO. Dissimilarity between samples was measured by unweighted UniFrac distance. (b) PCoA2D plots of β diversity analysis for BALF by days of mechanical ventilation in patients. Dissimilarity between samples was measured by unweighted UniFrac distance. \u0026lt;1 day indicates within the first day of mechanical ventilation. 2 days-7 days indicates from the second day to the seventh day of mechanical ventilation. \u0026gt;7 days indicates from day 7 onward of mechanical ventilation. (c) PCoA2D plots of β diversity analysis for BALF in patients with and without ECMO. Dissimilarity between samples was measured by weighted UniFrac distance. (d) PCoA2D plots of β diversity analysis for BALF by days of mechanical ventilation in the patients. Dissimilarity between samples was measured by weighted UniFrac distance. \u0026lt;1 day indicates within the first day of mechanical ventilation. 2 days-7 days indicates from the second day to the seventh day of mechanical ventilation. \u0026gt;7 days indicates from day 7 onward of mechanical ventilation. \u003cem\u003eBALF\u003c/em\u003e bronchoalveolar lavage fluid, \u003cem\u003eECMO\u003c/em\u003eextracorporeal membrane oxygenation, \u003cem\u003ePCoA2D\u003c/em\u003e principle coordinate analysis 2-dimensional, \u003cem\u003eANOSIM\u003c/em\u003e analysis of similarities\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4225435/v1/7cf02b83a3b3f4c385751bbb.jpg"},{"id":54997225,"identity":"87c6fc86-7cf2-41fc-9eca-7d15289da2e2","added_by":"auto","created_at":"2024-04-19 18:09:20","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":523116,"visible":true,"origin":"","legend":"\u003cp\u003eLung virome at the species level. (A) Composition of the lung virome at the species level for all samples. The legend indicates the top 20 species. (B) Bar graph showing the top viruses by averaging the species with a relative abundance of 1% or greater in the patients with or without ECMO. (C) The relative changes in abundance of the top five viruses in patients with ECMO. \u003cem\u003eECMO \u003c/em\u003eextracorporeal membrane oxygenation\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4225435/v1/0f581d326b8d1887a54a284e.jpg"},{"id":59341231,"identity":"a3a25f7c-28e9-4d5b-8236-c34aa04cb86b","added_by":"auto","created_at":"2024-06-30 05:24:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3041346,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4225435/v1/151d9efc-6444-4f04-a913-82e208c06b3b.pdf"},{"id":54997224,"identity":"ed381cb2-9319-4d60-9909-b2e6ce5c49f9","added_by":"auto","created_at":"2024-04-19 18:09:20","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":67245,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4225435/v1/e7363d87970b197b0431903c.pdf"},{"id":54997223,"identity":"beacbccc-30f8-4466-8925-3eec8c9da000","added_by":"auto","created_at":"2024-04-19 18:09:20","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":66063,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4225435/v1/1e8eeb884a8f05ff08ae1679.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Lung microbiota of ARDS patients due to COVID-19 receiving ECMO","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe lungs of healthy adults contain a unique microbial community that is involved in the maintenance of respiratory physiology and immune homeostasis by forming a network of ecological interaction between the microbiota and the host [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Microorganisms abundant in the lower respiratory tract include the \u003cem\u003eFirmicutes\u003c/em\u003e (\u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003eVeillonella\u003c/em\u003e) and \u003cem\u003eBacteroidetes\u003c/em\u003e (\u003cem\u003ePrevotella\u003c/em\u003e) among bacteria; \u003cem\u003eAspergillus\u003c/em\u003e, \u003cem\u003eCandida\u003c/em\u003e, \u003cem\u003eCladosporium\u003c/em\u003e, \u003cem\u003eMalassezia\u003c/em\u003e, and \u003cem\u003eSaccharomyces\u003c/em\u003e among fungi; and \u003cem\u003eAnelloviridae\u003c/em\u003e and \u003cem\u003eRedondoviridae\u003c/em\u003e among the viruses [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Dysbiosis, defined as deviation from the normal microbial composition, is involved in the development and progression of respiratory disease [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In patients with acute respiratory distress syndrome (ARDS), lung diversity decreases over time and dysbiosis progresses due to both the progression of the disease itself and the effects of positive pressure ventilation.\u003c/p\u003e \u003cp\u003eExtracorporeal membrane oxygenation (ECMO) is a treatment for patients with severe ARDS in which gas-exchanged oxygenated blood is delivered by an extracorporeal artificial lung to replace inadequate tissue oxygen supply for days to weeks and to prevent the development of ventilator-induced lung injury due to exposure to high concentrations of oxygen and hyperventilation associated with ventilation. Thus, the lungs are temporarily rested to prevent irreversible damage to the injured lung while it is being treated and restored [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. To our knowledge, there are no reports to date on the lung microbiota of ARDS patients treated with ECMO.\u003c/p\u003e \u003cp\u003eThe purpose of this study was to determine the effect of ECMO on the lung microbiota of patients with ARDS.