Application of metagenomic next-generation sequencing technology in hematologic malignancy patients with sepsis following antibiotic use

Application of metagenomic next-generation sequencing technology in hematologic malignancy patients with sepsis following antibiotic use | 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 Application of metagenomic next-generation sequencing technology in hematologic malignancy patients with sepsis following antibiotic use Bingrong Chen, Wenxiu Shu, Jing Le, Dian Jin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7345606/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Dec, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted 10 You are reading this latest preprint version Abstract Background Metagenomic next-generation sequencing (mNGS) has been widely applied in clinical pathogen detection; however, its utility in patients with hematologic malignancies complicated by sepsis after antibiotic therapy requires further investigation. Methods A total of 119 patients with hematologic malignancies complicated by sepsis, who had received antibiotic treatment for ≥ 3 days without clinical improvement, were enrolled in the study. All patients underwent simultaneous blood culture and mNGS analysis. The diagnostic value of mNGS and its impact on optimizing anti-infective therapy were evaluated. Results For the detection of bacterial and fungal pathogens, mNGS demonstrated a significantly higher positive rate compared to blood culture (89.36% vs. 25.53%). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of mNGS were 58.33%, 0.00%, 16.67%, and 0.00%, respectively. The overall agreement rate between the two methods was 13.21% (kappa = -0.202). Based on mNGS results, anti-infective treatment regimens were modified in 47 patients (39.49%). Granulocytopenia related to antitumor therapy was identified as a high-risk factor for polymicrobial infections ( P < 0.05). Conclusions Patients with hematologic malignancies and sepsis, particularly those with antitumor therapy-induced granulocytopenia, are at increased risk for polymicrobial infections. Blood mNGS offers a rapid and comprehensive approach to pathogen identification, showing significant potential for guiding anti-infective therapy in this patient population. mNGS Blood Hematological malignancies Sepsis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Patients with hematological malignancies, including leukemia, lymphoma, and multiple myeloma, are at increased risk of sepsis due to immune dysfunction associated with both the underlying disease and its treatment modalities, such as chemotherapy and hematopoietic stem cell transplantation[ 1 ]. Clinical evidence demonstrates that the incidence of sepsis in this patient population is significantly higher compared to the general population, with a notably elevated mortality rate. Particularly, patients undergoing intensive chemotherapy or those in the neutropenic phase following transplantation face an even greater risk of infection, with correspondingly higher mortality [ 2 ]. Prompt administration of appropriate antibiotics is a critical intervention in reducing mortality among patients with sepsis. However, clinical management is challenged by two major issues. First, the overuse of broad-spectrum antibiotics can contribute to the emergence of multidrug-resistant pathogens, including carbapenem-resistant Enterobacteriaceae and methicillin-resistant Staphylococcus aureus. Available data suggest that approximately 45% of infections in patients with hematologic malignancies are caused by drug-resistant organisms[ 3 ]. Second, the hepatic and renal functions of post-chemotherapy patients are often compromised, and inappropriate antimicrobial use may further exacerbate organ dysfunction and prolong hospitalization. Thus, accurate pathogen identification and targeted use of narrow-spectrum antibiotics are essential for optimizing clinical outcomes[ 4 ]. Although blood culture remains the "gold standard" for diagnosing sepsis, its diagnostic yield is suboptimal in patients with hematological malignancies. The positivity rate of blood cultures is often below 30% due to factors such as post-chemotherapy myelosuppression and prior antibiotic administration. In early-onset sepsis (defined as onset within 72 hours), the detection rate is even lower, ranging from 15–20%[ 5 ]. Furthermore, conventional culture techniques require 3 to 5 days for pathogen identification. Given the rapid progression of sepsis in immunocompromised patients, where mortality rises significantly with every hour that effective treatment is delayed, such timeframes are often incompatible with clinical urgency. Additionally, the spectrum of pathogen detection using traditional methods is limited, often failing to identify fastidious organisms such as Legionella pneumophila, anaerobes such as Bacteroides fragilis, viruses such as cytomegalovirus, and fungi such as Aspergillus species—pathogens that are frequently encountered in immunosuppressed individuals[ 6 ]. Metagenomic next-generation sequencing (mNGS) enables pathogen identification without prior culture by directly extracting nucleic acids from all microorganisms present in the sample for high-throughput sequencing. Its technical advantages are particularly evident in patients with hematological malignancies[ 7 ]. This technology allows for the simultaneous detection of over 1,000 types of pathogens, including bacteria, fungi, viruses, and parasites, and is especially indispensable in diagnosing mixed infections, which account for approximately 18% of cases among patients with hematological malignancies. Regarding timeliness, the turnaround time from sample receipt to report generation can be reduced to 24–48 hours, which is 3–5 days faster than traditional culture methods, thereby providing a critical treatment window for critically ill patients. Furthermore, mNGS can reliably detect pathogen nucleic acids even after antibiotic administration, effectively addressing the issue of false negatives caused by antimicrobial suppression in conventional methods. Recent clinical studies have further validated the value of blood-based mNGS in hematological disorders. For patients with hematological diseases presenting with fever—particularly those with hematological malignancies who have unexplained fever and negative routine test results—peripheral blood mNGS demonstrates high clinical utility and diagnostic accuracy. Research indicates that its positive detection rate is significantly higher than that of blood culture and conventional laboratory tests, strongly supporting the recommendation for early application of mNGS in infection detection, which aligns with current clinical practice guidelines[ 8 ]. Moreover, in patients with hematological malignancies complicated by sepsis, mNGS exhibits significantly higher sensitivity compared to traditional blood culture, particularly when applied to blood samples [9] . It enables broader pathogen detection, including organisms often missed by conventional culture methods, and its clinical application has demonstrated favorable outcomes. Adjusting therapeutic strategies based on mNGS findings can improve patient management, further underscoring its clinical significance[ 9 ]. Despite the considerable advantages of mNGS, its widespread adoption still faces practical challenges. At the technical level, issues such as host DNA contamination, false negatives for low-abundance pathogens, and the lack of standardization in interpreting drug resistance genes remain unresolved. These limitations can be mitigated by optimizing sequencing depth (recommended ≥ 10,000×) and incorporating negative controls[ 10 ]. At the clinical level, the relatively high cost per test (approximately 1,500–3,000 yuan) may be a barrier for some patients and their families, while the absence of bioinformatics analysis platforms in primary hospitals further restricts its accessibility. Therefore, this study focuses on the specific clinical scenario of “post-antibiotic administration” and compares the sensitivity and specificity of mNGS with conventional methods. This comparison not only provides evidence for the clinical application of mNGS but also offers data-driven support for optimizing testing protocols and reducing costs, ultimately facilitating the standardized use of this technology in the field of infections associated with hematological malignancies. 2. Materials and Methods 2.1 Patients and study design A retrospective analysis was conducted on 119 patients with hematologic malignancies complicated by sepsis who were admitted to the Department of Hematology, Li Huili Hospital, Ningbo Medical Center between May 2022 and April 2025, and who showed poor therapeutic response after receiving antibiotic treatment for ≥ 3 days. Blood samples from all patients were subjected to pathogen detection using both blood culture and mNGS. Inclusion criteria: (1) No restrictions on age or gender; (2) Patients consented to undergo both blood culture and mNGS testing; (3) Patients with hematologic malignancies presented to the Department of Hematology with fever and clinical signs of infection, and had positive results by either blood culture or mNGS, fulfilling the diagnostic criteria for definite sepsis; (4) Availability of complete clinical data. Exclusion criteria: (1) Fever caused by non-infectious etiologies; (2) Refusal by patients to undergo either blood culture or mNGS testing; (3) Incomplete clinical data unavailable for analysis. Clinical data collected from each patient included age, gender, underlying malignancy, anti-tumor therapy, comorbidities, laboratory findings, treatment modalities, and clinical outcomes. The study was approved by the Ethical Review Committee of Ningbo Medical Center Lihuili Hospital (approval No. KY2025SL154-02). 2.2 Collection, preservation and processing of specimens Blood sample collection for mNGS was performed in accordance with the protocol described in reference [ 11 ]. At least 5 mL of blood was collected for blood culture. For mNGS, blood samples were collected in tubes containing anticoagulants that preserve cell-free DNA or RNA, with a minimum collection volume of 2 mL. After collection, the blood samples were jointly verified by attending physicians and personnel from Hangzhou Matridx Biotechnology Co., Ltd. Specimens were stored at 6–35°C and transported by personnel from Hangzhou Matridx Biotechnology Co., Ltd. to the laboratory for subsequent testing. 2.3 Blood samples collection and mNGS analysis The mNGS workflow for blood samples included nucleic acid extraction, library preparation, sequencing, and data analysis. The collected peripheral blood samples were centrifuged in accordance with the manufacturer's instructions, and the supernatant (typically plasma components) was collected as the test sample to remove impurities such as blood cells, thereby minimizing interference from host nucleic acids. The pretreated specimens were processed using Genewiz's NGSmaster™ Automated Nucleic Acid Detection Reaction System to perform a series of steps: automated nucleic acid extraction (including simultaneous reverse transcription of RNA to enable concurrent detection of both DNA and RNA pathogens), nucleic acid fragmentation, end repair, 3' end single-base A tailing, sequencing adapter ligation, and purification, ultimately generating the sequencing library. The quality of the extracted total DNA was assessed using Qubit (Thermo Fisher Scientific) to ensure library integrity, and the library preparation utilized Genewiz's bloodstream infection library preparation kit (reversible terminator sequencing method). Genewiz's NGS Library Quantification Kit (probe-based method) was used to determine the library concentration via quantitative real-time PCR, and libraries meeting sequencing requirements were selected for downstream analysis. High-throughput sequencing was conducted on the Illumina Nextseq550 platform using Genewiz's Universal Sequencing Reaction Kit (reversible terminator sequencing method) with a 50-bp single-end sequencing mode to ensure effective pathogen sequence readout. A comprehensive quality control system was also implemented. Each experiment strictly included multiple controls: negative controls (to monitor background contamination), positive controls (to evaluate the sensitivity and effectiveness of the detection system), and internal reference sequences added to each sample (to monitor the stability of all steps, including nucleic acid extraction, library preparation, and sequencing). The raw sequencing data were processed using Genewiz's pathogenic metagenomic data analysis system, Gentellix, to filter out human genomic sequence data (GRCh38.p13). The remaining sequence data were aligned against microbial reference databases (NCBI GenBank and Genewiz's internal microbial genome database) to identify microbial species and their relative abundances. 2.4 Blood samples collection and mNGS analysis All statistical analyses were performed using SPSS 22.0 software (IBM Corp., Armonk, NY, USA). Continuous variables, which exhibited non-normal distributions, were expressed as medians with interquartile ranges and compared using the Mann-Whitney U test. Categorical variables were presented as counts and percentages and compared using the chi-square test. A 2×2 contingency table was used to calculate the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of mNGS. Diagnostic performance was compared using the McNemar test, and test agreement was assessed using the kappa statistic. All tests were two-tailed, with statistical significance defined as p < 0.05. 