Analysis of coinfections in patients with hematologic malignancies and COVID-19 by next-generation sequencing of bronchoalveolar lavage fluid

Analysis of coinfections in patients with hematologic malignancies and COVID-19 by next-generation sequencing of bronchoalveolar lavage fluid | 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 Analysis of coinfections in patients with hematologic malignancies and COVID-19 by next-generation sequencing of bronchoalveolar lavage fluid Wenxiu Shu, Qianqian Yang, Jing Le, Qianqian Cai, Hui Dai, Liufei Luo, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3940109/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 Coinfections in patients with coronavirus disease 2019 (COVID-19) affect patient prognosis. Patients with hematologic malignancies (HMs) are usually immunosuppressed and may be at high risk of coinfection, but few related data have been reported. Here, we conducted a retrospective study to explore coinfections in patients with HMs and COVID-19 by next-generation sequencing (NGS) of bronchoalveolar lavage fluid (BALF). Methods The data of hospitalized patients with pneumonia who underwent NGS analysis of BALF were reviewed. COVID-19 patients with HMs were enrolled in the HM group, and those without HMs were enrolled in the non-HM group. The coinfections of the two groups identified by NGS were analyzed. Results Fifteen patients were enrolled in the HM group, and 14 patients were enrolled in the non-HM group. The coinfection rates in the HM group and non-HM group were 80.0% and 85.7%, respectively. The percentage of coinfected bacteria in the HM group was significantly lower than that in the non-HM group (20.0% vs 71.4%, p = 0.005). The coinfection rates of fungi and viruses were 60.0% and 35.7%, respectively, in the HM group and 35.7% and 78.6%, respectively, in the non-HM group, with no significant differences. The most common coexisting pathogen in patients with HMs was Pneumocystis jirovecii (33.3%), and the most common coexisting pathogen in patients without HMs was human gammaherpesvirus 4 (50%). Coinfection with herpesviruses occurred frequently in both groups. Conclusions Our study showed that hospitalized patients with COVID-19 had a high incidence of coinfection. Pneumocystis jiroveci and herpesvirus are commonly coinfected pathogens in patients with HMs. Bacterial coinfection is rare in patients with HMs but is more common in patients without HMs. COVID-19 hematologic malignancy coinfection next-generation sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in December 2019, and its impact on the world is still ongoing, affecting millions of people. SARS-CoV-2 is mainly transmitted by respiratory droplets. The main clinical symptoms include fever, cough, expectoration, fatigue, and dyspnea( 1 ), which are sometimes difficult to distinguish from infections caused by other respiratory agents, such as bacteria, fungi, and other viruses. Viral infections cause a decrease in host immunity, which may lead to coinfection by other pathogens, and coinfections can significantly increase the mortality rate ( 2 – 4 ). Although most patients with COVID-19 develop mild illness with low coinfection rates, an increasing number of hospitalized patients are being diagnosed with coinfections, especially patients with severe illness( 5 – 7 ). Patients with hematologic malignancies (HMs) are usually in a state of severe immunosuppression due to bone marrow suppression, cytotoxic chemotherapy, glucocorticoids, and B-cell depletion therapy, resulting in a greater risk of severe COVID-19 and mortality( 8 ). Despite concerns that these patients with COVID-19 may be at high risk of coinfection, few related data have been reported. Next-generation sequencing (NGS) is a novel technique for providing rapid and objective pathogenic diagnosis that has been proven to be especially suitable for immunodeficient patients( 9 , 10 ). Moreover, the analysis of bronchoalveolar lavage fluid (BALF) by NGS is a very effective method for diagnosing pneumonia( 11 , 12 ). Therefore, we conducted a retrospective study to explore coinfections in HM patients with COVID-19 via NGS of BALF and compared the outcomes between patients with HMs and patients without HMs. Methods Patients Patients (≥ 16 years old) with pneumonia who underwent NGS analysis of BALF from January 2023 to October 2023 at Ningbo Medical Center Li Huili Hospital were reviewed. Patients with SARS-CoV-2-positive results according to NGS were enrolled in this study. We divided the enrolled patients into two groups: the HM group (patients with HMs) and the non-HM group (patients without HMs). Outcomes were compared between the two groups. Patients without HMs but with other hematologic diseases, such as aplastic anemias and autoimmune anemias, were excluded. Baseline data collection The baseline characteristics of the patients at the time of hospitalization were collected, such as sex, age, smoking history, performance status (PS) according to the Eastern Cooperative Oncology Group (ECOG)( 13 ), comorbidities (diabetes, pulmonary comorbidities and cardiac comorbidities), history of malignancy, previous treatments, laboratory parameters, radiological findings for interstitial pneumonia (IP) and severity of COVID-19. The necessary radiological findings of IP include diffuse pulmonary interstitial infiltration and other manifestations, such as traction bronchiectasis, bilateral reticular opacities, loss of lobe volume, and opacity in the lower lungs on computed tomography (CT) scans ( 14 – 16 ). Severe COVID-19 was defined as an SpO2 < 94% on room air, a ratio of arterial partial pressure of oxygen to fraction of inspired oxygen (PaO2/FiO2) 30 breaths/min, or > 50% lung infiltrates according to the National Institutes of Health ( 17 ). BALF collection and NGS analysis Senior respiratory physicians performed bronchoscopy and BAFL acquisition according to standard procedures( 18 ). To avoid contamination, the initial 20 ml BAFL sample was discarded, and another 20 ml BALF sample was collected for NGS analysis. NGS testing was performed at Matridx Biotechnology Co., Ltd. (Hangzhou, China). Total nucleic acid was extracted from 5 ml of BALF. DNA or RNA sequencing libraries were prepared by automatic nucleic acid extraction, reverse transcription (for RNA), enzymatic fragmentation, end repair, terminal adenylation and adaptor ligation (NGSmaster ™ library preparation, Cat# MAR002, Matridx, Hangzhou, China). The concentrations of the libraries were quantified real-time polymerase chain reaction (KAPA). Libraries were pooled and subsequently sequenced on an Illumina NextSeq platform. Approximately 20 million 75 bp single-end reads were generated for each library. For each run, one negative control and one positive control (with the RNA fragment of the adenovirus) were included for quality control. The sequencing data were first demultiplexed to obtain the sequence reads of each sample in fastq format. High-quality sequencing data were generated after removing short (< 35 bp) reads and low-quality and low-complexity reads. Then, the sequence reads of each sample were aligned to the human reference genome (GRCh38.p13) to eliminate human sequences. The remaining reads were aligned to a reference database (the NCBI nt database and GenBank) to identify microbial species. Microbial reads identified from a library were reported if they met the following criteria: 1) the sequencing data passed quality control filters (library concentration > 50 pM, Q20 > 85%, Q30 > 80%); 2) the species were different from the negative control (NC) of the