Testing Performance of mNGS in Febrile Patients with Hematological Disease and Its Guiding Value for Fever Management in Clinical Treatment

Testing Performance of mNGS in Febrile Patients with Hematological Disease and Its Guiding Value for Fever Management in Clinical Treatment | 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 Testing Performance of mNGS in Febrile Patients with Hematological Disease and Its Guiding Value for Fever Management in Clinical Treatment Yu-hua Song, Xi Chen, Wei Wang, Yun Lian, Peng Peng, Qi-qiang Long This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7387629/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 Objective To explore the testing performance of metagenomic next-generation sequencing (mNGS) in identifying pathogenic microbes in febrile patients with hematological disease and its significant role in guiding clinical treatment. Methods The data of pathogens from the blood cultures of neutropenic patients with hematological disease and/or febrile patients with bloodstream infection (BSI) were summarized and the features of infection were analyzed, through a retrieval of the WoS, PubMed, CNKI, Wanfang, and VIP databases. A retrospective study was conducted on 96 febrile patients with hematological disease (104 specimens) presented to our hospital between May 2022 and May 2024. These patients underwent both routine and mNGS tests for a comparison of the testing performance, and were assigned to mNGS-positive and mNGS-negative groups according to the mNGS results, respectively. Based on an analysis of the data and indexes of the two groups, the contributing factors of mNGS positivity were determined using the univarient and the logistic regression while a prediction model was developed to assess predictive value and summarize the prognosis information using the Receiver Operating Characteristic Curve (ROC). Results According to the included six papers, among totally 3614 isolates from positive blood cultures, Gram-negative bacteria, Gram-positive bacteria, and fungi accounted for 66.66%, 31.02%, and 2.05%, respectively. In this retrospective study, out of 104 peripheral blood tests using mNGS technology, pathogens were detected in 71 tests, with a positive detection rate of 68.27%, which was substantially higher than that of blood culture (7.69%) and routine test (16.35%). Out of 131 isolated pathogenic microbes, viruses held the maximum ratio (60.74%). The identification rate of combined infections by the mNGS test exceeded those by complete blood count (CBC) and routine test. When clinical diagnosis was employed as the gold standard, mNGS test had greater values of sensitivity, positive prediction and negative prediction than those of routine test. The univariant analysis revealed that the mNGS-positive group had higher incidences of pulmonary infection and neutropenia and lower natural killer (NK) cell levels, compared with the mNGS-negative group. The multivariate analysis result of logistic regression model showed that contributing factors of mNGS positivity in febrile patients with hematological disease included pulmonary infection [Odds ratio (OR): 2.389; 95%confidence level (CI): 1.199–4.763)], neutropenia (OR: 4.092; 95% CI: 1.179–14.209), and low NK cell levels (OR: 1.127; 95% CI: 1.117–1.139). According to the analysis result of the ROC curve, the area under curve (AUC) values used for single and combined predictive values of mNGS positivity in febrile patients with hematological disease ranged from 0.623 to 0.849, with the maximum value of 0.849 for combined prediction. The sensitivity and specificity values were 78.79% and 93.33%, respectively. Following relevant guidelines, physicians modified medication for patients with poor disease management based on mNGS results, achieving a survival rate of 75.0%. Conclusion mNGS allows a more comprehensive and accurate identification of pathogenic microbes in patients with hematological disease. However, the positive rate of mNGS test may be affected by pulmonary infection, neutropenia, and low NK cell levels. Therefore, appropriate timing of the mNGS use can provide critical information for the formulation of clinical protocol and enables individualized and accurate treatment. Hematological disease Fever (febrile) Metagenomic next-generation sequencing (mNGS) Testing performance Clinical guidance Figures Figure 1 Introduction Infection, in particular bloodstream infection (BSI), is the most common complication in patients with hematological disease and is also a major cause of death in these patients, with a mortality rate of 20%–35.4% [1] . Nonetheless, due to the insidious onset of infection in patients with hematological disease, the initial symptom is generally manifested as fever [2] . Therefore, it is important to understand the epidemiological characteristics of pathogens in febrile patients with hematological disease in the Hematology Department of our hospital and to early detect pathogenic microorganism. This can provide key guidance for empirical antibacterial treatment and subsequent modification, thereby contributing to the improvement of patient prognosis. Currently, the BSI data provide the mainstay source for epidemiological literature on pathogens in febrile patients with hematological disease in China. Sporadic studies on the epidemiological characteristics of pathogens in these patients may affect physicians' empirical decision-making about antibacterial treatment to some extent [3–4] . Moreover, the detection of pathogenic microorganisms primarily relies on bronchoscopy, blood (sputum/fecal) culture, as well as (1,3)-β-D-Glucan Assay and Galactomannan Antigen Assay (G/GM). Bronchoscopy is an invasive procedure, to which some patients with hematological disease have contraindications, hence limiting its application in these patients. Blood (sputum/stool) cultures requires a long incubation time but has low a positive rate of only 10–25% even in febrile neutropenic (FN) patients with hematological disease; the G/GM is mainly used for the early diagnosis of fungal infections such as Candida and Aspergillus spp. but still entails a high risk of false positives and false negatives [5-6] . Accordingly, it is critical to find an efficient, rapid, accurate, and non-invasive method for detecting pathogens. Thanks to the advancement of pathogen testing technologies, metagenomic next-generation sequencing (mNGS) has emerged. This is a novel sequencing technique based on the concept of sequencing-by-synthesis, which determines the DNA sequence by capturing newly synthesized end labels. Compared with traditional sequencing methods, mNGS has advantages of low cost and superior sensitivity. Unlike conventional method of pathogen testing, mNGS is featured by unbiased, rapid detection, and extensive coverage, suitable for identifying pathogens in immunocompromised individuals [7–8] . Evidence has shown that mNGS can considerably increase the pathogen detection rate in patients with sepsis [9] . However, scarce studies exist regarding the application of mNGS in detecting pathogens in febrile patients with hematological disease and its guiding value for clinical treatment. Accordingly, the present study was conducted in two parts. The first part involved searching domestic and international databases for studies published by Chinese scholars to assess the epidemiological characteristics of pathogens in blood cultures of patients with hematological disease complicated with BSI in China. The second part contains a retrospective investigation into febrile patients with hematological disease admitted to our hospital, aiming to evaluate the mNGS's testing performance of pathogenic microbes and to further ascertain the contributing factors of mNGS. This can provide key references for selecting the timing of mNGS test and for guiding the follow-up treatment. The details are presented as follows. I Data and Methods 1.1 General Data A retrospective study was conducted on 96 febrile patients with hematological disease (with a total of 104 samples) admitted to The Second Hospital of Nanjing between May 2022 and May 2024. The inclusion criteria were defined as below: 1. Meeting the diagnostic criteria for hematological diseases complicated by fever (i.e., a single axillary temperature ≥ 38.3 ℃ or an axillary temperature ≥ 38.0 ℃ for 1 h); 2. Age ranging from 3 to 90 years; 3. Undergoing both routine and mNGS tests. Exclusion criteria were defined as below: 1. Insufficient blood sample size or the inability to collect blood samples, and the failure to comply with the testing requirements; 2. Incomplete clinical data. The study was approved by The Second Hospital of Nanjing Review Board, and informed consent was obtained from the patients and their families. 1.2 Methods 1.2.1 Routine test Routine testing methods comprised blood culture, stool culture, sputum culture, and G/GM, all of which were performed by professional personnel in the hospital's laboratory in strict accordance with criteria. 1.2.2 mNGS test A total of 5 mL of peripheral venous blood was collected from patients under fasting condition, and was stored in the free nucleic acid preservation tube. After centrifugation, the supernatant was collected. The circulating cell-free DNA and RNA were extracted using the Magnetic Circulating DNA Maxi Kit and the TIANamp Virus RNA Kit, respectively. The circulating cell-free DNA and RNA libraries were created using the ieff NGS UItima DNA Library Prep Kit and the Hieff NGS UItima Dual-mode RNA Library Prep Kit. Aided by the BWA software, the NextSeq 550 System (a benchtop sequencer from Illumina, USA) was used to compare the sequencing data with those of the human genome and to remove the human-derived and repetitive sequences. The remaining sequencing data were compared with the microorganism database integrated with bacteria, fungi, and viruses, etc., to determine the number of microbiology sequences. The potential pathogens were evaluated based on the sequence number and clinical information. 1.3 Observation Indexes The testing performance values of the routine and mNGS tests for pathogenic microbes were compared. Patients were assigned to the mNGS-positive and mNGS-negative groups based on the mNGS results, respectively. Demographic information and clinical indexes of these patients in the two groups were collected. The analysis of the univarient and of the logistic regression model-related multivariate showed that, the contributing factors of mNGS positivity in febrile patients with hematological disease were identified while a prediction model was created. Using the receiver operating characteristic (ROC) curve, the single and combined predictive values of mNGS positivity in febrile patients with hematological disease were assessed, and the prognosis outcomes were summarized. 