Metagenomics next generation sequencing for diagnosis of invasive fungal diseases in patients with hematological diseases

Metagenomics next generation sequencing for diagnosis of invasive fungal diseases in patients with hematological diseases | 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 Metagenomics next generation sequencing for diagnosis of invasive fungal diseases in patients with hematological diseases Xi Chen, Yun Lian, Yuhua Song, Qiqiang Long This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3433576/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 PURPOSE: To investigate the clinical characteristics and risk factors of invasive fungal disease (IFD) in patients with hematological disorders. METHODS: From January 2022 to January 2023, seventy patients with blood diseases hospitalized at the Second Hospital of Nanjing who were suspected of infection with IFD underwent mNGS and fungal pathogen detection. The clinical characteristics and laboratory data of 32 fungal infected individuals (IFD group) and 38 non-IFD groups were compared. SPSS and R software were used for the statistical analysis of risk factors associated with IFD. RESULTS: Using the results of conventional fungal cultures as a “gold standard”, the sensitivity, specificity, positive predictive value, and negative predictive value of mNGS for the evaluation of fungal pathogen infections in patients with hematological disorders were found to be 100% (10/10), 63.3% (38/60), 31.3% (10/32), and 100% (38/38), respectively. Multivariate logistic regression analysis revealed six independent risk factors associated with IFD in patients with hematological disorders: CD4+T cell count < 400 cells/µL (odds ratio (OR)=7.43, p =3.79x10 -4 ), elevated C-reactive protein (OR=3.71, p =0.01), elevated interleukin (IL)-6 (OR=6.5, p =2.93x10 -4 ), elevated IL-10 (OR=3.03, p =0.041), hypoproteinemia (OR=7.04, p =0.025), and neutropenia persisting for >10 days (OR=3.03, p =0.002). CONCLUSION: mNGS has high sensitivity in detecting IFD in patients with hematological diseases. CD4+cell count below 400/ul, increased level of C-reactive protein, IL-6, and IL-10, hypoalbuminemia, and neutropenia lasting for more than 10 days are independent risk factors for IFD in patients with hematological diseases. hematologic disorders invasive fungal disease metagenomics next-generation sequencing risk factors clinical characteristics Figures Figure 1 Figure 2 Figure 3 Introduction Patients with hematological disorders are at a higher risk invasive fungal disease (IFD) due to various factors such as chemotherapy or transplantation, persistent neutropenia, immunosuppression, molecular targeted therapy, deep venous catheter placement, and use of broad-spectrum antibiotics. [1] Studies have shown that 10% of patients with hematological malignancies develop life-threatening IFDs during treatment, with a mortality rate approaching 29%. [2] According to the European Organisation for Research and Treatment of Cancer/Mycoses Study Group Education & Research Consortium (EORTC/MSGERC) criteria, [3] the diagnosis of IFD is categorized into three levels as follows: proven IFD, probable IFD, and possible IFD. Sterile body fluid culture and histopathological identification remain the “gold standard” for the diagnosis of proven IFD; however, their feasibility and positivity rates remain unsatisfactory, and existing diagnostic methods are limited. [4] Therefore, early and precise identification of fungal pathogens carries great importance for the selection of treatment regimens and in improving outcomes in patients with hematological disorders combined with IFD. Metagenomics next-generation sequencing (mNGS) is a new form of testing widely used for detection of viruses, bacteria, and fungi in a variety of host samples and can assist in diagnosing pathogens rapidly, thus enabling precision treatment of infectious diseases. [5] In order to gain an in-depth understanding of the clinical characteristics and related risk factors of patients with hematological disorders diagnosed with IFD, the present study was conducted as a retrospective analysis of the case data of patients with hematological malignancies diagnosed with IFD. Data and methods 1.1 Data Patients admitted to our Hematology Department between January 1, 2022, and January 1, 2023, were selected. The study was approved by the Institutional Review Board of the hospital. In total, 70 patients were enrolled, of which 32 comprised the IFD group and 38 comprised the non-IFD group. 1.2 Inclusion and exclusion criteria The inclusion criteria were as follows: 1) patients diagnosed with hematological disorders in accordance with the criteria of the Hematological Disease Diagnostic and Treatment Specifications, and 2) diagnosis of IFD in accordance with the EORTC/MSGERC criteria, the Infectious Diseases Society of America (IDSA) guidelines, the American Society for the Fight Against Infections (IDSA), and the guidelines of the European Conference on Infections in Leukaemia. [3] The exclusion criteria were inconsistent diagnosis or incomplete clinical data. 1.3 Observational indicators The complete blood count, biochemical indicators, C-reactive protein, cytokine testing results, lymphocyte subset testing results, mNGS results, and fungal and microbial culture results of all included patients were observed. 1.4 Statistics SPSS 23.0 and R Studio software were used for statistical analysis. The independent sample t-test was used for measurement data conforming to the normal distribution, non-parametric tests were used for measurement data not conforming to the normal distribution, and the X 2 test was used for count data. To exclude the effect of confounding factors, risk factors with p < 0.05 in the univariate analysis were entered into multivariate logistic regression analysis. Results 2.1 General characteristics The IFD group consisted of 32 patients with a median age of 41 years (range: 5–84 years), of which 21 were male and 11 were female. The clinical diagnosis was aplastic anemia in five cases, myelodysplastic syndromes in four cases, leukemia in one case, lymphoma in two cases, myeloma in two cases, and other diseases in nine cases. The non-IFD group consisted of 38 patients with a median age of 67 years (range: 4–88 years), of which 19 were male and 19 were female. The clinical diagnosis was aplastic anemia in six cases, myelodysplastic syndromes in eight cases, leukemia in 11 cases, lymphoma in two cases, myeloma in ine case, and other diseases in 10 cases. The difference between the two groups was not statistically significant ( p > 0.05) (Table 1). 