same sequencing run or the ratio of RPM (sample) to RPM (NC) reached the cutoff that can discriminate true positives from contaminants and backgrounds (RPM (sample)/RPM (NC) ≥ 5). Statistical analysis Absolute and percentage frequencies were used for categorical variables, and differences between groups were analyzed by Fisher’s exact test. Medians and ranges were used for continuous variables, and differences between groups were analyzed by the Mann‒Whitney test. Kaplan‒Meier curves were generated to display survival after SARS-CoV-2 infection, and the log-rank test was used for comparison. Multivariate logistic regression was performed to assess the risk factors for severe COVID-19. Factors significant in the univariate logistic regression at the 0.10 level were included in the multivariate model. Forest plots were generated to present the outcomes of the multivariate analysis. The 95% confidence intervals (CIs) were used to estimate odds ratios (ORs). All tests were two-tailed, and P values ≤ 0.05 were considered statistically significant. All analyses were performed using the statistical software SPSS v. 25, and figures were drawn with GraphPad Prism 9. Results Patient characteristics Between January 2023 and October 2023, 784 patients with pneumonia underwent NGS analysis of BALF, and 31 patients were SARS-CoV-2 positive. One patient with aplastic anemia and one patient with autoimmune anemia were excluded. Overall, 15 patients with HMs (14 patients with lymphoma and one patient with multiple myeloma) and 14 patients without HMs were enrolled in the study. The flowchart is shown in Fig. 1 . The baseline characteristics are shown in Table 1 . The median ages of patients in the HM group and no-HM group were 63 and 67 years, respectively. Patients in the HM group had better PS (p = 0.014) and a lower incidence of comorbidities (p = 0.050) than those in the no-HM group. All the patients in the HM group received previous antitumor therapy, and 86.7% of them accepted anti-CD20 monoclonal antibody (mAB) therapy. Two patients in the non-HM group had a history of lung cancer, and 1 of them had previously received PD-1 therapy. Half of the patients in the non-HM group presented with severe pneumonia, whereas 33.3% of the patients in the HM group presented severe pneumonia (p = 0.462). Table 1 Baseline characteristics of patients Characteristics Patients with HMs(n = 15) Patients without HMs (n = 14) P value Age, median(range) 63(43–77) 67(17–88) 0.382 Sex Male 9(60.0%) 9(64.3%) 1.000 Female 6(40.0%) 5(35.7%%) Smoking 2(13.3%) 2(14.3%) 1.000 ECOG PS score 0.014 ≤ 2 14(93.3%) 7(50.0%) >2 1(6.7%) 7(50.0%) Comorbidities † 2(13.3%) 7(50.0%) 0.050 Malignancy 15(100%) 2(14.3%) < 0.001 Antitumor treatments 15(100%) 1(7.1%) < 0.001 Anti-CD20 mABs 13(86.7%) / CART 1(6.7%) / Stem cell transplantation 1(6.7%) / Neutrophil 2.6(0.9–8.7) 8.0(1.7–20.7) 0.055 Lymphocyte 0.8(0.2–2.7) 1.1(0.2–1.8) 0.759 High-sensitivity C-reactive protein 34.4(5.6-106.9) 28.2(0.5–346.0) 0.663 Albumin 37.5(22.2–41.5) 32.2(23.2–46.8) 0.077 Lactic dehydrogenase 271(151–505) 184(134–434) 0.169 Interstitial pneumonia 11(73.3%) 7(50.0%) 0.264 Severe COVID-19 5(33.3%) 7(50.0%) 0.462 † Comorbidities included diabetes, pulmonary comorbidities, and cardiac comorbidities. Abbreviations: ECOG PS: Eastern Co-operative Oncology Group Performance Status; mAB: monoclonal antibody; CART: Chimeric antigen receptor-T cell; COVID-19: coronavirus disease 2019; HM: hematologic malignancy. Pathogens detected by NGS A heatmap was drawn to show the pathogens and their abundance detected by NGS (Fig. 2 ). The most common coexisting pathogens in patients with HMs were Pneumocystis jirovecii (33.3%), Candida albicans (26.7%), human alphaherpesvirus 1 (26.7%) and human betaherpesvirus 5 (20.0% ) . The most common coexisting pathogens in patients without HMs were human gammaherpesvirus 4 ( ( Epstein-Barr virus, 50%), human alphaherpesvirus 1 (cytomegalovirus, 35.7%), human betaherpesvirus 5 (21.4%), Candida albicans (21.4%) and Enterococcus faecalis (21.4%). The sequence numbers of detected species-specific pathogens are shown by the color depth in the heatmap. Comparison of coinfections in the HM and non-HM groups The overall coinfection rates in the HM group and non-HM group were 80.0% and 85.7%, respectively, with no significant difference. The coinfection rate of bacteria in patients with HMs was significantly lower than that in patients without HMs (20.0% vs 71.4%, p = 0.005). The coinfection rates of fungi and viruses were 60.0% and 35.7%, respectively, in patients with HMs and 35.7% and 78.6%, respectively, in patients without HMs. There was no significant difference between the two groups (Fig. 3 A). We then listed the common coinfected pathogens between the two groups at the genus level (Fig. 3 B, 3 C, 3 D). Only three patients had coinfections with bacteria in patients with HMs, namely, Elizabethkingia, Escherichia , and Enterobacter , at the genus level. The most commonly detected coinfections of bacterial genera in patients without HMs were Enterococcus (21.4%), Escherichia (14.3%), Corynebacterium (14.3%), and Streptococcus (14.3%). There was no significant difference in the coinfection rate of each bacterium at the genus level between the two groups. The largest proportion of fungal genera in patients without HMs was Pneumocystis (33.3%), which seems to be greater than the proportion in patients without HMs (7.1%), but the difference was not statistically significant. The other fungal genera coinfected with HMs at high rates were Candida (26.7%) and Aspergillus (6.7%), which were similar to the findings in patients without HMs. Lymphocryptovirus was highly detected in patients without HMs, which was significantly greater than that in patients with HMs (50% vs 0.0%, p = 0.002). Other coinfected viral genera with high rates in the two groups were simplex virus (26.7% in the HM group vs 35.7% in the non-HM group, p = 0.700) and cytomegalovirus (20.0% in the HM group vs 21.4% in the non-HM group, p = 1.000). The 90-day survivals of the two groups are shown in Fig. 4 . The mortality rate was 13.3% (2/15) in the HM group and 28.6% (4/14) in the non-HM group, with no significant difference. Risk factors for severe COVID-19 We performed multivariate logistic regression analyses of the factors associated with severe COVID-19, and the results are shown in Fig. 5 . Coinfection with bacteria was an independent risk factor for severe disease (OR 19.61, 95% CI 1.32-292.05; p = 0.031). No other factors were found to be associated with severe disease, probably because of the small sample size. Discussion Although COVID-19 has been effectively controlled, it can still cause severe pneumonia and death, especially in immunocompromised patients and elderly patients. Respiratory virus infections can increase susceptibility to secondary bacterial or fungal infections, and coinfections can have an adverse effect on prognosis( 6 , 7 , 19 , 20 ). Previous studies reported that the probability of COVID-19 coinfection was 8%-14.5%( 5 , 6 , 21 ). In a study of all hospitals or outpatient patients with malignancies, the incidence of coinfections was 16.6%( 22 ). In another study of patients with malignancies or who underwent organ transplantation in the intensive care unit, the incidence of coinfections was 27%, whereas it was as high as 46.7% in patients with HMs( 23 ). However, the main microbiological detection methods used in previous studies were traditional methods, and their sensitivity remains to be evaluated. To the best of our knowledge, this is the first study to describe