Enhanced Pathogen Detection in Hematology Patients with Respiratory Infections Using Targeted Next-Generation Sequencing (tNGS) 

Enhanced Pathogen Detection in Hematology Patients with Respiratory Infections Using Targeted Next-Generation Sequencing (tNGS) | 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 Article Enhanced Pathogen Detection in Hematology Patients with Respiratory Infections Using Targeted Next-Generation Sequencing (tNGS) Yue Yao, Yuhu Feng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6951707/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Respiratory infections, involving both upper and lower respiratory tracts, represent a common and potentially life-threatening complication in patients with hematologic disorders. These patients exhibit compromised immunity and require highly sensitive diagnostic methods for both timing and capability. Traditional microbiological techniques often demonstrate inadequate diagnostic performance and delayed results, necessitating the exploration of alternative approaches. Methods: Sputum samples were collected from patients with hematologic diseases and concurrent respiratory infections (as the majority could not undergo bronchoalveolar lavage). The samples underwent Targeted Next-Generation Sequencing (tNGS), and the results were compared with those from traditional microbiological testing (TMT). The study aimed to evaluate the diagnostic performance, economic benefits, and pathogen prevalence—including fungi, viruses, atypical pathogens, and bacteria—along with the clinical impact of tNGS-guided treatment modifications. Results: tNGS demonstrated superior pathogen detection (98.6%) compared to TMT (47.2%), identifying bacteria in 79.7%, viruses in 87.8%, fungi in 58.1%, and atypical pathogens in 0.02% of cases. Common bacterial pathogens included Haemophilus influenzae, Streptococcus pyogenes, Klebsiella pneumoniae, and Stenotrophomonas maltophilia. Predominant fungal pathogens included Aspergillus flavus, Aspergillus niger, Aspergillus fumigatus, and Candida glabrata, while viral pathogens included Epstein-Barr virus, novel coronavirus, Herpes simplex virus type 1, and rhinovirus type A. The mean turnaround time for tNGS was 24 hours, significantly faster than TMT (3–5 days), and tNGS proved more cost-effective. Treatment modifications based on tNGS results were implemented in 55.6% of cases. Conclusion: tNGS offers notable improvements in pathogen detection, especially for fungi and atypical pathogens, enabling timely, targeted antimicrobial therapy for immunocompromised hematologic malignancy patients. Additionally, tNGS demonstrates excellent cost-effectiveness, rapid result turnaround, and extensive pathogen coverage, emphasizing its potential to enhance clinical outcomes. These findings advocate for the integration of tNGS into clinical practice, particularly for hematologic patients requiring more precise diagnostic capabilities. Health sciences/Oncology/Cancer/Haematological cancer Biological sciences/Cancer/Haematological cancer respiratory infections immunocompromised hematology patients Infectious Disease Diagnostics Next-Generation Sequencing (NGS) Technologies Clinical Pathogen Detection Figures Figure 1 Figure 2 1. Introduction Respiratory infections (including upper and lower respiratory tract infections) represent a frequent and severe complication in patients with hematological diseases, particularly those with compromised immunity( 1 ). Early diagnosis and prompt treatment are essential for improving patient outcomes, as delayed intervention significantly elevates mortality risks( 2 ). Traditional microbiological methods (TMT), such as general bacterial and blood cultures, fungal cultures or smear tests, respiratory hexapathogen screenings, and SARS-CoV-2 nucleic acid testing, face various limitations when applied in hematology departments: Low Sensitivity and Diagnostic Efficacy : Traditional methods only detect pathogens in 30%-40% of patients with respiratory infections( 3 ). Inability to Perform Bronchoalveolar Lavage (BAL) : Due to low platelet counts, many hematology patients are not candidates for BAL, which results in specimen collection limitations to pharyngeal swabs, sputum, or blood cultures, yielding even lower positivity rates( 4 ). Prolonged Time to Results : Bacterial and fungal cultures typically require 3–5 days for results( 5 ). Additional Diagnostic Burdens : Viral infections, including COVID-19, require additional tests, adding to the economic burden. Non-typical Pathogens Detection : Pathogens such as Mycoplasma, Chlamydia, and Coxiella burnetii are often undetected by routine methods, leaving up to 62% of infections unidentified ( 6 ). A more effective diagnostic approach is urgently needed to overcome these limitations, offering rapid results, improved sensitivity and specificity, cost-effectiveness, and broader coverage of pathogens. Metagenomic next-generation sequencing (mNGS) has significantly improved pathogen detection rates for respiratory infections( 7 ). However, its high cost, susceptibility to host nucleic acid interference, and the need for separate DNA and RNA detection have limited its widespread application. In comparison, targeted next-generation sequencing (tNGS), when paired with polymerase chain reaction (PCR) and high-throughput sequencing, allows for the concurrent detection of numerous common pathogens( 8 ). Though tNGS detects fewer pathogens compared to mNGS, it offers clear advantages in cost-effectiveness and diagnostic efficiency, with studies demonstrating it is a more affordable alternative to mNGS ( 9 ). Moreover, tNGS has shown comparable diagnostic performance to mNGS( 10 ). Sputum-based tNGS provides a rapid, efficient, and cost-effective alternative to traditional microbiological tests for pathogen detection( 11 ). This method has proven particularly valuable for diagnosing complex infectious diseases( 12 , 13 ). However, most studies have been limited to small case series and have not included hematology patients ( 13 ). In this context, tNGS shows considerable potential for overcoming diagnostic challenges in hematology patients. Sputum-based tNGS has exhibited a significantly higher positive detection rate (96.7%) compared to conventional methods (36.7%-38%)( 8 ), and is capable of identifying common pathogen resistance genes ( 8 ). Therefore, tNGS shows great potential in addressing the clinical diagnostic and treatment needs of hematology patients with respiratory infections, providing a valuable alternative to the limitations of conventional microbiological tests( 10 ). However, there remains a lack of retrospective studies specifically assessing the diagnostic efficacy of tNGS in hematology patients with respiratory tract infections. In a previous study conducted by our team, a small sample prospective study compared the diagnostic capabilities of tNGS and traditional microbiological testing (TMT) in patients with myelosuppression( 4 ). To further address this gap, we conducted a retrospective study with a larger sample size and expanded the research cohort to include all hematology patients. This study aimed to evaluate the early diagnostic performance of tNGS relative to conventional microbiological tests in a broad range of hematological diseases and respiratory infections. Additionally, we sought to explore the distribution of bacterial, viral, fungal, and atypical pathogens in this cohort and assess the impact of tNGS on clinical decision-making, especially regarding pathogen resistance information. The range of pathogens detected is crucial for diagnostic performance. In this study, we used an updated pathogen panel comprising 198 common respiratory pathogens, significantly broader than those in previous studies (typically covering 98–158 pathogens). This panel detects a wide range of pathogens, including bacteria (such as Mycobacterium tuberculosis and non-tuberculous mycobacteria), fungi, viruses, Mycoplasma, Chlamydia, Coxiella burnetii, and resistance genes associated with common pathogens, encompassing more than 98% of known microorganisms that cause respiratory infections. This comprehensive panel is designated as 198#tNGS (see Supplementary file, Table S1 ; for academic reference only; commercial use is prohibited). 