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003e This single-center, prospective, observational clinical study was conducted in patients with ARDS due to COVID-19 admitted to the Division of Trauma and Surgical Critical Care, Osaka General Medical Center between April 2021 and March 2022. The diagnosis of COVID-19 was confirmed by polymerase chain reaction testing of nasal swabs on admission. The diagnosis of ARDS followed the Berlin definition [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Clinical and biological parameters such as patient demographic characteristics, duration of mechanical ventilation and hospitalization, and comorbidities were collected from the electronic medical record. Severity scores were recorded using the Acute Physiology and Chronic Health Evaluation (APACHE) II score (range 0\u0026ndash;71) and Sequential Organ Failure Assessment (SOFA) score (range 0\u0026ndash;24), with ARDS severity rated as mild, moderate, or severe [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This study was approved by the institutional review board of Osaka General Medical Center (approval number: 2021-002). Written informed consent was obtained from all patients. The study was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSample collection\u003c/h2\u003e \u003cp\u003eBronchoalveolar lavage fluid (BALF) was collected using a bronchial fiberscope within 24 hours after the diagnosis of ARDS. The bronchoalveolar lavage procedure was performed under aseptic conditions using a disposable AMBU\u0026reg;ASCOPE\u003csup\u003eTM\u003c/sup\u003e4 (Ambu A/S, Ballerup, Denmark). In the patients with ECMO, BALF was collected as needed after the initial BALF collection. Bronchoalveolar lavage was performed according to standardized procedures, specifically by injecting 3 \u0026times; 20 mL of sterile saline solution into the bronchi. After each injection, the largest volume of fluid in the bronchioles (nearly 10 mL total) was collected in 50 mL sterile plastic tubes and centrifuged to separate the supernatant from the sediment. The tubes were stored at -80\u0026deg;C until use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAmplicon library construction and sequencing\u003c/h2\u003e \u003cp\u003eBacterial and fungal DNA were extracted from the precipitate fraction of BALF using a PI-1200 nucleic acid extraction system (Kurabo, Japan). For bacterial metagenome analysis, the V1-V2 variable region of the 16S rRNA gene was sequenced in 251-bp paired-end mode on an Illumina MiSeq platform (Illumina, San Diego, CA, USA). For fungal metagenome analysis, the fungal ITS1 region was sequenced in 301-bp paired-end mode on the Illumina MiSeq.\u0026nbsp;The resulting paired-end sequences were merged, filtered, and denoised using DADA2 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://benjjneb.github.io/dada2/\u003c/span\u003e\u003cspan address=\"https://benjjneb.github.io/dada2/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Taxonomic assignments were made using the QIIME2 feature-classifier plug-in in the Greengenes database (release 13_8) for bacteria and the ntF-ITS1 database for fungi. The QIIME2 pipeline, version 2020.2, was used as the bioinformatics environment for processing all relevant raw sequence data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMetagenomic shotgun sequencing\u003c/h2\u003e \u003cp\u003eViral RNA was extracted from the precipitate fraction using a QIAamp MinElute Virus Spin Kit (Qiagen, Hilden, Germany). The extracted RNA was then used to synthesize double-stranded DNA using a ProtoScript II First Strand cDNA Synthesis Kit (New England Biolabs, Ipswich, MA, USA), NEBNext Ultra II Non-Directional RNA Second Strand Synthesis Module (New England Biolabs), and Random Primer 6 (random hexanucleotides; New England Biolabs). Next, viral metagenome shotgun libraries were prepared for each sample using a Twist Library Preparation Enzymatic Fragmentation Kit (Twist Bioscience, South San Francisco, CA, USA) and the Twist Comprehensive Viral Research Panel (Twist Bioscience). All libraries were converted to libraries for DNBSEQ using a MGIEasy Universal Library Conversion Kit (App-A). Sequencing was performed using a DNBSEQ-G400RS High-throughput Sequencing Kit (MGI Tech, Tokyo, Japan) in 100-bp paired-end mode. Each read was subjected to Kraken2 analysis against the PlusPFP database, which includes archaeal, bacterial, viral, plasmid, human, UniVec_core, protozoan, fungal, and plant sequences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are shown as the median and interquartile range (IQR), and categorical variables are shown as frequencies and percentages. The Wilcoxon rank-sum test was used to test continuous variables, and Fisher\u0026rsquo;s exact test was used to test the nominal variables. A p-value of \u0026lt;\u0026thinsp;0.05 was considered to indicate statistical significance. All analyses were performed using JMP Pro17 (SAS Institute Inc., Cary, NC, USA) and Prism 9 (GraphPad Software, Boston, MA, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eIn total, 13 patients were included in the study: 5 patients with ECMO and 8 patients without ECMO. Patient characteristics are shown in \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e. The median age (IQR) of the patients with ECMO was significantly younger than that of those without ECMO (44 [36\u0026ndash;48] years vs. 64 [53\u0026ndash;74] years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.007). The median APACHE II score was significantly higher in the patients with ECMO versus