coinfections in HM patients with SARS-CoV-2-caused pneumonia by detecting the BALF of patients using the highly sensitive NGS method. Our study showed that the coinfection rates of patients with HMs and those without HMs were 80.0% and 85.7%, respectively, which were significantly greater than those previously reported. The NGS method we used in this study was highly more sensitive than traditional microbiological detection methods used in previous studies, which may account for the greater rate of coinfection in our study. Pneumocystis jirovecii was the most common coinfected pathogen, with a coinfection rate of 33.3%. Pneumocystis jirovecii is a common opportunistic infection pathogen in immunocompromised patients. Pneumocystis jiroveci pneumonia may also present as diffuse pulmonary interstitial infiltration( 24 – 26 ), which is sometimes difficult to distinguish from SARS-CoV-2 pneumonia. The traditional detection methods for Pneumocystis jiroveci infection have poor sensitivity, but NGS has been proven to be an effective method for detecting this disease( 27 – 30 ) ( 9 ). In our previous study of lymphoma patients with chemotherapy-related IP, Pneumocystis jirovecii was detected in the BALF of 12 of 15 patients by NGS( 29 ). In this study, all the patients with HMs had previously received chemotherapy, and 13 of 15 (86.7%) patients had received anti-CD20 mAbs, which may have resulted in severe immunodeficiency and increased susceptibility to Pneumocystis jirovecii . These data suggest that identifying Pneumocystis jirovecii coinfected with COVID-19 is necessary in HM patients after chemotherapy. In patients with a long course of SARS-CoV-2 pneumonia, NGS testing of BALF and anti-pneumocystis therapy may be considered. Previous studies have reported that the probability of bacterial coinfection in patients with COVID-19 is approximately 8%-15%, while the incidence is relatively high in critically ill patients (approximately 20%-30%)( 5 , 6 , 31 ). Our study showed that the probability of bacterial coinfection in patients with HMs was significantly lower than that in patients without HMs. This may be related to the differences in baseline characteristics between the two groups. Patients in the non-HM group had worse performance status and more comorbidities. Moreover, half of the patients in the non-HM group had severe disease. This selection bias may be due to the differences between hematologists and respiratory physicians in deciding which patients to perform bronchoscopy and NGS. For COVID-19 patients without HMs, respiratory physicians may suggest bronchoscopy for more critically ill patients. Multivariate analysis in our study also showed that bacterial coinfection was associated with severe disease. Notably, according to previous reports, the majority of hospitalized COVID-19 patients received antibiotics, despite the low incidence of bacterial coinfection( 6 , 32 ). The overuse of antibiotics can increase the risk of multidrug-resistant infections and lead to poor prognosis( 33 ). Therefore, we should carefully evaluate the use of antibiotics in HM patients with mild COVID-19. The incidence of viral coinfection reported in previous literature was 2.1%( 22 ), which was significantly lower than that in our study. This may be due to the poor sensitivity of traditional virus detection methods. In our study, coinfection with herpesviruses occurred frequently in the two groups. Previous studies showed that herpesviruses, such as Epstein-Barr virus and cytomegalovirus, are common in critically ill patients, patients with hematologic disorders, and patients treated with immunosuppressive agents( 34 – 37 ). Moreover, the reactivation of herpesviruses is associated with the severity and length of COVID-19 symptoms( 38 , 39 ). Gold et al. suggested that long COVID-19 symptoms may not be a direct result of the SARS-CoV-2 virus but may be the result of COVID-19-induced Epstein-Barr virus reactivation( 40 ). Furthermore, anti-herpesvirus therapy with ganciclovir may reduce the risk of death in patients with severe COVID-19( 41 ). Therefore, coinfection with herpesviruses may affect the prognosis of patients with COVID-19. The high detection rate of herpesviruses in our study suggested that we need to pay attention to coinfections caused by these viruses and provide effective treatment. There are several limitations of our study. First, this was a single-center study, and the results only represent coinfections around that center. Second, because this was a retrospective study, the baseline characteristics of patients in the HM group and non-HM group were not completely compared. Patients in the non-HM group had worse performance status and more comorbidities and seemed to have more severe disease. Finally, the sample size was small, resulting in no significant differences in the comparison of some outcomes between the two groups. Conclusions Our study showed that hospitalized patients with COVID-19 had a high proportion of coinfections. Pneumocystis jiroveci and herpesvirus are commonly coinfected pathogens in patients with HMs. Bacterial coinfection is rare in patients with HMs but is more common in patients without HMs. Abbreviations BALF bronchoalveolar lavage fluid CI confidence interval COVID-19 coronavirus disease 2019 CT computed tomography ECOG Eastern Cooperative Oncology Group HM hematologic malignancy IP interstitial pneumonia mAB monoclonal antibody NC negative control NGS next-generation sequencing) OR odds ratio PS performance status SARS-CoV-2 severe acute respiratory syndrome coronavirus 2 Declarations Ethics approval and consent to participate The study was approved by the Ethical Review Committee of Ningbo Medical Center Li Huili Hospital (approval No. YJZ2023SL2). The procedures used in this study adhere to the tenets of the Declaration of Helsinki. Informed consent was obtained from all subjects and/or their legal guardian(s). Consent for publication Not applicable. Availability of data and materials The DNA and RNA sequencing data are available from Matridx Biotechnology Co., Ltd. but restrictions apply to the availability of these data, which were used for the current study, and so are not publicly available. However, data are available from the authors upon reasonable request and with permission of Matridx Biotechnology Co., Ltd. Other datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by by Ningbo Medical Science and Technology Project (reference:2018A64). Authors' contributions DJ conceived the study; WS and QY analyzed data and wrote the paper; JL revised the paper; QC, HD, LL, JT, YS, BC, YT collected data;All authors read and approved the final manuscript. 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Zubchenko S, Kril I, Nadizhko O, Matsyura O, Chopyak V. Herpesvirus infections and post-COVID-19 manifestations: a pilot observational study. Rheumatol Int. 2022;42(9):1523-30. Simonnet A, Engelmann I, Moreau AS, Garcia B, Six S, El Kalioubie A, et al. High incidence of Epstein-Barr virus, cytomegalovirus, and human-herpes virus-6 reactivations in critically ill patients with COVID-19. Infect Dis Now. 2021;51(3):296-9. Gold JE, Okyay RA, Licht WE, Hurley DJ. Investigation of Long COVID Prevalence and Its Relationship to Epstein-Barr Virus Reactivation. Pathogens. 2021;10(6). Liu J, Zhang S, Wu Z, Shang Y, Dong X, Li G, et al. Clinical outcomes of COVID-19 in Wuhan, China: a large cohort study. Ann Intensive Care. 2020;10(1):99. 