1.4 Statistical Processing SPSS 22.0 software was used. Measurement information was denoted by "_x ± s", and the non-normally distributed quantitative data were denoted by interquartile ranges. The t-tests and nonparametric tests were conducted, respectively. Count data were expressed as "%" and analyzed using the χ 2 test. If the P value was less than 0.05, it was deemed statistically significant. II Results 2.1 General Information of Included Papers Among the initially retrieved 537 papers, 32 papers were excluded using Note Express for removing duplicates, 489 papers were eliminated based on titles and abstracts, ten papers were omitted due to low quality or unclear key data. Finally, six articles were included in this study. A total of 3614 isolates from positive blood cultures were identified, including 2409 Gram-negative bacteria (66.66%), 1121 Gram-positive bacteria (31.02%), and 74 fungi (2.05%). See Table 1 . Table 1 General Data of Papers Included in the Study Author Year of Publication Study Subjects Sample Size Isolates from Positive Blood Cultures Yan Chenhua et al. [10] 2016 Patients with hematological disease complicated with BSI 1139 104 Yao et al. [11] 2017 Pediatric patients with hematological disease complicated with BSI 231 619 Zhu et al. [12] 2017 Patients with hematological disease complicated with BSI / 246 Xu Chunhui et al. [13] 2020 Patients with hematological disease complicated with BSI 1478 2025 Zhu Guoqing et al. [14] 2020 Pediatric patients with hematological disease complicated with BSI 427 550 Wu et al. [15] 2024 Patients with hematological disease complicated with BSI 56 70 Table 1 (Continued) Author Bacteria Fungi Gram-Negative Gram-Positive Yan Chenhua et al. [10] 57 ( Escherichia coli 22, Klebsiella pneumoniae 19, Pseudomonas aeruginosa 8) 38 ( Staphylococcus epidermidis 12, Coagulase-negative staphylococci 4, Staphylococcus aureus 3) 9 Yao et al. [11] 371 ( Pseudomonas aeruginosa 72, Enterobacter cloacae 69, Escherichia coli 66, Klebsiella pneumoniae 64) 243 ( Staphylococcus hominis 60, Streptococcus spp. 59) 5 Zhu et al. [12] 127 ( Stenotrophomonas maltophilia , Klebsiella pneumoniae ) 120 ( Coagulase-negative staphylococci ) 0 Xu Chunhui et al. [13] 1551 ( Escherichia coli 553, Klebsiella pneumoniae 394, Pseudomonas aeruginosa 229) 423 ( Streptococcus viridans 112, Coagulase-negative staphylococci 93, Staphylococcus aureus 62) 51 Zhu Guoqing et al. [14] 253 ( Escherichia coli 99, Klebsiella pneumoniae 67, Pseudomonas aeruginosa 28) 281 ( Streptococcus viridans 109, Staphylococcus epidermidis 75, Staphylococcus aureus 32) 5 Wu et al. [15] 50 ( Klebsiella pneumoniae 16, Escherichia coli 10, Pseudomonas aeruginosa 9) 16 ( Enterococcus faecium 5, Streptococcus spp. 2, Staphylococcus hominis 2, Staphylococcus epidermidis 2) 4 2.2 General Data Among the enrolled 96 febrile patients with hematological disease, there were 54 males (56.30%) and 42 females (43.70%), with a median age of 62 (within the range from 40 to 70) years. Wherein, 11 patients aged < 18 years (11.46%) and 85 patients (88.54%) aged ≥ 18 years. The types of hematological diseases were as follows: acute myeloid leukemia (AML) in 21 cases, severe aplastic anemia (SAA) in 21 cases, lymphoma in 11 cases, myelodysplastic syndromes (MDS) in 12 cases, hemophagocytic syndrome (HPS) in 6 cases, hemolytic disease in 5 cases, multiple myeloma (MM) in 3 cases, myelofibrosis (MF) in 3 cases, chronic NK cell lymphoproliferative disease (LPD) in 3 cases, T-cell large granular lymphocyte leukemia (T-LGLL) in 2 cases, acute lymphoblastic leukemia (ALL) in 1 case, acute promyelocytic leukemia (APL) in 1 case, chronic myelomonocytic leukemia (CMML) in 1 case, and other diseases in 6 cases. Comorbidities were present in 56 cases (a single patient may have multiple comorbidities), including hypertension in 11 cases, diabetes mellitus in 11 cases, tumor in 8 cases, pulmonary disease in 5 cases, hepatic disease in 12 cases, cardiovascular and cerebrovascular diseases in 10 cases, and renal disease in 3 cases. 2.3 Results of Pathogen Tests Using the Routine and mNGS Methods Among 104 peripheral blood sample tests using mNGS, pathogens were detected in 71 tests; simple infections were found in 36 tests; and combined infections were identified in 35 tests. A total of 131 pathogenic microbes were isolated. In terms of the detected bacteria, 23 strains (17.56%) were identified, with Klebsiella pneumoniae , Stenotrophomonas maltophilia , and Pseudomonas aeruginosa being the top three Gram-negative bacteria detected. In terms of Gram-positive bacteria, 14 strains (10.69%) were identified, with Mycobacterium spp. being most frequently detected. In terms of detected viruses, 82 strains (62.60%) were identified, with human herpesvirus, parvovirus B19, and cytomegalovirus being the top three viruses detected. In terms of fungi, 12 strains (9.16%) were identified, with Aspergillus spp. being most frequently detected. Among 104 peripheral blood sample tests using mNGS, pathogens were detected in 71 tests, with a positive detection rate of 68.27%; pathogens were detected in 8 blood culture tests, with a positive detection rate of 7.69%. Pathogens were detected in 17 routine tests, with a positive detection rate of 16.35%. The positive detection rate of the peripheral blood test using mNGS was remarkably more than those of blood culture and routine test methods ( P < 0.05). The detection rate of combined infections by the peripheral blood test using mNGS was 33.65% (35/104), which was higher than 0.96% by complete blood count (CBC) (1/104) and 1.92% by routine method (2/104), with statistically significant differences ( P < 0.05). See Table 2 . Table 2 Results of Routine and mNGS Pathogen Tests Pathogenic Bacteria Blood Culture Routine Test mNGS Test Gram-negative Bacteria Klebsiella pneumoniae 2 4 5 Klebsiella oxytoca 1 1 2 Escherichia coli 0 0 2 Pseudomonas aeruginosa 0 2 3 Stenotrophomonas maltophilia 1 3 4 Prevotella spp. 0 0 2 Legionella pneumophila 0 0 1 Coxiella burnetii 0 0 1 Enterobacter hormaechei 0 0 1 Ureaplasma urealyticum 0 0 1 Gram-Positive Bacteria Bacteroides fragilis 0 0 1 Coagulase-negative staphylococci 0 1 1 Staphylococcus aureus 0 1 1 Staphylococcus epidermidis 1 2 0 Streptococcus spp. 1 1 1 Enterococcus faecium 1 1 2 Staphylococcus hominis 1 1 2 Listeria monocytogenes 0 0 1 Nocardia spp. 0 0 1 Mycobacterium spp. 0 0 5 Virus Human herpesvirus 0 0 42 Parvovirus 0 0 19 Cytomegalovirus 0 0 9 Polyomavirus 0 0 6 Hepatitis virus 0 0 4 Others 0 0 2 Fungi Candida tropicalis 1 1 1 Candida albicans 0 1 1 Candida krusei 0 0 1 Aspergillus spp. 0 1 4 Candida glabrata 0 2 0 Rhizopus microsporus 0 0 2 Sporothrix fungi 0 0 3 2.4 Consistency Analysis of Routine and mNGS Test Results With routine test as the reference standard, the sensitivity, specificity, positive predictive value, negative predictive value, and the consistency rates of mNGS test were 21.13% (15/71), 93.94% (31/33), 88.24% (15/17), 35.63% (31/87), and 44.23% (46/104), respectively. The Kappa value was 0.105, suggesting poor consistency. See Table 3 . Table 3 Consistency Analysis of Routine and mNGS Test Results mNGS Test Subtotal Positive Negative Routine Test Positive 15 2 17 Negative 56 31 87 Total 71 33 104 2.5 Diagnostic performance Analysis of Routine and mNGS Tests With clinical diagnosis as the gold standard, among 96 febrile patients with hematological disease, 80 patients experienced pathogen infections and 16 did not. The sensitivity, positive predictive value, and negative predictive value of the mNGS test were 80.00%, 96.97%, and 46.67%, respectively, all of which exceeded those of routine test (13.75%, 73.33%, and 14.81%, respectively). The differences were statistically significant ( P < 0.05). See Table 4 . Table 4 Diagnostic Performance Analysis of Routine and mNGS Tests B Routine Test mNGS P Sensitivity 13.75 (11/80) 80.00 (64/80) < 0.001 Specificity 75.00 (12/16) 87.50 (14/16) - Positive Predictive Value 73.33 (11/15) 96.97 (64/66) 0.009 Negative Predictive Value 14.81 (12/81) 46.67 (14/30) < 0.001 2.6 Univariate Analysis of mNGS Positivity in Febrile Patients with Hematological Disease The univariate analysis results showed that the incidence of pulmonary infection in the mNGS-positive group was 74.24% (49/66), higher than 46.67% in the mNGS-negative group (14/30); the incidence of neutropenia in the mNGS-positive group was 92.42%, higher than 66.67% in the mNGS-negative group (20/30); the NK cell level in the mNGS-positive group was 59.00 (23.50, 147.00), lower than 129.00 in the mNGS-negative group (56.75, 177.75). See Table 5 . Table 5 Univariate Analysis of mNGS Positivity in Febrile Patients with Hematological Disease Factor mNGS-positive Group (n = 66) mNGS-Negative Group (n = 30) χ 2 P Gender Male 38 16 0.151 0.698 Female 28 14 Age (Years) 62.50 (48.25,70.00) 61.50 (29.75,69.25) 0.446 0.504 Type of Hematological Disease Malignant hematology 36 19 0.651 0.420 Non-malignant hematology 30 11 Hypoproteinemia Yes 54 21 1.686 0.194 No 12 9 Pulmonary Infection Yes 49 14 6.952 0.009 No 17 16 Neutropenia Yes 61 20 8.518 0.004 No 5 10 WBC (×10 9 /L) 2.49 (1.21,4.85) 3.79 (1.27,6.80) 1.016 0.314 NE (×10 9 /L) 1.55 (0.19,3.36) 1.95 (0.57,4.37) 0.558 0.455 CRP (mg/L) 48.12 (13.45,90.49) 53.56 (5.91,106.67) 0.187 0.665 HB (g/L) 68.00 (60.00,82.00) 66.50 (59.00,83.50) 0.001 0.981 PLT (×10 9 /L) 34.00 (13.00,98.00) 22.00 (11.75,96.00) 0.425 0.514 LDH (U/L) 240.00 (149.50,392.50) 224.00 (178.50,427.00) 0.024 0.877 ALT (U/L) 20.60 (12.25,31.95) 17.35 (10.10,40.73) 0.042 0.837 Cr (µmol/L) 57.00 (47.00,78.00) 58.50 (47.00,70.50) 0.501 0.479 r-GGT (U/L) 38.00 (21.50,72.20) 36.00 (19.75,59.75) 0.268 0.605 CD4 294.00 (141.50,454.00) 300.50 (107.50,396.00) 0.000 0.994 CD8 192.00 (113.00,373.00) 209.50 (115.25,283.25) 0.121 0.728 Th/Ts 1.33 (0.77,1.84) 1.42 (0.87,1.80) 0.095 0.758 NK 59.00 (23.50,147.00) 129.00 (56.75,177.75) 5.494 0.019 B 32.00 (6.00,85.00) 72.50 (17.75,158.00) 3.497 0.061 2.7 Multivariate Analysis of mNGS Positivity in Febrile Patients with Hematological Disease The results of the multivariate analysis using the logistic regression model showed that the contributing factor of mNGS positivity in febrile patients with hematological disease included pulmonary infection (OR: 2.389; 95% CI: 1.199–4.763), Neutropenia (OR: 4.092; 95% CI: 1.179–14.209), low NK level (OR: 1.127; 95% CI: 1.117–1.139), and chemotherapy (OR: 3.158; 95% CI: 1.206–8.268). See Table 6 . Table 6 Multivariate Analysis of mNGS Positivity in Febrile Patients with Hematological Disease Using Logistic Regression Model Factor B SE Waldχ2 P OR (95%CI) Intercept -2.388 0.663 12.977 0.000 - Pulmonary Infection 0.871 0.352 3.834 0.32 2.389 (1.199–4.763) Neutropenia 1.409 0.635 4.923 0.027 4.092 (1.179–14.209) NK 0.120 0.005 2.126 0.045 1.127 (1.117–1.139) 2.8 ROC Curve Analysis Results Based on the results of the multivariate analysis using the logistic regression model, a model was developed: Logit (P1) ≈ -2.388 + 0.871 × Pulmonary Infection + 1.409 × Neutropenia + 0.120 × NK. The findings of the ROC curve analysis revealed that the AUC values used for single and combined predictive values of mNGS positivity in febrile patients with hematological disease ranged from 0.623 to 0.849, with the maximum value of 0.849 for combined prediction. The sensitivity and specificity values were 78.79% and 93.33%, respectively. See Table 7 and Fig. 1 . Table 7 ROC Curve Analysis Results Test Method ROC Curve Optimal Cutoff Value Sensitivity (%) Specificity (%) AUC 95% CI P Pulmonary Infection 0.623 0.499–0.746 0.048 / 71.21 (47/66) 53.33 (16/30) Neutropenia 0.629 0.501–0.757 0.044 / 92.42 (61/66) 33.33 (10/30) NK 0.656 0.546–0.766 0.015 67.90 66.67 (44/66) 83.33 (25/30) Combined 0.849 0.775–0.924 < 0.001 / 78.79 (54/66) 93.33 (28/30) 2.9 Prognostic Outcomes According to the guidance stipulated in the Chinese Guidelines for the Clinical Application of Antibacterial Drugs for Patients with Febrile Neutropenia (2020 Edition) , physicians initially administered empirical anti-infection treatment. For 48 patients with poor disease management, the medication was modified based on mNGS results. Twelve patients died, whereas remaining patients had their conditions under effective management, with a survival rate of 75.0%. 