that in the patients without ECMO (20 [\u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] vs. 15 [\u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16 CR17\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], p\u0026thinsp;=\u0026thinsp;0.018). The median SOFA score of the patients with ECMO was also significantly higher than that of those without ECMO (10 [\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] vs. 8 [\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], p\u0026thinsp;=\u0026thinsp;0.022). As adjunctive therapy for ARDS, 80% of the patients without ECMO were treated in the prone position, whereas all of the patients with ECMO were treated in the lateral position. The median length of ECMO was 11 (10\u0026ndash;22) days. There was no significant difference in the duration of mechanical ventilation between the patients with ECMO and those without ECMO. The median length of stay in the intensive care unit (ICU) was significantly longer in the patients with ECMO (13 [\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] days vs. 9 [\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10 CR11\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] days, p\u0026thinsp;=\u0026thinsp;0.045). There was no significant difference in mortality between the two groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eLung bacterial microbiota\u003c/h2\u003e \u003cp\u003eIn all samples, \u003cem\u003eProteobacteria\u003c/em\u003e and \u003cem\u003eFirmicutes\u003c/em\u003e were predominant in the composition of flora at the phylum level (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB shows the top bacterial phyla with relative abundance greater than 1% in the ECMO and non-ECMO groups. The top three of these bacterial phyla were the same in both groups, but their frequencies differed: the relative frequencies of \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eFirmicutes\u003c/em\u003e, and \u003cem\u003eActinobacteria\u003c/em\u003e in the non-ECMO group were 51.3%, 29.3%, and 6.3%, whereas in the ECMO group they were 76.6%, 8.0%, and 6.5%. The composition of the bacterial flora at the genus level in all samples is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC. The top bacterial genera averaging over 1% relative abundance in the ECMO and non-ECMO groups are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD. \u003cem\u003ePseudomonas\u003c/em\u003e was the most predominant in both groups. The top three Gram-negative rods (\u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eCurvibacter\u003c/em\u003e, and \u003cem\u003eSphingomonas\u003c/em\u003e) tended to be more common in the ECMO group, whereas \u003cem\u003eStreptococcus\u003c/em\u003e tended to be higher in the non-ECMO group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). The relative changes in abundance of the top six bacterial genera in patients with ECMO are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF. \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eCurvibacter\u003c/em\u003e, and \u003cem\u003eSphingomonas\u003c/em\u003e showed similar trends within the same patient. In two patients, \u003cem\u003eKlebsiella\u003c/em\u003e tended to increase through day 21. There was little similarity in beta diversity between the patients with and without ECMO and in terms of the number of days on mechanical ventilation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The patients with ECMO did not differ in alpha diversity from those without ECMO (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLung mycobiota\u003c/h2\u003e \u003cp\u003eThe composition of the fungal flora at the species level in all samples is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA. Each sample was dominated by only a few fungal species. The top fungal species averaging over 1% relative abundance are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB. In the ECMO group, unclassified fungi was the most predominant, followed in abundance by \u003cem\u003eMalassezia restricta\u003c/em\u003e. In the non-ECMO group, \u003cem\u003eEmmia lacerata\u003c/em\u003e was the most predominant, followed in abundance by \u003cem\u003eM. restricta\u003c/em\u003e. The patients with ECMO tended to have less diverse lung mycobiota than those without ECMO in terms of the Shannon and Simpson diversity indexes (\u003cb\u003eFig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). There were no differences in beta diversity based on ECMO status or number of days on mechanical ventilation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLung virome\u003c/h2\u003e \u003cp\u003eThe composition of viral flora in all samples is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eA. A bar graph of the viruses averaged by relative abundance greater than 1% in each patient group with and without ECMO is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB. All patients had COVID-19, but \u003cem\u003ehuman betaherpesvirus 5\u003c/em\u003e and \u003cem\u003ehuman alphaherpesvirus 1\u003c/em\u003e, not \u003cem\u003esevere acute respiratory syndrome coronavirus\u003c/em\u003e, were predominant. ECMO patients tended to have a lower relative abundance of \u003cem\u003ehuman betaherpesvirus 5\u003c/em\u003e and a greater relative abundance of \u003cem\u003esevere acute respiratory syndrome coronavirus\u003c/em\u003e. The changes in relative abundance of the top five viruses in the patients with ECMO are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eC. The relative abundance of \u003cem\u003ehuman betaherpesvirus 5\u003c/em\u003e tended to decrease over the 21 days, but it increased in one patient after this time.