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-3940109","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274818690,"identity":"b2bc8c0a-9d19-46e7-baf9-84302ed04452","order_by":0,"name":"Wenxiu Shu","email":"","orcid":"","institution":"Ningbo Medical Center Li Huili Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenxiu","middleName":"","lastName":"Shu","suffix":""},{"id":274818691,"identity":"18ff11a2-375c-4a07-852c-34ac6427ecce","order_by":1,"name":"Qianqian Yang","email":"","orcid":"","institution":"Ningbo Medical Center Li Huili Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qianqian","middleName":"","lastName":"Yang","suffix":""},{"id":274818692,"identity":"de8873d6-4bbc-47c3-b097-67d9c1d46d24","order_by":2,"name":"Jing Le","email":"","orcid":"","institution":"Ningbo Medical Center Li Huili Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Le","suffix":""},{"id":274818693,"identity":"17b03863-01b4-487b-9fa2-661248e6549a","order_by":3,"name":"Qianqian Cai","email":"","orcid":"","institution":"Ningbo Medical Center Li Huili Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qianqian","middleName":"","lastName":"Cai","suffix":""},{"id":274818694,"identity":"120d9df7-5477-435a-b0f6-c1a577d2b844","order_by":4,"name":"Hui Dai","email":"","orcid":"","institution":"Ningbo Medical Center Li Huili Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Dai","suffix":""},{"id":274818695,"identity":"c9ef8c64-d93a-486a-bd71-931c122fff3b","order_by":5,"name":"Liufei Luo","email":"","orcid":"","institution":"Ningbo Medical Center Li Huili Hospital","correspondingAuthor":false,"prefix":"","firstName":"Liufei","middleName":"","lastName":"Luo","suffix":""},{"id":274818696,"identity":"f818c675-a094-40d3-a437-b9f73063430f","order_by":6,"name":"Jiaqi Tong","email":"","orcid":"","institution":"Ningbo Medical Center Li Huili Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiaqi","middleName":"","lastName":"Tong","suffix":""},{"id":274818697,"identity":"c2959621-5349-466b-b309-7aca6253545e","order_by":7,"name":"Yanping Song","email":"","orcid":"","institution":"Ningbo Medical Center Li Huili Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yanping","middleName":"","lastName":"Song","suffix":""},{"id":274818698,"identity":"4633df56-6c33-4620-881b-d8a61521c8a9","order_by":8,"name":"Bingrong Chen","email":"","orcid":"","institution":"Ningbo Medical Center Li Huili Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bingrong","middleName":"","lastName":"Chen","suffix":""},{"id":274818699,"identity":"eb9d74a9-4738-41de-bb3a-bafbcc74d208","order_by":9,"name":"Yaodong Tang","email":"","orcid":"","institution":"Ningbo Medical Center Li Huili Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yaodong","middleName":"","lastName":"Tang","suffix":""},{"id":274818700,"identity":"4e525856-dd46-4dbe-84f2-1f4c6b57868b","order_by":10,"name":"Dian Jin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYJCCAwwMbIz9DAwJQDYzCVpmNpCiBQQYNxwA00RokZ/d+/BwwS8+2c3nDzyTYKiwTmxgP3sArxaDO8cNDs/sYzPeduBAmgTDmfTEBp68BPxaJNIYDvP2sCVuO9iQJsHYdjixQYLHAL/DZkC1bG5mAGr5R4QWhhtALTw/2BI3sIG0NBChxeDOMaAtDWzGM84wJFskHEs3buPJIeCw2W3Mn3n+HJPt7z+TeONDjbVsP/sZAg6TAGLGtmNAkicBHJls+NVDtTD8qQES7AcIKh4Fo2AUjIKRCQA6gkeaJGk9XgAAAABJRU5ErkJggg==","orcid":"","institution":"Ningbo Medical Center Li Huili Hospital","correspondingAuthor":true,"prefix":"","firstName":"Dian","middleName":"","lastName":"Jin","suffix":""}],"badges":[],"createdAt":"2024-02-08 13:51:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3940109/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3940109/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51715342,"identity":"02e8f121-b402-4404-9ac4-60c5cf7fc4fc","added_by":"auto","created_at":"2024-02-27 20:57:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":28340,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of patient selection in this study. Abbreviations: NGS:next-generation sequencing; BALF: bronchoalveolar lavage fluid; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; HM: hematologic malignancy.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3940109/v1/77e3c45badfe022b56a79aa5.png"},{"id":51715346,"identity":"2323ea5c-02dd-43f0-bbb6-32622ebdc973","added_by":"auto","created_at":"2024-02-27 20:57:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":841898,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of the pathogens and their sequence numbersdetected by next-generation sequencingin patients with hematologic malignancies (A) and patients without hematologic malignancies (B). Abbreviations: SARS-CoV-2: severe acute respiratory syndrome coronavirus 2.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3940109/v1/ac6d40b775b1bb7e167ff3d1.png"},{"id":51715343,"identity":"2c8d5d29-d493-4023-b7e7-e3bf805a9300","added_by":"auto","created_at":"2024-02-27 20:57:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":510921,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of coexisting pathogens between the HM and non-HM groups. (A) Overall coinfection, bacterial coinfection, fungal coinfection, and viral coinfection in the two groups. (B) Coinfection of bacteria in the two groups at the genus level. (C) Coinfection of fungus in the two groups at the genus level. (D) Coinfection of viruses in the two groups at the genus level. Abbreviations: HM: hematologic malignancy.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3940109/v1/c38ca9923b6b7394c3af6635.png"},{"id":51715345,"identity":"ce10130c-a72c-4f0a-88dc-87fc10684e70","added_by":"auto","created_at":"2024-02-27 20:57:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":74770,"visible":true,"origin":"","legend":"\u003cp\u003e90-day survival in the HM and non-HM groups. Abbreviations: HM: hematologic malignancy.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3940109/v1/dcf84125a4b2295323b69e14.png"},{"id":51716222,"identity":"b90528bf-be55-4da2-853e-ebea8a707deb","added_by":"auto","created_at":"2024-02-27 21:05:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":165903,"visible":true,"origin":"","legend":"\u003cp\u003eRisk factors for severe COVID-19. Abbreviations: ECOG: Eastern Co-operative Oncology Group; OR:odds ratio; CI: confidence interval.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3940109/v1/d30fe560ec44f12794c0078f.png"},{"id":61285648,"identity":"1cb6ea20-c399-4dd6-a2a5-63f0c3479f17","added_by":"auto","created_at":"2024-07-29 06:25:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1956652,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3940109/v1/85bb22e3-2634-4194-9620-b1511e480652.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of coinfections in patients with hematologic malignancies and COVID-19 by next-generation sequencing of bronchoalveolar lavage fluid","fulltext":[{"header":"Background","content":"\u003cp\u003eCoronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in December 2019, and its impact on the world is still ongoing, affecting millions of people. SARS-CoV-2 is mainly transmitted by respiratory droplets. The main clinical symptoms include fever, cough, expectoration, fatigue, and dyspnea(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), which are sometimes difficult to distinguish from infections caused by other respiratory agents, such as bacteria, fungi, and other viruses. Viral infections cause a decrease in host immunity, which may lead to coinfection by other pathogens, and coinfections can significantly increase the mortality rate (\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Although most patients with COVID-19 develop mild illness with low coinfection rates, an increasing number of hospitalized patients are being diagnosed with coinfections, especially patients with severe illness(\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePatients with hematologic malignancies (HMs) are usually in a state of severe immunosuppression due to bone marrow suppression, cytotoxic chemotherapy, glucocorticoids, and B-cell depletion therapy, resulting in a greater risk of severe COVID-19 and mortality(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Despite concerns that these patients with COVID-19 may be at high risk of coinfection, few related data have been reported.