2. Materials and Methods 2.1 Study Subjects and Methods 2.1 Eligibility Criteria:1. Admission to the hematology department at Puyang People's Hospital from January 1, 2024, to May 31, 2024. 2. Age ≥ 15 years. 3. Informed consent obtained for participation in the study. 4.Diagnosis of respiratory infections in conjunction with hematological diseases. 5.Patients who underwent both 198#tNGS and conventional microbiological testing (CMT) were included. Data collected after the onset of respiratory tract infection included general bacterial culture, blood culture, fungal culture, fungal smear examination, respiratory hexapathogen testing (for viruses), SARS-CoV-2 nucleic acid testing, and the results from 198#tNGS. All enrolled patients complied with sputum collection requirements, which involved expectorating deep respiratory sputum in the morning. 2.2 Exclusion Criteria : Patients who declined sample collection for 198#tNGS, did not adhere to sputum specimen collection requirements, or had incomplete clinical data were excluded from the study. Participation was voluntary, and safety-related information was given priority throughout the study. Recruitment was suspended if any safety concerns were identified. The study received approval from the Ethics Committee of Fuyang People's Hospital (approval number: Medical Ethics Review [2024]96) and adhered to the principles outlined in the Declaration of Helsinki. Patient data were anonymized for the purpose of this study, with informed consent obtained from all participants. 2.3 Evaluation Indicators The primary objective was to evaluate the diagnostic performance of 198#tNGS in detecting respiratory pathogens in patients with respiratory tract infections. Secondary objectives included comparing 198#tNGS to current clinical standard microbiological tests (TMT), assessing the positive and negative concordance rates of 198#tNGS, and determining its clinical relevance in guiding therapeutic decisions. Positive Concordance Defined as the identification of at least one pathogen by 198#tNGS that was also detected by TMT. The positive concordance rate was determined by dividing the number of positive concordances identified by 198#tNGS by the total number of positives detected by TMT. Negative Concordance Defined as results where both methods yielded negative outcomes. The negative concordance rate was calculated similarly. Diagnoses were established by three independent hematology experts based on both test results and clinical data. These experts also evaluated the clinical relevance of the detected pathogens and assessed the need for antibiotic adjustments based on the findings from 198#tNGS. The effectiveness of any therapeutic modifications was also reviewed. 2.4 Statistical Analysis For continuous variables, the number of observations, mean, median, standard deviation, minimum, and maximum values were reported. Categorical variables were presented as frequencies and percentages for each group. Data processing and analysis were carried out using SPSS 20.0 and R software. To compare continuous variables between groups, a t-test was used, while categorical variables were analyzed with the chi-square test. A p-value of less than 0.05 was considered indicative of statistical significance. 3. Results 3.1 Patient Characteristics A total of 74 198#tNGS test results were analyzed, involving 54 patients with hematological diseases. Half of the patients had two or more hospitalizations and infections. The median age of the enrolled patients was 58 years, with 57.4% (31/54) being male. Ultimately, 25.9% (14/54) of the patients died from infection. Agranulocytosis (neutrophils < 0.5 × 10⁹/L) was present in 36.4% (27/74) of patients, while platelet counts of < 20 × 10⁹/L and < 50 × 10⁹/L were seen in 28.3% (21/74) and 37.8% (28/74) of patients, respectively. Additionally, a significant percentage of patients—92% (50/54)—were either unsuitable or unwilling to undergo bronchoalveolar lavage due to specific reasons outlined in the Discussion section (Table 1). 3.2 Pathogen Detection Using 198#tNGS and TMT Out of 74 198#tNGS test results, 44 potential pathogens were detected by 198#tNGS, while TMT identified 16 pathogens (Fig. 1 ; Supplementary file, Table S2 ). The overall microbiological detection rates for 198#tNGS and TMT differed significantly, with 198#tNGS detecting pathogens in 98.6% (73/74) of samples and TMT identifying pathogens in 47.2% (31/74) (P < 0.001). The sensitivity and positive predictive value of tNGS were high, at 98.6% and 100%, respectively. Of the test results, 41.89% (31/74) were positive by both 198#tNGS and TMT, 1.35% (1/74) were negative by both methods, and 56.76% (42/74) tested positive only by 198#tNGS. No specimens were positive only by TMT. Among the 31 double-positive results, only 1 (1/31) showed complete agreement between 198#tNGS and TMT, while 24 (23/31) showed partial agreement and 7 (7/31) showed complete disagreement (Fig. 2 ). Additionally, the positive consistency rate between tNGS and blood culture was relatively high at 77.4%, with a low negative consistency rate of 2%. The mean assay turnaround time (TAT) for tNGS was approximately 24 hours from sample receipt to electronic report delivery, excluding holidays. 3.2 Pathogen Detection Using 198#tNGS and TMT Out of 74 specimens analyzed, 198#tNGS detected a total of 44 potential pathogens, while TMT identified only 16 pathogens (Fig. 1 ; Supplementary Table S2 ). The overall microbiological detection rates between the two methods were significantly different, with 198#tNGS detecting pathogens in 98.6% (73/74) of the samples, compared to TMT’s 47.2% (31/74) (P < 0.001). The sensitivity and positive predictive value of 198#tNGS were high, at 98.6% and 100%, respectively. Of the test results, 41.9% (31/74) were positive by both 198#tNGS and TMT, 1.4% (1/74) were negative by both methods, and 56.8% (42/74) tested positive only by 198#tNGS. No specimens were positive exclusively by TMT. Among the 31 samples with dual-positive results, complete concordance between the two methods was observed in only 1 (1/31) sample, while 24 (23/31) demonstrated partial agreement, and 7 (7/31) showed complete disagreement (Fig. 2 ). Additionally, the positive concordance rate between 198#tNGS and blood culture was 77.4%, with a low negative concordance rate of 2%. he average assay turnaround time (TAT) for 198#tNGS was approximately 24 hours from the receipt of the sample to the delivery of the electronic report, excluding holidays. The detected pathogens included a variety of bacterial, viral, fungal, and atypical microorganisms. Specifically, 198#tNGS identified bacterial pathogens in 59 samples (79.7%). The most commonly identified bacterium was Haemophilus influenzae, which accounted for 25.4% (15/59) of all bacterial findings, followed by Streptococcus anginosus, Klebsiella pneumoniae, Stenotrophomonas maltophilia, and Pseudomonas aeruginosa. In contrast, TMT detected bacterial infections in only 13 specimens (11/74, 17.6%). For viral infections, 198#tNGS identified viruses in 65 samples (87.8%), with Epstein-Barr virus being the most prevalent (53.8%), followed by 2019-nCoV, Herpes simplex virus type 1 (HSV-1), rhinovirus type A (HRV), and human respiratory syncytial virus type B (RSV B). TMT, on the other hand, identified viruses in just 19 specimens (25.7%). Fungal infections were found in 43 specimens (58.1%) by 198#tNGS, with Aspergillus flavus being the most common species, representing 51% of all fungal detections. Other fungal species included Aspergillus niger, Aspergillus fumigatus, Candida glabrata, Pneumocystis jirovecii, and Candida albicans. TMT identified fungi in only 10 specimens (13.5%). Additionally, Mycoplasma pneumoniae, an atypical pathogen, was detected in one specimen by 198#tNGS, but was not identified by TMT in any sample. 