3. Results 3.1. Patient characteristics From May 2022 to April 2025, a total of 93 patients with hematological malignancies complicated by sepsis were enrolled in this study, comprising 49 males (41.17%) and 70 females (58.82%), with a median age of 64 years. The majority of patients were over 60 years old (75.63%). All patients had confirmed diagnoses of hematological malignancies, including 58 cases of lymphoma, 32 cases of multiple myeloma, and 29 cases of acute leukemia. Among them, 58 patients (48.73%) developed neutropenia [ 12 ] as a result of antitumor therapy. The median oxygenation index was 405 mmHg. Additionally, 30 patients (25.21%) progressed to septic shock. Despite active clinical interventions, the 30-day mortality rate reached 15.12%, indicating that sepsis in patients with hematological malignancies is characterized by rapid progression, severe illness, therapeutic challenges, and high mortality. Detailed clinical data, including demographic characteristics, laboratory findings, and outcomes, are summarized in Table 1 . Table 1 Clinical characteristics of 119 enrolled patients Characteristic Value (median (IQRs 1 ) or no.(%)) Gender Male 49(41.17%) Female 70(58.82%) Age(years) <60 90(75.63%) ≥ 60 29(24.36%) Malignancy Lymphoma 58(48.73%) Multiple Myeloma 32(26.89%) Acute Leukemia 29(24.36%) Comorbidity Diabetes mellitus 34(28.57%) Chronic Obstructive Pulmonary Disease 17(14.28%) Hypertension 11(9.24%) Disease severity Septic shock 30(25.21%) Oxygenation index>300 (mmHg) 98(82.35%) Inflammation biomarker Peripheral blood neutrophils (10 9 /L) 1.5(0.45,6.2) C reactive protein (mg/L) 60.9(34.85,130) Procalcitonin (ng/mL) 0.48(0.136,1.99) Outcomes Total 30-day mortality 18(15.12%) 1 IQR, interquartile range 3.2. Comparison and concordance of mNGS and culture method Given the limited capacity of conventional culture methods to detect a broad spectrum of fungi and bacteria, this study compared the diagnostic performance of blood mNGS and paired blood culture (targeting bacteria and fungi) in patients with hematological malignancies complicated by sepsis. A total of 47 patients were identified as having fungal and/or bacterial infections. The positive detection rate of mNGS was significantly higher than that of blood culture (89.36% vs. 25.53%). Compared with the culture method, mNGS demonstrated a diagnostic sensitivity of 58.33% and a specificity of 0.00%, with a positive predictive value (PPV) of 16.67% and a negative predictive value (NPV) of 0.00% (Table 2 ). Table 2 Diagnostic performance and concordance of mNGS relative to culture method Culture+ Culture- Total Diagnostic performance of mNGS mNGS 1 + 7 35 42 Sensitivity% Specificity% PPV 2 % NPV 3 % mNGS- 5 0 5 58.33 0.00 16.67 0.00 Total 12 35 47 Consensus analysis P value <0.001 Kappa -0.229 1 mNGS, metagenomic next-generation sequencing; 2 PPV, positive predictive value; 3 NPV, negative predictive value. Furthermore, among the 47 patients with fungal or bacterial sepsis, 7 cases (14.90%) tested positive by both mNGS and culture, yielding an overall agreement rate of 13.21%. Concordance analysis revealed a kappa coefficient of -0.202, indicating poor agreement between the two methods (Table 2 ). In this cohort, 35 cases (74.46%) were exclusively positive by mNGS, while 5 cases (10.64%) were detected only by culture. Among the 7 cases with dual positivity, the results of mNGS and culture were fully consistent in 4 cases (57.14%), partially consistent in 1 case (14.28%), and inconsistent in 2 cases (28.57%) (Fig. 1 ). Overall, mNGS identified a greater number of bacterial (42 vs. 11) and fungal (16 vs. 1) pathogens compared to culture(Fig. S1 ). Additionally, mNGS demonstrated a significantly higher detection rate of polymicrobial infections (involving two or more pathogens) than culture (25.00% vs. 0.00%, P < 0.001). Among the 35 patients with negative culture results, mNGS identified monomicrobial infections in 26 cases and polymicrobial infections in 9 cases. In total, mNGS detected 19 bacterial species and 9 fungal species, demonstrating enhanced diagnostic performance for bloodstream infections. For patients with dual positive results, all 4 cases with complete agreement involved monomicrobial infections. In the analysis of partially concordant and discordant cases, mNGS detected more bacteria (8 vs. 3), more fungi (2 vs. 0), and more polymicrobial infections (3 vs. 0) than culture (Fig. S2 ). These findings indicate that even among patients with positive culture results, mNGS provides broader pathogen detection, including rare or fastidious organisms. In summary, these findings highlight the complex etiology of sepsis in patients with hematological malignancies and underscore the value of mNGS in efficiently identifying a wide range of causative pathogens. 3.3. Pathogens detected by mNGS and clinical impact of mNGS results on treatment Subsequently, we analyzed the pathogen strains identified by mNGS to determine the predominant causative microorganisms, thereby providing valuable insights for optimizing therapeutic strategies. A total of 190 pathogen strains were detected across 119 cases using mNGS. Viruses were the most commonly identified pathogens (70.52%), followed by bacteria (22.11%) and fungi (7.37%) (Fig. 2 ). Among bacterial pathogens, 42 strains were detected in 42 patients, with the six most frequently encountered being Klebsiella pneumoniae (9 cases), Propionibacterium acnes (5 cases), Pseudomonas aeruginosa (3 cases), Escherichia coli (3 cases), Stenotrophomonas maltophilia (2 cases), and Bacillus cereus (2 cases) (Fig. 3 ). In comparison, the culture method identified 11 bacterial strains in 12 patients, with the top three being Klebsiella pneumoniae (2 cases), Escherichia coli (2 cases), and Staphylococcus intermedius (2 cases) (Figure S3 ). A total of 16 fungal strains were detected in 13 patients, with Aspergillus fumigatus (3 cases) and Aspergillus flavus (3 cases) being the most prevalent, followed by Rhizopus microsporus (2 cases) and Candida glabrata (2 cases) (Fig. 3 ). In contrast, only one fungal strain, Aspergillus fumigatus (1 case), was identified by conventional culture. Concurrently, 134 viral strains were confirmed in 89 patients, with Human herpesvirus 5 (45 cases), Human herpesvirus 4 (44 cases), and Human polyomavirus 1 (11 cases) being the most frequently detected (Fig. 3 ). Based on mNGS findings, a total of 52 cases (43.69%) were diagnosed with polymicrobial infections. Among these, the most common combinations included viral co-infections (31 cases), bacteria-fungi-virus co-infections (3 cases), virus-fungus co-infections (3 cases), and bacteria-virus co-infections (11 cases) (Fig. 4 ). In this study, all patients received empirical antibiotic therapy, with treatment adjustments made upon availability of microbiological results. A total of 47 cases (39.49%) underwent treatment modifications based on blood mNGS findings, while the remaining 18 cases (15.12%) continued the initial therapy, as the empirically administered antibiotics had already covered the identified pathogens (Table S1 ). Notably, in cases requiring treatment adjustments, mNGS identified a range of pathogens necessitating specific targeted therapies. Consequently, modifications or additions to antibiotic regimens were frequently required, with particular attention given to the inclusion of antifungal agents. Additionally, the turnaround time for mNGS remained relatively consistent, typically ranging between 24 and 48 hours. Through rapid optimization of anti-infective regimens, improvement in febrile symptoms was observed in more than half of the patients within 7 days. Laboratory findings indicated reductions in white blood cell count, C-reactive protein, and procalcitonin levels in 57.98%, 60.50%, and 73.10% of patients, respectively; 40.00% of patients demonstrated improvement in septic shock, and 83.19% experienced resolution of fever (Table S3 ). In conclusion, patients with hematological malignancies are particularly susceptible to polymicrobial infections. mNGS provides more comprehensive and timely etiological information, enabling early adjustments to anti-infective therapy and thereby improving patient outcomes. 3.4. Effect of granulocytopenia on the distribution of pathogens To investigate the impact of granulocytopenia on microbiota distribution, patients were divided into two groups: the observation group, comprising 58 patients with granulocytopenia, and the control group, consisting of 61 patients without granulocytopenia. Patients in the observation group developed post-chemotherapy myelosuppression or drug-induced granulocytopenia, which were secondary to prior antineoplastic therapies, including chemotherapy, targeted therapy, and immunotherapy. In contrast, patients in the control group had not received any antineoplastic treatment within the preceding six months. No significant differences were observed in median age or 30-day overall mortality between the two groups. However, indicators of disease severity, including oxygenation index and incidence of septic shock, were significantly higher in the observation group compared to the control group (p < 0.05). With respect to inflammatory biomarkers, no significant differences were found in levels of C-reactive protein or procalcitonin between the two groups (Table 3 ). Furthermore, mNGS identified 108 and 88 pathogenic strains in the observation and control groups, respectively. Although no significant differences were observed in the number of cases with viral or fungal detection between the two groups, the observation group exhibited a significantly higher number of bacterial detections and polymicrobial infections compared to the control group ( p < 0.05) (Table 3 ). Table 3 Clinical characteristics of the two groups of patients. The observation group consisted of patients with granulocytopenia, while the control group comprised those without granulocytopenia. Characteristic(median or no. (%)) Observation group (n = 58) Control group (n = 61) p value Age(years) 65.00(52.0,73.0) 64.00(44.0,71.0) 0.242 Oxygenation index(mmHg) 395.00(304.8,427.0) 415.00(375.0,445.0) 0.038 Septic shock 21(36.21%) 9(14.75) 0.007 Total 30-day mortality 12(20.69%) 6(9.84%) 0.099 Inflammation biomarker Peripheral blood neutrophils (109 /L) 0.40(0.2,0.6) 6.20(3.1,14.1) <0.001 C reactive protein (mg/L) 68.35(41.2,145.8) 52.00(24.6,122.8) 0.069 Procalcitonin (ng/mL) 0.48(0.1,1.8) 0.48(0.2,2.0) 0.369 Blood mNGS results Bacteria(%) 25(43.10%) 11(18.30%) <0.001 Viruses(%) 41(70.69%) 48(78.69%) 0.664 Fungi(%) 8(13.79%) 7(11.48%) 0.657 Poly-microbial infection(%) 14(24.14%) 6(9.84%) 0.038 To assess differences in pathogen distribution, analysis revealed that Klebsiella pneumoniae was the most commonly identified bacterium in both groups. In the observation group, the subsequent most frequently detected bacteria were Propionibacterium acnes , Pseudomonas aeruginosa , and Escherichia coli , in descending order. In contrast, only Propionibacterium acnes was identified in the control group, with no detection of Pseudomonas aeruginosa or Escherichia coli . Regarding fungal pathogens, the top three species identified in the observation group were Aspergillus flavus , Aspergillus fumigatus , and Cunninghamella bertholletiae . In the control group, Candida glabrata and Rhizopus microsporus were the most frequently identified, followed by a joint occurrence of Aspergillus flavus and Aspergillus fumigatus at the third position. Both groups shared the same top three viral pathogens: Epstein-Barr virus , Cytomegalovirus , and Human alphaherpesvirus 1 (Fig. 5 ). Although mNGS revealed quantitative differences in certain pathogens between the two groups—such as five cases of Propionibacterium acnes in the observation group versus one in the control group, and three cases each of Pseudomonas aeruginosa and Escherichia coli exclusively in the observation group—the overall distribution of microbial species did not differ significantly between the two groups (Fig. 6 ). 