\u003c/p\u003e \u003cp\u003eNext-generation sequencing (NGS) is a novel technique for providing rapid and objective pathogenic diagnosis that has been proven to be especially suitable for immunodeficient patients(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Moreover, the analysis of bronchoalveolar lavage fluid (BALF) by NGS is a very effective method for diagnosing pneumonia(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Therefore, we conducted a retrospective study to explore coinfections in HM patients with COVID-19 via NGS of BALF and compared the outcomes between patients with HMs and patients without HMs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003ePatients (\u0026ge;\u0026thinsp;16 years old) with pneumonia who underwent NGS analysis of BALF from January 2023 to October 2023 at Ningbo Medical Center Li Huili Hospital were reviewed. Patients with SARS-CoV-2-positive results according to NGS were enrolled in this study. We divided the enrolled patients into two groups: the HM group (patients with HMs) and the non-HM group (patients without HMs). Outcomes were compared between the two groups. Patients without HMs but with other hematologic diseases, such as aplastic anemias and autoimmune anemias, were excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eBaseline data collection\u003c/h2\u003e \u003cp\u003eThe baseline characteristics of the patients at the time of hospitalization were collected, such as sex, age, smoking history, performance status (PS) according to the Eastern Cooperative Oncology Group (ECOG)(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), comorbidities (diabetes, pulmonary comorbidities and cardiac comorbidities), history of malignancy, previous treatments, laboratory parameters, radiological findings for interstitial pneumonia (IP) and severity of COVID-19. The necessary radiological findings of IP include diffuse pulmonary interstitial infiltration and other manifestations, such as traction bronchiectasis, bilateral reticular opacities, loss of lobe volume, and opacity in the lower lungs on computed tomography (CT) scans (\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Severe COVID-19 was defined as an SpO2\u0026thinsp;\u0026lt;\u0026thinsp;94% on room air, a ratio of arterial partial pressure of oxygen to fraction of inspired oxygen (PaO2/FiO2)\u0026thinsp;\u0026lt;\u0026thinsp;300 mmHg, a respiratory rate\u0026thinsp;\u0026gt;\u0026thinsp;30 breaths/min, or \u0026gt;\u0026thinsp;50% lung infiltrates according to the National Institutes of Health (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eBALF collection and NGS analysis\u003c/h2\u003e \u003cp\u003eSenior respiratory physicians performed bronchoscopy and BAFL acquisition according to standard procedures(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). To avoid contamination, the initial 20 ml BAFL sample was discarded, and another 20 ml BALF sample was collected for NGS analysis.\u003c/p\u003e \u003cp\u003eNGS testing was performed at Matridx Biotechnology Co., Ltd. (Hangzhou, China). Total nucleic acid was extracted from 5 ml of BALF. DNA or RNA sequencing libraries were prepared by automatic nucleic acid extraction, reverse transcription (for RNA), enzymatic fragmentation, end repair, terminal adenylation and adaptor ligation (NGSmaster\u003csup\u003e\u0026trade;\u003c/sup\u003e library preparation, Cat# MAR002, Matridx, Hangzhou, China). The concentrations of the libraries were quantified real-time polymerase chain reaction (KAPA). Libraries were pooled and subsequently sequenced on an Illumina NextSeq platform. Approximately 20\u0026nbsp;million 75 bp single-end reads were generated for each library. For each run, one negative control and one positive control (with the RNA fragment of the adenovirus) were included for quality control.\u003c/p\u003e \u003cp\u003eThe sequencing data were first demultiplexed to obtain the sequence reads of each sample in fastq format. High-quality sequencing data were generated after removing short (\u0026lt;\u0026thinsp;35 bp) reads and low-quality and low-complexity reads. Then, the sequence reads of each sample were aligned to the human reference genome (GRCh38.p13) to eliminate human sequences. The remaining reads were aligned to a reference database (the NCBI nt database and GenBank) to identify microbial species.\u003c/p\u003e \u003cp\u003eMicrobial reads identified from a library were reported if they met the following criteria: 1) the sequencing data passed quality control filters (library concentration\u0026thinsp;\u0026gt;\u0026thinsp;50 pM, Q20\u0026thinsp;\u0026gt;\u0026thinsp;85%, Q30\u0026thinsp;\u0026gt;\u0026thinsp;80%); 2) the species were different from the negative control (NC) of the same sequencing run or the ratio of RPM (sample) to RPM (NC) reached the cutoff that can discriminate true positives from contaminants and backgrounds (RPM (sample)/RPM (NC)\u0026thinsp;\u0026ge;\u0026thinsp;5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAbsolute and percentage frequencies were used for categorical variables, and differences between groups were analyzed by Fisher\u0026rsquo;s exact test. Medians and ranges were used for continuous variables, and differences between groups were analyzed by the Mann‒Whitney test. Kaplan‒Meier curves were generated to display survival after SARS-CoV-2 infection, and the log-rank test was used for comparison. Multivariate logistic regression was performed to assess the risk factors for severe COVID-19. Factors significant in the univariate logistic regression at the 0.10 level were included in the multivariate model. Forest plots were generated to present the outcomes of the multivariate analysis. The 95% confidence intervals (CIs) were used to estimate odds ratios (ORs). All tests were two-tailed, and P values\u0026thinsp;\u0026le;\u0026thinsp;0.05 were considered statistically significant. All analyses were performed using the statistical software SPSS v. 25, and figures were drawn with GraphPad Prism 9.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eBetween January 2023 and October 2023, 784 patients with pneumonia underwent NGS analysis of BALF, and 31 patients were SARS-CoV-2 positive. One patient with aplastic anemia and one patient with autoimmune anemia were excluded. Overall, 15 patients with HMs (14 patients with lymphoma and one patient with multiple myeloma) and 14 patients without HMs were enrolled in the study. The flowchart is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe baseline characteristics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median ages of patients in the HM group and no-HM group were 63 and 67 years, respectively. Patients in the HM group had better PS (p\u0026thinsp;=\u0026thinsp;0.014) and a lower incidence of comorbidities (p\u0026thinsp;=\u0026thinsp;0.050) than those in the no-HM group. All the patients in the HM group received previous antitumor therapy, and 86.7% of them accepted anti-CD20 monoclonal antibody (mAB) therapy. Two patients in the non-HM group had a history of lung cancer, and 1 of them had previously received PD-1 therapy. Half of the patients in the non-HM group presented with severe pneumonia, whereas 33.3% of the patients in the HM group presented severe pneumonia (p\u0026thinsp;=\u0026thinsp;0.462).