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe profiled the bacterial, fungal, and viral flora of the lower respiratory tract of patients with ARDS due to COVID-19. The microbiota of the lower respiratory tract of patients with ECMO was rich in \u003cem\u003ePseudomonas\u003c/em\u003e among the bacteria, unclassified fungi among the fungi, and \u003cem\u003ehuman betaherpesvirus 5\u003c/em\u003e (\u003cem\u003eCytomegalovirus\u003c/em\u003e: CMV\u003cem\u003e)\u003c/em\u003e among the viruses.\u003c/p\u003e \u003cp\u003eThe Proteobacteria phylum is predominant in the lower respiratory tract of patients with ARDS due to COVID-19, and an increase in the abundance of the \u003cem\u003ePseudomonas\u003c/em\u003e genus and \u003cem\u003eEnterobacter\u003c/em\u003e has been reported as a characteristic of the lower respiratory tract of patients with pneumonia due to COVID-19, which is consistent with the results of the present study [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Notably, patients with ECMO had a higher relative abundances of \u003cem\u003ePseudomonas\u003c/em\u003e and \u003cem\u003eKlebsiella\u003c/em\u003e than those without ECMO, whereas patients without ECMO had a higher abundances of \u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003eStaphylococcus aureus\u003c/em\u003e. Viral infections damage tissues of the respiratory tract pathway, leading to dysbiosis and the promotion of bacterial colony formation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In COVID-19, \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e has been reported to promote colony formation, and enrichment of \u003cem\u003eP. aeruginosa\u003c/em\u003e is associated with poor prognosis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Enrichment of \u003cem\u003ePseudomonas\u003c/em\u003e may have been greater in ECMO patients with severe ARDS, who have more severe lung injury. The oral microbiota of the elderly is rich in staphylococci and streptococci, and an increased relative abundance of \u003cem\u003eEnterobacter\u003c/em\u003e in the gut has been reported in patients with severe ARDS [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Aspiration in patients with ARDS also affects the microbiota of the lower respiratory tract [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Thus, there may be an increase in streptococci in patients without ECMO and a more confirmed presence of Enterobacter predominantly in severely ill ECMO patients. Despite the enrichment and severity of highly pathogenic microorganisms in the ECMO patients, clinical outcomes and diversity were not inferior to those of the non-ECMO patients. ECMO replaces oxygenation and carbon dioxide removal that the lungs would normally do and allows for protective ventilation that significantly reduces plateau and driving pressures [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This results in a significant reduction in the concentrations of plasma sRAGE, interleukin-6, and monocyte chemotaxis protein-1, thus limiting pulmonary biotrauma caused by mechanical ventilation [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. ECMO has also been reported to promote recovery of alveolar epithelial function in rat experiments [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The fact that changes in lung microbiota did not lead to a decrease in diversity in the ECMO patients may have contributed to the protective effect of ECMO on the lungs.\u003c/p\u003e \u003cp\u003eThe fungal flora of ARDS patients has been reported to be enriched with \u003cem\u003eCandida albicans\u003c/em\u003e, and \u003cem\u003eC. albicans\u003c/em\u003e is a risk factor for death [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This was similarly observed in ARDS with COVID-19, in which an increase in unidentified ascomycetes in ARDS patients who were not contaminated with \u003cem\u003eCandida\u003c/em\u003e spp. was reported [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This is consistent with the results of the present study, which showed patients contaminated with \u003cem\u003eC. albicans\u003c/em\u003e from an early stage and an increase in unidentified fungi in the uncontaminated cases. Notably, \u003cem\u003eM. restricta\u003c/em\u003e was abundant in the present study. \u003cem\u003eMalassezia restricta\u003c/em\u003e is endemic to the skin and intestinal tract [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and in the intestinal fungal flora, \u003cem\u003eM. restricta\u003c/em\u003e and \u003cem\u003eC. albicans\u003c/em\u003e are prominent [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Similar to the bacterial flora, \u003cem\u003eM. restricta\u003c/em\u003e may have been enriched through the gut-lung axis in ARDS patients.