3 Discussion This paper summarized a total of 6 studies (published by Chinese scholars) on the pathogen of patients with hematological disease complicated with BSI. Among overall 3614 isolates from positive blood cultures, 2409 strains of Gram-negative bacteria accounted for 66.66%; 1121 strains of Gram-positive bacteria accounted for 31.02%; 74 strains of fungi accounted for 2.05%. This suggests that the mainstay pathogenic bacteria isolated from patients with hematological disease complicated with BSI remained Gram-negative bacteria, Escherichia coli , Klebsiella pneumoniae , Pseudomonas aeruginosa , and Stenotrophomonas maltophilia . This is inconsistent with the results that pathogens in patients with hematological disease complicated with BSI were dominated by Gram-positive bacteria in European and American countries [16–17] . The fact is probably associated with fewer use of central venous catheters and prophylactic quinolone drugs in developing countries. This situation is similar to a study from central India, which showed that major pathogenic microbes in patients with hematological disease experiencing BSI were Gram-negative bacteria (64.0%), followed by Gram-positive bacteria (25.0%) and fungi (9.0%) [18] . As fever in patients with hematological disease is not a specific sign of infection, some patients may experience fever of unknown origin, which is probably related to the disease itself or relapse. The results of this study in this paper revealed that, out of 104 peripheral blood sample tests using the mNGS, pathogens were detected in 71 tests. About one-third of tests did not detect pathogenic microorganisms, which corroborates with the above conclusion. Compared with blood culture and routine testing methods, the test using mNGS has a higher positive detection rate (68.27% vs. 16.35% and 7.69%). This is consistent with the study by Wang et al. [19] , which showed that the positive detection rate using mNGS for pathogens was higher than that by the routine test method (67.70% vs. 22.0%). The positive detection rate of less than 20% by blood culture and routine testing method in this study may be due to a lack of RNA pathogen testing in this study, which indicates the shortage of this study. (This explanation is appropriate because the study by the scholar Wang et al. included the RNA pathogen test, which was collectively referred to as routine test. Nonetheless, only bacteria and fungi tests were considered as routine tests in this study. Therefore, fewer types of pathogens were detected, leading to a lower positive detection rate.) Unlike other studies [20] , the results of this study showed that viruses were major pathogens in febrile patients with hematological disease, accounting for up to 60.74%. This is primarily attributable to the fact that viruses are usually carried during the treatment with blood transfusion, which causes the immunodeficiency in patients with hematological disease and the combined infection. In clinical setting, bacterial and fungal infections should still be given priority. It is worth noting that Stenotrophomonas maltophilia among Gram-negative bacteria and Mycobacterium spp. among Gram-positive bacteria have become dominant pathogens, which is consistent with the conclusions of the study by Trecarichi et al [21] . Therefore, for febrile patients with hematological disease in the Hematology Department of our hospital, it is necessary to test the RNA pathogen and to empirically administer antimicrobial drugs targeting Stenotrophomonas maltophilia and Mycobacterium spp . The mNGS test is able to detect a wide range of pathogens including bacteria, fungi, and viruses. Once an mNGS-positivity is confirmed, it is substantially possible to ascertain the specific etiological evidence. Compared with blood culture and routine testing methods, mNGS test can identify two or more pathogens, with a high detection rate of combined infection being up to 33.65%. In contrast, either blood culture or routine test methods generally only identifies one or two pathogens. Moreover, mNGS can identify pathogens that are not detectable by routine methods (such as Aspergillus spp. , Pneumocystis jirovecii , and polyomavirus) and rare pathogens (such as Nocardia and Rhizomucor pusillus ) [22–25] . There is a low consistency between the results of routine and the mNGS tests. Moreover, with clinical diagnosis of infection as the gold standard, the results of mNGS test showed the greater values of sensitivity and positive prediction. This undoubtedly confirms that mNGS findings can offer important guidance for the formulation of individualized antibacterial regimens for patients, which is consistent with previous studies [26] . Albeit numerous advantages of mNGS test, its high cost usually results in its low frequency of only once. Therefore, choosing a right timing for mNGS test is also critical. To improve the positive detection rate of mNGS in febrile patients with hematological disease, it is necessary to further clarify the contributing factors. The deficiency of neutrophils, the important immune cells in the human body, can impair intestinal and respiratory mucosae, thereby adding the risks of bacterial, viral, and fungal colonization in patients [27–28] . Low NK cell levels may increase the human body's susceptibility to bacterial and fungal infections through two pathways: 1. Activating IFN-γ-mediated macrophages/neutrophils; 2. impairing their natural killing ability against infectious pathogens [29] . Pulmonary infection can cause an inflammatory response, which in turn accelerates the entry of pathogens into the bloodstream [30] . The findings of this study reveal that the contributing factors of mNGS positivity in febrile patients with hematological disease are pulmonary infection, neutropenia, and low NK cell levels. Accordingly, the study recommends an early test using mNGS in patients with signs of pulmonary infection, neutropenia, and low NK cell levels. Moreover, based on the mNGS testing results with fever status, it is possible to locate the cause of fever. If patients are tested mNGS-negative with fever, they might develop fever due to the progression of hematological disease, and chemotherapy can be considered. If patients are tested mNGS-positive with fever, it indicates an infectious fever, and the decision to modify antibiotic therapy can be based on the response to empirical antibiotic treatment. The prognosis findings indicated a survival rate of 75.0%, which represents an improvement compared to the survival rate of febrile patients with hematological disease previously treated at our hospital. It suggested that mNGS test can also guide clinical treatment. However, it was still probable that mNGS results were false positive and false negative. False positive result may be attributable to improper operation, reagent contamination, or gene mismatch, while false negative results may be related to the difficulty in detecting thick-walled or intracellular microorganisms, as well as the susceptibility of cell-free DNA in the peripheral blood samples to degradation and the interference from human nucleic acids. Accordingly, when the test is conducted using mNGS technology, its combination with the routine test is still warranted for comprehensive evaluation. However, this study is bound by following restrictions: 1. Clinical diagnosis is empirical with diagnostic bias. Using it as the "gold standard" to analyze the diagnostic value of mNGS for pathogen infection in patients with hematological patients may compromise the accuracy of the study conclusions; 2. the RNA pathogen test is not included in the routine test methods of this study. In summary, mNGS allows a more comprehensive and accurate identification of pathogenic microbes in patients with hematological disease. However, the positive rate of mNGS test may be affected by pulmonary infection, neutropenia, and low NK cell levels. Therefore, appropriate timing for the mNGS use can provide critical information for the formulation of clinical protocol and enables individualized and accurate treatment. Declarations Conflict of Interest: The authors declare no conflict of interest. Funding: This research received no external funding. Author Contribution Long proposed a writing approach, Peng conducted statistical work, and Song, Chen, Wang, and Lian all collected, tracked, and followed up on cases; Song wrote the main manuscript text ; Long, Peng and Song revised the article； All authors reviewed the manuscript. Acknowledgment: Not applicable. Availability of Data and Materials: The datasets used during the current study are available from the corresponding author upon reasonable request. References Amanati A, Sajedianfard S, Khajeh S, et al. Bloodstream infections in adult patients with malignancy, epidemiology, microbiology, and risk factors associated with mortality and multi-drug resistance[J]. BMC Infect Dis. 2021, 21(1):636. Lehrnbecher T, Robinson PD, Ammann RA, et al. Guideline for the Management of Fever and Neutropenia in Pediatric Patients With Cancer and Hematopoietic Cell Transplantation Recipients: 2023 Update[J]. J Clin Oncol. 2023, 41(9):1774–1785. Wang J, Mu M, Zhu J, et al. Adult acute leukemia patients with gram-negative bacteria bloodstream infection: Risk factors and outcomes of antibiotic-resistant bacteria[J]. Ann Hematol. 2024, 103(10):4021–4031. Wang J, Liu J, Lin Q, et al. A comparative analysis of clinical outcomes in hematological patients afflicted with bacteremia attributable to carbapenem-resistant Klebsiella pneumoniae versus Escherichia coli[J]. Front Cell Infect Microbiol. 2025, 15:1600746. Alexander BD, Lamoth F, Heussel CP, et al. Guidance on Imaging for Invasive Pulmonary Aspergillosis and Mucormycosis: From the Imaging Working Group for the Revision and Update of the Consensus Definitions of Fungal Disease from the EORTC/MSGERC[J]. Clin Infect Dis. 2021, 72(l 2):79–88. Feng S, Rao G, Wei X, et al. Clinical metagenomic sequencing of plasma microbial cell-free DNA for febrile neutropenia in patients with acute leukaemia[J]. Clin Microbiol Infect. 2024, 30(1):107–113. Hao SF, Wang YH, Li LJ, et al.Clinical application value of peripheral blood metagenomic next-generation sequencing test for patients with hematological diseases accompanied by fever [J]. Zhonghua Xue Ye Xue Za Zhi. 2022, 43(9):766–770. Hogan CA, Yang S, Garner OB, et al. Clinical Impact of Metagenomic Next-Generation Sequencing of Plasma Cell-Free DNA for the Diagnosis of Infectious Diseases: A Multicenter Retrospective Cohort Study[J]. Clin Infect Dis. 2021, 72(2):239–245. Blauwkamp TA, Thair S, Rosen MJ, et al. Analytical and clinical validation of a microbial cell-free DNA sequencing test for infectious disease[J]. Nat Microbiol. 2019, 4(4):663–674. 闫晨华,徐婷,郑晓云,等. 中国血液病患者中性粒细胞缺乏伴发热的多中心、前瞻性流行病学研究[J]. 中华血液学杂志,2016,37(3):177–182. Yao JF, Li N, Jiang J. Clinical Characteristics of Bloodstream Infections in Pediatric Acute Leukemia: A Single-center Experience with 231 Patients[J]. Chin Med J (Engl). 