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatients with HMs(n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatients without HMs (n\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, median(range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63(43\u0026ndash;77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67(17\u0026ndash;88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(60.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(64.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(35.7%%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECOG PS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(93.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntitumor treatments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-CD20 mABs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(86.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCART\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStem cell transplantation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.6(0.9\u0026ndash;8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.0(1.7\u0026ndash;20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8(0.2\u0026ndash;2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1(0.2\u0026ndash;1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-sensitivity C-reactive protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.4(5.6-106.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.2(0.5\u0026ndash;346.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.5(22.2\u0026ndash;41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.2(23.2\u0026ndash;46.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactic dehydrogenase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e271(151\u0026ndash;505)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e184(134\u0026ndash;434)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterstitial pneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(73.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere COVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003eComorbidities included diabetes, pulmonary comorbidities, and cardiac comorbidities. Abbreviations: ECOG PS: Eastern Co-operative Oncology Group Performance Status; mAB: monoclonal antibody; CART: Chimeric antigen receptor-T cell; COVID-19: coronavirus disease 2019; HM: hematologic malignancy.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePathogens detected by NGS\u003c/h2\u003e \u003cp\u003eA heatmap was drawn to show the pathogens and their abundance detected by NGS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The most common coexisting pathogens in patients with HMs were \u003cem\u003ePneumocystis jirovecii\u003c/em\u003e (33.3%), \u003cem\u003eCandida albicans\u003c/em\u003e (26.7%), \u003cem\u003ehuman alphaherpesvirus 1\u003c/em\u003e (26.7%) and \u003cem\u003ehuman betaherpesvirus 5\u003c/em\u003e (20.0%\u003cem\u003e)\u003c/em\u003e. The most common coexisting pathogens in patients without HMs were \u003cem\u003ehuman gammaherpesvirus 4\u003c/em\u003e (\u003cem\u003e(\u003c/em\u003eEpstein-Barr virus, 50%), \u003cem\u003ehuman alphaherpesvirus 1\u003c/em\u003e (cytomegalovirus, 35.7%), \u003cem\u003ehuman betaherpesvirus 5 (21.4%), Candida albicans\u003c/em\u003e (21.4%) and \u003cem\u003eEnterococcus faecalis\u003c/em\u003e (21.4%). The sequence numbers of detected species-specific pathogens are shown by the color depth in the heatmap.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eComparison of coinfections in the HM and non-HM groups\u003c/h2\u003e \u003cp\u003eThe overall coinfection rates in the HM group and non-HM group were 80.0% and 85.7%, respectively, with no significant difference. The coinfection rate of bacteria in patients with HMs was significantly lower than that in patients without HMs (20.0% vs 71.4%, p\u0026thinsp;=\u0026thinsp;0.005). The coinfection rates of fungi and viruses were 60.0% and 35.7%, respectively, in patients with HMs and 35.7% and 78.6%, respectively, in patients without HMs. There was no significant difference between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eWe then listed the common coinfected pathogens between the two groups at the genus level (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Only three patients had coinfections with bacteria in patients with HMs, namely, \u003cem\u003eElizabethkingia, Escherichia\u003c/em\u003e, and \u003cem\u003eEnterobacter\u003c/em\u003e, at the genus level. The most commonly detected coinfections of bacterial genera in patients without HMs were \u003cem\u003eEnterococcus\u003c/em\u003e (21.4%), \u003cem\u003eEscherichia\u003c/em\u003e (14.3%), \u003cem\u003eCorynebacterium\u003c/em\u003e (14.3%), and \u003cem\u003eStreptococcus\u003c/em\u003e (14.3%). There was no significant difference in the coinfection rate of each bacterium at the genus level between the two groups. The largest proportion of fungal genera in patients without HMs was \u003cem\u003ePneumocystis\u003c/em\u003e (33.3%), which seems to be greater than the proportion in patients without HMs (7.1%), but the difference was not statistically significant. The other fungal genera coinfected with HMs at high rates were \u003cem\u003eCandida\u003c/em\u003e (26.7%) and \u003cem\u003eAspergillus\u003c/em\u003e (6.7%), which were similar to the findings in patients without HMs.\u003c/p\u003e \u003cp\u003e \u003cem\u003eLymphocryptovirus\u003c/em\u003e was highly detected in patients without HMs, which was significantly greater than that in patients with HMs (50% vs 0.0%, p\u0026thinsp;=\u0026thinsp;0.002). Other coinfected viral genera with high rates in the two groups were \u003cem\u003esimplex virus\u003c/em\u003e (26.7% in the HM group vs 35.7% in the non-HM group, p\u0026thinsp;=\u0026thinsp;0.700) and \u003cem\u003ecytomegalovirus\u003c/em\u003e (20.0% in the HM group vs 21.4% in the non-HM group, p\u0026thinsp;=\u0026thinsp;1.000).\u003c/p\u003e \u003cp\u003eThe 90-day survivals of the two groups are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The mortality rate was 13.3% (2/15) in the HM group and 28.6% (4/14) in the non-HM group, with no significant difference.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRisk factors for severe COVID-19\u003c/h2\u003e \u003cp\u003eWe performed multivariate logistic regression analyses of the factors associated with severe COVID-19, and the results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Coinfection with bacteria was an independent risk factor for severe disease (OR 19.61, 95% CI 1.32-292.05; p\u0026thinsp;=\u0026thinsp;0.031). No other factors were found to be associated with severe disease, probably because of the small sample size.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAlthough COVID-19 has been effectively controlled, it can still cause severe pneumonia and death, especially in immunocompromised patients and elderly patients. Respiratory virus infections can increase susceptibility to secondary bacterial or fungal infections, and coinfections can have an adverse effect on prognosis(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Previous studies reported that the probability of COVID-19 coinfection was 8%-14.5%(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). In a study of all hospitals or outpatient patients with malignancies, the incidence of coinfections was 16.6%(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). In another study of patients with malignancies or who underwent organ transplantation in the intensive care unit, the incidence of coinfections was 27%, whereas it was as high as 46.7% in patients with HMs(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). However, the main microbiological detection methods used in previous studies were traditional methods, and their sensitivity remains to be evaluated. To the best of our knowledge, this is the first study to describe coinfections in HM patients with SARS-CoV-2-caused pneumonia by detecting the BALF of patients using the highly sensitive NGS method.