\u003c/p\u003e \u003cp\u003eThe respiratory virus flora are thought to play an important role in the pathogenesis of respiratory disease by interacting with the immune system [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. \u003cem\u003eTobacco mosaic virus\u003c/em\u003e is reported to be abundant in COVID-19 and both \u003cem\u003eAnelloviridae\u003c/em\u003e and \u003cem\u003eRedondoviridae\u003c/em\u003e are abundant in severe cases, and the presence of these viruses is positively correlated with intubation during hospitalization [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In the present study, there was marked enrichment of CMV and \u003cem\u003eHuman alphaherpesvirus 1 (Herpes simplex virus 1\u003c/em\u003e: HSV-1\u003cem\u003e)\u003c/em\u003e, but not \u003cem\u003eAnelloviridae\u003c/em\u003e and \u003cem\u003eRedondoviridae\u003c/em\u003e. The patients with ECMO tended to have less \u003cem\u003eHuman betaherpesvirus 5\u003c/em\u003e than those without ECMO. \u003cem\u003eHuman betaherpesvirus 5\u003c/em\u003e and \u003cem\u003eHuman alphaherpesvirus\u003c/em\u003e are the most commonly identified viruses from patients on mechanical ventilation, and viral reactivation among ICU patients, especially among the herpes group, significantly changes the virome [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. It was reported that CMV pulmonary infections, not HSV-1, were associated with increased length of mechanical ventilation and increased ICU length of stay and mortality in ventilated patients [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Given the decreasing trend in the relative abundance of CMV with the increasing duration of mechanical ventilation in ECMO patients, the lung rest provided by ECMO may contribute to the suppression of pathogenic viral enrichment. The subjects of the previously reported studies were early in the initiation of mechanical ventilation, and there are no reports on the progression of single-virus enrichment with increased duration of mechanical ventilation.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, although we detected a large number of microorganisms using metagenomic sequencing, contamination in the airways and of bronchoscopes should always be considered. We used disposable bronchoscopes to minimize contamination. Second, the present results are based on measurements from a single center and have not been validated in other cohorts. Third, we did not evaluate whether the microorganisms were truly pathogenic or only present in the airways. Further studies using animal models should provide important insights into the pathogenic role of microbiome alterations in patients with ECMO.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe ARDS patients treated with ECMO had a different lung microbiota than those without ECMO. It is speculated that critical illness, respiratory management, and a variety of other factors contribute to the lung microbiota of ARDS patients. Further studies are needed to determine how the unique respiratory management of ECMO affects the lung microbiota.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eARDS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Acute respiratory distress syndrome\u003c/p\u003e\n\u003cp\u003eECMO\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Extracorporeal membrane oxygenation\u003c/p\u003e\n\u003cp\u003eCOVID-19\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Coronavirus disease 2019\u003c/p\u003e\n\u003cp\u003eAPACHE II\u0026nbsp; \u0026nbsp; \u0026nbsp;Acute Physiologic Assessment and Chronic Health Evaluation II\u003c/p\u003e\n\u003cp\u003eSOFA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Sequential Organ Failure Assessment\u003c/p\u003e\n\u003cp\u003eIQR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Interquartile range\u003c/p\u003e\n\u003cp\u003eBALF\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Bronchoalveolar lavage fluid\u003c/p\u003e\n\u003cp\u003eCMV\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Cytomegalovirus\u003c/p\u003e\n\u003cp\u003eHSV-1\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Herpes simplex virus 1\u003c/p\u003e\n\u003cp\u003eICU \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Intensive care unit\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the\u0026nbsp;institutional review board of\u0026nbsp;Osaka General Medical Center\u0026nbsp;(approval number: 2021-002). Written informed consent was obtained from all patients. The study was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study were submitted to DDBJ/EMBL/GenBank databases under accession number PRJDB17654.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by a Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science [grant number 22K0913].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.M. designed the study, analyzed the data, and wrote the manuscript; D.M. performed sequencing and helped analyze the data;\u0026nbsp;K.S. supervised the conduction of the study. H.O., S.F., and J.O. critically revised the manuscript for intellectual content. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe appreciate the cooperation of the patients and families involved in this study. We also thank all of the medical staff for their cooperation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMan WH, de Steenhuijsen Piters WAA, Bogaert D. The microbiota of the respiratory tract: gatekeeper to respiratory health. Nat Rev Microbiol. 2017;15(5):259\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlszak T, An D, Zeissig S, Vera MP, Richter J, Franke A, et al. Microbial exposure during early life has persistent effects on natural killer T cell function. 