3.3 Clinical Implications of 198#tNGS The results from 198#tNGS provided valuable diagnostic insights that guided clinical decision-making. Pathogen identification was combined with clinical factors, including patient medical history, symptoms, and imaging results. The findings from 198#tNGS influenced antibiotic therapy decisions for 30 patients, including those with infections caused by SARS-CoV-2, Pneumocystis jirovecii, Mycoplasma pneumoniae, Rhizobium, and Stenotrophomonas maltophilia. Notably, the detection of the methicillin-resistant Staphylococcus gene mecA:1413 led to changes in antibiotic treatment for two patients. In contrast to conventional bacterial cultures, which typically require 3–5 days for results, 198#tNGS provided results in just 24 hours, offering a significant reduction in turnaround time. This speed highlights the superior diagnostic efficiency of tNGS compared to traditional techniques such as blood and fungal cultures. Rapid and precise pathogen identification is critical for informing clinical decisions and ensuring timely administration of appropriate antibiotic treatments. 4. Discussion Consequently, respiratory tract infections in hematology patients exhibit distinct characteristics compared to those in respiratory medicine( 14 ). These patients frequently present with unique infection patterns, including a higher prevalence of fungal and atypical pathogen infections( 15 , 16 ). Furthermore, the risk of these infections advancing to severe disease is elevated, along with an increased incidence of drug-resistant strains( 17 ). Our study revealed that in the hematology department, the proportions of fungal, viral, and atypical pathogen infections were 58.1%, 87.8%, and 0.02%, respectively. In contrast, for patients with respiratory infections in the Respiratory department, similarly based on sputum tNGS testing, the proportions were 26.8%, 84.2%, and 0%, respectively. Specifically, we found that the most common bacterial pathogens in the hematology department were Haemophilus influenzae, Streptococcus anginosus, Klebsiella pneumoniae, and Stenotrophomonas maltophilia. The predominant fungal species were Aspergillus flavus, Aspergillus niger, Aspergillus fumigatus, and Candida glabrata. Among viral infections, Epstein-Barr virus, 2019-nCoV, Herpes simplex virus type 1, rhinovirus A, and atypical pathogens such as Mycoplasma pneumoniae were most frequently detected. In comparison, the most common bacterial pathogens in the Respiratory department were Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Stenotrophomonas maltophilia, while Candida albicans, Candida yersinia, and Candida fumigatus were the most common fungal pathogens. Viral pathogens in this group included EBV, CMV, HSV-1, HHV-7, and HHV-6, with no atypical pathogens detected( 8 ). These findings highlight the significant differences in pathogen profiles between hematology and respiratory departments( 8 ). Hematology patients exhibited a notably higher frequency of fungal infections, particularly with Aspergillus species, which predominated over Candida species seen in the Respiratory department( 18 ). Additionally, atypical pathogens were more common in the hematology cohort compared to the respiratory group.( 19 ) Given the rapid disease progression and severity often seen in hematology patients, many of whom are unable to undergo bronchoscopy, there is an urgent clinical need for non-invasive diagnostic methods like sputum-based tNGS( 8 ). This study retrospectively assess the diagnostic performance of tNGS in hematology patients. Prior to this, tNGS had not been systematically explored in the context of hematologic diseases. Our findings underscore its efficacy in diagnosing respiratory infections in this patient group and provide strong evidence for its utility in clinical practice. Diagnostic Performance of 198#tNGS This study demonstrated that 198#tNGS achieved a sensitivity of 98.6% and a positive predictive value of 100% in diagnosing respiratory tract infections, surpassing the performance of TMT. The average turnaround time (TAT) for 198#tNGS was 24 hours, notably faster than the 3–5 days typically required for conventional bacterial, blood, and fungal cultures( 20 ). Although 198#tNGS might seem more costly than traditional bacterial culture, a comprehensive cost analysis shows that when factoring in the combined costs of bacterial, blood, and fungal cultures, fungal smear examinations, respiratory hexapathogen detection, and COVID-19 nucleic acid tests, the overall cost of 198#tNGS is lower. Moreover, TMT often necessitates multiple testing rounds and specimen collections to enhance its detection rate, further highlighting the cost-effectiveness of sputum-based 198#tNGS. Advantages Over Bronchoscopy and mNGS Although bronchoalveolar lavage fluid obtained through bronchoscopy remains the gold standard for pathogen identification in respiratory infections, it is not feasible for many hematology patients. Several factors contribute to this limitation: ( 1 ) patients with platelet counts < 20 × 10⁹/L are at high risk for spontaneous bleeding, making bronchoscopy contraindicated, and even with platelet counts < 50 × 10⁹/L, the bleeding risk remains significant. ( 2 ) Hematology patients often experience rapid disease progression, and those with severe hypoxia (requiring oxygen supplementation) are unsuitable for bronchoscopy. In cases of neutropenia associated with fever or impending respiratory failure, bronchoscopy becomes impractical. ( 3 ) Many patients are apprehensive about undergoing bronchoscopy due to concerns of hemoptysis, especially when platelet counts are low. Given these constraints, sputum testing provides a viable alternative. Our study demonstrates that 198#tNGS, using sputum samples, offers superior diagnostic efficiency, with results comparable to those obtained from bronchoalveolar lavage fluid. Other studies have also shown that tNGS for respiratory pathogens performs similarly to metagenomic next-generation sequencing (mNGS) in samples obtained from bronchoscopy. Consequently, for hematology patients who cannot undergo bronchoscopy, sputum-based 198#tNGS represents the optimal diagnostic approach, effectively addressing the clinical challenges these patients face. Detection of Non-Bacterial Pathogens An important advantage of 198#tNGS is its ability to detect non-bacterial pathogens, which are often overlooked by conventional microbiological testing methods (TMT). Hematology patients, who are frequently immunocompromised due to treatments such as chemotherapy and immunosuppressive drugs, are particularly susceptible to infections caused by fungi, viruses, mycoplasma, and chlamydia. These pathogens are often not detected by traditional methods but are significant contributors to the morbidity and mortality in this population. By providing a more comprehensive diagnostic approach, 198#tNGS allows for the identification of a broader spectrum of pathogens, including those that are typically missed by other diagnostic techniques. Impact on Clinical Decision-Making Moreover, 198#tNGS proved instrumental in guiding clinical decision-making. In this study, treatment modifications were made for 55.6% (30/54) of patients based on the results of 198#tNGS. For patients whose pathogens were not identified by TMT, 198#tNGS provided essential clarification of the underlying infection. Additionally, 198#tNGS enabled the