2.2 Sources of mNGS specimens and distribution of fungal strains in the IFD group In the IFD group, bronchoalveolar lavage fluid was used for mNGS in 14 patients, blood in 11 patients, urine in five patients, feces in one patient, and pleural fluid and ascites in one patient. In the non-IFD group, bronchoalveolar lavage fluid was used for mNGS in eight patients, blood in 25 patients, urine in three patients, feces in zero patients, and pleural fluid and ascites in two patients. The differences between the two groups were not statistically significant ( p = 0.076, > 0.05) (Table 2). In the IFD group, Candida was detected in 15 cases (47%), Aspergillus in six cases (19%), Pneumocystis in four cases (12.5%), Rhizomucor in four cases (12.5%), Saccharomyces in two cases (6%), and Malassezia in one case (3%) (Figure 1). 2.3 mNGS diagnostic test evaluation indicators for fungal microbiological cultures Fungal pathogens were detected by microbiological culture in 10/70 patients, which had a fungal detection rate of 14.3%, compared to 45.7% (32/70) by mNGS. Using the results of conventional fungal cultures as a “gold standard”, the sensitivity, specificity, positive predictive value, and negative predictive value of mNGS for the evaluation of fungal pathogen infections in patients with hematological disorders were found to be 100% (10/10), 63.3% (38/60), 31.3% (10/32), and 100% (38/38), respectively. 2.3 Analysis of factors associated with infection in IFD patients The sex, age, history of diabetes mellitus, degree of neutropenia at the time of initial diagnosis, duration of neutropenia, lymphocyte count, C-reactive protein, cytokines, CD4+ T cell count, and presence of concomitant bacterial infection in the IFD and non-IFD groups were included in univariate analysis using SPSS 23.0 software. The results suggested that the differences in the duration of neutropenia, C-reactive protein, CD4+ T cell count, interleukin (IL)-6, IL-10, and albumin level were statistically significant ( p < 0.05) (Table 3). Multivariate logistic regression analysis of the six risk factors with p < 0.05 in the univariate analysis was performed using R 4.2.1 software. The results indicated that CD4+ T cell count < 400 cells/µL (odds ratio (OR) = 7.43, p = 3.79 x 10 -4 ), elevated C-reactive protein (OR = 3.71, p = 0.01), elevated IL-6 (OR = 6.5, p = 2.93 x 10 -4 ), elevated IL-10 (OR = 3.03, p = 0.041), hypoproteinemia (OR = 7.04, p = 0.025), and neutropenia persisting for > 10 days (OR = 3.03, p = 0.002) were independent risk factors for IFD infection in patients with hematological disorders (Figure 2). These independent risk factors for IFD were used to construct a nomogram using R 4.2.1 software. The nomogram had a C-index of 0.862 with a 95% confidence interval of 0.772–0.951, indicating that it could reflect the risk factors of IFD infection in patients with hematological disorders and predict the chance of IFD infection in these patients (Figure 3). Discussion IFD is one of the major complications in the diagnosis and treatment of malignant hematological disorders. [6] The overall incidence of IFD is 2.1% in patients with hematological malignancies undergoing chemotherapy and 26.7% in patients undergoing hematopoietic stem cell transplantation. [7] IFD remains a major cause of death in hematological disorders. [8] Studies have shown that the overall mortality rate for patients with hematological disorders treated with chemotherapy is only 1.5%, whereas the mortality rate for patients with IFD is as high as 11.7%. [7] Therefore, IFD severely impacts the long-term survival of patients with hematological disorders. The development of timely, effective, and standardized prevention and treatment strategies for IFD is crucial for clinical purposes. Due to the atypical clinical features of IFD, sterile body fluid culture and histopathological identification remain the “gold standard” for diagnosis. However, fungal culture is time-consuming and has a low positive rate; therefore, the diagnosis of IFD is still based on stratified criteria. [4] In contrast, mNGS can detect a wide range of pathogenic microorganisms in an unbiased manner, thus providing reference for the precise clinical diagnosis and treatment of acute and critical illnesses and complex infectious diseases. [5] In the present study, the positive detection rates of fungal microbiological culture and mNGS were compared; the results showed that mNGS exhibited a higher detection rate for fungal pathogens compared to conventional culture, suggesting that mNGS has a strong advantage in the diagnosis of fungal pathogens. In addition, the evaluation indicators of mNGS for fungal diagnostic testing was calculated using the results of conventional fungal cultures as a “gold standard”. The results showed that the specificity and positive predictive value were relatively low (positive predictive value for fungal culture: 14.3%), which resulted in a higher false-positive rate with mNGS, affecting its evaluation indicators. Before the introduction of routine systemic prophylactic antifungal therapy in the clinic, Candida was the most common pathogen in IFD. [9] With the widespread use of fluconazole prophylaxis, the incidence of Aspergillus infection has gradually increased. [9] In recent years, with the application of new broad-spectrum triazoles, the incidence of infection by rare fungi, such as Mucor and Rhizomucor, has also increased. [9] In the present study, mNGS testing of patients with hematological disorders revealed that Candida (15 cases, 47%) and Aspergillus (six cases, 19%) infections were the most prevalent, followed by Rhizomucor (four cases, 12.5%) and Sporothrix (four cases, 12.5%), which is generally consistent with previous findings. Antifungal prophylaxis is a protective factor for IFD and reduces the risk of IFD and death to some extent. [2] Studies have suggested a benefit from antifungal prophylaxis in populations with ≥ 5% prevalence of IFD and a significant benefit in those at ≥ 10% high risk for IFD. [10] Therefore, patients with risk factors for IFD should receive antifungal prophylaxis, the course of which depends on whether the patient’s risk factors for IFD improve. [8] In the present study, CD4+ T cell count < 400 cells/µL (OR = 7.43, p = 3.79 x 10 -4 ), elevated C-reactive protein (OR = 3.71, p = 0.01), elevated IL-6 (OR = 6.5, p = 2.93 x 10 -4 ), elevated IL-10 (OR = 3.03, p = 0.041), hypoproteinemia (OR = 7.04, p = 0.025), and neutropenia persisting for > 10 days (OR = 3.03, p = 0.002) were found to be independent risk factors for IFD. First, neutropenia persisting for > 10 days and hypoproteinemia have been previously shown to be independent risk factors for IFD [7,11] ; neutropenia persisting for >10 days can weaken the immune system and increase the risk of opportunistic fungal infections, [7,11] and hypoproteinemia can lead to weakening of the immune system and impairment of the mucosal barrier of the body, which increases the risk of IFD infection. [7,11] Second, according to Lionakis et al. , [12] CD4+ T cells can play a direct antifungal role by releasing cytokines and mediating