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Acute respiratory distress syndrome: the Berlin Definition. JAMA. 2012;307(23):2526\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985;13(10):818\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVincent JL, Moreno R, Takala J, Willatts S, De Mendon\u0026ccedil;a A, Bruining H, et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine. Intensive Care Med. 1996;22(7):707\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKullberg RFJ, de Brabander J, Boers LS, Biemond JJ, Nossent EJ, Heunks LMA, et al. Lung microbiota of critically ill patients with COVID-19 are associated with nonresolving acute respiratory distress syndrome. Am J Respir Crit Care Med. 2022;206(7):846\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaibani P, Viciani E, Bartoletti M, Lewis RE, Tonetti T, Lombardo D, et al. The lower respiratory tract microbiome of critically ill patients with COVID-19. Sci Rep. 2021;11:10103.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBakaletz LO. Viral-bacterial co-infections in the respiratory tract. Curr Opin Microbiol. 2017;35:30\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQu J, Cai Z, Liu Y, Duan X, Han S, Liu J, et al. Persistent bacterial coinfection of a COVID-19 patient caused by a genetically adapted pseudomonas aeruginosa chronic colonizer. Front Cell Infect Microbiol. 2021;11:641920.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKitsios GD, Yang H, Yang L, Qin S, Fitch A, Wang XH, et al. Respiratory tract dysbiosis is associated with worse outcomes in mechanically ventilated patients. Am J Respir Crit Care Med. 2020;202(12):1666\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhelan FJ, Verschoor CP, Stearns JC, Rossi L, Luinstra K, Loeb M, et al. The loss of topography in the microbial communities of the upper respiratory tract in the elderly. Ann Am Thorac Soc. 2014;11(4):513\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDickson RP, Singer BH, Newstead MW, Falkowski NR, Erb-Downward JR, Standiford TJ, et al. Enrichment of the lung microbiome with gut bacteria in sepsis and the acute respiratory distress syndrome. Nat Microbiol. 2016;1(10):16113.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSantacruz A, Collado MC, Garc\u0026iacute;a-Vald\u0026eacute;s L, Segura MT, Mart\u0026iacute;n-Lagos JA, Anjos T, et al. Gut microbiota composition is associated with body weight, weight gain and biochemical parameters in pregnant women. Br J Nutr. 2010;104(1):83\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLamont RJ, Koo H, Hajishengallis G. The oral microbiota: dynamic communities and host interactions. Nat Rev Microbiol. 2018;16(12):745\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNatalini JG, Singh S, Segal LN. The dynamic lung microbiome in health and disease. Nat Rev Microbiol. 2023;21(4):222\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmidt M, Pham T, Arcadipane A, Agerstrand C, Ohshimo S, Pellegrino V, et al. Mechanical ventilation management during extracorporeal membrane oxygenation for acute respiratory distress syndrome. an international multicenter prospective cohort. Am J Respir Crit Care Med. 2019;200(8):1002\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRozencwajg S, Guihot A, Franchineau G, Lescroat M, Br\u0026eacute;chot N, H\u0026eacute;kimian G, et al. Ultra-protective ventilation reduces biotrauma in patients on venovenous extracorporeal membrane oxygenation for severe acute respiratory distress syndrome. Crit Care Med. 2019;47(11):1505\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang J, Zhang R, Zhai K, Li J, Yao M, Wei S, et al. Venovenous extracorporeal membrane oxygenation promotes alveolar epithelial recovery by activating Hippo/YAP signaling after lung injury. J Heart Lung Transpl. 2022;41(10):1391\u0026ndash;400.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBritton N, Yang H, Fitch A, Li K, Seyed K, Guo R et al. Respiratory fungal communities are associated with systemic inflammation and predict survival in patients with acute respiratory failure. MedRxiv. 2023;2023.05.11.23289861. [Preprint].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang P, Liu B, Zheng W, Chen Y, Wu Z, Lu Y, et al. Pulmonary microbial composition in sepsis-induced acute respiratory distress syndrome. Front Mol Biosci. 2022;9:862570.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eViciani E, Gaibani P, Castagnetti A, Liberatore A, Bartoletti M, Viale P, et al. Critically ill patients with COVID-19 show lung fungal dysbiosis with reduced microbial diversity in patients colonized with \u003cem\u003eCandida\u003c/em\u003e spp. Int J Infect Dis. 2022;117:233\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoxberger M, Cenizo V, Cassir N, La Scola B. Challenges in exploring and manipulating the human skin microbiome. Microbiome. 2021;9:125.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNash AK, Auchtung TA, Wong MC, Smith DP, Gesell JR, Ross MC, et al. The gut mycobiome of the Human Microbiome Project healthy cohort. Microbiome. 2017;5(1):153.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoung JC, Chehoud C, Bittinger K, Bailey A, Diamond JM, Cantu E, et al. Viral metagenomics reveal blooms of anelloviruses in the respiratory tract of lung transplant recipients. Am J Transpl. 2015;15(1):200\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan Y, Jia Z, Shi J, Wang W, He K. The active lung microbiota landscape of COVID-19 patients through the metatranscriptome data analysis. Bioimpacts. 2022;12(2):139\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMerenstein C, Liang G, Whiteside SA, et al. Signatures of COVID-19 Severity and Immune Response in the Respiratory Tract Microbiome. mBio. 2021;12(4):e0177721.