2017, 130(17):2076–2081. Zhu J, Hu J, Mao YF, et al. A multicenter, retrospective study of pathogenic bacteria distribution and drug resistance in febrile neutropenic patients with hematological diseases in Shanghai[J]. Zhonghua Xue Ye Xue Za Zhi. 2017, 38(11):945–950. 徐春晖,朱国庆,林青松,等. 2014–2018年成人血液病患者血流感染病原菌分布及耐药性单中心结果分析[J]. 中华血液学杂志,2020,41(8):643–648. 朱国庆,徐春晖,林青松,等. 2014–2018年儿童恶性血液病患者中性粒细胞缺乏期血流感染病原学和临床特征分析[J]. 中华血液学杂志,2020,41(8):655–660. Wu H, Li M, Shou C, et al. Pathogenic spectrum and drug resistance of bloodstream infection in patients with acute myeloid leukaemia: a single centre retrospective study[J]. Front Cell Infect Microbiol. 2024, 14:1390053. Nesher L, Rolston KV. The current spectrum of infection in cancer patients with chemotherapy related neutropenia. Infection. 2014, 42(1):5–13. Joudeh N, Sawafta E, Abu Taha A, Hamed Allah M, Amer R, Odeh RY, Salameh H, Sabateen A, Aiesh BM, Zyoud SH. Epidemiology and source of infection in cancer patients with febrile neutropenia: an experience from a developing country. BMC Infect Dis. 2023, 23(1):106. Choudhari S, Gawande R, Watchmaker J, et al. Bloodstream infections in cancer patients in central India: pathogens and trends of antimicrobial resistance over a 5-year period[J]. Access Microbiol. 2024, 6(10):000673.v5. Wang X, Zhang H, Zhang N, Zhang S, Shuai Y, Miao X, Liu Y, Qiu L, Ren S, Lai S, Han Y, Yao H, Zhang X, Fan F, Sun H, Yi H. Application value of metagenomic next-generation sequencing in hematological patients with high-risk febrile neutropenia. Front Cell Infect Microbiol. 2024, 14:1366908. Kara Ali R, Surme S, Balkan II, Salihoglu A, Sahin Ozdemir M, Ozdemir Y, Mete B, Can G, Ar MC, Tabak F, Saltoglu N. An eleven-year cohort of bloodstream infections in 552 febrile neutropenic patients: resistance profiles of Gram-negative bacteria as a predictor of mortality. Ann Hematol. 2020, 99(8):1925–1932. Trecarichi EM, Tumbarello M. Antimicrobial-resistant Gram-negative bacteria in febrile neutropenic patients with cancer: current epidemiology and clinical impact. Curr Opin Infect Dis. 2014, 27(2):200 − 10. Chen Y, Ma T. Hematologic cancers and infections: how to detect infections in advance and determine the type? Front Cell Infect Microbiol. 2024 Nov 4;14:1476543. Zhang Y, Zhou D, Xia H, et al. Metagenomic next-generation sequencing for detection of pathogens in children with hematological diseases complicated with infection[J]. Mol Cell Probes. 2023, 67:101889. Zhou Y, Shi W, Wen Y, et al. Comparison of pathogen detection consistency between metagenomic next-generation sequencing and blood culture in patients with suspected bloodstream infection[J]. Sci Rep. 2023, 13(1):9460. Lai LM, Chen QG, Liu Y, et al. The value of metagenomic next-generation sequencing in the diagnosis of fever of unknown origin[J]. Sci Rep. 2025, 15(1):1963. Li J, Feng X, Wang J, et al. Acinetobacter spp. bloodstream infection in hematological patients: a 10-year single-center study[J]. 2023, 23(1):796. Öztop H, Hunutlu FÇ. Neutrophil-to-ferritin ratio can predict hematological causes of fever of unknown origin[J]. Sci Rep. 2024, 14(1):22983. Xie N, Zhang W, Tian F, et al. Fever of unknown origin: Clinical significance of the etiology and common inflammatory parameters[J]. Diagn Microbiol Infect Dis. 2025, 112(3):116801. Wang H, Fu BB, Gale RP, et al. NK-/T-cell lymphomas[J]. Leukemia. 2021, 35(9):2460–2468. Ma Q, Yao C, Wu Y, et al. Neurological disorders after severe pneumonia are associated with translocation of endogenous bacteria from the lung to the brain[J]. Sci Adv. 2023, 9(42):eadi0699. Additional Declarations No competing interests reported. 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Nonetheless, due to the insidious onset of infection in patients with hematological disease, the initial symptom is generally manifested as fever \u003csup\u003e[2]\u003c/sup\u003e. Therefore, it is important to understand the epidemiological characteristics of pathogens in febrile patients with hematological disease in the Hematology Department of our hospital and to early detect pathogenic microorganism. This can provide key guidance for empirical antibacterial treatment and subsequent modification, thereby contributing to the improvement of patient prognosis.\u003c/p\u003e\n\u003cp\u003eCurrently, the BSI data provide the mainstay source for epidemiological literature on pathogens in febrile patients with hematological disease in China. Sporadic studies on the epidemiological characteristics of pathogens in these patients may affect physicians' empirical decision-making about antibacterial treatment to some extent \u003csup\u003e[3–4]\u003c/sup\u003e. Moreover, the detection of pathogenic microorganisms primarily relies on bronchoscopy, blood (sputum/fecal) culture, as well as (1,3)-β-D-Glucan Assay and Galactomannan Antigen Assay (G/GM). Bronchoscopy is an invasive procedure, to which some patients with hematological disease have contraindications, hence limiting its application in these patients. Blood (sputum/stool) cultures requires a long incubation time but has low a positive rate of only 10–25% even in febrile neutropenic (FN) patients with hematological disease; the G/GM is mainly used for the early diagnosis of fungal infections such as \u003cem\u003eCandida\u003c/em\u003e and\u003cem\u003e\u0026nbsp;Aspergillus spp.\u003c/em\u003e but still entails a high risk of false positives and false negatives \u003csup\u003e[5-6]\u003c/sup\u003e. Accordingly, it is critical to find an efficient, rapid, accurate, and non-invasive method for detecting pathogens.\u003c/p\u003e\n\u003cp\u003eThanks to the advancement of pathogen testing technologies, metagenomic next-generation sequencing (mNGS) has emerged. This is a novel sequencing technique based on the concept of sequencing-by-synthesis, which determines the DNA sequence by capturing newly synthesized end labels. Compared with traditional sequencing methods, mNGS has advantages of low cost and superior sensitivity. Unlike conventional method of pathogen testing, mNGS is featured by unbiased, rapid detection, and extensive coverage, suitable for identifying pathogens in immunocompromised individuals \u003csup\u003e[7–8]\u003c/sup\u003e. Evidence has shown that mNGS can considerably increase the pathogen detection rate in patients with sepsis\u003csup\u003e\u0026nbsp;[9]\u003c/sup\u003e. However, scarce studies exist regarding the application of mNGS in detecting pathogens in febrile patients with hematological disease and its guiding value for clinical treatment. Accordingly, the present study was conducted in two parts. The first part involved searching domestic and international databases for studies published by Chinese scholars to assess the epidemiological characteristics of pathogens in blood cultures of patients with hematological disease complicated with BSI in China. The second part contains a retrospective investigation into febrile patients with hematological disease admitted to our hospital, aiming to evaluate the mNGS's testing performance of pathogenic microbes and to further ascertain the contributing factors of mNGS. This can provide key references for selecting the timing of mNGS test and for guiding the follow-up treatment. The details are presented as follows.\u003c/p\u003e"},{"header":"I Data and Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1 General Data\u003c/h2\u003e\u003cp\u003eA retrospective study was conducted on 96 febrile patients with hematological disease (with a total of 104 samples) admitted to The Second Hospital of Nanjing between May 2022 and May 2024. The inclusion criteria were defined as below: 1. Meeting the diagnostic criteria for hematological diseases complicated by fever (i.e., a single axillary temperature\u0026thinsp;\u0026ge;\u0026thinsp;38.3 ℃ or an axillary temperature\u0026thinsp;\u0026ge;\u0026thinsp;38.0 ℃ for 1 h); 2. Age ranging from 3 to 90 years; 3. Undergoing both routine and mNGS tests. Exclusion criteria were defined as below: 1. Insufficient blood sample size or the inability to collect blood samples, and the failure to comply with the testing requirements; 2. Incomplete clinical data. The study was approved by The Second Hospital of Nanjing Review Board, and informed consent was obtained from the patients and their families.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Methods\u003c/h2\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003ch2\u003e1.2.1 Routine test\u003c/h2\u003e\u003cp\u003eRoutine testing methods comprised blood culture, stool culture, sputum culture, and G/GM, all of which were performed by professional personnel in the hospital's laboratory in strict accordance with criteria.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e1.2.2 mNGS test\u003c/h2\u003e\u003cp\u003eA total of 5 mL of peripheral venous blood was collected from patients under fasting condition, and was stored in the free nucleic acid preservation tube. After centrifugation, the supernatant was collected. The circulating cell-free DNA and RNA were extracted using the Magnetic Circulating DNA Maxi Kit and the TIANamp Virus RNA Kit, respectively. The circulating cell-free DNA and RNA libraries were created using the ieff NGS UItima DNA Library Prep Kit and the Hieff NGS UItima Dual-mode RNA Library Prep Kit. Aided by the BWA software, the NextSeq 550 System (a benchtop sequencer from Illumina, USA) was used to compare the sequencing data with those of the human genome and to remove the human-derived and repetitive sequences. The remaining sequencing data were compared with the microorganism database integrated with bacteria, fungi, and viruses, etc., to determine the number of microbiology sequences. The potential pathogens were evaluated based on the sequence number and clinical information.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e1.3 Observation Indexes\u003c/h2\u003e\u003cp\u003eThe testing performance values of the routine and mNGS tests for pathogenic microbes were compared. Patients were assigned to the mNGS-positive and mNGS-negative groups based on the mNGS results, respectively. Demographic information and clinical indexes of these patients in the two groups were collected. The analysis of the univarient and of the logistic regression model-related multivariate showed that, the contributing factors of mNGS positivity in febrile patients with hematological disease were identified while a prediction model was created. Using the receiver operating characteristic (ROC) curve, the single and combined predictive values of mNGS positivity in febrile patients with hematological disease were assessed, and the prognosis outcomes were summarized.