B-cell immunity; therefore, their role in host defense against fungi cannot be neglected. Thus, decreased CD4+ T cells may exacerbate fungal infections in patients. Furthermore, C-reactive protein is one of the most common markers of inflammation as it can be elevated during injury, infection, and inflammation. Sidharta et al. retrospectively analyzed the correlation between C-reactive protein and fungal infections in 61 patients with acute leukemia, and the results suggest that elevated C-reactive protein can be used as a marker for the screening of fungal infections. [13] In addition, IL-6 is a cytokine, or intercellular signaling molecule, that plays an important role in the inflammatory immune response and other immune processes, including regulation of the immune response, inflammatory response, thermoregulation, and cell proliferation. In a study of 106 patients with hematological disorders, Rawlings et al. found elevated levels of IL-6 in the blood and bronchoalveolar lavage fluid of patients with IFD, particularly cases involving Aspergillus . Thus, IL-6 can be used as a clinical predictor for the long-term prognosis and mortality of patients with hematological disorders. [14] In addition, IL-10 has major multifunctional roles in modulating the immune response to fungal infections. Antachopoulos et al. identified a key role for IL-10 in modulating normal host resistance to fungal pathogens using a mouse model of Aspergillus infection. [15] This study has some potential limitations. First, due to ethical considerations, a randomized control population could not be established. Second, based on time and resource limitations, the sample size of the present study is relatively small, and the observational indicators are insufficiently comprehensive; thus, it is necessary to adjust the study design and increase the sample size in future studies to further investigate the high-risk factors for IFD infection in patients with hematological disorders and provide a more reliable clinical foundation. In conclusion, it is important to understand the clinical characteristics of IFD combined with hematological diseases, to focus on early intervention in groups at high risk of invasive mycoses, to fully leverage modern and advanced microbiological testing, to improve the early diagnosis rate of IFD, and to formulate rational antifungal treatment protocols to improving patient outcomes and quality of life. Declarations Authors’ contributions Xi Chen wrote the manuscript, Yun Lian and Yuhua Song performed the data analysis. All the authors read and approved the final manuscript. Funding National Natural Science Foundation of China (81900109). Data Availability All data generated or analyzed during this study are included in this published article. The data that support the findings of this study are available from the corresponding author upon reasonable request. Competing interests The authors declare no competing interests. Ethics approval and consent to participate ： The study was registered with the Chinese Clinical Trial Registry (ChiCTR2100045895) and was approved by the hospital ethics committees in accordance with the Declaration of Helsinki and Good Clinical Practice. Patients were required signing informed consent forms prior to participation in the study. Consent for publication Not Applicable. References Douglas AP, Slavin MA. Risk factors and prophylaxis against invasive fungal disease for hematology and stem cell transplant recipients: an evolving field. Expert Rev Anti Infect Ther. 2016 Dec;14(12):1165-1177. doi: 10.1080/14787210.2016.1245613. Epub 2016 Oct 22. PMID: 27710140. Teh BW, Yeoh DK, Haeusler GM, Yannakou CK, Fleming S, Lindsay J, Slavin MA; Australasian Antifungal Guidelines Steering Committee. Consensus guidelines for antifungal prophylaxis in haematological malignancy and haemopoietic stem cell transplantation, 2021. Intern Med J. 2021 Nov;51 Suppl 7:67-88. doi: 10.1111/imj.15588. PMID: 3493714 Donnelly JP, Chen SC, Kauffman CA, et al. Revision and Update of the Consensus Definitions of Invasive Fungal Disease From the European Organization for Research and Treatment of Cancer and the Mycoses Study Group Education and Research Consortium. Clin Infect Dis. 2020 Sep 12;71(6):1367-1376. doi: 10.1093/cid/ciz1008. PMID: 31802125; PMCID: PMC7486838. Bassetti M, Azoulay E, Kullberg B J, et al. EORTC/MSGERC Definitions of Invasive Fungal Diseases: Summary of Activities of the Intensive Care Unit Working Group. Clin Infect Dis 2021 Mar 12;72 Suppl 2:S121-S127. doi:10.1093/cid/ciaa1751. PNID:33709127. Li H. Editorial: mNGS for Fungal Pulmonary Infection Diagnostics. Front Cell Infect Microbiol. 2022 Mar 11; 12:864163. doi: 10.3389/fcimb.2022.864163. PMID: 35360103; PMCID: PMC8963408. Ruhnke M, Behre G, Buchheidt D, et al. Diagnosis of invasive fungal diseases in haematology and oncology: 2018 update of the recommendations of the infectious diseases working party of the German society for hematology and medical oncology (AGIHO). Mycoses. 2018 Nov;61(11):796-813. doi: 10.1111/myc.12838. Epub 2018 Sep 3. PMID: 30098069. Sun Y, Huang H, Chen J, et al. Invasive fungal infection in patients receiving chemotherapy for hematological malignancy: a multicenter, prospective, observational study in China. Tumour Biol. 2015 Feb;36(2):757- 767. doi:10.1007/s13277-014-2649-7. Epub 2014 Oct 8. PMID: 25293517. Tissot F, Agrawal S, Pagano L, et al. ECIL-guidelines for the treatment of invasive candidiasis, aspergillosis and mucormycosis in leukemia and hematopoietic stem cell transplant patients. Haematologica 2017 Mar;102(3) 433- 444. Doi: 10.3324/haematol.2016.152900. PMID: 28011902. Zilberberg MD, Nathanson BH, Harrington R, Spalding JR, Shorr AF. Epidemiology and Outcomes of Hospitalizations with Invasive Aspergillosis in the United States, 2009-2013. Clin Infect Dis. 2018 Aug 16;67(5):727-735. Doi: 10.1093/cird/ciy181. PMID: 29718296. Rogers TR, Slavin MA, Donnelly JP. Antifungal prophylaxis during treatment for haematological malignancies: are we there yet? Br J Haematol. 2011 Jun;153(6):681-97. doi: 10.1111/j.1365-2141.2011.08650. x. Epub 2011 Apr 20. PMID: 21504422. Xiao H, Tang Y, Cheng Q, Liu J, Li X. Risk Prediction and Prognosis of Invasive Fungal Disease in Hematological Malignancies Patients Complicated with Bloodstream Infections. Cancer Manag Res. 2020 Mar 24;12:2167-2175. doi: 10.2147/CMAR.S238166. PMID: 32273756; PMCID: PMC7102877. Lionakis MS, Levitz SM. Host Control of Fungal Infections: Lessons from Basic Studies and Human Cohorts. Annu Rev Immunol. 2018 Apr 26;36:157-191. doi: 10.1146/annurev-immunol-042617-053318. Epub 2017 Dec 13. PMID: 29237128. Sidharta BRA, Suparyatmo J, Astuti AF. C-Reactive Protein as A Fungal Infection Marker in Acute Leukemia Patients. Indonesian J Clin Pathol Med Lab. 2021 Mar;27(2):212-216. doi: 10.24293/IJCPML.V27I2.1639. Rawlings SA, Heldt S, Prattes J, et al. Using Interleukin 6 and 8 in Blood and Bronchoalveolar Lavage Fluid to Predict Survival in Hematological Malignancy Patients with Suspected Pulmonary Mold Infection. Front Immunol. 