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrantzeskaki FG, Karampi ES, Kottaridi C, Alepaki M, Routsi C, Tzanela M, et al. Cytomegalovirus reactivation in a general, nonimmunosuppressed intensive care unit population: incidence, risk factors, associations with organ dysfunction, and inflammatory biomarkers. J Crit Care. 2015;30(2):276\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiche L, Forel JM, Roch A, Guervilly C, Pauly V, Allardet-Servent J, Gainnier M, Zandotti C, Papazian L. Active cytomegalovirus infection is common in mechanically ventilated medical intensive care unit patients. Crit Care Med. 2009;37(6):1850\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"873\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\" colspan=\"11\" style=\"width: 99.5973%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1 Characteristics of the population\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" colspan=\"2\"\u003e\n \u003cp\u003eECMO (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" colspan=\"2\"\u003e\n \u003cp\u003eECMO (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" colspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" colspan=\"2\"\u003e\n \u003cp\u003en=8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" colspan=\"2\"\u003e\n \u003cp\u003en=5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eN=13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" colspan=\"2\"\u003e\n \u003cp\u003esample n=8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" colspan=\"2\"\u003e\n \u003cp\u003esample n=13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003esample N=21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eDemographics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eAge (years), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(53-74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(36-48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(44-70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eSex, male (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(84.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eBMI, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e28.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(24.8-30.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e33.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(20.3-36.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e28.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(24.6-32.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0.558\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eFormer smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(30.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eComorbidities, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0.715\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eImmunocompromise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eCardiovascular compromise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eChronic obstructive pulmonary disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eRenal insufficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eDays after onset, days, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(4-8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(7-17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(6-12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eSeverity of disease on admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eAPACHE II score, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(12-18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(17-22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(14-19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eSOFA score, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(4-9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(9-13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(6-10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eSeverity of ARDS, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Severe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(76.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eTreatment of disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eAntibiotics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(30.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0.571\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eTazobactam and piperacillin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eAmpicillin and sulbactam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eTrimethoprim and sulfamethoxazole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eAntivirals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0.592\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eLopinavir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eFavipiravir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eRemdesivir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(38.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eGlucocorticoid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eAdjunctive therapies for ARDS, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eNeuromuscular blockade\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eProne position\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(61.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eLateral position\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(38.