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e1.4 Statistical Processing\u003c/h2\u003e\u003cp\u003eSPSS 22.0 software was used. Measurement information was denoted by \"_x \u0026plusmn; s\", and the non-normally distributed quantitative data were denoted by interquartile ranges. The t-tests and nonparametric tests were conducted, respectively. Count data were expressed as \"%\" and analyzed using the \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e test. If the \u003cem\u003eP\u003c/em\u003e value was less than 0.05, it was deemed statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"II Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.1 General Information of Included Papers\u003c/h2\u003e\u003cp\u003eAmong the initially retrieved 537 papers, 32 papers were excluded using Note Express for removing duplicates, 489 papers were eliminated based on titles and abstracts, ten papers were omitted due to low quality or unclear key data. Finally, six articles were included in this study. A total of 3614 isolates from positive blood cultures were identified, including 2409 Gram-negative bacteria (66.66%), 1121 Gram-positive bacteria (31.02%), and 74 fungi (2.05%). See Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eGeneral Data of Papers Included in the Study\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAuthor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYear of Publication\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStudy Subjects\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSample Size\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIsolates from Positive Blood Cultures\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYan Chenhua et al.\u003csup\u003e[10]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePatients with hematological disease complicated with BSI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e104\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYao et al. \u003csup\u003e[11]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePediatric patients with hematological disease complicated with BSI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e619\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZhu et al. \u003csup\u003e[12]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePatients with hematological disease complicated with BSI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e246\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eXu Chunhui et al. \u003csup\u003e[13]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePatients with hematological disease complicated with BSI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZhu Guoqing et al. \u003csup\u003e[14]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePediatric patients with hematological disease complicated with BSI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e550\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWu et al. \u003csup\u003e[15]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePatients with hematological disease complicated with BSI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e(Continued)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAuthor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eBacteria\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFungi\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGram-Negative\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGram-Positive\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYan Chenhua et al.\u003csup\u003e[10]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57 (\u003cem\u003eEscherichia coli\u003c/em\u003e 22, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e 19, \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e 8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38 (\u003cem\u003eStaphylococcus epidermidis\u003c/em\u003e 12, \u003cem\u003eCoagulase-negative staphylococci\u003c/em\u003e 4, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e 3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYao et al. \u003csup\u003e[11]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e371 (\u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e 72, \u003cem\u003eEnterobacter cloacae\u003c/em\u003e 69, \u003cem\u003eEscherichia coli\u003c/em\u003e 66, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e 64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e243 (\u003cem\u003eStaphylococcus hominis\u003c/em\u003e 60, \u003cem\u003eStreptococcus spp.\u003c/em\u003e 59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZhu et al. \u003csup\u003e[12]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e127 (\u003cem\u003eStenotrophomonas maltophilia\u003c/em\u003e, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e120 (\u003cem\u003eCoagulase-negative staphylococci\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eXu Chunhui et al. \u003csup\u003e[13]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1551 (\u003cem\u003eEscherichia coli\u003c/em\u003e 553, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e 394, \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e 229)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e423 (\u003cem\u003eStreptococcus viridans\u003c/em\u003e 112, \u003cem\u003eCoagulase-negative staphylococci\u003c/em\u003e 93, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e 62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZhu Guoqing et al. \u003csup\u003e[14]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e253 (\u003cem\u003eEscherichia coli\u003c/em\u003e 99, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e 67, \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e 28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e281 (\u003cem\u003eStreptococcus viridans\u003c/em\u003e 109, \u003cem\u003eStaphylococcus epidermidis\u003c/em\u003e 75, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e 32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWu et al. \u003csup\u003e[15]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50 (\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e 16, \u003cem\u003eEscherichia coli\u003c/em\u003e 10, \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e 9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (\u003cem\u003eEnterococcus faecium\u003c/em\u003e 5, \u003cem\u003eStreptococcus spp.\u003c/em\u003e 2, \u003cem\u003eStaphylococcus hominis\u003c/em\u003e 2, \u003cem\u003eStaphylococcus epidermidis\u003c/em\u003e 2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.2 General Data\u003c/h2\u003e\u003cp\u003eAmong the enrolled 96 febrile patients with hematological disease, there were 54 males (56.30%) and 42 females (43.70%), with a median age of 62 (within the range from 40 to 70) years. Wherein, 11 patients aged\u0026thinsp;\u0026lt;\u0026thinsp;18 years (11.46%) and 85 patients (88.54%) aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years. The types of hematological diseases were as follows: acute myeloid leukemia (AML) in 21 cases, severe aplastic anemia (SAA) in 21 cases, lymphoma in 11 cases, myelodysplastic syndromes (MDS) in 12 cases, hemophagocytic syndrome (HPS) in 6 cases, hemolytic disease in 5 cases, multiple myeloma (MM) in 3 cases, myelofibrosis (MF) in 3 cases, chronic NK cell lymphoproliferative disease (LPD) in 3 cases, T-cell large granular lymphocyte leukemia (T-LGLL) in 2 cases, acute lymphoblastic leukemia (ALL) in 1 case, acute promyelocytic leukemia (APL) in 1 case, chronic myelomonocytic leukemia (CMML) in 1 case, and other diseases in 6 cases. Comorbidities were present in 56 cases (a single patient may have multiple comorbidities), including hypertension in 11 cases, diabetes mellitus in 11 cases, tumor in 8 cases, pulmonary disease in 5 cases, hepatic disease in 12 cases, cardiovascular and cerebrovascular diseases in 10 cases, and renal disease in 3 cases.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Results of Pathogen Tests Using the Routine and mNGS Methods\u003c/h2\u003e\u003cp\u003eAmong 104 peripheral blood sample tests using mNGS, pathogens were detected in 71 tests; simple infections were found in 36 tests; and combined infections were identified in 35 tests. A total of 131 pathogenic microbes were isolated. In terms of the detected bacteria, 23 strains (17.56%) were identified, with \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, \u003cem\u003eStenotrophomonas maltophilia\u003c/em\u003e, and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e being the top three Gram-negative bacteria detected. In terms of Gram-positive bacteria, 14 strains (10.69%) were identified, with \u003cem\u003eMycobacterium spp.\u003c/em\u003e being most frequently detected. In terms of detected viruses, 82 strains (62.60%) were identified, with human herpesvirus, parvovirus B19, and cytomegalovirus being the top three viruses detected. In terms of fungi, 12 strains (9.16%) were identified, with \u003cem\u003eAspergillus spp.\u003c/em\u003e being most frequently detected.\u003c/p\u003e\u003cp\u003eAmong 104 peripheral blood sample tests using mNGS, pathogens were detected in 71 tests, with a positive detection rate of 68.27%; pathogens were detected in 8 blood culture tests, with a positive detection rate of 7.69%. Pathogens were detected in 17 routine tests, with a positive detection rate of 16.35%. The positive detection rate of the peripheral blood test using mNGS was remarkably more than those of blood culture and routine test methods (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The detection rate of combined infections by the peripheral blood test using mNGS was 33.65% (35/104), which was higher than 0.96% by complete blood count (CBC) (1/104) and 1.92% by routine method (2/104), with statistically significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). See Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of Routine and mNGS Pathogen Tests\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePathogenic Bacteria\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBlood Culture\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRoutine Test\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003emNGS Test\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e\u003cp\u003eGram-negative Bacteria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eKlebsiella oxytoca\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eStenotrophomonas maltophilia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePrevotella spp.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLegionella pneumophila\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCoxiella burnetii\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eEnterobacter hormaechei\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eUreaplasma urealyticum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e\u003cp\u003eGram-Positive Bacteria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eBacteroides fragilis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCoagulase-negative staphylococci\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eStaphylococcus epidermidis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eStreptococcus spp.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eEnterococcus faecium\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eStaphylococcus hominis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eListeria monocytogenes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eNocardia spp.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMycobacterium spp.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eVirus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eHuman herpesvirus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eParvovirus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCytomegalovirus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePolyomavirus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eHepatitis virus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eFungi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCandida tropicalis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCandida albicans\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCandida krusei\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAspergillus spp.