2019 Aug 2; 10:1798. doi: 10.3389/fimmu.2019.01798. PMID: 31428097; PMCID: PMC6687868. Antachopoulos C, Roilides E. Cytokines and fungal infections. Br J Haematol. 2005 Jun;129(5):583-96. doi: 10.1111/j.1365-2141.2005.05498. x. PMID: 15916680. Tables Table 1. General characteristics of patients in the invasive fungal disease (IFD) group and non-IFD group Characteristic Experimental group (n = 32) Control group (n = 38) p Sex Male 21 19 0.188 Female 11 19 Age (median/range) 41 (5-84) 67 (4-88) 0.447 Clinical diagnosis Aplastic anemia 5 6 0.929 MDS 4 8 Leukemia 10 11 Lymphoma 2 2 Myeloma 2 1 Other 9 10 Table 2. Metagenomics next-generation sequencing (mNGS) sample sources Sample type Experimental group Control group p Blood 11 25 0.076 Urine 5 3 Feces 1 0 Bronchoalveolar lavage fluid 14 8 Pleural fluid 1 2 Table 3. Univariate analysis of factors associated with invasive fungal disease (IFD) Clinical characteristic Experimental group Control group p Age > 60 years 19 19 0.433 ≤ 60 years 13 19 Diabetes Yes 9 11 0.94 No 23 27 Neutropenia at initial diagnosis Yes 8 8 0.695 No 24 30 Duration of neutropenia > 10 days 13 7 0.041 ≤ 10 days 19 31 Elevated C-reactive protein Yes 24 17 0.01 No 8 21 Lymphocytopenia Yes 13 11 0.305 No 19 27 Thrombocytopenia Yes 21 27 0.626 No 11 11 Elevated LDH Yes 17 15 0.253 No 15 23 CD4+ T cell count ≥ 400/µL 7 24 3.79*E-04 < 400/µL 26 14 Elevated IL-2 Yes 0 1 0.355 No 32 37 Elevated IL-4 Yes 0 1 0.355 No 32 37 Elevated IL-6 Yes 24 12 2.93*E-04 No 8 26 Elevated IL-8 Yes 15 10 0.074 No 17 28 Elevated IL-10 Yes 13 7 0.041 No 19 31 Elevated TNF-α Yes 3 3 0.826 No 29 35 Elevated IFN-α Yes 1 2 0.66 No 31 36 Elevated IFN-γ Yes 2 0 0.118 No 30 38 Albumin > 30 g/L 23 36 0.009 ≤ 30 g/L 9 2 Additional Declarations No competing interests reported. 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IFDs during treatment, with a mortality rate approaching 29%. \u003csup\u003e[2]\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the European Organisation for Research and Treatment of Cancer/Mycoses Study Group Education \u0026amp; Research Consortium (EORTC/MSGERC) criteria, \u003csup\u003e[3]\u003c/sup\u003e the diagnosis of IFD is categorized into three levels as follows: proven IFD, probable IFD, and possible IFD.\u0026nbsp;Sterile body fluid culture and histopathological identification remain the \u0026ldquo;gold standard\u0026rdquo; for the diagnosis of proven IFD; however, their feasibility and positivity rates remain unsatisfactory, and existing diagnostic methods are limited. \u003csup\u003e[4]\u003c/sup\u003e Therefore, early and precise identification of fungal pathogens carries great importance for the selection of treatment regimens and in improving outcomes in patients with hematological disorders combined with IFD.\u003c/p\u003e\n\u003cp\u003eMetagenomics next-generation sequencing (mNGS) is a new form of testing widely used for detection of viruses, bacteria, and fungi in a variety of host samples and can assist in diagnosing pathogens rapidly, thus enabling precision treatment of infectious diseases. \u003csup\u003e[5]\u003c/sup\u003e In order to gain an in-depth understanding of the clinical characteristics and related risk factors of patients with hematological disorders diagnosed with IFD, the present study was conducted as a retrospective analysis of the case data of patients with hematological malignancies diagnosed with IFD.\u003c/p\u003e"},{"header":"Data and methods","content":"\u003cp\u003e1.1 Data\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePatients admitted to our Hematology Department between January 1, 2022, and January 1, 2023, were selected. The study was approved by the Institutional Review Board of the hospital. In total, 70 patients were enrolled, of which 32 comprised the IFD group and 38 comprised the non-IFD group.\u003c/p\u003e\n\u003cp\u003e1.2 Inclusion and exclusion criteria\u003c/p\u003e\n\u003cp\u003eThe inclusion criteria were as follows: 1) patients diagnosed with hematological disorders in accordance with the criteria of the Hematological Disease Diagnostic and Treatment Specifications, and 2) diagnosis of IFD in accordance with the EORTC/MSGERC criteria, the Infectious Diseases Society of America (IDSA) guidelines, the American Society for the Fight Against Infections (IDSA), and the guidelines of the European Conference on Infections in Leukaemia. \u003csup\u003e[3]\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe exclusion criteria were inconsistent diagnosis or incomplete clinical data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1.3 Observational indicators\u003c/p\u003e\n\u003cp\u003eThe complete blood count, biochemical indicators, C-reactive protein, cytokine testing results, lymphocyte subset testing results, mNGS results, and fungal and microbial culture results of all included patients were observed.\u003c/p\u003e\n\u003cp\u003e1.4 Statistics\u003c/p\u003e\n\u003cp\u003eSPSS 23.0 and R Studio software were used for statistical analysis. The independent sample t-test was used for measurement data conforming to the normal distribution, non-parametric tests were used for measurement data not conforming to the normal distribution, and the X\u003csup\u003e2\u003c/sup\u003e test was used for count data. To exclude the effect of confounding factors, risk factors with \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 in the univariate analysis were entered into multivariate logistic regression analysis.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e2.1 General characteristics\u003c/p\u003e\n\u003cp\u003eThe IFD group consisted of 32 patients with a median age of 41 years (range: 5\u0026ndash;84 years), of which 21 were male and 11 were female. The clinical diagnosis was aplastic anemia in five cases, myelodysplastic syndromes in four cases, leukemia in one case, lymphoma in two cases, myeloma in two cases, and other diseases in nine cases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe non-IFD group consisted of 38 patients with a median age of 67 years (range: 4\u0026ndash;88 years), of which 19 were male and 19 were female. The clinical diagnosis was aplastic anemia in six cases, myelodysplastic syndromes in eight cases, leukemia in 11 cases, lymphoma in two cases, myeloma in ine case, and other diseases in 10 cases. The difference between the two groups was not statistically significant (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026gt; 0.05) (Table 1).