identification of pathogen resistance genes, which directly informed antibiotic choices, thus improving patient outcomes. Study Limitations While this study provides valuable insights, it does have limitations. The sample size was relatively small, and larger, multi-center studies are needed to further validate these findings. Additionally, sputum liquefaction tests predominantly reflect pathogens from the upper respiratory tract, which may limit their ability to detect pathogens originating in the lower respiratory tract. To mitigate this limitation, patients were instructed to collect sputum specimens from the deep respiratory tract in the morning, optimizing the detection of lower respiratory tract pathogens. Another challenge with sputum testing is the potential detection of colonizing or non-pathogenic bacteria. To address this, three independent hematology experts evaluated the clinical relevance of the pathogens identified through both TMT and 198#tNGS, in conjunction with clinical symptoms, imaging results, and patient history. Conclusion In conclusion, this study demonstrates the superiority of sputum-based 198#tNGS in diagnosing the etiology of respiratory tract infections in hematology patients who are unsuitable for bronchoalveolar lavage. Compared to TMT, 198#tNGS exhibited significantly higher detection rates, enabling the early identification of pathogenic microorganisms, particularly those with high pathogenicity, minimal colonization, or drug-resistant genes( 21 ). Furthermore, 198#tNGS is faster, more convenient, efficient, and cost-effective. Its broad pathogen detection, including resistance genes, offers enhanced specificity and clinical relevance. This technology has the potential to transform the diagnostic and treatment approach for respiratory infections in hematology patients, significantly improving patient outcomes. Declarations Availability of data and materials The original contributions presented in the study are included in the supplementary material. Ethics approval and consent to participate The study was approved by the Ethics Committee of Fuyang People's Hospital. All methods were carried out in accordance with relevant guidelines and regulations. Informed consent was obtained from all subjects and/or their legal guardian(s). Author contributions Yue Yao proposed, designed, and analyzed the research. Yue Yao and Yuhu Feng also curated data and verified the statistical analysis. Authors advised and revised the manuscript. All authors approved publication. Conflict of Interest The authors declare no conflict of interest, financial or otherwise. Funding This study was supported by the Fuyang Municipal Key Research and Development Program for Clinical Medicine Research and Translational Applications (FK20245516), the Fuyang Municipal Health and Wellness Research Project (FY2024-016), and the Fuyang City 14th Five-Year Plan Key Specialty Development Program (Department of Hematology). Acknowledgments N/A References Periselneris J, Brown JS. A clinical approach to respiratory disease in patients with hematological malignancy, with a focus on respiratory infection. Med Mycol. 2019;57(Supplement_3):S318-s27. Chemaly RF, Shah DP, Boeckh MJ. Management of respiratory viral infections in hematopoietic cell transplant recipients and patients with hematologic malignancies. Clin Infect Dis. 2014;59 Suppl 5(Suppl 5):S344-51. Ye J, Huang K, Xu Y, Chen N, Tu Y, Huang J, et al. Clinical application of nanopore-targeted sequencing technology in bronchoalveolar lavage fluid from patients with pulmonary infections. Microbiol Spectr. 2024;12(6):e0002624. Li C, Wu J, Feng Y. Prospective study on diagnostic efficacy of targeted NGS vs. traditional testing for respiratory infections in myelosuppressed hematology patients. Front Med (Lausanne). 2025;12:1488652. Li D, Li Q, Huang Z, Wu W, Fan X, Liu J, et al. Comparison of the Impact of tNGS with mNGS on Antimicrobial Management in Patients with LRTIs: A Multicenter Retrospective Cohort Study. Infect Drug Resist. 2025;18:93-105. Waites KB, Balish MF, Atkinson TP. New insights into the pathogenesis and detection of Mycoplasma pneumoniae infections. Future Microbiol. 2008;3(6):635-48. Xie G, Zhao B, Wang X, Bao L, Xu Y, Ren X, et al. Exploring the Clinical Utility of Metagenomic Next-Generation Sequencing in the Diagnosis of Pulmonary Infection. Infect Dis Ther. 2021;10(3):1419-35. Deng Z, Li C, Wang Y, Wu F, Liang C, Deng W, et al. Targeted next-generation sequencing for pulmonary infection diagnosis in patients unsuitable for bronchoalveolar lavage. Front Med (Lausanne). 2023;10:1321515. Zheng YR, Chen XH, Chen Q, Cao H. Comparison of targeted next-generation sequencing and metagenomic next-generation sequencing in the identification of pathogens in pneumonia after congenital heart surgery: a comparative diagnostic accuracy study. Ital J Pediatr. 2024;50(1):174. Wei M, Mao S, Li S, Gu K, Gu D, Bai S, et al. Comparing the diagnostic value of targeted with metagenomic next-generation sequencing in immunocompromised patients with lower respiratory tract infection. Ann Clin Microbiol Antimicrob. 2024;23(1):88. Ma H, Wang H, Han X, Fei J. Efficacy of targeted next generation sequencing for pathogen detection in lower respiratory tract infections. Am J Transl Res. 2024;16(8):3637-45. Li S, Tong J, Li H, Mao C, Shen W, Lei Y, et al. L. pneumophila Infection Diagnosed by tNGS in a Lady with Lymphadenopathy. Infect Drug Resist. 2023;16:4435-42. Xu JH, Cui YB, Wang LJ, Nan HJ, Yang PY, Bai YL, et al. Pathogen detection by targeted next-generation sequencing test in adult hematological malignancies patients with suspected infections. Front Med (Lausanne). 2024;11:1443596. Zhang X, Wang F, Yu J, Jiang Z. Clinical application value of metagenomic second-generation sequencing technology in hematologic diseases with and without transplantation. Front Cell Infect Microbiol. 2023;13:1135460. Cordonnier C, Alanio A, Cesaro S, Maschmeyer G, Einsele H, Donnelly JP, et al. Pneumocystis jirovecii pneumonia: still a concern in patients with haematological malignancies and stem cell transplant recipients-authors' response. J Antimicrob Chemother. 2017;72(4):1266-8. Milano F, Campbell AP, Guthrie KA, Kuypers J, Englund JA, Corey L, et al. Human rhinovirus and coronavirus detection among allogeneic hematopoietic stem cell transplantation recipients. Blood. 2010;115(10):2088-94. Blennow O, Ljungman P. Infections in Hematology Patients. In: Lazarus HM, Schmaier AH, editors. Concise Guide to Hematology. Cham: Springer International Publishing; 2019. p. 503-18. Ghajari A, Lotfali E, Azari M, Fateh R, Kalantary S. Fungal Airborne Contamination as a Serious Threat for Respiratory Infection in the Hematology Ward. Tanaffos. 2015;14(4):257-61. Cheng Q, Tang Y, Liu J, Liu F, Li X. The Differential Diagnostic Value of Chest Computed Tomography for the Identification of Pathogens Causing Pulmonary Infections in Patients with Hematological Malignancies. Infect Drug Resist. 2024;17:4557-66. Qin L, Liang M, Song J, Chen P, Zhang S, Zhou Y, et al. Utilizing Targeted Next-Generation Sequencing for Rapid, Accurate, and Cost-Effective Pathogen Detection in Lower Respiratory Tract Infections. Infect Drug Resist. 2025;18:329-40. Li Y, Jiang Y, Liu H, Fu Y, Lu J, Li H, et al. Targeted next-generation sequencing for antimicrobial resistance detection in ventilator-associated pneumonia. Front Cell Infect Microbiol. 