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCandida glabrata\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eRhizopus microsporus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSporothrix fungi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\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=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Consistency Analysis of Routine and mNGS Test Results\u003c/h2\u003e\u003cp\u003eWith routine test as the reference standard, the sensitivity, specificity, positive predictive value, negative predictive value, and the consistency rates of mNGS test were 21.13% (15/71), 93.94% (31/33), 88.24% (15/17), 35.63% (31/87), and 44.23% (46/104), respectively. The Kappa value was 0.105, suggesting poor consistency. See Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eConsistency Analysis of Routine and mNGS Test Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003emNGS Test\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSubtotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRoutine Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e104\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=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Diagnostic performance Analysis of Routine and mNGS Tests\u003c/h2\u003e\u003cp\u003eWith clinical diagnosis as the gold standard, among 96 febrile patients with hematological disease, 80 patients experienced pathogen infections and 16 did not. The sensitivity, positive predictive value, and negative predictive value of the mNGS test were 80.00%, 96.97%, and 46.67%, respectively, all of which exceeded those of routine test (13.75%, 73.33%, and 14.81%, respectively). The differences were statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). See Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDiagnostic Performance Analysis of Routine and mNGS Tests\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\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRoutine Test\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003emNGS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.75 (11/80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80.00 (64/80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75.00 (12/16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87.50 (14/16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive Predictive Value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73.33 (11/15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96.97 (64/66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative Predictive Value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.81 (12/81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46.67 (14/30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\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=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Univariate Analysis of mNGS Positivity in Febrile Patients with Hematological Disease\u003c/h2\u003e\u003cp\u003eThe univariate analysis results showed that the incidence of pulmonary infection in the mNGS-positive group was 74.24% (49/66), higher than 46.67% in the mNGS-negative group (14/30); the incidence of neutropenia in the mNGS-positive group was 92.42%, higher than 66.67% in the mNGS-negative group (20/30); the NK cell level in the mNGS-positive group was 59.00 (23.50, 147.00), lower than 129.00 in the mNGS-negative group (56.75, 177.75). See Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate Analysis of mNGS Positivity in Febrile Patients with Hematological Disease\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003emNGS-positive Group (n\u0026thinsp;=\u0026thinsp;66)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003emNGS-Negative Group (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.698\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAge (Years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62.50 (48.25,70.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e61.50 (29.75,69.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.446\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.504\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eType of Hematological Disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMalignant hematology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.651\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.420\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-malignant hematology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHypoproteinemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.686\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.194\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePulmonary Infection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.952\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNeutropenia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eWBC (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.49 (1.21,4.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.79 (1.27,6.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.314\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNE (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.55 (0.19,3.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.95 (0.57,4.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.455\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCRP (mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.12 (13.45,90.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53.56 (5.91,106.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.665\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHB (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.00 (60.00,82.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66.50 (59.00,83.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.981\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePLT (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.00 (13.00,98.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.00 (11.75,96.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.514\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLDH (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e240.00 (149.50,392.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e224.00 (178.50,427.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.877\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eALT (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.60 (12.25,31.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.35 (10.10,40.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.837\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCr (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57.00 (47.00,78.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58.50 (47.00,70.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.479\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003er-GGT (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.00 (21.50,72.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.00 (19.75,59.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.605\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCD4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e294.00 (141.50,454.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e300.50 (107.50,396.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.994\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCD8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e192.00 (113.00,373.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e209.50 (115.25,283.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.728\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTh/Ts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.33 (0.77,1.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.42 (0.87,1.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.758\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59.00 (23.50,147.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e129.00 (56.75,177.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.00 (6.00,85.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72.50 (17.75,158.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.061\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=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Multivariate Analysis of mNGS Positivity in Febrile Patients with Hematological Disease\u003c/h2\u003e\u003cp\u003eThe results of the multivariate analysis using the logistic regression model showed that the contributing factor of mNGS positivity in febrile patients with hematological disease included pulmonary infection (OR: 2.389; 95% CI: 1.199\u0026ndash;4.763), Neutropenia (OR: 4.092; 95% CI: 1.179\u0026ndash;14.209), low NK level (OR: 1.127; 95% CI: 1.117\u0026ndash;1.139), and chemotherapy (OR: 3.158; 95% CI: 1.206\u0026ndash;8.268). See Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate Analysis of mNGS Positivity in Febrile Patients with Hematological Disease Using Logistic Regression Model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWaldχ2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.388\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.663\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePulmonary Infection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.871\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.834\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.389 (1.199\u0026ndash;4.763)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutropenia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.923\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.092 (1.179\u0026ndash;14.209)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.127 (1.117\u0026ndash;1.139)\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=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e2.8 ROC Curve Analysis Results\u003c/h2\u003e\u003cp\u003eBased on the results of the multivariate analysis using the logistic regression model, a model was developed: Logit (P1) \u0026asymp; -2.388\u0026thinsp;+\u0026thinsp;0.871 \u0026times; Pulmonary Infection\u0026thinsp;+\u0026thinsp;1.409 \u0026times; Neutropenia\u0026thinsp;+\u0026thinsp;0.120 \u0026times; NK. The findings of the ROC curve analysis revealed that the AUC values used for single and combined predictive values of mNGS positivity in febrile patients with hematological disease ranged from 0.623 to 0.849, with the maximum value of 0.849 for combined prediction. The sensitivity and specificity values were 78.79% and 93.33%, respectively. See Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eROC Curve Analysis Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTest Method\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eROC Curve\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOptimal Cutoff Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSensitivity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSpecificity (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePulmonary Infection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.623\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.499\u0026ndash;0.746\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e71.21 (47/66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e53.33 (16/30)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutropenia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.501\u0026ndash;0.757\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e92.42 (61/66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e33.33 (10/30)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.656\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.546\u0026ndash;0.766\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e67.