4. Discussion In conclusion, this study demonstrates that blood mNGS possesses significant diagnostic value for patients with hematological malignancies complicated by sepsis after antibiotic use. Its high sensitivity and broad-spectrum detection capability can compensate for the limitations of traditional blood culture, particularly in identifying mixed infections and fastidious organisms, thereby guiding clinical optimization of treatment strategies. Building upon insights from the reference literature, blood mNGS holds promise as a pivotal tool in infection management for this high-risk population; however, its integration with conventional diagnostic methods and clinical data is essential to enhance diagnostic specificity. Patients with hematological malignancies complicated by sepsis are particularly prone to complex infections due to immunosuppression resulting from both the underlying disease and antitumor therapies. The rapid progression and therapeutic challenges of sepsis underscore the critical need for prompt and accurate pathogen identification to improve clinical outcomes [ 13 ]. Traditional blood culture, although widely used in sepsis diagnosis, is constrained by a prolonged turnaround time and suboptimal sensitivity, especially following antibiotic administration, thereby failing to meet the clinical demands of this patient cohort [ 14 ]. Blood mNGS, a high-throughput nucleic acid sequencing technology, enables rapid and comprehensive pathogen detection and has garnered increasing attention in infectious disease diagnostics [ 15 ]. This study focuses on patients with hematological malignancies complicated by sepsis who received antibiotic therapy for ≥ 3 days without clinical improvement, aiming to evaluate the clinical utility of blood mNGS. The findings of this study revealed that the positive detection rate of blood mNGS for bacterial and fungal pathogens (89.36%) was markedly higher than that of blood culture (25.53%), with a more pronounced advantage in detecting mixed infections (25.00% vs. 0.00%, P < 0.001). This aligns with findings from the reference literature, which demonstrated that BALF mNGS exhibits a significantly higher detection rate compared with culture-based methods in cancer patients with severe pneumonia, further confirming the superior ability of mNGS to identify pathogens in complex infections [ 16 ]. The reference document highlights the enhanced detection of bacteria, fungi, and mixed infections with mNGS, a trend similarly observed with blood mNGS in this study, suggesting that mNGS can provide a more comprehensive etiological profile of infections in immunocompromised individuals regardless of sample type (e.g., bronchoalveolar lavage fluid or blood). Nonetheless, the present study revealed a suboptimal concordance between blood mNGS and blood culture (concordance rate 13.21%, kappa = -0.202), with relatively low sensitivity (58.33%) and specificity (0.00%) compared to bronchoalveolar lavage fluid mNGS (BALF mNGS) (sensitivity 100%, specificity 16.67%) [ 17 ]. These discrepancies may be attributed to differences in sample type: pathogen load in blood is generally lower than in bronchoalveolar lavage fluid, and the inhibitory effects of antibiotics on blood culture are more pronounced. Additionally, the pathogen spectrum in septic patients (e.g., a higher prevalence of viral infections) differs from that observed in severe pneumonia, which may further impact the concordance between the two methods. Previous studies have demonstrated that traditional culture techniques are significantly affected by prior antibiotic use [ 18 ], and all patients in this study had received antibiotic therapy for ≥ 3 days, which likely further diminished the sensitivity of blood culture and highlighted the diagnostic advantage of blood mNGS in this clinical context. In our study, a total of 190 pathogens were detected by blood mNGS, predominantly viruses (70.52%), among which human cytomegalovirus and Epstein-Barr virus accounted for the highest proportions. Common bacteria included Klebsiella pneumoniae and Pseudomonas aeruginosa , while Aspergillus was the main fungal pathogen. The distribution of these pathogens shares both similarities and unique features compared with findings from relevant studies, reflecting characteristics specific to the population and infection type. In terms of similarities, the high detection rates of herpesviruses (such as Epstein-Barr virus ) and opportunistic fungi (such as Aspergillus ) in this study are consistent with the dominant presence of human herpesviruses detected by mNGS in patients with community-acquired and hospital-acquired sepsis [ 19 ], and also align with the tendency of immunocompromised populations (including those with hematological malignancies) to be susceptible to opportunistic pathogens [ 20 ], confirming the high pathogenicity of these pathogens in immunocompromised states. In terms of differences, the virus-dominated pathogen spectrum in sepsis observed in this study contrasts with the predominance of bacteria detected by BALF mNGS in cancer patients with severe pneumonia [ 17 ]. This discrepancy is closely related to the biological characteristics of the infection site—blood, as a systemic circulatory system, is more prone to viral dissemination through the bloodstream, whereas pulmonary infections, due to the local microenvironment, are more conducive to bacterial colonization and reproduction (significant differences in pathogen spectra exist across different infection sites) [ 20 ]. Additionally, the detection of Gram-negative bacteria such as Klebsiella pneumoniae and Pseudomonas aeruginosa in this study echoes the high sensitivity of mNGS in detecting Gram-negative bacteria in abdominal sepsis [ 21 ], suggesting the widespread pathogenicity of such bacteria in sepsis. Moreover, the high detection rate of Aspergillus , a typical opportunistic fungus in immunocompromised populations, is also consistent with the advantage of mNGS in detecting difficult-to-culture fungi [ 20 ]. In summary, the pathogen distribution revealed by blood mNGS not only reflects the characteristics of susceptible pathogens in patients with hematological malignancies under immunosuppression but also demonstrates the site-specific differences in pathogen spectra between sepsis and pulmonary infections, providing an etiological basis for targeted clinical interventions. Previous studies have demonstrated that mNGS holds significant value in guiding adjustments to anti-infective therapy. Particularly in immunocompromised populations, its high detection rate of opportunistic pathogens such as Pneumocystis jirovecii and Aspergillus enables most patients to receive optimized targeted treatment regimens [ 22 ]. In this study, 39.49% of patients had their anti-infective treatment strategies optimized based on blood mNGS results, with significant improvements in inflammatory markers (e.g., procalcitonin) and clinical symptoms (e.g., alleviation of fever) within 7 days. This result further validates the core role of mNGS in precision treatment: for instance, when Pneumocystis jirovecii is detected by mNGS, trimethoprim-sulfamethoxazole is promptly administered, or when Aspergillus is identified, the treatment is adjusted to voriconazole. Such targeted interventions are difficult to achieve when traditional cultures are negative—primarily because Pneumocystis jirovecii is challenging to culture, and Aspergillus is easily missed in traditional cultures. However, mNGS effectively overcomes these limitations through unbiased detection, which is consistent with conclusions confirmed by studies that mNGS can break through the diagnostic limitations of traditional culture for difficult-to-culture or rare pathogens [ 23 ]. In this study, the incidence of mixed infections was significantly higher among patients undergoing antitumor therapy, particularly those with neutropenia, compared to untreated patients ( P < 0.05), accompanied by an increased 30-day mortality rate. These findings align with existing evidence confirming that antitumor therapy represents a high-risk factor for mixed infections. Studies on patients with acute myeloid leukemia receiving high-dose cytarabine (HiDAC) consolidation therapy have shown that 36% developed at least one infection, with infections affecting 18% of treatment cycles, further highlighting the elevated infection risk during antitumor therapy [ 24 ]. The underlying mechanism primarily involves myelosuppression and immunodeficiency induced by antitumor treatments such as chemotherapy, which increases host susceptibility to various pathogens and consequently raises the risk of mixed infections. Previous reports indicate a higher detection rate of fungal pathogens, including Pneumocystis jirovecii and Aspergillus species, in patients undergoing antitumor therapy, a trend also observed in this study. These findings suggest the necessity for heightened clinical vigilance regarding opportunistic fungal infections in this patient population. Blood-based mNGS can provide earlier etiological evidence, facilitating timely clinical intervention. Furthermore, when combined with evidence indicating that short-course antibiotic therapy (7 days) is non-inferior to long-course therapy (14 days) in treating bloodstream infections, the implementation of tailored anti-infective regimens guided by mNGS pathogen identification may further optimize infection management strategies during antitumor therapy [ 25 ]. Despite its notable advantages in infectious disease diagnostics, blood mNGS has inherent limitations. First, its specificity is relatively low (0.00%), potentially influenced by background microorganisms or sample contamination, thereby complicating the differentiation between colonization and active infection. This limitation is consistent with findings in relevant literature indicating that mNGS results may be affected by background flora in complex samples [ 26 ]. Second, mNGS does not provide direct antimicrobial susceptibility data, necessitating conventional culture methods for drug sensitivity testing to guide targeted antimicrobial therapy. Third, the cost of mNGS testing remains relatively high. Studies have reported that mNGS expenses may account for 30–50% of total microbiology testing costs [ 27 ], which limits its widespread adoption as a routine diagnostic tool. This study has several limitations. First, the single-center design may introduce selection bias due to the homogeneity of enrolled patient characteristics, and the sample size (n = 119) is relatively small, consistent with the recognized limitations of generalizability in single-center, small-sample studies [ 28 ]. Second, RNA virus detection was not included, potentially leading to missed identification of certain RNA viral pathogens. The comprehensive coverage of pathogen detection remains a key determinant of mNGS diagnostic performance [ 28 ]. Third, long-term follow-up of patient survival outcomes was not conducted, although longitudinal assessment of clinical endpoints is essential for determining the true clinical utility of mNGS [ 29 ]. Additionally, the diagnostic accuracy of mNGS may be influenced by factors such as sampling timing. For example, testing within two weeks of symptom onset may improve the true negative rate; however, this study did not comprehensively evaluate the impact of sampling timing, which may represent an additional limitation [ 29 ]. In summary, this study demonstrates that blood mNGS possesses substantial diagnostic utility in patients with hematological malignancies complicated by sepsis who are undergoing antibiotic therapy. Its high sensitivity and broad-spectrum detection capacity can effectively compensate for the limitations of conventional blood culture, particularly in identifying mixed infections, fastidious organisms, and guiding the optimization of clinical treatment strategies. Building upon findings from relevant studies, blood mNGS has the potential to become a pivotal tool for infection management in this high-risk patient population. However, integrating conventional diagnostic methods and clinical context remains essential to enhance diagnostic specificity. 5. Conclusions Blood mNGS technology offers significant advantages in the diagnosis of sepsis among patients with hematological malignancies, particularly in scenarios where conventional diagnostic methods yield limited results following antibiotic administration. Evidence indicates that the pathogen detection rate using blood mNGS is notably higher than that of blood culture, with superior performance in identifying polymicrobial infections and fastidious organisms, thereby providing critical support for refining clinical antimicrobial strategies. Its broad-spectrum detection capability encompasses various pathogens, including viruses, bacteria, and fungi. In immunocompromised individuals, mNGS can effectively detect clinically relevant pathogens such as cytomegalovirus , Epstein-Barr virus , Klebsiella pneumoniae , and Aspergillus species , reveal variations in pathogen distribution across different infection sites, and support targeted therapeutic interventions. Abbreviations IQR Interquartile range mNGS Metagenomic next-generation sequencing PPV Positive predictive value NPV Negative predictive value Declarations Acknowledgements : Not applicable. Author Contributions: Conceptualization, B.C. and D.J.; methodology, W.S. and D.J.; project administration, J.L.; formal analysis, D.J. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Data Availability : The data presented in this study are available on request. Declarations Ethics approval and consent to participate: The study was approved by the Ethical Review Committee of Ningbo Medical Center Lihuili Hospital (Approval No. KY2025SL154-02). The procedures used in this study adhere to the tenets of the Declaration of Helsinki. The need for consent to participate was waived by the Ethical Review Committee of Ningbo Medical Center Lihuili Hospital. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. Author details: 1 Department of Hematology, Ningbo Medical Center Lihuili Hospital, Ningbo 315000, China; [email protected] (B.C.); [email protected] (W.S.); [email protected] (J.L.) * Correspondence: [email protected] References Ar, M. C., El Fakih, R., Gabbassova, S., Alhuraiji, A., Nasr, F., Alsaeed, A., Sayinalp, N., & Marashi, M. (2023). 