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eDisease course\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eLength of ECMO, days, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(10-22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(10-22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eLength of mechanical ventilation, days, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(6-20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(11-29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(8-21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eLength of stay in ICU, days, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(6-12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(11-29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(7-16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eLength of stay in hospital, days, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(11-27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(22-39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(16-33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eICU mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eHospital mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\n \u003cp\u003e(20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\n \u003cp\u003e(30.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eNumber of days from admission date of sample collection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003eWithin 24 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003e2-7days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003e7-14days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.955326460481096%\"\u003e\n \u003cp\u003e\u0026gt;14dayes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.841924398625429%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.621993127147766%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.145475372279496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"11\"\u003e\n \u003cp\u003eARDS, Acute Respiratory Distress Syndrome; IQR, interquartile range; BMI, body mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"11\"\u003e\n \u003cp\u003eAPACHE, Acute Physiology and Chronic Health Evaluation; SOFA; Sequential Organ Failure Assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"11\"\u003e\n \u003cp\u003eECMO, Extracorporeal membrane oxygenation; ICU, intensive care unit; NA, not available\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\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":"Microbiota, ECMO, ARDS, Mycobiota, Virome, COVID-19","lastPublishedDoi":"10.21203/rs.3.rs-4225435/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4225435/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDiversity of the microbiota, which is essential for lower airway homeostasis, is greatly altered in acute respiratory distress syndrome (ARDS). Extracorporeal membrane oxygenation (ECMO) is the ultimate protective treatment for the lungs of patients with severe ARDS, but little is known about its effect on the lung microbiota of these patients. The aim of this study was to evaluate the effect of ECMO on the lung microbiota of ARDS patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis was a prospective, observational clinical study of ARDS patients with COVID-19. We performed 16S rRNA and fungal ITS1 profiling and shotgun sequencing on bronchoalveolar lavage fluid (BALF) samples collected from patients with ARDS due to COVID-19.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBALF was collected from 13 patients, five of whom underwent ECMO. The median age of the patients with ECMO was significantly younger than that of those without ECMO (44 [IQR: 36\u0026ndash;48] years vs. 64 [IQR: 53\u0026ndash;74] years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.007). The median APACHE II score was significantly higher in the patients with ECMO versus those without ECMO (20 [IQR: 17\u0026ndash;22] vs. 15 [IQR: 12\u0026ndash;18], p\u0026thinsp;=\u0026thinsp;0.018). In all ARDS patients, \u003cem\u003ePseudomonas\u003c/em\u003e was the most abundant of the bacteria. The patients with ECMO had more \u003cem\u003ePseudomonas\u003c/em\u003e and more \u003cem\u003eKlebsiella\u003c/em\u003e than those without ECMO. The most abundant fungi were unspecified fungi in the patients with ECMO and \u003cem\u003eEmmia lacerata\u003c/em\u003e in the patients without ECMO. Alpha diversity of bacteria and fungi did not differ significantly between the two groups. \u003cem\u003eHuman betaherpesvirus 5\u003c/em\u003e and \u003cem\u003ehuman alphaherpesvirus 1\u003c/em\u003e were predominant in all patients, with \u003cem\u003ehuman betaherpesvirus 5\u003c/em\u003e decreasing over time in the ECMO patients.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe patients with ARDS due to COVID-19 who received ECMO had a different lung microbiota than those who did not receive ECMO.\u003c/p\u003e","manuscriptTitle":"Lung microbiota of ARDS patients due to COVID-19 receiving ECMO","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-19 18:09:15","doi":"10.21203/rs.3.rs-4225435/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":"12faaa4d-0033-4e54-b57c-b8fb4bd75845","owner":[],"postedDate":"April 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-11T06:18:49+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-19 18:09:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4225435","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4225435","identity":"rs-4225435","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}