2025;15:1526087. Tables Tables are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.xlsx Table 1 Baseline characteristics of 54 patients (74 198# tNGS test results). Table2.xlsx Table 2 Prices for different items. SupplementaryTablelegend.docx TableS1.xlsx TableS2.xlsx rawdata.xlsx Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6951707","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":475319887,"identity":"e81e4ddb-c889-4655-823d-01b99f7be95d","order_by":0,"name":"Yue Yao","email":"","orcid":"","institution":"Fuyang People’s Hospital (The Affiliated Fuyang People’s Hospital of Anhui Medical University)","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Yao","suffix":""},{"id":475319888,"identity":"f1a953aa-9ed6-4671-8e63-9e1afee5264f","order_by":1,"name":"Yuhu Feng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYBAC9gYwdYCBTf78wwcfDGzsCGrhOQDVwi/Bw2w4oyAtmXgtkjN42KR5PhxibCCohb338GvetjuJG273Hja2MTjAzMB++OgGvFp4zqVZ87Y9S9xw51zi4xyDO3wMPGlpN/BpsZfIMTPmbTucuOFAgrFxjsEzZgYJHjO8Wnjk38C1mElbGBxmbCCoRYLH+DFIy8wZOWbSDERp4ckxY5xz7rBxP8+xZMMeg7RkNkJ+4WE/Y/zhTdlh2Tb25oMPfvyxseNnP3wMrxYgYJPiQeESUA4CzB9/EKFqFIyCUTAKRjAAAGTDUK27kZlMAAAAAElFTkSuQmCC","orcid":"","institution":"Fuyang People’s Hospital (The Affiliated Fuyang People’s Hospital of Anhui Medical University)","correspondingAuthor":true,"prefix":"","firstName":"Yuhu","middleName":"","lastName":"Feng","suffix":""}],"badges":[],"createdAt":"2025-06-23 00:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6951707/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6951707/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85371110,"identity":"a15d440f-9587-424d-bab1-6bdd3ac6682e","added_by":"auto","created_at":"2025-06-25 07:35:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":503733,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of pathogens in study cohort and detection rate heterogeneity between tNGS and TMT.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6951707/v1/3e42fbc2e5ff4f71a5c81910.png"},{"id":85371109,"identity":"9ad16f0e-623a-4121-9e41-e6233209ca70","added_by":"auto","created_at":"2025-06-25 07:35:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":61042,"visible":true,"origin":"","legend":"\u003cp\u003eA\u0026amp;B: Consistency of pathogen detection between tNGS and CMTs.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6951707/v1/b9dcf57a5a38f17b6d6b69ce.png"},{"id":102029127,"identity":"f0f4578f-5d6c-4faf-923c-52abd06e27b2","added_by":"auto","created_at":"2026-02-06 10:27:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1231531,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6951707/v1/4e7b80e3-8903-483f-9ac5-9ceff40a0911.pdf"},{"id":85370714,"identity":"f5a24d7b-b40f-4f6c-bfa9-a8aab47f1bb3","added_by":"auto","created_at":"2025-06-25 07:27:32","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10110,"visible":true,"origin":"","legend":"\u003cp\u003eTable 1 Baseline characteristics of 54 patients (74 198# tNGS test results).\u003c/p\u003e","description":"","filename":"Table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6951707/v1/d5c19237a0f82be955c27ba5.xlsx"},{"id":85370710,"identity":"4a8d6752-b829-468e-ae9e-23ee248f5e21","added_by":"auto","created_at":"2025-06-25 07:27:32","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":9909,"visible":true,"origin":"","legend":"\u003cp\u003eTable 2 Prices for different items.\u003c/p\u003e","description":"","filename":"Table2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6951707/v1/3369a16ac6b47b154e34c63b.xlsx"},{"id":85372212,"identity":"3fc8f0a4-bd8c-4d0f-9aa7-a6c37489c40d","added_by":"auto","created_at":"2025-06-25 07:43:33","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":12571,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTablelegend.docx","url":"https://assets-eu.researchsquare.com/files/rs-6951707/v1/0e9470890c8f68abfc3da188.docx"},{"id":85370719,"identity":"a30f6131-a9b9-4211-b1be-d8a5213282ed","added_by":"auto","created_at":"2025-06-25 07:27:33","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":14658,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6951707/v1/9beafd466f6f6573661a015d.xlsx"},{"id":85370720,"identity":"ff0dbe7a-4563-4508-9e7e-aa43f44d2c1d","added_by":"auto","created_at":"2025-06-25 07:27:33","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":92972,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6951707/v1/2b43d563896fbe1f90d6dbf5.xlsx"},{"id":85372214,"identity":"ea9191bb-64a5-4246-920c-d2ee508a3199","added_by":"auto","created_at":"2025-06-25 07:43:33","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":18800,"visible":true,"origin":"","legend":"","description":"","filename":"rawdata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6951707/v1/71b950bc3fa723a2a0578598.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhanced Pathogen Detection in Hematology Patients with Respiratory Infections Using Targeted Next-Generation Sequencing (tNGS) ","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRespiratory infections (including upper and lower respiratory tract infections) represent a frequent and severe complication in patients with hematological diseases, particularly those with compromised immunity(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Early diagnosis and prompt treatment are essential for improving patient outcomes, as delayed intervention significantly elevates mortality risks(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Traditional microbiological methods (TMT), such as general bacterial and blood cultures, fungal cultures or smear tests, respiratory hexapathogen screenings, and SARS-CoV-2 nucleic acid testing, face various limitations when applied in hematology departments: \u003cb\u003eLow Sensitivity and Diagnostic Efficacy\u003c/b\u003e: Traditional methods only detect pathogens in 30%-40% of patients with respiratory infections(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). \u003cb\u003eInability to Perform Bronchoalveolar Lavage (BAL)\u003c/b\u003e: Due to low platelet counts, many hematology patients are not candidates for BAL, which results in specimen collection limitations to pharyngeal swabs, sputum, or blood cultures, yielding even lower positivity rates(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). \u003cb\u003eProlonged Time to Results\u003c/b\u003e: Bacterial and fungal cultures typically require 3\u0026ndash;5 days for results(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). \u003cb\u003eAdditional Diagnostic Burdens\u003c/b\u003e: Viral infections, including COVID-19, require additional tests, adding to the economic burden. \u003cb\u003eNon-typical Pathogens Detection\u003c/b\u003e: Pathogens such as Mycoplasma, Chlamydia, and Coxiella burnetii are often undetected by routine methods, leaving up to 62% of infections unidentified (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA more effective diagnostic approach is urgently needed to overcome these limitations, offering rapid results, improved sensitivity and specificity, cost-effectiveness, and broader coverage of pathogens. Metagenomic next-generation sequencing (mNGS) has significantly improved pathogen detection rates for respiratory infections(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). However, its high cost, susceptibility to host nucleic acid interference, and the need for separate DNA and RNA detection have limited its widespread application. In comparison, targeted next-generation sequencing (tNGS), when paired with polymerase chain reaction (PCR) and high-throughput sequencing, allows for the concurrent detection of numerous common pathogens(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Though tNGS detects fewer pathogens compared to mNGS, it offers clear advantages in cost-effectiveness and diagnostic efficiency, with studies demonstrating it is a more affordable alternative to mNGS (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Moreover, tNGS has shown comparable diagnostic performance to mNGS(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSputum-based tNGS provides a rapid, efficient, and cost-effective alternative to traditional microbiological tests for pathogen detection(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). This method has proven particularly