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Metagenomic next-generation sequencing testing from the perspective of clinical benefits. Clinica chimica acta; international journal of clinical chemistry , 553 , 117730. https://doi.org/10.1016/j.cca.2023.117730. Lisha, W., Jiao, Q., Mengyuan, C., Jiajia, Q., Tianbin, T., Yilan, W., Linjie, H., & Sufei, Y. (2025). Clinical evaluation of negative mNGS reports in sterile body fluids and tissues. Microbiology spectrum , 13 (7), e0201324. https://doi.org/10.1128/spectrum.02013-24. Additional Declarations No competing interests reported. Supplementary Files FigureS1.png Figure S1: The comparison of detected results between mNGS and culture method. mNGS identified more bacteria (42 versus 11) and fungi (16 versus 1) than culture method; FigureS2.png Figure S2: Comparison of pathogens detected by mNGS and culture method in the double positive patients; FigureS3.png Figure S3: Number of pathogens identified by culture method. The the top three were Klebsiella pneumoniae , Escherichia coli , and Staphylococcus intermedius. The most common fungus was Aspergillus fumigatus . TableS1.docx Table S1: Clinical impact of mNGS results on anti-infective treatment; TableS2.docx Table S2: Changes in patient indicators within the subsequent 7 days after optimizing anti-infective treatment. Cite Share Download PDF Status: Published Journal Publication published 01 Dec, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted Editorial decision: Revision requested 23 Oct, 2025 Reviews received at journal 10 Oct, 2025 Reviewers agreed at journal 07 Oct, 2025 Reviews received at journal 01 Oct, 2025 Reviewers agreed at journal 18 Sep, 2025 Reviewers agreed at journal 17 Sep, 2025 Reviewers invited by journal 16 Sep, 2025 Editor assigned by journal 14 Aug, 2025 Submission checks completed at journal 14 Aug, 2025 First submitted to journal 14 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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14:50:38","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":30134,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7345606/v1/dcf861a4fc54644117315213.png"},{"id":92189606,"identity":"0ab2c232-9e31-43ce-94a9-0bdc96c556e6","added_by":"auto","created_at":"2025-09-25 14:58:39","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":13070,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7345606/v1/7f7e3acf56daa9c2971ba651.png"},{"id":92188830,"identity":"7ad728e8-6562-4592-b82f-405910d3c6cc","added_by":"auto","created_at":"2025-09-25 14:50:39","extension":"xml","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":131396,"visible":true,"origin":"","legend":"","description":"","filename":"55e2d17c21df4d89965d1daeb80f170a1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7345606/v1/144eaf2467c4987391d74e18.xml"},{"id":92188829,"identity":"8bb85f46-6d75-4659-a377-4bbd3a885a43","added_by":"auto","created_at":"2025-09-25 14:50:39","extension":"html","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":142275,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7345606/v1/4a39c963e6d5956a5059993d.html"},{"id":92188800,"identity":"04dc82dc-4ebc-4a9e-b2f6-395aee0d79e5","added_by":"auto","created_at":"2025-09-25 14:50:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":77563,"visible":true,"origin":"","legend":"\u003cp\u003eConcordance between mNGS and culture method. The results of mNGS and culture method were double positive in 7 (14.89%) cases and only mNGS positive in 35 (74.46%) cases. For the double positive subset, 4 (57.14%) cases were consistent, 1 (14.28%) cases were partially consistent, and 2 (28.57%) cases were completely inconsistent. Abbreviations: mNGS: metagenomic next-generation sequencing.\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-7345606/v1/6c324d99162c834df856bf97.png"},{"id":92188799,"identity":"88a46393-0ead-4976-85ff-64f36c3f7057","added_by":"auto","created_at":"2025-09-25 14:50:38","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":44061,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of various pathogens identified by mNGS. Viruses were the most frequently pathogens detected by mNGS (70.52%), followed by bacteria (22.11%) and fungi (7.37%).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7345606/v1/3b8664f85b43b5c9dbcea035.jpeg"},{"id":92189605,"identity":"fa84a7ea-73b9-4d19-b4af-e4874b9122b9","added_by":"auto","created_at":"2025-09-25 14:58:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":42990,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of pathogens identified by mNGS. The most common bacteria, fungi, viruses, and were \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, \u003cem\u003eAspergillus\u003c/em\u003e \u003cem\u003efumigatus\u003c/em\u003e, \u003cem\u003eAspergillus flavus\u003c/em\u003e, and \u003cem\u003eHuman herpesvirus 5\u003c/em\u003erespectively.\u003c/p\u003e","description":"","filename":"floatimage31.png","url":"https://assets-eu.researchsquare.com/files/rs-7345606/v1/98663a16a244819fe9dd2b84.png"},{"id":92189598,"identity":"47de2af6-57be-429d-8b56-73bbec713331","added_by":"auto","created_at":"2025-09-25 14:58:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":73809,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of patients with poly-microbial infection. viruses, vacteria-fungi-viruses, viruses -fungi, and bacteria-viruses were the top four co-pathogens.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7345606/v1/8d3f549395078af68859f1e4.png"},{"id":92188824,"identity":"122b91bd-78ef-47d9-b580-6d8df1d54d5a","added_by":"auto","created_at":"2025-09-25 14:50:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":66445,"visible":true,"origin":"","legend":"\u003cp\u003eThe most common pathogens detected by mNGS in observation group and control group.Both groups most common bacterium: \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e; observation group next: \u003cem\u003ePropionibacterium acnes\u003c/em\u003e, \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, \u003cem\u003eEscherichia coli \u003c/em\u003e(control group only \u003cem\u003eP. acnes\u003c/em\u003e, no \u003cem\u003eP. aeruginosa\u003c/em\u003e/\u003cem\u003eE. coli\u003c/em\u003e). Observation group top fungi: \u003cem\u003eAspergillus flavus\u003c/em\u003e, \u003cem\u003eA. fumigatus\u003c/em\u003e, \u003cem\u003eCunninghamella bertholletiae\u003c/em\u003e; control group: \u003cem\u003eCandida glabrata\u003c/em\u003e, \u003cem\u003eRhizopus microsporus\u003c/em\u003e (top 2), \u003cem\u003eA. flavus\u003c/em\u003e/\u003cem\u003eA. fumigatus\u003c/em\u003e (joint 3rd). Shared top 3 viruses: \u003cem\u003eEpstein-Barr virus\u003c/em\u003e, \u003cem\u003eCytomegalovirus\u003c/em\u003e, \u003cem\u003eHuman alphaherpesvirus 1\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7345606/v1/7e146c73dd93aa28bf074288.png"},{"id":92188823,"identity":"d0b025ec-284b-47db-86b1-95a3a02df103","added_by":"auto","created_at":"2025-09-25 14:50:39","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":27563,"visible":true,"origin":"","legend":"\u003cp\u003eThe species distribution of pathogens detected by mNGS in the observation group and the control group is as follows. There was no difference in the proportion of each pathogenic species between the two groups.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7345606/v1/04dd16a1ef725daa4072da7b.png"},{"id":97724632,"identity":"ca27538c-63f2-4bcd-b03d-d6aa7de89b55","added_by":"auto","created_at":"2025-12-08 16:13:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1249290,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7345606/v1/1627f0b0-d4cb-445b-a7de-a804cba371a0.pdf"},{"id":92189599,"identity":"9dd541dd-4413-4ac8-bccc-fa57de9ee7e8","added_by":"auto","created_at":"2025-09-25 14:58:38","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9521,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1: The comparison of detected results between mNGS and culture method. mNGS identified more bacteria (42 versus 11) and fungi (16 versus 1) than culture method;\u003c/p\u003e","description":"","filename":"FigureS1.png","url":"https://assets-eu.researchsquare.com/files/rs-7345606/v1/b37cd1e3f2fb5f8e74be44b6.png"},{"id":92188804,"identity":"bc4037df-ad6c-4303-9af5-bbbe123fe8ee","added_by":"auto","created_at":"2025-09-25 14:50:38","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":17819,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S2: Comparison of pathogens detected by mNGS and culture method in the double positive patients;\u003c/p\u003e","description":"","filename":"FigureS2.png","url":"https://assets-eu.researchsquare.com/files/rs-7345606/v1/dc138ee4e4698795d1bdfb01.png"},{"id":92188809,"identity":"c55c2e69-c5fb-41f9-9bc4-e56b9f0679e1","added_by":"auto","created_at":"2025-09-25 14:50:38","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":39467,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S3: Number of pathogens identified by culture method. The the top three were \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, \u003cem\u003eEscherichia coli\u003c/em\u003e, and \u003cem\u003eStaphylococcus\u003c/em\u003e intermedius. The most common fungus was \u003cem\u003eAspergillus fumigatus\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"FigureS3.png","url":"https://assets-eu.researchsquare.com/files/rs-7345606/v1/ff8667f3357f91ebf181cb35.png"},{"id":92190749,"identity":"083f7a94-813c-441b-a467-fbcde1c13616","added_by":"auto","created_at":"2025-09-25 15:06:39","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":17285,"visible":true,"origin":"","legend":"\u003cp\u003eTable S1: Clinical impact of mNGS results on anti-infective treatment;\u003c/p\u003e","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7345606/v1/6e3f21080081c0064827fe82.docx"},{"id":92188822,"identity":"081e5efa-f8f8-437d-862d-682265355802","added_by":"auto","created_at":"2025-09-25 14:50:39","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":17387,"visible":true,"origin":"","legend":"\u003cp\u003eTable S2: Changes in patient indicators within the subsequent 7 days after optimizing anti-infective treatment.\u003c/p\u003e","description":"","filename":"TableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7345606/v1/5f4b419e3659e8bbf9e788b7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Application of metagenomic next-generation sequencing technology in hematologic malignancy patients with sepsis following antibiotic use","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003ePatients with hematological malignancies, including leukemia, lymphoma, and multiple myeloma, are at increased risk of sepsis due to immune dysfunction associated with both the underlying disease and its treatment modalities, such as chemotherapy and hematopoietic stem cell transplantation[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Clinical evidence demonstrates that the incidence of sepsis in this patient population is significantly higher compared to the general population, with a notably elevated mortality rate. Particularly, patients undergoing intensive chemotherapy or those in the neutropenic phase following transplantation face an even greater risk of infection, with correspondingly higher mortality [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrompt administration of appropriate antibiotics is a critical intervention in reducing mortality among patients with sepsis. However, clinical management is challenged by two major issues. First, the overuse of broad-spectrum antibiotics can contribute to the emergence of multidrug-resistant pathogens, including carbapenem-resistant Enterobacteriaceae and methicillin-resistant Staphylococcus aureus. Available data suggest that approximately 45% of infections in patients with hematologic malignancies are caused by drug-resistant organisms[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Second, the hepatic and renal functions of post-chemotherapy patients are often compromised, and inappropriate antimicrobial use may further exacerbate organ dysfunction and prolong hospitalization. Thus, accurate pathogen identification and targeted use of narrow-spectrum antibiotics are essential for optimizing clinical outcomes[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough blood culture remains the \"gold standard\" for diagnosing sepsis, its diagnostic yield is suboptimal in patients with hematological malignancies. The positivity rate of blood cultures is often below 30% due to factors such as post-chemotherapy myelosuppression and prior antibiotic administration. In early-onset sepsis (defined as onset within 72 hours), the detection rate is even lower, ranging from 15\u0026ndash;20%[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Furthermore, conventional culture techniques require 3 to 5 days for pathogen identification. Given the rapid progression of sepsis in immunocompromised patients, where mortality rises significantly with every hour that effective treatment is delayed, such timeframes are often incompatible with clinical urgency. Additionally, the spectrum of pathogen detection using traditional methods is limited, often failing to identify fastidious organisms such as Legionella pneumophila, anaerobes such as Bacteroides fragilis, viruses such as cytomegalovirus, and fungi such as Aspergillus species\u0026mdash;pathogens that are frequently encountered in immunosuppressed individuals[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMetagenomic next-generation sequencing (mNGS) enables pathogen identification without prior culture by directly extracting nucleic acids from all microorganisms present in the sample for high-throughput sequencing. Its technical advantages are particularly evident in patients with hematological malignancies[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This technology allows for the simultaneous detection of over 1,000 types