\u003c/p\u003e \u003cp\u003eOur study showed that the coinfection rates of patients with HMs and those without HMs were 80.0% and 85.7%, respectively, which were significantly greater than those previously reported. The NGS method we used in this study was highly more sensitive than traditional microbiological detection methods used in previous studies, which may account for the greater rate of coinfection in our study. \u003cem\u003ePneumocystis jirovecii\u003c/em\u003e was the most common coinfected pathogen, with a coinfection rate of 33.3%. \u003cem\u003ePneumocystis jirovecii\u003c/em\u003e is a common opportunistic infection pathogen in immunocompromised patients. \u003cem\u003ePneumocystis jiroveci\u003c/em\u003e pneumonia may also present as diffuse pulmonary interstitial infiltration(\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), which is sometimes difficult to distinguish from SARS-CoV-2 pneumonia. The traditional detection methods for \u003cem\u003ePneumocystis jiroveci\u003c/em\u003e infection have poor sensitivity, but NGS has been proven to be an effective method for detecting this disease(\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In our previous study of lymphoma patients with chemotherapy-related IP, \u003cem\u003ePneumocystis jirovecii\u003c/em\u003e was detected in the BALF of 12 of 15 patients by NGS(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). In this study, all the patients with HMs had previously received chemotherapy, and 13 of 15 (86.7%) patients had received anti-CD20 mAbs, which may have resulted in severe immunodeficiency and increased susceptibility to \u003cem\u003ePneumocystis jirovecii\u003c/em\u003e. These data suggest that \u003cem\u003eidentifying Pneumocystis jirovecii\u003c/em\u003e coinfected with COVID-19 is necessary in HM patients after chemotherapy. In patients with a long course of SARS-CoV-2 pneumonia, NGS testing of BALF and anti-pneumocystis therapy may be considered.\u003c/p\u003e \u003cp\u003ePrevious studies have reported that the probability of bacterial coinfection in patients with COVID-19 is approximately 8%-15%, while the incidence is relatively high in critically ill patients (approximately 20%-30%)(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Our study showed that the probability of bacterial coinfection in patients with HMs was significantly lower than that in patients without HMs. This may be related to the differences in baseline characteristics between the two groups. Patients in the non-HM group had worse performance status and more comorbidities. Moreover, half of the patients in the non-HM group had severe disease. This selection bias may be due to the differences between hematologists and respiratory physicians in deciding which patients to perform bronchoscopy and NGS. For COVID-19 patients without HMs, respiratory physicians may suggest bronchoscopy for more critically ill patients. Multivariate analysis in our study also showed that bacterial coinfection was associated with severe disease. Notably, according to previous reports, the majority of hospitalized COVID-19 patients received antibiotics, despite the low incidence of bacterial coinfection(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The overuse of antibiotics can increase the risk of multidrug-resistant infections and lead to poor prognosis(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Therefore, we should carefully evaluate the use of antibiotics in HM patients with mild COVID-19.\u003c/p\u003e \u003cp\u003eThe incidence of viral coinfection reported in previous literature was 2.1%(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), which was significantly lower than that in our study. This may be due to the poor sensitivity of traditional virus detection methods. In our study, coinfection with herpesviruses occurred frequently in the two groups. Previous studies showed that herpesviruses, such as Epstein-Barr virus and cytomegalovirus, are common in critically ill patients, patients with hematologic disorders, and patients treated with immunosuppressive agents(\u003cspan additionalcitationids=\"CR35 CR36\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Moreover, the reactivation of herpesviruses is associated with the severity and length of COVID-19 symptoms(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Gold et al. suggested that long COVID-19 symptoms may not be a direct result of the SARS-CoV-2 virus but may be the result of COVID-19-induced Epstein-Barr virus reactivation(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Furthermore, anti-herpesvirus therapy with ganciclovir may reduce the risk of death in patients with severe COVID-19(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Therefore, coinfection with herpesviruses may affect the prognosis of patients with COVID-19. The high detection rate of herpesviruses in our study suggested that we need to pay attention to coinfections caused by these viruses and provide effective treatment.\u003c/p\u003e \u003cp\u003eThere are several limitations of our study. First, this was a single-center study, and the results only represent coinfections around that center. Second, because this was a retrospective study, the baseline characteristics of patients in the HM group and non-HM group were not completely compared. Patients in the non-HM group had worse performance status and more comorbidities and seemed to have more severe disease. Finally, the sample size was small, resulting in no significant differences in the comparison of some outcomes between the two groups.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study showed that hospitalized patients with COVID-19 had a high proportion of coinfections. \u003cem\u003ePneumocystis jiroveci\u003c/em\u003e and herpesvirus are commonly coinfected pathogens in patients with HMs. Bacterial coinfection is rare in patients with HMs but is more common in patients without HMs.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBALF bronchoalveolar lavage fluid\u003c/p\u003e\u003cp\u003eCI confidence interval\u003c/p\u003e\u003cp\u003eCOVID-19 coronavirus disease 2019\u003c/p\u003e\u003cp\u003eCT computed tomography\u003c/p\u003e\u003cp\u003eECOG Eastern Cooperative Oncology Group\u003c/p\u003e\u003cp\u003eHM hematologic malignancy\u003c/p\u003e\u003cp\u003eIP interstitial pneumonia\u003c/p\u003e\u003cp\u003emAB monoclonal antibody\u003c/p\u003e\u003cp\u003eNC negative control\u003c/p\u003e\u003cp\u003eNGS next-generation sequencing)\u003c/p\u003e\u003cp\u003eOR odds ratio\u003c/p\u003e\u003cp\u003ePS performance status\u003c/p\u003e\u003cp\u003eSARS-CoV-2 severe acute respiratory syndrome coronavirus 2\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethical Review Committee of Ningbo Medical Center Li Huili Hospital (approval No. YJZ2023SL2). The procedures used in this study adhere to the tenets of the Declaration of Helsinki. Informed consent was obtained from all subjects and/or their legal guardian(s).