\u003c/p\u003e\n\u003cp\u003e2.2 Sources of mNGS specimens and distribution of fungal strains in the IFD group\u003c/p\u003e\n\u003cp\u003eIn the IFD group, bronchoalveolar lavage fluid was used for mNGS in 14 patients, blood in 11 patients, urine in five patients, feces in one patient, and pleural fluid and ascites in one patient. In the non-IFD group, bronchoalveolar lavage fluid was used for mNGS in eight patients, blood in 25 patients, urine in three patients, feces in zero patients, and pleural fluid and ascites in two patients. The differences between the two groups were not statistically significant (\u003cem\u003ep\u003c/em\u003e = 0.076, \u0026gt; 0.05) (Table 2).\u003c/p\u003e\n\u003cp\u003eIn the IFD group, \u003cem\u003eCandida\u003c/em\u003e was detected in 15 cases (47%), \u003cem\u003eAspergillus\u003c/em\u003e in six cases (19%), \u003cem\u003ePneumocystis\u003c/em\u003e in four cases (12.5%), \u003cem\u003eRhizomucor\u003c/em\u003e in four cases (12.5%), \u003cem\u003eSaccharomyces\u003c/em\u003e in two cases (6%), and \u003cem\u003eMalassezia\u003c/em\u003e in one case (3%) (Figure 1).\u003c/p\u003e\n\u003cp\u003e2.3 mNGS diagnostic test evaluation indicators for fungal microbiological cultures\u003c/p\u003e\n\u003cp\u003eFungal pathogens were detected by microbiological culture in 10/70 patients, which had a fungal detection rate of 14.3%, compared to 45.7% (32/70) by mNGS. Using the results of conventional fungal cultures as a \u0026ldquo;gold standard\u0026rdquo;, the sensitivity, specificity, positive predictive value, and negative predictive value of mNGS for the evaluation of fungal pathogen infections in patients with hematological disorders were found to be 100% (10/10), 63.3% (38/60), 31.3% (10/32), and 100% (38/38), respectively.\u003c/p\u003e\n\u003cp\u003e2.3 Analysis of factors associated with infection in IFD patients\u003c/p\u003e\n\u003cp\u003eThe sex, age, history of diabetes mellitus, degree of neutropenia at the time of initial diagnosis, duration of neutropenia, lymphocyte count, C-reactive protein, cytokines, CD4+ T cell count, and presence of concomitant bacterial infection in the IFD and non-IFD groups were included in univariate analysis using SPSS 23.0 software. The results suggested that the differences in the duration of neutropenia, C-reactive protein, CD4+ T cell count, interleukin (IL)-6, IL-10, and albumin level were statistically significant (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05) (Table 3).\u003c/p\u003e\n\u003cp\u003eMultivariate logistic regression analysis of the six risk factors with \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 in the univariate analysis was performed using R 4.2.1 software. The results indicated that CD4+ T cell count \u0026lt; 400 cells/\u0026micro;L (odds ratio (OR) = 7.43, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 3.79 x 10\u003csup\u003e-4\u003c/sup\u003e), elevated C-reactive protein (OR = 3.71, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.01), elevated IL-6 (OR = 6.5, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 2.93 x 10\u003csup\u003e-4\u003c/sup\u003e), elevated IL-10 (OR = 3.03, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.041), hypoproteinemia (OR = 7.04, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.025), and neutropenia persisting for \u0026gt; 10 days (OR = 3.03, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.002) were independent risk factors for IFD infection in patients with hematological disorders (Figure 2).\u003c/p\u003e\n\u003cp\u003eThese independent risk factors for IFD were used to construct a nomogram using R 4.2.1 software. The nomogram had a C-index of 0.862 with a 95% confidence interval of 0.772\u0026ndash;0.951, indicating that it could reflect the risk factors of IFD infection in patients with hematological disorders and predict the chance of IFD infection in these patients (Figure 3).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIFD is one of the major complications in the diagnosis and treatment of malignant hematological disorders. \u003csup\u003e[6]\u003c/sup\u003e The overall incidence of IFD is 2.1% in patients with hematological malignancies undergoing chemotherapy and 26.7% in patients undergoing hematopoietic stem cell transplantation. \u003csup\u003e[7]\u003c/sup\u003e IFD remains a major cause of death in hematological disorders. \u003csup\u003e[8]\u003c/sup\u003e Studies have shown that the overall mortality rate for patients with hematological disorders treated with chemotherapy is only 1.5%, whereas the mortality rate for patients with IFD is as high as 11.7%. \u003csup\u003e[7]\u003c/sup\u003e Therefore, IFD severely impacts the long-term survival of patients with hematological disorders. The development of timely, effective, and standardized prevention and treatment strategies for IFD is crucial for clinical purposes.\u003c/p\u003e\n\u003cp\u003eDue to the atypical clinical features of IFD, sterile body fluid culture and histopathological identification remain the \u0026ldquo;gold standard\u0026rdquo; for diagnosis. However, fungal culture is time-consuming and has a low positive rate; therefore, the diagnosis of IFD is still based on stratified criteria. \u003csup\u003e[4]\u003c/sup\u003e In contrast, mNGS can detect a wide range of pathogenic microorganisms in an unbiased manner, thus providing reference for the precise clinical diagnosis and treatment of acute and critical illnesses and complex infectious diseases. \u003csup\u003e[5]\u003c/sup\u003e In the present study, the positive detection rates of fungal microbiological culture and mNGS were compared; the results showed that mNGS exhibited a higher detection rate for fungal pathogens compared to conventional culture, suggesting that mNGS has a strong advantage in the diagnosis of fungal pathogens. In addition, the evaluation indicators of mNGS for fungal diagnostic testing was calculated using the results of conventional fungal cultures as a \u0026ldquo;gold standard\u0026rdquo;. The results showed that the specificity and positive predictive value were relatively low (positive predictive value for fungal culture: 14.3%), which resulted in a higher false-positive rate with mNGS, affecting its evaluation indicators.\u003c/p\u003e\n\u003cp\u003eBefore the introduction of routine systemic prophylactic antifungal therapy in the clinic, \u003cem\u003eCandida\u003c/em\u003e was the most common pathogen in IFD. \u003csup\u003e[9]\u003c/sup\u003e With the widespread use of fluconazole prophylaxis, the incidence of \u003cem\u003eAspergillus\u003c/em\u003e infection has gradually increased. \u003csup\u003e[9]\u003c/sup\u003e In recent years, with the application of new broad-spectrum triazoles, the incidence of infection by rare fungi, such as \u003cem\u003eMucor\u0026nbsp;\u003c/em\u003eand \u003cem\u003eRhizomucor,\u0026nbsp;\u003c/em\u003ehas also increased. \u003csup\u003e[9]\u003c/sup\u003e In the present study, mNGS testing of patients with hematological disorders revealed that \u003cem\u003eCandida\u003c/em\u003e (15 cases, 47%) and \u003cem\u003eAspergillus\u003c/em\u003e (six cases, 19%) infections were the most prevalent, followed by \u003cem\u003eRhizomucor\u003c/em\u003e (four cases, 12.5%) and \u003cem\u003eSporothrix\u003c/em\u003e (four cases, 12.5%), which is generally consistent with previous findings.