of pathogens, including bacteria, fungi, viruses, and parasites, and is especially indispensable in diagnosing mixed infections, which account for approximately 18% of cases among patients with hematological malignancies. Regarding timeliness, the turnaround time from sample receipt to report generation can be reduced to 24\u0026ndash;48 hours, which is 3\u0026ndash;5 days faster than traditional culture methods, thereby providing a critical treatment window for critically ill patients. Furthermore, mNGS can reliably detect pathogen nucleic acids even after antibiotic administration, effectively addressing the issue of false negatives caused by antimicrobial suppression in conventional methods. Recent clinical studies have further validated the value of blood-based mNGS in hematological disorders. For patients with hematological diseases presenting with fever\u0026mdash;particularly those with hematological malignancies who have unexplained fever and negative routine test results\u0026mdash;peripheral blood mNGS demonstrates high clinical utility and diagnostic accuracy. Research indicates that its positive detection rate is significantly higher than that of blood culture and conventional laboratory tests, strongly supporting the recommendation for early application of mNGS in infection detection, which aligns with current clinical practice guidelines[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Moreover, in patients with hematological malignancies complicated by sepsis, mNGS exhibits significantly higher sensitivity compared to traditional blood culture, particularly when applied to blood samples\u003csup\u003e[9]\u003c/sup\u003e. It enables broader pathogen detection, including organisms often missed by conventional culture methods, and its clinical application has demonstrated favorable outcomes. Adjusting therapeutic strategies based on mNGS findings can improve patient management, further underscoring its clinical significance[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite the considerable advantages of mNGS, its widespread adoption still faces practical challenges. At the technical level, issues such as host DNA contamination, false negatives for low-abundance pathogens, and the lack of standardization in interpreting drug resistance genes remain unresolved. These limitations can be mitigated by optimizing sequencing depth (recommended\u0026thinsp;\u0026ge;\u0026thinsp;10,000\u0026times;) and incorporating negative controls[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. At the clinical level, the relatively high cost per test (approximately 1,500\u0026ndash;3,000 yuan) may be a barrier for some patients and their families, while the absence of bioinformatics analysis platforms in primary hospitals further restricts its accessibility. Therefore, this study focuses on the specific clinical scenario of \u0026ldquo;post-antibiotic administration\u0026rdquo; and compares the sensitivity and specificity of mNGS with conventional methods. This comparison not only provides evidence for the clinical application of mNGS but also offers data-driven support for optimizing testing protocols and reducing costs, ultimately facilitating the standardized use of this technology in the field of infections associated with hematological malignancies.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Patients and study design\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eA retrospective analysis was conducted on 119 patients with hematologic malignancies complicated by sepsis who were admitted to the Department of Hematology, Li Huili Hospital, Ningbo Medical Center between May 2022 and April 2025, and who showed poor therapeutic response after receiving antibiotic treatment for \u0026ge;\u0026thinsp;3 days. Blood samples from all patients were subjected to pathogen detection using both blood culture and mNGS. Inclusion criteria: (1) No restrictions on age or gender; (2) Patients consented to undergo both blood culture and mNGS testing; (3) Patients with hematologic malignancies presented to the Department of Hematology with fever and clinical signs of infection, and had positive results by either blood culture or mNGS, fulfilling the diagnostic criteria for definite sepsis; (4) Availability of complete clinical data. Exclusion criteria: (1) Fever caused by non-infectious etiologies; (2) Refusal by patients to undergo either blood culture or mNGS testing; (3) Incomplete clinical data unavailable for analysis. Clinical data collected from each patient included age, gender, underlying malignancy, anti-tumor therapy, comorbidities, laboratory findings, treatment modalities, and clinical outcomes.\u003c/p\u003e\u003cp\u003e The study was approved by the Ethical Review Committee of Ningbo Medical Center Lihuili Hospital (approval No. KY2025SL154-02).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Collection, preservation and processing of specimens\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eBlood sample collection for mNGS was performed in accordance with the protocol described in reference [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. At least 5 mL of blood was collected for blood culture. For mNGS, blood samples were collected in tubes containing anticoagulants that preserve cell-free DNA or RNA, with a minimum collection volume of 2 mL. After collection, the blood samples were jointly verified by attending physicians and personnel from Hangzhou Matridx Biotechnology Co., Ltd. Specimens were stored at 6\u0026ndash;35\u0026deg;C and transported by personnel from Hangzhou Matridx Biotechnology Co., Ltd. to the laboratory for subsequent testing.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Blood samples collection and mNGS analysis\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe mNGS workflow for blood samples included nucleic acid extraction, library preparation, sequencing, and data analysis.\u003c/p\u003e\u003cp\u003eThe collected peripheral blood samples were centrifuged in accordance with the manufacturer's instructions, and the supernatant (typically plasma components) was collected as the test sample to remove impurities such as blood cells, thereby minimizing interference from host nucleic acids. The pretreated specimens were processed using Genewiz's NGSmaster\u0026trade; Automated Nucleic Acid Detection Reaction System to perform a series of steps: automated nucleic acid extraction (including simultaneous reverse transcription of RNA to enable concurrent detection of both DNA and RNA pathogens), nucleic acid fragmentation, end repair, 3' end single-base A tailing, sequencing adapter ligation, and purification, ultimately generating the sequencing library. The quality of the extracted total DNA was assessed using Qubit (Thermo Fisher Scientific) to ensure library integrity, and the library preparation utilized Genewiz's bloodstream infection library preparation kit (reversible terminator sequencing method). Genewiz's NGS Library Quantification Kit (probe-based method) was used to determine the library concentration via quantitative real-time PCR, and libraries meeting sequencing requirements were selected for downstream analysis. High-throughput sequencing was conducted on the Illumina Nextseq550 platform using Genewiz's Universal Sequencing Reaction Kit (reversible terminator sequencing method) with a 50-bp single-end sequencing mode to ensure effective pathogen sequence readout. A comprehensive quality control system was also implemented. Each experiment strictly included multiple controls: negative controls (to monitor background contamination), positive controls (to evaluate the sensitivity and effectiveness of the detection system), and internal reference sequences added to each sample (to monitor the stability of all steps, including nucleic acid extraction, library preparation, and sequencing). The raw sequencing data were processed using Genewiz's pathogenic metagenomic data analysis system, Gentellix, to filter out human genomic sequence data (GRCh38.p13). The remaining sequence data were aligned against microbial reference databases (NCBI GenBank and Genewiz's internal microbial genome database) to identify microbial species and their relative abundances.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Blood samples collection and mNGS analysis\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAll statistical analyses were performed using SPSS 22.0 software (IBM Corp., Armonk, NY, USA). Continuous variables, which exhibited non-normal distributions, were expressed as medians with interquartile ranges and compared using the Mann-Whitney U test. Categorical variables were presented as counts and percentages and compared using the chi-square test. A 2\u0026times;2 contingency table was used to calculate the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of mNGS. Diagnostic performance was compared using the McNemar test, and test agreement was assessed using the kappa statistic. All tests were two-tailed, with statistical significance defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Patient characteristics\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eFrom May 2022 to April 2025, a total of 93 patients with hematological malignancies complicated by sepsis were enrolled in this study, comprising 49 males (41.17%) and 70 females (58.82%), with a median age of 64 years. The majority of patients were over 60 years old (75.63%). All patients had confirmed diagnoses of hematological malignancies, including 58 cases of lymphoma, 32 cases of multiple myeloma, and 29 cases of acute leukemia. Among them, 58 patients (48.73%) developed neutropenia [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] as a result of antitumor therapy. The median oxygenation index was 405 mmHg. Additionally, 30 patients (25.21%) progressed to septic shock. Despite active clinical interventions, the 30-day mortality rate reached 15.12%, indicating that sepsis in patients with hematological malignancies is characterized by rapid progression, severe illness, therapeutic challenges, and high mortality. Detailed clinical data, including demographic characteristics, laboratory findings, and outcomes, are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003c/div\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\u003eClinical characteristics of 119 enrolled patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eValue (median (IQRs\u003csup\u003e1\u003c/sup\u003e) or no.(%))\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=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e49(41.17%)\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=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70(58.82%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAge(years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90(75.63%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29(24.36%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eMalignancy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLymphoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58(48.73%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMultiple Myeloma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32(26.89%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAcute Leukemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29(24.36%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eComorbidity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDiabetes mellitus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34(28.57%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChronic Obstructive Pulmonary Disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17(14.28%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11(9.24%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDisease severity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSeptic shock\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30(25.21%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOxygenation index\u0026gt;300 (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98(82.35%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eInflammation biomarker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePeripheral blood neutrophils (10\u003csup\u003e9\u003c/sup\u003e /L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.5(0.45,6.