\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 DNA and RNA sequencing data are available from Matridx Biotechnology Co., Ltd. but restrictions apply to the availability of these data, which were used for the current study, and so are not publicly available. However, data are available from the authors upon reasonable request and with permission of Matridx Biotechnology Co., Ltd.\u0026nbsp;Other\u0026nbsp;datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by\u0026nbsp;by Ningbo Medical Science and Technology Project (reference:2018A64).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDJ conceived the study; WS and QY analyzed data and wrote the paper; JL revised the paper; QC, HD, LL, JT, YS, BC, YT collected data;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 thank the patients for cooperating with our investigation and acknowledge Matridx Biotechnology Co., Ltd. for their support of this study. \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eXie J, Wang Q, Xu Y, Zhang T, Chen L, Zuo X, et al. 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Infectious events in patients with severe COVID-19: results of a cohort of patients with high prevalence of underlying immune defect. Ann Intensive Care. 2021;11(1):83.\u003c/li\u003e\n\u003cli\u003ePark SY, Kim MY, Choi WJ, Yoon DH, Lee SO, Choi SH, et al. Pneumocystis pneumonia versus rituximab-induced interstitial lung disease in lymphoma patients receiving rituximab-containing chemotherapy. Med Mycol. 2017;55(4):349-57.\u003c/li\u003e\n\u003cli\u003eKim T, Choi SH, Kim SH, Jeong JY, Woo JH, Kim YS, et al. Point prevalence of Pneumocystis pneumonia in patients with non-Hodgkin lymphoma according to the number of cycles of R-CHOP chemotherapy. Ann Hematol. 2013;92(2):231-8.\u003c/li\u003e\n\u003cli\u003eMartin-Garrido I, Carmona EM, Specks U, Limper AH. Pneumocystis pneumonia in patients treated with rituximab. Chest. 2013;144(1):258-65.\u003c/li\u003e\n\u003cli\u003eFlori P, Bellete B, Durand F, Raberin H, Cazorla C, Hafid J, et al. Comparison between real-time PCR, conventional PCR and different staining techniques for diagnosing Pneumocystis jiroveci pneumonia from bronchoalveolar lavage specimens. J Med Microbiol. 2004;53(Pt 7):603-7.\u003c/li\u003e\n\u003cli\u003eBrakemeier S, Pfau A, Zukunft B, Budde K, Nickel P. Prophylaxis and treatment of Pneumocystis Jirovecii pneumonia after solid organ transplantation. Pharmacol Res. 2018;134:61-7.\u003c/li\u003e\n\u003cli\u003eJin D, Le J, Yang Q, Cai Q, Dai H, Luo L, et al. Pneumocystis jirovecii with high probability detected in bronchoalveolar lavage fluid of chemotherapy-related interstitial pneumonia in patients with lymphoma using metagenomic next-generation sequencing technology. Infect Agent Cancer. 2023;18(1):80.\u003c/li\u003e\n\u003cli\u003eLin P, Chen Y, Su S, Nan W, Zhou L, Zhou Y, et al. Diagnostic value of metagenomic next-generation sequencing of bronchoalveolar lavage fluid for the diagnosis of suspected pneumonia in immunocompromised patients. 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Crit Care. 2017;21(1):230.\u003c/li\u003e\n\u003cli\u003eWalton AH, Muenzer JT, Rasche D, Boomer JS, Sato B, Brownstein BH, et al. Reactivation of multiple viruses in patients with sepsis. PLoS One. 2014;9(2):e98819.\u003c/li\u003e\n\u003cli\u003eOng DSY, Bonten MJM, Spitoni C, Verduyn Lunel FM, Frencken JF, Horn J, et al. Epidemiology of Multiple Herpes Viremia in Previously Immunocompetent Patients With Septic Shock. Clin Infect Dis. 2017;64(9):1204-10.\u003c/li\u003e\n\u003cli\u003eLibert N, Bigaillon C, Chargari C, Bensalah M, Muller V, Merat S, et al. Epstein-Barr virus reactivation in critically ill immunocompetent patients. Biomed J. 2015;38(1):70-6.\u003c/li\u003e\n\u003cli\u003eZubchenko S, Kril I, Nadizhko O, Matsyura O, Chopyak V. Herpesvirus infections and post-COVID-19 manifestations: a pilot observational study. Rheumatol Int. 2022;42(9):1523-30.\u003c/li\u003e\n\u003cli\u003eSimonnet A, Engelmann I, Moreau AS, Garcia B, Six S, El Kalioubie A, et al. High incidence of Epstein-Barr virus, cytomegalovirus, and human-herpes virus-6 reactivations in critically ill patients with COVID-19. Infect Dis Now. 2021;51(3):296-9.\u003c/li\u003e\n\u003cli\u003eGold JE, Okyay RA, Licht WE, Hurley DJ. Investigation of Long COVID Prevalence and Its Relationship to Epstein-Barr Virus Reactivation. Pathogens. 2021;10(6).\u003c/li\u003e\n\u003cli\u003eLiu J, Zhang S, Wu Z, Shang Y, Dong X, Li G, et al. Clinical outcomes of COVID-19 in Wuhan, China: a large cohort study. Ann Intensive Care. 2020;10(1):99.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, hematologic malignancy, coinfection, next-generation sequencing","lastPublishedDoi":"10.21203/rs.3.rs-3940109/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3940109/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCoinfections in patients with coronavirus disease 2019 (COVID-19) affect patient prognosis. Patients with hematologic malignancies (HMs) are usually immunosuppressed and may be at high risk of coinfection, but few related data have been reported. Here, we conducted a retrospective study to explore coinfections in patients with HMs and COVID-19 by next-generation sequencing (NGS) of bronchoalveolar lavage fluid (BALF).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe data of hospitalized patients with pneumonia who underwent NGS analysis of BALF were reviewed. COVID-19 patients with HMs were enrolled in the HM group, and those without HMs were enrolled in the non-HM group. The coinfections of the two groups identified by NGS were analyzed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFifteen patients were enrolled in the HM group, and 14 patients were enrolled in the non-HM group. The coinfection rates in the HM group and non-HM group were 80.0% and 85.7%, respectively. The percentage of coinfected bacteria in the HM group was significantly lower than that in the non-HM group (20.0% vs 71.4%, p\u0026thinsp;=\u0026thinsp;0.005). The coinfection rates of fungi and viruses were 60.0% and 35.7%, respectively, in the HM group and 35.7% and 78.6%, respectively, in the non-HM group, with no significant differences. The most common coexisting pathogen in patients with HMs was \u003cem\u003ePneumocystis jirovecii\u003c/em\u003e (33.3%), and the most common coexisting pathogen in patients without HMs \u003cem\u003ewas human gammaherpesvirus 4\u003c/em\u003e (50%). Coinfection with herpesviruses occurred frequently in both groups.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur study showed that hospitalized patients with COVID-19 had a high incidence of coinfection. \u003cem\u003ePneumocystis jiroveci\u003c/em\u003e and herpesvirus are commonly coinfected pathogens in patients with HMs. Bacterial coinfection is rare in patients with HMs but is more common in patients without HMs.\u003c/p\u003e","manuscriptTitle":"Analysis of coinfections in patients with hematologic malignancies and COVID-19 by next-generation sequencing of bronchoalveolar lavage fluid","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-27 20:57:25","doi":"10.21203/rs.3.rs-3940109/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":"2b539531-ed7b-46e6-857c-17c05cb43b0f","owner":[],"postedDate":"February 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-02T08:05:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-27 20:57:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3940109","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3940109","identity":"rs-3940109","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}