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e66.67 (44/66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e83.33 (25/30)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCombined\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.849\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.775\u0026ndash;0.924\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e78.79 (54/66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e93.33 (28/30)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Prognostic Outcomes\u003c/h2\u003e\u003cp\u003eAccording to the guidance stipulated in the \u003cem\u003eChinese Guidelines for the Clinical Application of Antibacterial Drugs for Patients with Febrile Neutropenia (2020 Edition)\u003c/em\u003e, physicians initially administered empirical anti-infection treatment. For 48 patients with poor disease management, the medication was modified based on mNGS results. Twelve patients died, whereas remaining patients had their conditions under effective management, with a survival rate of 75.0%.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Discussion","content":"\u003cp\u003eThis paper summarized a total of 6 studies (published by Chinese scholars) on the pathogen of patients with hematological disease complicated with BSI. Among overall 3614 isolates from positive blood cultures, 2409 strains of Gram-negative bacteria accounted for 66.66%; 1121 strains of Gram-positive bacteria accounted for 31.02%; 74 strains of fungi accounted for 2.05%. This suggests that the mainstay pathogenic bacteria isolated from patients with hematological disease complicated with BSI remained Gram-negative bacteria, \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, and \u003cem\u003eStenotrophomonas maltophilia\u003c/em\u003e. This is inconsistent with the results that pathogens in patients with hematological disease complicated with BSI were dominated by Gram-positive bacteria in European and American countries \u003csup\u003e[16\u0026ndash;17]\u003c/sup\u003e. The fact is probably associated with fewer use of central venous catheters and prophylactic quinolone drugs in developing countries. This situation is similar to a study from central India, which showed that major pathogenic microbes in patients with hematological disease experiencing BSI were Gram-negative bacteria (64.0%), followed by Gram-positive bacteria (25.0%) and fungi (9.0%) \u003csup\u003e[18]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAs fever in patients with hematological disease is not a specific sign of infection, some patients may experience fever of unknown origin, which is probably related to the disease itself or relapse. The results of this study in this paper revealed that, out of 104 peripheral blood sample tests using the mNGS, pathogens were detected in 71 tests. About one-third of tests did not detect pathogenic microorganisms, which corroborates with the above conclusion. Compared with blood culture and routine testing methods, the test using mNGS has a higher positive detection rate (68.27% vs. 16.35% and 7.69%). This is consistent with the study by Wang et al. \u003csup\u003e[19]\u003c/sup\u003e, which showed that the positive detection rate using mNGS for pathogens was higher than that by the routine test method (67.70% vs. 22.0%). The positive detection rate of less than 20% by blood culture and routine testing method in this study may be due to a lack of RNA pathogen testing in this study, which indicates the shortage of this study. (This explanation is appropriate because the study by the scholar Wang et al. included the RNA pathogen test, which was collectively referred to as routine test. Nonetheless, only bacteria and fungi tests were considered as routine tests in this study. Therefore, fewer types of pathogens were detected, leading to a lower positive detection rate.) Unlike other studies \u003csup\u003e[20]\u003c/sup\u003e, the results of this study showed that viruses were major pathogens in febrile patients with hematological disease, accounting for up to 60.74%. This is primarily attributable to the fact that viruses are usually carried during the treatment with blood transfusion, which causes the immunodeficiency in patients with hematological disease and the combined infection. In clinical setting, bacterial and fungal infections should still be given priority. It is worth noting that \u003cem\u003eStenotrophomonas maltophilia\u003c/em\u003e among Gram-negative bacteria and \u003cem\u003eMycobacterium spp.\u003c/em\u003e among Gram-positive bacteria have become dominant pathogens, which is consistent with the conclusions of the study by \u003cem\u003eTrecarichi\u003c/em\u003e et al \u003csup\u003e[21]\u003c/sup\u003e. Therefore, for febrile patients with hematological disease in the Hematology Department of our hospital, it is necessary to test the RNA pathogen and to empirically administer antimicrobial drugs targeting \u003cem\u003eStenotrophomonas maltophilia\u003c/em\u003e and \u003cem\u003eMycobacterium spp\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eThe mNGS test is able to detect a wide range of pathogens including bacteria, fungi, and viruses. Once an mNGS-positivity is confirmed, it is substantially possible to ascertain the specific etiological evidence. Compared with blood culture and routine testing methods, mNGS test can identify two or more pathogens, with a high detection rate of combined infection being up to 33.65%. In contrast, either blood culture or routine test methods generally only identifies one or two pathogens. Moreover, mNGS can identify pathogens that are not detectable by routine methods (such as \u003cem\u003eAspergillus spp.\u003c/em\u003e, \u003cem\u003ePneumocystis jirovecii\u003c/em\u003e, and polyomavirus) and rare pathogens (such as \u003cem\u003eNocardia\u003c/em\u003e and \u003cem\u003eRhizomucor pusillus\u003c/em\u003e) \u003csup\u003e[22\u0026ndash;25]\u003c/sup\u003e. There is a low consistency between the results of routine and the mNGS tests. Moreover, with clinical diagnosis of infection as the gold standard, the results of mNGS test showed the greater values of sensitivity and positive prediction. This undoubtedly confirms that mNGS findings can offer important guidance for the formulation of individualized antibacterial regimens for patients, which is consistent with previous studies \u003csup\u003e[26]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAlbeit numerous advantages of mNGS test, its high cost usually results in its low frequency of only once. Therefore, choosing a right timing for mNGS test is also critical. To improve the positive detection rate of mNGS in febrile patients with hematological disease, it is necessary to further clarify the contributing factors. The deficiency of neutrophils, the important immune cells in the human body, can impair intestinal and respiratory mucosae, thereby adding the risks of bacterial, viral, and fungal colonization in patients \u003csup\u003e[27\u0026ndash;28]\u003c/sup\u003e. Low NK cell levels may increase the human body's susceptibility to bacterial and fungal infections through two pathways: 1. Activating IFN-γ-mediated macrophages/neutrophils; 2. impairing their natural killing ability against infectious pathogens \u003csup\u003e[29]\u003c/sup\u003e. Pulmonary infection can cause an inflammatory response, which in turn accelerates the entry of pathogens into the bloodstream \u003csup\u003e[30]\u003c/sup\u003e. The findings of this study reveal that the contributing factors of mNGS positivity in febrile patients with hematological disease are pulmonary infection, neutropenia, and low NK cell levels. Accordingly, the study recommends an early test using mNGS in patients with signs of pulmonary infection, neutropenia, and low NK cell levels. Moreover, based on the mNGS testing results with fever status, it is possible to locate the cause of fever. If patients are tested mNGS-negative with fever, they might develop fever due to the progression of hematological disease, and chemotherapy can be considered. If patients are tested mNGS-positive with fever, it indicates an infectious fever, and the decision to modify antibiotic therapy can be based on the response to empirical antibiotic treatment. The prognosis findings indicated a survival rate of 75.0%, which represents an improvement compared to the survival rate of febrile patients with hematological disease previously treated at our hospital. It suggested that mNGS test can also guide clinical treatment. However, it was still probable that mNGS results were false positive and false negative. False positive result may be attributable to improper operation, reagent contamination, or gene mismatch, while false negative results may be related to the difficulty in detecting thick-walled or intracellular microorganisms, as well as the susceptibility of cell-free DNA in the peripheral blood samples to degradation and the interference from human nucleic acids. Accordingly, when the test is conducted using mNGS technology, its combination with the routine test is still warranted for comprehensive evaluation. However, this study is bound by following restrictions: 1. Clinical diagnosis is empirical with diagnostic bias. Using it as the \"gold standard\" to analyze the diagnostic value of mNGS for pathogen infection in patients with hematological patients may compromise the accuracy of the study conclusions; 2. the RNA pathogen test is not included in the routine test methods of this study.\u003c/p\u003e\u003cp\u003eIn summary, mNGS allows a more comprehensive and accurate identification of pathogenic microbes in patients with hematological disease. However, the positive rate of mNGS test may be affected by pulmonary infection, neutropenia, and low NK cell levels. Therefore, appropriate timing for the mNGS use can provide critical information for the formulation of clinical protocol and enables individualized and accurate treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of Interest:\u003c/h2\u003e\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThis research received no external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLong proposed a writing approach, Peng conducted statistical work, and Song, Chen, Wang, and Lian all collected, tracked, and followed up on cases; Song wrote the main manuscript text ; Long, Peng and Song revised the article； All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgment:\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAvailability of Data and Materials:\u003c/h2\u003e\u003cp\u003eThe datasets used during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmanati A, Sajedianfard S, Khajeh S, et al. Bloodstream infections in adult patients with malignancy, epidemiology, microbiology, and risk factors associated with mortality and multi-drug resistance[J]. BMC Infect Dis. 2021, 21(1):636.