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC reactive protein (mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60.9(34.85,130)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProcalcitonin (ng/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.48(0.136,1.99)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOutcomes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal 30-day mortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18(15.12%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003e1\u003c/sup\u003eIQR, interquartile range\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Comparison and concordance of mNGS and culture method\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eGiven the limited capacity of conventional culture methods to detect a broad spectrum of fungi and bacteria, this study compared the diagnostic performance of blood mNGS and paired blood culture (targeting bacteria and fungi) in patients with hematological malignancies complicated by sepsis. A total of 47 patients were identified as having fungal and/or bacterial infections. The positive detection rate of mNGS was significantly higher than that of blood culture (89.36% vs. 25.53%). Compared with the culture method, mNGS demonstrated a diagnostic sensitivity of 58.33% and a specificity of 0.00%, with a positive predictive value (PPV) of 16.67% and a negative predictive value (NPV) of 0.00% (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\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 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDiagnostic performance and concordance of mNGS relative to culture method\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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=\"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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCulture+\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCulture-\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u003cp\u003eDiagnostic performance of mNGS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emNGS\u003csup\u003e1\u003c/sup\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSensitivity%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSpecificity%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePPV\u003csup\u003e2\u003c/sup\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNPV\u003csup\u003e3\u003c/sup\u003e%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emNGS-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\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\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47\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\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConsensus analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKappa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\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=\"BlockQuote\"\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e mNGS, metagenomic next-generation sequencing; \u003csup\u003e2\u003c/sup\u003ePPV, positive predictive value;\u003csup\u003e3\u003c/sup\u003e NPV, negative predictive value.\u003c/p\u003e\u003cp\u003eFurthermore, among the 47 patients with fungal or bacterial sepsis, 7 cases (14.90%) tested positive by both mNGS and culture, yielding an overall agreement rate of 13.21%. Concordance analysis revealed a kappa coefficient of -0.202, indicating poor agreement between the two methods (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In this cohort, 35 cases (74.46%) were exclusively positive by mNGS, while 5 cases (10.64%) were detected only by culture. Among the 7 cases with dual positivity, the results of mNGS and culture were fully consistent in 4 cases (57.14%), partially consistent in 1 case (14.28%), and inconsistent in 2 cases (28.57%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eOverall, mNGS identified a greater number of bacterial (42 vs. 11) and fungal (16 vs. 1) pathogens compared to culture(Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Additionally, mNGS demonstrated a significantly higher detection rate of polymicrobial infections (involving two or more pathogens) than culture (25.00% vs. 0.00%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among the 35 patients with negative culture results, mNGS identified monomicrobial infections in 26 cases and polymicrobial infections in 9 cases. In total, mNGS detected 19 bacterial species and 9 fungal species, demonstrating enhanced diagnostic performance for bloodstream infections. For patients with dual positive results, all 4 cases with complete agreement involved monomicrobial infections. In the analysis of partially concordant and discordant cases, mNGS detected more bacteria (8 vs. 3), more fungi (2 vs. 0), and more polymicrobial infections (3 vs. 0) than culture (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). These findings indicate that even among patients with positive culture results, mNGS provides broader pathogen detection, including rare or fastidious organisms.\u003c/p\u003e\u003cp\u003eIn summary, these findings highlight the complex etiology of sepsis in patients with hematological malignancies and underscore the value of mNGS in efficiently identifying a wide range of causative pathogens.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Pathogens detected by mNGS and clinical impact of mNGS results on treatment\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eSubsequently, we analyzed the pathogen strains identified by mNGS to determine the predominant causative microorganisms, thereby providing valuable insights for optimizing therapeutic strategies. A total of 190 pathogen strains were detected across 119 cases using mNGS. Viruses were the most commonly identified pathogens (70.52%), followed by bacteria (22.11%) and fungi (7.37%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among bacterial pathogens, 42 strains were detected in 42 patients, with the six most frequently encountered being \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e (9 cases), \u003cem\u003ePropionibacterium acnes\u003c/em\u003e (5 cases), \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e (3 cases), \u003cem\u003eEscherichia coli\u003c/em\u003e (3 cases), \u003cem\u003eStenotrophomonas maltophilia\u003c/em\u003e (2 cases), and \u003cem\u003eBacillus cereus\u003c/em\u003e (2 cases) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In comparison, the culture method identified 11 bacterial strains in 12 patients, with the top three being \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e (2 cases), \u003cem\u003eEscherichia coli\u003c/em\u003e (2 cases), and \u003cem\u003eStaphylococcus intermedius\u003c/em\u003e (2 cases) (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eA total of 16 fungal strains were detected in 13 patients, with \u003cem\u003eAspergillus fumigatus\u003c/em\u003e (3 cases) and \u003cem\u003eAspergillus flavus\u003c/em\u003e (3 cases) being the most prevalent, followed by \u003cem\u003eRhizopus microsporus\u003c/em\u003e (2 cases) and \u003cem\u003eCandida glabrata\u003c/em\u003e (2 cases) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In contrast, only one fungal strain, \u003cem\u003eAspergillus fumigatus\u003c/em\u003e (1 case), was identified by conventional culture. Concurrently, 134 viral strains were confirmed in 89 patients, with \u003cem\u003eHuman herpesvirus 5\u003c/em\u003e (45 cases), \u003cem\u003eHuman herpesvirus 4\u003c/em\u003e (44 cases), and \u003cem\u003eHuman polyomavirus 1\u003c/em\u003e (11 cases) being the most frequently detected (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Based on mNGS findings, a total of 52 cases (43.69%) were diagnosed with polymicrobial infections. Among these, the most common combinations included viral co-infections (31 cases), bacteria-fungi-virus co-infections (3 cases), virus-fungus co-infections (3 cases), and bacteria-virus co-infections (11 cases) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In this study, all patients received empirical antibiotic therapy, with treatment adjustments made upon availability of microbiological results. A total of 47 cases (39.49%) underwent treatment modifications based on blood mNGS findings, while the remaining 18 cases (15.12%) continued the initial therapy, as the empirically administered antibiotics had already covered the identified pathogens (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eNotably, in cases requiring treatment adjustments, mNGS identified a range of pathogens necessitating specific targeted therapies. Consequently, modifications or additions to antibiotic regimens were frequently required, with particular attention given to the inclusion of antifungal agents. Additionally, the turnaround time for mNGS remained relatively consistent, typically ranging between 24 and 48 hours. Through rapid optimization of anti-infective regimens, improvement in febrile symptoms was observed in more than half of the patients within 7 days. Laboratory findings indicated reductions in white blood cell count, C-reactive protein, and procalcitonin levels in 57.98%, 60.50%, and 73.10% of patients, respectively; 40.00% of patients demonstrated improvement in septic shock, and 83.19% experienced resolution of fever (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). In conclusion, patients with hematological malignancies are particularly susceptible to polymicrobial infections. mNGS provides more comprehensive and timely etiological information, enabling early adjustments to anti-infective therapy and thereby improving patient outcomes.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Effect of granulocytopenia on the distribution of pathogens\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTo investigate the impact of granulocytopenia on microbiota distribution, patients were divided into two groups: the observation group, comprising 58 patients with granulocytopenia, and the control group, consisting of 61 patients without granulocytopenia. Patients in the observation group developed post-chemotherapy myelosuppression or drug-induced granulocytopenia, which were secondary to prior antineoplastic therapies, including chemotherapy, targeted therapy, and immunotherapy. In contrast, patients in the control group had not received any antineoplastic treatment within the preceding six months. No significant differences were observed in median age or 30-day overall mortality between the two groups. However, indicators of disease severity, including oxygenation index and incidence of septic shock, were significantly higher in the observation group compared to the control group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). With respect to inflammatory biomarkers, no significant differences were found in levels of C-reactive protein or procalcitonin between the two groups (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Furthermore, mNGS identified 108 and 88 pathogenic strains in the observation and control groups, respectively. Although no significant differences were observed in the number of cases with viral or fungal detection between the two groups, the observation group exhibited a significantly higher number of bacterial detections and polymicrobial infections compared to the control group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003c/div\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 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClinical characteristics of the two groups of patients. The observation group consisted of patients with granulocytopenia, while the control group comprised those without granulocytopenia.\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic(median or no. (%))\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObservation group (n\u0026thinsp;=\u0026thinsp;58)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl group\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge(years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65.00(52.0,73.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64.00(44.0,71.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.242\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOxygenation index(mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e395.00(304.8,427.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e415.00(375.0,445.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeptic shock\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21(36.21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9(14.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal 30-day mortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12(20.69%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6(9.84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInflammation biomarker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeripheral blood neutrophils (109 /L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.40(0.2,0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.20(3.1,14.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003eC reactive protein (mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68.35(41.2,145.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52.00(24.6,122.