valuable for diagnosing complex infectious diseases(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). However, most studies have been limited to small case series and have not included hematology patients (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this context, tNGS shows considerable potential for overcoming diagnostic challenges in hematology patients. Sputum-based tNGS has exhibited a significantly higher positive detection rate (96.7%) compared to conventional methods (36.7%-38%)(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), and is capable of identifying common pathogen resistance genes (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Therefore, tNGS shows great potential in addressing the clinical diagnostic and treatment needs of hematology patients with respiratory infections, providing a valuable alternative to the limitations of conventional microbiological tests(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, there remains a lack of retrospective studies specifically assessing the diagnostic efficacy of tNGS in hematology patients with respiratory tract infections. In a previous study conducted by our team, a small sample prospective study compared the diagnostic capabilities of tNGS and traditional microbiological testing (TMT) in patients with myelosuppression(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). To further address this gap, we conducted a retrospective study with a larger sample size and expanded the research cohort to include all hematology patients. This study aimed to evaluate the early diagnostic performance of tNGS relative to conventional microbiological tests in a broad range of hematological diseases and respiratory infections. Additionally, we sought to explore the distribution of bacterial, viral, fungal, and atypical pathogens in this cohort and assess the impact of tNGS on clinical decision-making, especially regarding pathogen resistance information.\u003c/p\u003e \u003cp\u003eThe range of pathogens detected is crucial for diagnostic performance. In this study, we used an updated pathogen panel comprising 198 common respiratory pathogens, significantly broader than those in previous studies (typically covering 98\u0026ndash;158 pathogens). This panel detects a wide range of pathogens, including bacteria (such as Mycobacterium tuberculosis and non-tuberculous mycobacteria), fungi, viruses, Mycoplasma, Chlamydia, Coxiella burnetii, and resistance genes associated with common pathogens, encompassing more than 98% of known microorganisms that cause respiratory infections. This comprehensive panel is designated as 198#tNGS (see Supplementary file, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e; for academic reference only; commercial use is prohibited).\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study Subjects and Methods\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e2.1 Eligibility Criteria:1.\u003c/strong\u003e Admission to the hematology department at Puyang People\u0026apos;s Hospital from January 1, 2024, to May 31, 2024. 2. Age\u0026thinsp;\u0026ge;\u0026thinsp;15 years. 3. Informed consent obtained for participation in the study. 4.Diagnosis of respiratory infections in conjunction with hematological diseases. 5.Patients who underwent both 198#tNGS and conventional microbiological testing (CMT) were included. Data collected after the onset of respiratory tract infection included general bacterial culture, blood culture, fungal culture, fungal smear examination, respiratory hexapathogen testing (for viruses), SARS-CoV-2 nucleic acid testing, and the results from 198#tNGS. All enrolled patients complied with sputum collection requirements, which involved expectorating deep respiratory sputum in the morning.\u003cbr\u003e\u003cstrong\u003e2.2 Exclusion Criteria\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003ePatients who declined sample collection for 198#tNGS, did not adhere to sputum specimen collection requirements, or had incomplete clinical data were excluded from the study. Participation was voluntary, and safety-related information was given priority throughout the study. Recruitment was suspended if any safety concerns were identified. The study received approval from the Ethics Committee of Fuyang People\u0026apos;s Hospital (approval number: Medical Ethics Review [2024]96) and adhered to the principles outlined in the Declaration of Helsinki. Patient data were anonymized for the purpose of this study, with informed consent obtained from all participants.\u003c/p\u003e\n\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.3 Evaluation Indicators\u003c/h2\u003e\n \u003cp\u003eThe primary objective was to evaluate the diagnostic performance of 198#tNGS in detecting respiratory pathogens in patients with respiratory tract infections. Secondary objectives included comparing 198#tNGS to current clinical standard microbiological tests (TMT), assessing the positive and negative concordance rates of 198#tNGS, and determining its clinical relevance in guiding therapeutic decisions.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePositive Concordance\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eDefined as the identification of at least one pathogen by 198#tNGS that was also detected by TMT. The positive concordance rate was determined by dividing the number of positive concordances identified by 198#tNGS by the total number of positives detected by TMT.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNegative Concordance\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eDefined as results where both methods yielded negative outcomes. The negative concordance rate was calculated similarly.\u003c/p\u003eDiagnoses were established by three independent hematology experts based on both test results and clinical data. These experts also evaluated the clinical relevance of the detected pathogens and assessed the need for antibiotic adjustments based on the findings from 198#tNGS. The effectiveness of any therapeutic modifications was also reviewed.\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e\n \u003cp\u003eFor continuous variables, the number of observations, mean, median, standard deviation, minimum, and maximum values were reported. Categorical variables were presented as frequencies and percentages for each group. Data processing and analysis were carried out using SPSS 20.0 and R software. To compare continuous variables between groups, a t-test was used, while categorical variables were analyzed with the chi-square test. A p-value of less than 0.05 was considered indicative of statistical significance.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Patient Characteristics\u003c/h2\u003e \u003cp\u003eA total of 74 198#tNGS test results were analyzed, involving 54 patients with hematological diseases. Half of the patients had two or more hospitalizations and infections. The median age of the enrolled patients was 58 years, with 57.4% (31/54) being male. Ultimately, 25.9% (14/54) of the patients died from infection. Agranulocytosis (neutrophils\u0026thinsp;\u0026lt;\u0026thinsp;0.5 \u0026times; 10⁹/L) was present in 36.4% (27/74) of patients, while platelet counts of \u0026lt;\u0026thinsp;20 \u0026times; 10⁹/L and \u0026lt;\u0026thinsp;50 \u0026times; 10⁹/L were seen in 28.3% (21/74) and 37.8% (28/74) of patients, respectively. Additionally, a significant percentage of patients\u0026mdash;92% (50/54)\u0026mdash;were either unsuitable or unwilling to undergo bronchoalveolar lavage due to specific reasons outlined in the Discussion section (Table\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Pathogen Detection Using 198#tNGS and TMT\u003c/h2\u003e \u003cp\u003eOut of 74 198#tNGS test results, 44 potential pathogens were detected by 198#tNGS, while TMT identified 16 pathogens (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Supplementary file, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The overall microbiological detection rates for 198#tNGS and TMT differed significantly, with 198#tNGS detecting pathogens in 98.6% (73/74) of samples and TMT identifying pathogens in 47.2% (31/74) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The sensitivity and positive predictive value of tNGS were high, at 98.6% and 100%, respectively. Of the test results, 41.89% (31/74) were positive by both 198#tNGS and TMT, 1.35% (1/74) were negative by both methods, and 56.76% (42/74) tested positive only by 198#tNGS. No specimens were positive only by TMT. Among the 31 double-positive results, only 1 (1/31) showed complete agreement between 198#tNGS and TMT, while 24 (23/31) showed partial agreement and 7 (7/31) showed complete disagreement (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Additionally, the positive consistency rate between tNGS and blood culture was relatively high at 77.4%, with a low negative consistency rate of 2%. The mean assay turnaround time (TAT) for tNGS was approximately 24 hours from sample receipt to electronic report delivery, excluding holidays.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Pathogen Detection Using 198#tNGS and TMT\u003c/h2\u003e \u003cp\u003eOut of 74 specimens analyzed, 198#tNGS detected a total of 44 potential pathogens, while TMT identified only 16 pathogens (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The overall microbiological detection rates between the two methods were significantly different, with 198#tNGS detecting pathogens in 98.6% (73/74) of the samples, compared to TMT\u0026rsquo;s 47.2% (31/74) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The sensitivity and positive predictive value of 198#tNGS were high, at 98.6% and 100%, respectively. Of the test results, 41.9% (31/74) were positive by both 198#tNGS and TMT, 1.4% (1/74) were negative by both methods, and 56.8% (42/74) tested positive only by 198#tNGS. No specimens were positive exclusively by TMT. Among the 31 samples with dual-positive results, complete concordance between the two methods was observed in only 1 (1/31) sample, while 24 (23/31) demonstrated partial agreement, and 7 (7/31) showed complete disagreement (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Additionally, the positive concordance rate between 198#tNGS and blood culture was 77.4%, with a low negative concordance rate of 2%.\u003c/p\u003e \u003cp\u003ehe average assay turnaround time (TAT) for 198#tNGS was approximately 24 hours from the receipt of the sample to the delivery of the electronic report, excluding holidays. The detected pathogens included a variety of bacterial, viral, fungal, and atypical microorganisms. Specifically, 198#tNGS identified bacterial pathogens in 59 samples (79.7%). The most commonly identified bacterium was Haemophilus influenzae, which accounted for 25.4% (15/59) of all bacterial findings, followed by Streptococcus anginosus, Klebsiella pneumoniae, Stenotrophomonas maltophilia, and Pseudomonas aeruginosa. In contrast, TMT detected bacterial infections in only 13 specimens (11/74, 17.6%).\u003c/p\u003e \u003cp\u003eFor viral infections, 198#tNGS identified viruses in 65 samples (87.8%), with Epstein-Barr virus being the most prevalent (53.8%), followed by 2019-nCoV, Herpes simplex virus type 1 (HSV-1), rhinovirus type A (HRV), and human respiratory syncytial virus type B (RSV B). TMT, on the other hand, identified viruses in just 19 specimens (25.7%).\u003c/p\u003e \u003cp\u003eFungal infections were found in 43 specimens (58.1%) by 198#tNGS, with Aspergillus flavus being the most common species, representing 51% of all fungal detections.\u003c/p\u003e \u003cp\u003eOther fungal species included Aspergillus niger, Aspergillus fumigatus, Candida glabrata, Pneumocystis jirovecii, and Candida albicans. TMT identified fungi in only 10 specimens (13.5%).\u003c/p\u003e \u003cp\u003eAdditionally, Mycoplasma pneumoniae, an atypical pathogen, was detected in one specimen by 198#tNGS, but was not identified by TMT in any sample.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Clinical Implications of 198#tNGS\u003c/h2\u003e \u003cp\u003eThe results from 198#tNGS provided valuable diagnostic insights that guided clinical decision-making. Pathogen identification was combined with clinical factors, including patient medical history, symptoms, and imaging results. The findings from 198#tNGS influenced antibiotic therapy decisions for 30 patients, including those with infections caused by SARS-CoV-2, Pneumocystis jirovecii, Mycoplasma pneumoniae, Rhizobium, and Stenotrophomonas maltophilia. Notably, the detection of the methicillin-resistant Staphylococcus gene mecA:1413 led to changes in antibiotic treatment for two patients.\u003c/p\u003e \u003cp\u003eIn contrast to conventional bacterial cultures, which typically require 3\u0026ndash;5 days for results, 198#tNGS provided results in just 24 hours, offering a significant reduction in turnaround time. This speed highlights the superior diagnostic efficiency of tNGS compared to traditional techniques such as blood and fungal cultures. Rapid and precise pathogen identification is critical for informing clinical decisions and ensuring timely administration of appropriate antibiotic treatments.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eConsequently, respiratory tract infections in hematology patients exhibit distinct characteristics compared to those in respiratory medicine(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). These patients frequently present with unique infection patterns, including a higher prevalence of fungal and atypical pathogen infections(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Furthermore, the risk of these infections advancing to severe disease is elevated, along with an increased incidence of drug-resistant strains(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study revealed that in the hematology department, the proportions of fungal, viral, and atypical pathogen infections were 58.1%, 87.8%, and 0.02%, respectively. In contrast, for patients with respiratory infections in the Respiratory department, similarly based on sputum tNGS testing, the proportions were 26.8%, 84.2%, and 0%, respectively. Specifically, we found that the most common bacterial pathogens in the hematology department were Haemophilus influenzae, Streptococcus anginosus, Klebsiella pneumoniae, and Stenotrophomonas maltophilia. The predominant fungal species were Aspergillus flavus, Aspergillus niger, Aspergillus fumigatus, and Candida glabrata. Among viral infections, Epstein-Barr virus, 2019-nCoV, Herpes simplex virus type 1, rhinovirus A, and atypical pathogens such as Mycoplasma pneumoniae were most frequently detected. In comparison, the most common bacterial pathogens in the Respiratory department were Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Stenotrophomonas maltophilia, while Candida albicans, Candida yersinia, and Candida fumigatus were the most common fungal pathogens. Viral pathogens in this group included EBV, CMV, HSV-1, HHV-7, and HHV-6, with no atypical pathogens detected(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese findings highlight the significant differences in pathogen profiles between hematology and respiratory departments(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Hematology patients exhibited a notably higher frequency of fungal infections, particularly with Aspergillus species, which predominated over Candida species seen in the Respiratory department(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Additionally, atypical pathogens were more common in the hematology cohort compared to the respiratory group.