\u003c/li\u003e\n\u003cli\u003eLehrnbecher T, Robinson PD, Ammann RA, et al. Guideline for the Management of Fever and Neutropenia in Pediatric Patients With Cancer and Hematopoietic Cell Transplantation Recipients: 2023 Update[J]. J Clin Oncol. 2023, 41(9):1774–1785.\u003c/li\u003e\n\u003cli\u003eWang J, Mu M, Zhu J, et al. Adult acute leukemia patients with gram-negative bacteria bloodstream infection: Risk factors and outcomes of antibiotic-resistant bacteria[J]. Ann Hematol. 2024, 103(10):4021–4031.\u003c/li\u003e\n\u003cli\u003eWang J, Liu J, Lin Q, et al. A comparative analysis of clinical outcomes in hematological patients afflicted with bacteremia attributable to carbapenem-resistant Klebsiella pneumoniae versus Escherichia coli[J]. Front Cell Infect Microbiol. 2025, 15:1600746.\u003c/li\u003e\n\u003cli\u003eAlexander BD, Lamoth F, Heussel CP, et al. Guidance on Imaging for Invasive Pulmonary Aspergillosis and Mucormycosis: From the Imaging Working Group for the Revision and Update of the Consensus Definitions of Fungal Disease from the EORTC/MSGERC[J]. Clin Infect Dis. 2021, 72(l 2):79–88.\u003c/li\u003e\n\u003cli\u003eFeng S, Rao G, Wei X, et al. Clinical metagenomic sequencing of plasma microbial cell-free DNA for febrile neutropenia in patients with acute leukaemia[J]. Clin Microbiol Infect. 2024, 30(1):107–113.\u003c/li\u003e\n\u003cli\u003eHao SF, Wang YH, Li LJ, et al.Clinical application value of peripheral blood metagenomic next-generation sequencing test for patients with hematological diseases accompanied by fever [J]. Zhonghua Xue Ye Xue Za Zhi. 2022, 43(9):766–770.\u003c/li\u003e\n\u003cli\u003eHogan CA, Yang S, Garner OB, et al. Clinical Impact of Metagenomic Next-Generation Sequencing of Plasma Cell-Free DNA for the Diagnosis of Infectious Diseases: A Multicenter Retrospective Cohort Study[J]. Clin Infect Dis. 2021, 72(2):239–245.\u003c/li\u003e\n\u003cli\u003eBlauwkamp TA, Thair S, Rosen MJ, et al. Analytical and clinical validation of a microbial cell-free DNA sequencing test for infectious disease[J]. Nat Microbiol. 2019, 4(4):663–674.\u003c/li\u003e\n\u003cli\u003e闫晨华,徐婷,郑晓云,等. 中国血液病患者中性粒细胞缺乏伴发热的多中心、前瞻性流行病学研究[J]. 中华血液学杂志,2016,37(3):177–182.\u003c/li\u003e\n\u003cli\u003eYao JF, Li N, Jiang J. Clinical Characteristics of Bloodstream Infections in Pediatric Acute Leukemia: A Single-center Experience with 231 Patients[J]. Chin Med J (Engl). 2017, 130(17):2076–2081.\u003c/li\u003e\n\u003cli\u003eZhu J, Hu J, Mao YF, et al. A multicenter, retrospective study of pathogenic bacteria distribution and drug resistance in febrile neutropenic patients with hematological diseases in Shanghai[J]. Zhonghua Xue Ye Xue Za Zhi. 2017, 38(11):945–950.\u003c/li\u003e\n\u003cli\u003e徐春晖,朱国庆,林青松,等. 2014–2018年成人血液病患者血流感染病原菌分布及耐药性单中心结果分析[J]. 中华血液学杂志,2020,41(8):643–648.\u003c/li\u003e\n\u003cli\u003e朱国庆,徐春晖,林青松,等. 2014–2018年儿童恶性血液病患者中性粒细胞缺乏期血流感染病原学和临床特征分析[J]. 中华血液学杂志,2020,41(8):655–660.\u003c/li\u003e\n\u003cli\u003eWu H, Li M, Shou C, et al. Pathogenic spectrum and drug resistance of bloodstream infection in patients with acute myeloid leukaemia: a single centre retrospective study[J]. Front Cell Infect Microbiol. 2024, 14:1390053.\u003c/li\u003e\n\u003cli\u003eNesher L, Rolston KV. The current spectrum of infection in cancer patients with chemotherapy related neutropenia. Infection. 2014, 42(1):5–13.\u003c/li\u003e\n\u003cli\u003eJoudeh N, Sawafta E, Abu Taha A, Hamed Allah M, Amer R, Odeh RY, Salameh H, Sabateen A, Aiesh BM, Zyoud SH. Epidemiology and source of infection in cancer patients with febrile neutropenia: an experience from a developing country. BMC Infect Dis. 2023, 23(1):106.\u003c/li\u003e\n\u003cli\u003eChoudhari S, Gawande R, Watchmaker J, et al. Bloodstream infections in cancer patients in central India: pathogens and trends of antimicrobial resistance over a 5-year period[J]. Access Microbiol. 2024, 6(10):000673.v5.\u003c/li\u003e\n\u003cli\u003eWang X, Zhang H, Zhang N, Zhang S, Shuai Y, Miao X, Liu Y, Qiu L, Ren S, Lai S, Han Y, Yao H, Zhang X, Fan F, Sun H, Yi H. Application value of metagenomic next-generation sequencing in hematological patients with high-risk febrile neutropenia. Front Cell Infect Microbiol. 2024, 14:1366908.\u003c/li\u003e\n\u003cli\u003eKara Ali R, Surme S, Balkan II, Salihoglu A, Sahin Ozdemir M, Ozdemir Y, Mete B, Can G, Ar MC, Tabak F, Saltoglu N. An eleven-year cohort of bloodstream infections in 552 febrile neutropenic patients: resistance profiles of Gram-negative bacteria as a predictor of mortality. Ann Hematol. 2020, 99(8):1925–1932.\u003c/li\u003e\n\u003cli\u003eTrecarichi EM, Tumbarello M. Antimicrobial-resistant Gram-negative bacteria in febrile neutropenic patients with cancer: current epidemiology and clinical impact. Curr Opin Infect Dis. 2014, 27(2):200 − 10.\u003c/li\u003e\n\u003cli\u003eChen Y, Ma T. Hematologic cancers and infections: how to detect infections in advance and determine the type? Front Cell Infect Microbiol. 2024 Nov 4;14:1476543.\u003c/li\u003e\n\u003cli\u003eZhang Y, Zhou D, Xia H, et al. Metagenomic next-generation sequencing for detection of pathogens in children with hematological diseases complicated with infection[J]. Mol Cell Probes. 2023, 67:101889.\u003c/li\u003e\n\u003cli\u003eZhou Y, Shi W, Wen Y, et al. Comparison of pathogen detection consistency between metagenomic next-generation sequencing and blood culture in patients with suspected bloodstream infection[J]. Sci Rep. 2023, 13(1):9460.\u003c/li\u003e\n\u003cli\u003eLai LM, Chen QG, Liu Y, et al. The value of metagenomic next-generation sequencing in the diagnosis of fever of unknown origin[J]. Sci Rep. 2025, 15(1):1963.\u003c/li\u003e\n\u003cli\u003eLi J, Feng X, Wang J, et al. Acinetobacter spp. bloodstream infection in hematological patients: a 10-year single-center study[J]. 2023, 23(1):796.\u003c/li\u003e\n\u003cli\u003eÖztop H, Hunutlu FÇ. Neutrophil-to-ferritin ratio can predict hematological causes of fever of unknown origin[J]. Sci Rep. 2024, 14(1):22983.\u003c/li\u003e\n\u003cli\u003eXie N, Zhang W, Tian F, et al. Fever of unknown origin: Clinical significance of the etiology and common inflammatory parameters[J]. Diagn Microbiol Infect Dis. 2025, 112(3):116801.\u003c/li\u003e\n\u003cli\u003eWang H, Fu BB, Gale RP, et al. NK-/T-cell lymphomas[J]. Leukemia. 2021, 35(9):2460–2468.\u003c/li\u003e\n\u003cli\u003eMa Q, Yao C, Wu Y, et al. Neurological disorders after severe pneumonia are associated with translocation of endogenous bacteria from the lung to the brain[J]. Sci Adv. 2023, 9(42):eadi0699.\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":"Hematological disease, Fever (febrile), Metagenomic next-generation sequencing (mNGS), Testing performance, Clinical guidance","lastPublishedDoi":"10.21203/rs.3.rs-7387629/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7387629/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo explore the testing performance of metagenomic next-generation sequencing (mNGS) in identifying pathogenic microbes in febrile patients with hematological disease and its significant role in guiding clinical treatment.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThe data of pathogens from the blood cultures of neutropenic patients with hematological disease and/or febrile patients with bloodstream infection (BSI) were summarized and the features of infection were analyzed, through a retrieval of the WoS, PubMed, CNKI, Wanfang, and VIP databases. A retrospective study was conducted on 96 febrile patients with hematological disease (104 specimens) presented to our hospital between May 2022 and May 2024. These patients underwent both routine and mNGS tests for a comparison of the testing performance, and were assigned to mNGS-positive and mNGS-negative groups according to the mNGS results, respectively. Based on an analysis of the data and indexes of the two groups, the contributing factors of mNGS positivity were determined using the univarient and the logistic regression while a prediction model was developed to assess predictive value and summarize the prognosis information using the Receiver Operating Characteristic Curve (ROC).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAccording to the included six papers, among totally 3614 isolates from positive blood cultures, Gram-negative bacteria, Gram-positive bacteria, and fungi accounted for 66.66%, 31.02%, and 2.05%, respectively. In this retrospective study, out of 104 peripheral blood tests using mNGS technology, pathogens were detected in 71 tests, with a positive detection rate of 68.27%, which was substantially higher than that of blood culture (7.69%) and routine test (16.35%). Out of 131 isolated pathogenic microbes, viruses held the maximum ratio (60.74%). The identification rate of combined infections by the mNGS test exceeded those by complete blood count (CBC) and routine test. When clinical diagnosis was employed as the gold standard, mNGS test had greater values of sensitivity, positive prediction and negative prediction than those of routine test. The univariant analysis revealed that the mNGS-positive group had higher incidences of pulmonary infection and neutropenia and lower natural killer (NK) cell levels, compared with the mNGS-negative group. The multivariate analysis result of logistic regression model showed that contributing factors of mNGS positivity in febrile patients with hematological disease included pulmonary infection [Odds ratio (OR): 2.389; 95%confidence level (CI): 1.199\u0026ndash;4.763)], neutropenia (OR: 4.092; 95% CI: 1.179\u0026ndash;14.209), and low NK cell levels (OR: 1.127; 95% CI: 1.117\u0026ndash;1.139). According to the analysis result of the ROC curve, the area under curve (AUC) values used for single and combined predictive values of mNGS positivity in febrile patients with hematological disease ranged from 0.623 to 0.849, with the maximum value of 0.849 for combined prediction. The sensitivity and specificity values were 78.79% and 93.33%, respectively. Following relevant guidelines, physicians modified medication for patients with poor disease management based on mNGS results, achieving a survival rate of 75.0%.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003emNGS allows a more comprehensive and accurate identification of pathogenic microbes in patients with hematological disease. However, the positive rate of mNGS test may be affected by pulmonary infection, neutropenia, and low NK cell levels. Therefore, appropriate timing of the mNGS use can provide critical information for the formulation of clinical protocol and enables individualized and accurate treatment.\u003c/p\u003e","manuscriptTitle":"Testing Performance of mNGS in Febrile Patients with Hematological Disease and Its Guiding Value for Fever Management in Clinical Treatment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-10 05:14:07","doi":"10.21203/rs.3.rs-7387629/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":"48039823-613d-49db-b069-633f5ebfc58e","owner":[],"postedDate":"September 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-05T14:23:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-10 05:14:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7387629","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7387629","identity":"rs-7387629","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}