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProcalcitonin (ng/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.48(0.1,1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.48(0.2,2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.369\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood mNGS results\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBacteria(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25(43.10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11(18.30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003eViruses(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41(70.69%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48(78.69%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.664\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFungi(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8(13.79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7(11.48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.657\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoly-microbial infection(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14(24.14%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6(9.84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.038\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=\"BlockQuote\"\u003e\u003cp\u003eTo assess differences in pathogen distribution, analysis revealed that \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e was the most commonly identified bacterium in both groups. In the observation group, the subsequent most frequently detected bacteria were \u003cem\u003ePropionibacterium acnes\u003c/em\u003e, \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, and \u003cem\u003eEscherichia coli\u003c/em\u003e, in descending order. In contrast, only \u003cem\u003ePropionibacterium acnes\u003c/em\u003e was identified in the control group, with no detection of \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e or \u003cem\u003eEscherichia coli\u003c/em\u003e. Regarding fungal pathogens, the top three species identified in the observation group were \u003cem\u003eAspergillus flavus\u003c/em\u003e, \u003cem\u003eAspergillus fumigatus\u003c/em\u003e, and \u003cem\u003eCunninghamella bertholletiae\u003c/em\u003e. In the control group, \u003cem\u003eCandida glabrata\u003c/em\u003e and \u003cem\u003eRhizopus microsporus\u003c/em\u003e were the most frequently identified, followed by a joint occurrence of \u003cem\u003eAspergillus flavus\u003c/em\u003e and \u003cem\u003eAspergillus fumigatus\u003c/em\u003e at the third position. Both groups shared the same top three viral pathogens: \u003cem\u003eEpstein-Barr virus\u003c/em\u003e, \u003cem\u003eCytomegalovirus\u003c/em\u003e, and \u003cem\u003eHuman alphaherpesvirus 1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Although mNGS revealed quantitative differences in certain pathogens between the two groups\u0026mdash;such as five cases of \u003cem\u003ePropionibacterium acnes\u003c/em\u003e in the observation group versus one in the control group, and three cases each of \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e and \u003cem\u003eEscherichia coli\u003c/em\u003e exclusively in the observation group\u0026mdash;the overall distribution of microbial species did not differ significantly between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn conclusion, this study demonstrates that blood mNGS possesses significant diagnostic value for patients with hematological malignancies complicated by sepsis after antibiotic use. Its high sensitivity and broad-spectrum detection capability can compensate for the limitations of traditional blood culture, particularly in identifying mixed infections and fastidious organisms, thereby guiding clinical optimization of treatment strategies. Building upon insights from the reference literature, blood mNGS holds promise as a pivotal tool in infection management for this high-risk population; however, its integration with conventional diagnostic methods and clinical data is essential to enhance diagnostic specificity.\u003c/p\u003e\u003cp\u003ePatients with hematological malignancies complicated by sepsis are particularly prone to complex infections due to immunosuppression resulting from both the underlying disease and antitumor therapies. The rapid progression and therapeutic challenges of sepsis underscore the critical need for prompt and accurate pathogen identification to improve clinical outcomes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Traditional blood culture, although widely used in sepsis diagnosis, is constrained by a prolonged turnaround time and suboptimal sensitivity, especially following antibiotic administration, thereby failing to meet the clinical demands of this patient cohort [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Blood mNGS, a high-throughput nucleic acid sequencing technology, enables rapid and comprehensive pathogen detection and has garnered increasing attention in infectious disease diagnostics [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This study focuses on patients with hematological malignancies complicated by sepsis who received antibiotic therapy for \u0026ge;\u0026thinsp;3 days without clinical improvement, aiming to evaluate the clinical utility of blood mNGS.\u003c/p\u003e\u003cp\u003eThe findings of this study revealed that the positive detection rate of blood mNGS for bacterial and fungal pathogens (89.36%) was markedly higher than that of blood culture (25.53%), with a more pronounced advantage in detecting mixed infections (25.00% vs. 0.00%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This aligns with findings from the reference literature, which demonstrated that BALF mNGS exhibits a significantly higher detection rate compared with culture-based methods in cancer patients with severe pneumonia, further confirming the superior ability of mNGS to identify pathogens in complex infections [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The reference document highlights the enhanced detection of bacteria, fungi, and mixed infections with mNGS, a trend similarly observed with blood mNGS in this study, suggesting that mNGS can provide a more comprehensive etiological profile of infections in immunocompromised individuals regardless of sample type (e.g., bronchoalveolar lavage fluid or blood).\u003c/p\u003e\u003cp\u003eNonetheless, the present study revealed a suboptimal concordance between blood mNGS and blood culture (concordance rate 13.21%, kappa = -0.202), with relatively low sensitivity (58.33%) and specificity (0.00%) compared to bronchoalveolar lavage fluid mNGS (BALF mNGS) (sensitivity 100%, specificity 16.67%) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These discrepancies may be attributed to differences in sample type: pathogen load in blood is generally lower than in bronchoalveolar lavage fluid, and the inhibitory effects of antibiotics on blood culture are more pronounced. Additionally, the pathogen spectrum in septic patients (e.g., a higher prevalence of viral infections) differs from that observed in severe pneumonia, which may further impact the concordance between the two methods. Previous studies have demonstrated that traditional culture techniques are significantly affected by prior antibiotic use [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and all patients in this study had received antibiotic therapy for \u0026ge;\u0026thinsp;3 days, which likely further diminished the sensitivity of blood culture and highlighted the diagnostic advantage of blood mNGS in this clinical context.\u003c/p\u003e\u003cp\u003eIn our study, a total of 190 pathogens were detected by blood mNGS, predominantly viruses (70.52%), among which \u003cem\u003ehuman cytomegalovirus\u003c/em\u003e and \u003cem\u003eEpstein-Barr virus\u003c/em\u003e accounted for the highest proportions. Common bacteria included \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, while \u003cem\u003eAspergillus\u003c/em\u003e was the main fungal pathogen. The distribution of these pathogens shares both similarities and unique features compared with findings from relevant studies, reflecting characteristics specific to the population and infection type. In terms of similarities, the high detection rates of herpesviruses (such as \u003cem\u003eEpstein-Barr virus\u003c/em\u003e) and opportunistic fungi (such as \u003cem\u003eAspergillus\u003c/em\u003e) in this study are consistent with the dominant presence of human herpesviruses detected by mNGS in patients with community-acquired and hospital-acquired sepsis [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and also align with the tendency of immunocompromised populations (including those with hematological malignancies) to be susceptible to opportunistic pathogens [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], confirming the high pathogenicity of these pathogens in immunocompromised states. In terms of differences, the virus-dominated pathogen spectrum in sepsis observed in this study contrasts with the predominance of bacteria detected by BALF mNGS in cancer patients with severe pneumonia [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This discrepancy is closely related to the biological characteristics of the infection site\u0026mdash;blood, as a systemic circulatory system, is more prone to viral dissemination through the bloodstream, whereas pulmonary infections, due to the local microenvironment, are more conducive to bacterial colonization and reproduction (significant differences in pathogen spectra exist across different infection sites) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Additionally, the detection of Gram-negative bacteria such as \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e in this study echoes the high sensitivity of mNGS in detecting Gram-negative bacteria in abdominal sepsis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], suggesting the widespread pathogenicity of such bacteria in sepsis. Moreover, the high detection rate of \u003cem\u003eAspergillus\u003c/em\u003e, a typical opportunistic fungus in immunocompromised populations, is also consistent with the advantage of mNGS in detecting difficult-to-culture fungi [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In summary, the pathogen distribution revealed by blood mNGS not only reflects the characteristics of susceptible pathogens in patients with hematological malignancies under immunosuppression but also demonstrates the site-specific differences in pathogen spectra between sepsis and pulmonary infections, providing an etiological basis for targeted clinical interventions.\u003c/p\u003e\u003cp\u003ePrevious studies have demonstrated that mNGS holds significant value in guiding adjustments to anti-infective therapy. Particularly in immunocompromised populations, its high detection rate of opportunistic pathogens such as \u003cem\u003ePneumocystis jirovecii\u003c/em\u003e and \u003cem\u003eAspergillus\u003c/em\u003e enables most patients to receive optimized targeted treatment regimens [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In this study, 39.49% of patients had their anti-infective treatment strategies optimized based on blood mNGS results, with significant improvements in inflammatory markers (e.g., procalcitonin) and clinical symptoms (e.g., alleviation of fever) within 7 days. This result further validates the core role of mNGS in precision treatment: for instance, when \u003cem\u003ePneumocystis jirovecii\u003c/em\u003e is detected by mNGS, trimethoprim-sulfamethoxazole is promptly administered, or when \u003cem\u003eAspergillus\u003c/em\u003e is identified, the treatment is adjusted to voriconazole. Such targeted interventions are difficult to achieve when traditional cultures are negative\u0026mdash;primarily because \u003cem\u003ePneumocystis jirovecii\u003c/em\u003e is challenging to culture, and \u003cem\u003eAspergillus\u003c/em\u003e is easily missed in traditional cultures. However, mNGS effectively overcomes these limitations through unbiased detection, which is consistent with conclusions confirmed by studies that mNGS can break through the diagnostic limitations of traditional culture for difficult-to-culture or rare pathogens [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this study, the incidence of mixed infections was significantly higher among patients undergoing antitumor therapy, particularly those with neutropenia, compared to untreated patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), accompanied by an increased 30-day mortality rate. These findings align with existing evidence confirming that antitumor therapy represents a high-risk factor for mixed infections. Studies on patients with acute myeloid leukemia receiving high-dose cytarabine (HiDAC) consolidation therapy have shown that 36% developed at least one infection, with infections affecting 18% of treatment cycles, further highlighting the elevated infection risk during antitumor therapy [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The underlying mechanism primarily involves myelosuppression and immunodeficiency induced by antitumor treatments such as chemotherapy, which increases host susceptibility to various pathogens and consequently raises the risk of mixed infections. Previous reports indicate a higher detection rate of fungal pathogens, including \u003cem\u003ePneumocystis jirovecii\u003c/em\u003e and \u003cem\u003eAspergillus\u003c/em\u003e species, in patients undergoing antitumor therapy, a trend also observed in this study. These findings suggest the necessity for heightened clinical vigilance regarding opportunistic fungal infections in this patient population. Blood-based mNGS can provide earlier etiological evidence, facilitating timely clinical intervention. Furthermore, when combined with evidence indicating that short-course antibiotic therapy (7 days) is non-inferior to long-course therapy (14 days) in treating bloodstream infections, the implementation of tailored anti-infective regimens guided by mNGS pathogen identification may further optimize infection management strategies during antitumor therapy [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite its notable advantages in infectious disease diagnostics, blood mNGS has inherent limitations. First, its specificity is relatively low (0.00%), potentially influenced by background microorganisms or sample contamination, thereby complicating the differentiation between colonization and active infection. This limitation is consistent with findings in relevant literature indicating that mNGS results may be affected by background flora in complex samples [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Second, mNGS does not provide direct antimicrobial susceptibility data, necessitating conventional culture methods for drug sensitivity testing to guide targeted antimicrobial therapy. Third, the cost of mNGS testing remains relatively high. Studies have reported that mNGS expenses may account for 30\u0026ndash;50% of total microbiology testing costs [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], which limits its widespread adoption as a routine diagnostic tool.