\u003c/p\u003e\n\u003cp\u003eAntifungal prophylaxis is a protective factor for IFD and reduces the risk of IFD and death to some extent. \u003csup\u003e[2]\u003c/sup\u003e Studies have suggested a benefit from antifungal prophylaxis in populations with \u0026ge; 5% prevalence of IFD and a significant benefit in those at \u0026ge; 10% high risk for IFD. \u003csup\u003e[10]\u003c/sup\u003e Therefore, patients with risk factors for IFD should receive antifungal prophylaxis, the course of which depends on whether the patient\u0026rsquo;s risk factors for IFD improve. \u003csup\u003e[8]\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eIn the present study, CD4+ T cell count \u0026lt; 400 cells/\u0026micro;L (OR = 7.43, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 3.79 x 10\u003csup\u003e-4\u003c/sup\u003e), elevated C-reactive protein (OR = 3.71, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.01), elevated IL-6 (OR = 6.5, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 2.93 x 10\u003csup\u003e-4\u003c/sup\u003e), elevated IL-10 (OR = 3.03, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.041), hypoproteinemia (OR = 7.04, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.025), and neutropenia persisting for \u0026gt; 10 days (OR = 3.03, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.002) were found to be independent risk factors for IFD. First, neutropenia persisting for \u0026gt; 10 days and hypoproteinemia have been previously shown to be independent risk factors for IFD \u003csup\u003e[7,11]\u003c/sup\u003e; neutropenia persisting for \u0026gt;10 days can weaken the immune system and increase the risk of opportunistic fungal infections, \u003csup\u003e[7,11]\u003c/sup\u003e and hypoproteinemia can lead to weakening of the immune system and impairment of the mucosal barrier of the body, which increases the risk of IFD infection. \u003csup\u003e[7,11]\u003c/sup\u003e Second, according to Lionakis \u003cem\u003eet al.\u003c/em\u003e, \u003csup\u003e[12]\u003c/sup\u003e CD4+ T cells can play a direct antifungal role by releasing cytokines and mediating B-cell immunity; therefore, their role in host defense against fungi cannot be neglected. Thus, decreased CD4+ T cells may exacerbate fungal infections in patients. Furthermore, C-reactive protein is one of the most common markers of inflammation as it can be elevated during injury, infection, and inflammation. Sidharta \u003cem\u003eet al.\u003c/em\u003e retrospectively analyzed the correlation between C-reactive protein and fungal infections in 61 patients with acute leukemia, and the results suggest that elevated C-reactive protein can be used as a marker for the screening of fungal infections. \u003csup\u003e[13]\u003c/sup\u003e In addition, IL-6 is a cytokine, or intercellular signaling molecule, that plays an important role in the inflammatory immune response and other immune processes, including regulation of the immune response, inflammatory response, thermoregulation, and cell proliferation. In a study of 106 patients with hematological disorders, Rawlings \u003cem\u003eet al.\u003c/em\u003e found elevated levels of IL-6 in the blood and bronchoalveolar lavage fluid of patients with IFD, particularly cases involving \u003cem\u003eAspergillus\u003c/em\u003e. Thus, IL-6 can be used as a clinical predictor for the long-term prognosis and mortality of patients with hematological disorders. \u003csup\u003e[14]\u003c/sup\u003e In addition, IL-10 has major multifunctional roles in modulating the immune response to fungal infections. Antachopoulos \u003cem\u003eet al.\u003c/em\u003e identified a key role for IL-10 in modulating normal host resistance to fungal pathogens using a mouse model of \u003cem\u003eAspergillus\u003c/em\u003e infection. \u003csup\u003e[15]\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThis study has some potential limitations. First, due to ethical considerations, a randomized control population could not be established. Second, based on time and resource limitations, the sample size of the present study is relatively small, and the observational indicators are insufficiently comprehensive; thus, it is necessary to adjust the study design and increase the sample size in future studies to further investigate the high-risk factors for IFD infection in patients with hematological disorders and provide a more reliable clinical foundation.\u003c/p\u003e\n\u003cp\u003eIn conclusion, it is important to understand the clinical characteristics of IFD combined with hematological diseases, to focus on early intervention in groups at high risk of invasive mycoses, to fully leverage modern and advanced microbiological testing, to improve the early diagnosis rate of IFD, and to formulate rational antifungal treatment protocols to improving patient outcomes and quality of life.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXi Chen wrote the manuscript, Yun Lian and Yuhua Song performed the data analysis. All the authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNational Natural Science Foundation of China (81900109).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article. The data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study was registered with the Chinese Clinical Trial Registry (ChiCTR2100045895) and was approved by the hospital ethics committees in accordance with the Declaration of Helsinki and Good Clinical Practice. Patients were required signing informed consent forms prior to participation in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDouglas AP, Slavin MA. Risk factors and prophylaxis against invasive fungal disease for hematology and stem cell transplant recipients: an evolving field. Expert Rev Anti Infect Ther. 2016 Dec;14(12):1165-1177. doi: 10.1080/14787210.2016.1245613. Epub 2016 Oct 22. PMID: 27710140.\u003c/li\u003e\n\u003cli\u003eTeh BW, Yeoh DK, Haeusler GM, Yannakou CK, Fleming S, Lindsay J, Slavin MA; Australasian Antifungal Guidelines Steering Committee. Consensus guidelines for antifungal prophylaxis in haematological malignancy and haemopoietic stem cell transplantation, 2021. Intern Med J. 2021 Nov;51 Suppl 7:67-88. doi: 10.1111/imj.15588. PMID: 3493714\u003c/li\u003e\n\u003cli\u003eDonnelly JP, Chen SC, Kauffman CA, et al. Revision and Update of the Consensus Definitions of Invasive Fungal Disease From the European Organization for Research and Treatment of Cancer and the Mycoses Study Group Education and Research Consortium. Clin Infect Dis. 2020 Sep 12;71(6):1367-1376. doi: 10.1093/cid/ciz1008. PMID: 31802125; PMCID: PMC7486838. \u003c/li\u003e\n\u003cli\u003eBassetti M, Azoulay E, Kullberg B J, et al. EORTC/MSGERC Definitions of Invasive Fungal Diseases: Summary of Activities of the Intensive Care Unit Working Group. Clin Infect Dis 2021 Mar 12;72 Suppl 2:S121-S127. doi:10.1093/cid/ciaa1751. PNID:33709127.\u003c/li\u003e\n\u003cli\u003eLi H. Editorial: mNGS for Fungal Pulmonary Infection Diagnostics. Front Cell Infect Microbiol. 2022 Mar 11; 12:864163. doi: 10.3389/fcimb.2022.864163. PMID: 35360103; PMCID: PMC8963408.\u003c/li\u003e\n\u003cli\u003eRuhnke M, Behre G, Buchheidt D, et al. Diagnosis of invasive fungal diseases in haematology and oncology: 2018 update of the recommendations of the infectious diseases working party of the German society for hematology and medical oncology (AGIHO). Mycoses. 2018 Nov;61(11):796-813. doi: 10.1111/myc.12838. Epub 2018 Sep 3. PMID: 30098069.\u003c/li\u003e\n\u003cli\u003eSun Y, Huang H, Chen J, et al. Invasive fungal infection in patients receiving chemotherapy for hematological malignancy: a multicenter, prospective, observational study in China. Tumour Biol. 2015 Feb;36(2):757- 767. doi:10.1007/s13277-014-2649-7. Epub 2014 Oct 8. PMID: 25293517.\u003c/li\u003e\n\u003cli\u003eTissot F, Agrawal S, Pagano L, et al. ECIL-guidelines for the treatment of invasive candidiasis, aspergillosis and mucormycosis in leukemia and hematopoietic stem cell transplant patients. Haematologica 2017 Mar;102(3) 433- 444. Doi: 10.3324/haematol.2016.152900. PMID: 28011902.\u003c/li\u003e\n\u003cli\u003eZilberberg MD, Nathanson BH, Harrington R, Spalding JR, Shorr AF. Epidemiology and Outcomes of Hospitalizations with Invasive Aspergillosis in the United States, 2009-2013. Clin Infect Dis. 2018 Aug 16;67(5):727-735. Doi: 10.1093/cird/ciy181. PMID: 29718296.\u003c/li\u003e\n\u003cli\u003eRogers TR, Slavin MA, Donnelly JP. Antifungal prophylaxis during treatment for haematological malignancies: are we there yet? Br J Haematol. 2011 Jun;153(6):681-97. doi: 10.1111/j.1365-2141.2011.08650. x. Epub 2011 Apr 20. PMID: 21504422. \u003c/li\u003e\n\u003cli\u003eXiao H, Tang Y, Cheng Q, Liu J, Li X. Risk Prediction and Prognosis of Invasive Fungal Disease in Hematological Malignancies Patients Complicated with Bloodstream Infections. Cancer Manag Res. 2020 Mar 24;12:2167-2175. doi: 10.2147/CMAR.S238166. PMID: 32273756; PMCID: PMC7102877.\u003c/li\u003e\n\u003cli\u003eLionakis MS, Levitz SM. Host Control of Fungal Infections: Lessons from Basic Studies and Human Cohorts. Annu Rev Immunol. 2018 Apr 26;36:157-191. doi: 10.1146/annurev-immunol-042617-053318. Epub 2017 Dec 13. PMID: 29237128.\u003c/li\u003e\n\u003cli\u003eSidharta BRA, Suparyatmo J, Astuti AF. C-Reactive Protein as A Fungal Infection Marker in Acute Leukemia Patients. Indonesian J Clin Pathol Med Lab. 2021 Mar;27(2):212-216. doi: 10.24293/IJCPML.V27I2.1639.\u003c/li\u003e\n\u003cli\u003eRawlings SA, Heldt S, Prattes J, et al. Using Interleukin 6 and 8 in Blood and Bronchoalveolar Lavage Fluid to Predict Survival in Hematological Malignancy Patients with Suspected Pulmonary Mold Infection. Front Immunol. 2019 Aug 2; 10:1798. doi: 10.3389/fimmu.2019.01798. PMID: 31428097; PMCID: PMC6687868.\u003c/li\u003e\n\u003cli\u003eAntachopoulos C, Roilides E. Cytokines and fungal infections. Br J Haematol. 2005 Jun;129(5):583-96. doi: 10.1111/j.1365-2141.2005.05498. x. PMID: 15916680.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. General characteristics of patients in the invasive fungal disease (IFD) group and non-IFD group\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"546\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.144424131627055%\" colspan=\"2\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.561243144424132%\"\u003e\n \u003cp\u003eExperimental group (n = 32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.6691042047532%\"\u003e\n \u003cp\u003eControl group\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n = 38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.625228519195613%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.497257769652652%\" rowspan=\"2\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.647166361974406%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.561243144424132%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.6691042047532%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.625228519195613%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.63063063063063%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.132132132132135%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.23723723723724%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.144424131627055%\" colspan=\"2\"\u003e\n \u003cp\u003eAge (median/range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.561243144424132%\"\u003e\n \u003cp\u003e41 (5-84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.6691042047532%\"\u003e\n \u003cp\u003e67 (4-88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.625228519195613%\"\u003e\n \u003cp\u003e0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.497257769652652%\" rowspan=\"6\"\u003e\n \u003cp\u003eClinical diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.647166361974406%\"\u003e\n \u003cp\u003eAplastic anemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.561243144424132%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.6691042047532%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.625228519195613%\" rowspan=\"6\"\u003e\n \u003cp\u003e0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.63063063063063%\"\u003e\n \u003cp\u003eMDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.132132132132135%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.23723723723724%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.63063063063063%\"\u003e\n \u003cp\u003eLeukemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.132132132132135%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.23723723723724%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.63063063063063%\"\u003e\n \u003cp\u003eLymphoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.132132132132135%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.23723723723724%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.63063063063063%\"\u003e\n \u003cp\u003eMyeloma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.132132132132135%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.23723723723724%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.63063063063063%\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.132132132132135%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.23723723723724%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Metagenomics next-generation sequencing (mNGS) sample sources\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"537\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.171641791044777%\"\u003e\n \u003cp\u003eSample