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, the single-center design may introduce selection bias due to the homogeneity of enrolled patient characteristics, and the sample size (n\u0026thinsp;=\u0026thinsp;119) is relatively small, consistent with the recognized limitations of generalizability in single-center, small-sample studies [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Second, RNA virus detection was not included, potentially leading to missed identification of certain RNA viral pathogens. The comprehensive coverage of pathogen detection remains a key determinant of mNGS diagnostic performance [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Third, long-term follow-up of patient survival outcomes was not conducted, although longitudinal assessment of clinical endpoints is essential for determining the true clinical utility of mNGS [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, the diagnostic accuracy of mNGS may be influenced by factors such as sampling timing. For example, testing within two weeks of symptom onset may improve the true negative rate; however, this study did not comprehensively evaluate the impact of sampling timing, which may represent an additional limitation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn summary, this study demonstrates that blood mNGS possesses substantial diagnostic utility in patients with hematological malignancies complicated by sepsis who are undergoing antibiotic therapy. Its high sensitivity and broad-spectrum detection capacity can effectively compensate for the limitations of conventional blood culture, particularly in identifying mixed infections, fastidious organisms, and guiding the optimization of clinical treatment strategies. Building upon findings from relevant studies, blood mNGS has the potential to become a pivotal tool for infection management in this high-risk patient population. However, integrating conventional diagnostic methods and clinical context remains essential to enhance diagnostic specificity.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eBlood mNGS technology offers significant advantages in the diagnosis of sepsis among patients with hematological malignancies, particularly in scenarios where conventional diagnostic methods yield limited results following antibiotic administration. Evidence indicates that the pathogen detection rate using blood mNGS is notably higher than that of blood culture, with superior performance in identifying polymicrobial infections and fastidious organisms, thereby providing critical support for refining clinical antimicrobial strategies. Its broad-spectrum detection capability encompasses various pathogens, including viruses, bacteria, and fungi. In immunocompromised individuals, mNGS can effectively detect clinically relevant pathogens such as \u003cem\u003ecytomegalovirus\u003c/em\u003e, \u003cem\u003eEpstein-Barr virus\u003c/em\u003e, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, and \u003cem\u003eAspergillus species\u003c/em\u003e, reveal variations in pathogen distribution across different infection sites, and support targeted therapeutic interventions.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"524\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eIQR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eInterquartile range\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003emNGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eMetagenomic next-generation sequencing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003ePPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003ePositive predictive value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eNPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eNegative predictive value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Conceptualization, B.C. and D.J.; methodology, W.S. and D.J.; project administration, J.L.; formal analysis, D.J. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eThe data presented in this study are available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThe study was approved by the Ethical Review Committee of Ningbo Medical Center Lihuili Hospital (Approval No. KY2025SL154-02). The procedures used in this study adhere to the tenets of the Declaration of Helsinki.\u0026nbsp;The need for consent to participate was waived by the Ethical Review Committee of Ningbo Medical Center Lihuili Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1\u0026nbsp; \u0026nbsp;\u0026nbsp;Department of Hematology, Ningbo Medical Center Lihuili Hospital, Ningbo 315000, China; [email protected](B.C.); [email protected](W.S.); [email protected](J.L.)\u003c/p\u003e\n\u003cp\u003e* \u0026nbsp; \u0026nbsp;Correspondence: [email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAr, M. 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The clinical importance of metagenomic next-generation sequencing in detecting disease-causing microorganisms in cases of sepsis acquired in the community or hospital setting. \u003cem\u003eFrontiers in microbiology\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e, 1384166. https://doi.org/10.3389/fmicb.2024.1384166.\u003c/li\u003e\n\u003cli\u003eXu, F., Chen, C., Lu, S., Xue, M., Ding, H., Song, Y., Zhang, Y., Sun, K., Tang, L., Wang, W., Wang, M., Tang, Y., Tan, D., Yao, C., Shi, D., Mao, E., Shao, M., Ying, Y., Zhou, C., Huang, L., \u0026hellip; Song, Z. (2025). Impact of metagenomics next-generation sequencing on etiological diagnosis and early outcomes in sepsis. \u003cem\u003eJournal of translational medicine\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(1), 394. https://doi.org/10.1186/s12967-025-06332-6.\u003c/li\u003e\n\u003cli\u003eLi, D., Gai, W., Zhang, J., Cheng, W., Cui, N., \u0026amp; Wang, H. (2022). 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Comparison of the Impact of tNGS with mNGS on Antimicrobial Management in Patients with LRTIs: A Multicenter Retrospective Cohort Study. \u003cem\u003eInfection and drug resistance\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e, 93\u0026ndash;105. https://doi.org/10.2147/IDR.S493575.\u003c/li\u003e\n\u003cli\u003eMcClellan, K., Messina, J., Saullo, J., \u0026amp; Huggins, J. (2024). Incidence of infection in patients with acute myeloid leukemia receiving high-dose cytarabine consolidation. \u003cem\u003eAnnals of hematology\u003c/em\u003e, \u003cem\u003e103\u003c/em\u003e(12), 5351\u0026ndash;5358. https://doi.org/10.1007/s00277-024-06069-0.\u003c/li\u003e\n\u003cli\u003eBALANCE Investigators, for the Canadian Critical Care Trials Group, the Association of Medical Microbiology and Infectious Disease Canada Clinical Research Network, the Australian and New Zealand Intensive Care Society Clinical Trials Group, and the Australasian Society for Infectious Diseases Clinical Research Network, Daneman, N., Rishu, A., Pinto, R., Rogers, B. A., Shehabi, Y., Parke, R., Cook, D., Arabi, Y., Muscedere, J., Reynolds, S., Hall, R., Dwivedi, D. B., McArthur, C., McGuinness, S., Yahav, D., Coburn, B., Geagea, A., Das, P., Shin, P., \u0026hellip; The BALANCE Investigators, for the Canadian Critical Care Trials Group, the Association of Medical Microbiology and Infectious Disease Canada Clinical Research Network, the Australian and New Zealand Intensive Care Society Clinical Trials Group, and the Australasian Society for Infectious Diseases Clinical Research Network (2025). Antibiotic Treatment for 7 versus 14 Days in Patients with Bloodstream Infections. \u003cem\u003eThe New England journal of medicine\u003c/em\u003e, \u003cem\u003e392\u003c/em\u003e(11), 1065\u0026ndash;1078. https://doi.org/10.1056/NEJMoa2404991.\u003c/li\u003e\n\u003cli\u003eZuo, Y. H., Wu, Y. X., Hu, W. P., Chen, Y., Li, Y. P., Song, Z. J., Luo, Z., Ju, M. J., Shi, M. H., Xu, S. Y., Zhou, H., Li, X., Jie, Z. J., Liu, X. D., \u0026amp; Zhang, J. (2023). The Clinical Impact of Metagenomic Next-Generation Sequencing (mNGS) Test in Hospitalized Patients with Suspected Sepsis: A Multicenter Prospective Study. \u003cem\u003eDiagnostics (Basel, Switzerland)\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(2), 323. https://doi.org/10.3390/diagnostics13020323.\u003c/li\u003e\n\u003cli\u003eShean, R. C., Garrett, E., Malleis, J., Lieberman, J. A., \u0026amp; Bradley, B. T. (2024). A retrospective observational study of mNGS test utilization to examine the role of diagnostic stewardship at two academic medical centers. \u003cem\u003eJournal of clinical microbiology\u003c/em\u003e, \u003cem\u003e62\u003c/em\u003e(9), e0060524. https://doi.org/10.1128/jcm.00605-24.\u003c/li\u003e\n\u003cli\u003eChen, M., Cai, Y., Wang, L., Jiang, Y., Qian, J., Qin, J., Xu, J., Li, J., Yu, S., \u0026amp; Shen, B. (2024). Metagenomic next-generation sequencing testing from the perspective of clinical benefits. \u003cem\u003eClinica chimica acta; international journal of clinical chemistry\u003c/em\u003e, \u003cem\u003e553\u003c/em\u003e, 117730. https://doi.org/10.1016/j.cca.2023.117730.\u003c/li\u003e\n\u003cli\u003eLisha, W., Jiao, Q., Mengyuan, C., Jiajia, Q., Tianbin, T., Yilan, W., Linjie, H., \u0026amp; Sufei, Y. (2025). Clinical evaluation of negative mNGS reports in sterile body fluids and tissues. \u003cem\u003eMicrobiology spectrum\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(7), e0201324. https://doi.org/10.1128/spectrum.02013-24. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"mNGS, Blood, Hematological malignancies, Sepsis","lastPublishedDoi":"10.21203/rs.3.rs-7345606/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7345606/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eMetagenomic next-generation sequencing (mNGS) has been widely applied in clinical pathogen detection; however, its utility in patients with hematologic malignancies complicated by sepsis after antibiotic therapy requires further investigation.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA total of 119 patients with hematologic malignancies complicated by sepsis, who had received antibiotic treatment for \u0026ge;\u0026thinsp;3 days without clinical improvement, were enrolled in the study. All patients underwent simultaneous blood culture and mNGS analysis. The diagnostic value of mNGS and its impact on optimizing anti-infective therapy were evaluated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eFor the detection of bacterial and fungal pathogens, mNGS demonstrated a significantly higher positive rate compared to blood culture (89.36% vs. 25.53%). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of mNGS were 58.33%, 0.00%, 16.67%, and 0.00%, respectively. The overall agreement rate between the two methods was 13.21% (kappa = -0.202). Based on mNGS results, anti-infective treatment regimens were modified in 47 patients (39.49%). Granulocytopenia related to antitumor therapy was identified as a high-risk factor for polymicrobial infections (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003ePatients with hematologic malignancies and sepsis, particularly those with antitumor therapy-induced granulocytopenia, are at increased risk for polymicrobial infections. Blood mNGS offers a rapid and comprehensive approach to pathogen identification, showing significant potential for guiding anti-infective therapy in this patient population.\u003c/p\u003e","manuscriptTitle":"Application of metagenomic next-generation sequencing technology in hematologic malignancy patients with sepsis following antibiotic use","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-25 14:50:34","doi":"10.21203/rs.3.rs-7345606/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-23T17:59:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-10T14:22:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"26411801940503712084587491761302437728","date":"2025-10-08T00:50:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-01T11:36:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"139297577656441609217659338574708581746","date":"2025-09-18T14:58:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"251616930082365460992327312543325672667","date":"2025-09-17T15:36:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-16T13:59:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-14T08:35:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-14T08:32:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2025-08-14T08:28:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2b539531-ed7b-46e6-857c-17c05cb43b0f","owner":[],"postedDate":"September 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T16:09:19+00:00","versionOfRecord":{"articleIdentity":"rs-7345606","link":"https://doi.org/10.1186/s12879-025-12111-x","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2025-12-01 15:57:55","publishedOnDateReadable":"December 1st, 2025"},"versionCreatedAt":"2025-09-25 14:50:34","video":"","vorDoi":"10.1186/s12879-025-12111-x","vorDoiUrl":"https://doi.org/10.1186/s12879-025-12111-x","workflowStages":[]},"version":"v1","identity":"rs-7345606","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7345606","identity":"rs-7345606","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}