type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.62686567164179%\"\u003e\n \u003cp\u003eExperimental group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.171641791044777%\"\u003e\n \u003cp\u003eControl group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.029850746268657%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.171641791044777%\"\u003e\n \u003cp\u003eBlood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.62686567164179%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.171641791044777%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.029850746268657%\" rowspan=\"5\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.7926267281106%\"\u003e\n \u003cp\u003eUrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.4147465437788%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.7926267281106%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.7926267281106%\"\u003e\n \u003cp\u003eFeces\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.4147465437788%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.7926267281106%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.7926267281106%\"\u003e\n \u003cp\u003eBronchoalveolar lavage fluid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.4147465437788%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.7926267281106%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.7926267281106%\"\u003e\n \u003cp\u003ePleural fluid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.4147465437788%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.7926267281106%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 3. Univariate analysis of factors associated with invasive fungal disease (IFD)\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eClinical characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eExperimental group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eControl group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt; 60 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le; 60 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eNeutropenia at initial diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eDuration of neutropenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt; 10 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le; 10 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eElevated C-reactive protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eLymphocytopenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eThrombocytopenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.626\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eElevated LDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eCD4+ T cell count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge; 400/\u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e3.79*E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;400/\u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eElevated IL-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eElevated IL-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eElevated IL-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e2.93*E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eElevated IL-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eElevated IL-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eElevated TNF-\u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eElevated IFN-\u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eElevated IFN-\u0026gamma;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt; 30 g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le; 30 g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"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":"hematologic disorders, invasive fungal disease, metagenomics next-generation sequencing, risk factors, clinical characteristics","lastPublishedDoi":"10.21203/rs.3.rs-3433576/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3433576/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePURPOSE:\u003c/strong\u003e To investigate the clinical characteristics and risk factors of invasive fungal disease (IFD) in patients with hematological disorders.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMETHODS:\u003c/strong\u003e From January 2022 to January 2023, seventy patients with blood diseases hospitalized at the Second Hospital of Nanjing who were suspected of infection with IFD underwent mNGS and fungal pathogen detection. The clinical characteristics and laboratory data of 32 fungal infected individuals (IFD group) and 38 non-IFD groups were compared. SPSS and R software were used for the statistical analysis of risk factors associated with IFD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRESULTS: \u003c/strong\u003eUsing the results of conventional fungal cultures as a “gold standard”, the sensitivity, specificity, positive predictive value, and negative predictive value of mNGS for the evaluation of fungal pathogen infections in patients with hematological disorders were found to be 100% (10/10), 63.3% (38/60), 31.3% (10/32), and 100% (38/38), respectively. Multivariate logistic regression analysis revealed six independent risk factors associated with IFD in patients with hematological disorders: CD4+T cell count \u0026lt; 400 cells/µL (odds ratio (OR)=7.43, \u003cem\u003ep\u003c/em\u003e=3.79x10\u003csup\u003e-4\u003c/sup\u003e), elevated C-reactive protein (OR=3.71, \u003cem\u003ep\u003c/em\u003e=0.01), elevated interleukin (IL)-6 (OR=6.5, \u003cem\u003ep\u003c/em\u003e=2.93x10\u003csup\u003e-4\u003c/sup\u003e), elevated IL-10 (OR=3.03, \u003cem\u003ep\u003c/em\u003e=0.041), hypoproteinemia (OR=7.04, \u003cem\u003ep\u003c/em\u003e=0.025), and neutropenia persisting for \u0026gt;10 days (OR=3.03, \u003cem\u003ep\u003c/em\u003e=0.002).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONCLUSION:\u003c/strong\u003e mNGS has high sensitivity in detecting IFD in patients with hematological diseases. CD4+cell count below 400/ul, increased level of C-reactive protein, IL-6, and IL-10, hypoalbuminemia, and neutropenia lasting for more than 10 days are independent risk factors for IFD in patients with hematological diseases.\u003c/p\u003e","manuscriptTitle":"Metagenomics next generation sequencing for diagnosis of invasive fungal diseases in patients with hematological diseases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-10-23 23:39:42","doi":"10.21203/rs.3.rs-3433576/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":"c7d4a38e-29dc-43f4-b29a-bba27556e904","owner":[],"postedDate":"October 23rd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-11-27T04:44:23+00:00","versionOfRecord":[],"versionCreatedAt":"2023-10-23 23:39:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3433576","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3433576","identity":"rs-3433576","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}