(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eGiven the rapid disease progression and severity often seen in hematology patients, many of whom are unable to undergo bronchoscopy, there is an urgent clinical need for non-invasive diagnostic methods like sputum-based tNGS(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). This study retrospectively assess the diagnostic performance of tNGS in hematology patients. Prior to this, tNGS had not been systematically explored in the context of hematologic diseases. Our findings underscore its efficacy in diagnosing respiratory infections in this patient group and provide strong evidence for its utility in clinical practice.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDiagnostic Performance of 198#tNGS\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study demonstrated that 198#tNGS achieved a sensitivity of 98.6% and a positive predictive value of 100% in diagnosing respiratory tract infections, surpassing the performance of TMT. The average turnaround time (TAT) for 198#tNGS was 24 hours, notably faster than the 3–5 days typically required for conventional bacterial, blood, and fungal cultures(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Although 198#tNGS might seem more costly than traditional bacterial culture, a comprehensive cost analysis shows that when factoring in the combined costs of bacterial, blood, and fungal cultures, fungal smear examinations, respiratory hexapathogen detection, and COVID-19 nucleic acid tests, the overall cost of 198#tNGS is lower. Moreover, TMT often necessitates multiple testing rounds and specimen collections to enhance its detection rate, further highlighting the cost-effectiveness of sputum-based 198#tNGS.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAdvantages Over Bronchoscopy and mNGS\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAlthough bronchoalveolar lavage fluid obtained through bronchoscopy remains the gold standard for pathogen identification in respiratory infections, it is not feasible for many hematology patients. Several factors contribute to this limitation: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) patients with platelet counts \u0026lt; 20 × 10⁹/L are at high risk for spontaneous bleeding, making bronchoscopy contraindicated, and even with platelet counts \u0026lt; 50 × 10⁹/L, the bleeding risk remains significant. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Hematology patients often experience rapid disease progression, and those with severe hypoxia (requiring oxygen supplementation) are unsuitable for bronchoscopy. In cases of neutropenia associated with fever or impending respiratory failure, bronchoscopy becomes impractical. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Many patients are apprehensive about undergoing bronchoscopy due to concerns of hemoptysis, especially when platelet counts are low. Given these constraints, sputum testing provides a viable alternative.\u003c/p\u003e \u003cp\u003e Our study demonstrates that 198#tNGS, using sputum samples, offers superior diagnostic efficiency, with results comparable to those obtained from bronchoalveolar lavage fluid. Other studies have also shown that tNGS for respiratory pathogens performs similarly to metagenomic next-generation sequencing (mNGS) in samples obtained from bronchoscopy. Consequently, for hematology patients who cannot undergo bronchoscopy, sputum-based 198#tNGS represents the optimal diagnostic approach, effectively addressing the clinical challenges these patients face.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDetection of Non-Bacterial Pathogens\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAn important advantage of 198#tNGS is its ability to detect non-bacterial pathogens, which are often overlooked by conventional microbiological testing methods (TMT). Hematology patients, who are frequently immunocompromised due to treatments such as chemotherapy and immunosuppressive drugs, are particularly susceptible to infections caused by fungi, viruses, mycoplasma, and chlamydia. These pathogens are often not detected by traditional methods but are significant contributors to the morbidity and mortality in this population. By providing a more comprehensive diagnostic approach, 198#tNGS allows for the identification of a broader spectrum of pathogens, including those that are typically missed by other diagnostic techniques.\u003c/p\u003e \u003cp\u003e \u003cb\u003eImpact on Clinical Decision-Making\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMoreover, 198#tNGS proved instrumental in guiding clinical decision-making. In this study, treatment modifications were made for 55.6% (30/54) of patients based on the results of 198#tNGS. For patients whose pathogens were not identified by TMT, 198#tNGS provided essential clarification of the underlying infection. Additionally, 198#tNGS enabled the identification of pathogen resistance genes, which directly informed antibiotic choices, thus improving patient outcomes.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStudy Limitations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWhile this study provides valuable insights, it does have limitations. The sample size was relatively small, and larger, multi-center studies are needed to further validate these findings. Additionally, sputum liquefaction tests predominantly reflect pathogens from the upper respiratory tract, which may limit their ability to detect pathogens originating in the lower respiratory tract. To mitigate this limitation, patients were instructed to collect sputum specimens from the deep respiratory tract in the morning, optimizing the detection of lower respiratory tract pathogens. Another challenge with sputum testing is the potential detection of colonizing or non-pathogenic bacteria. To address this, three independent hematology experts evaluated the clinical relevance of the pathogens identified through both TMT and 198#tNGS, in conjunction with clinical symptoms, imaging results, and patient history.\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study demonstrates the superiority of sputum-based 198#tNGS in diagnosing the etiology of respiratory tract infections in hematology patients who are unsuitable for bronchoalveolar lavage. Compared to TMT, 198#tNGS exhibited significantly higher detection rates, enabling the early identification of pathogenic microorganisms, particularly those with high pathogenicity, minimal colonization, or drug-resistant genes(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Furthermore, 198#tNGS is faster, more convenient, efficient, and cost-effective. Its broad pathogen detection, including resistance genes, offers enhanced specificity and clinical relevance. This technology has the potential to transform the diagnostic and treatment approach for respiratory infections in hematology patients, significantly improving patient outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the supplementary material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of Fuyang People\u0026apos;s Hospital. All methods were carried out in accordance with relevant guidelines and regulations. Informed consent was obtained from all subjects and/or their legal guardian(s).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYue Yao proposed, designed, and analyzed the research. Yue Yao and Yuhu Feng also curated data and verified the statistical analysis. Authors advised and revised the manuscript. All authors approved publication.\u003c/p\u003e\n\u003cp\u003eConflict of Interest\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest, financial or otherwise.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Fuyang Municipal Key Research and Development Program for Clinical Medicine Research and Translational Applications (FK20245516), the Fuyang Municipal Health and Wellness Research Project (FY2024-016), and the Fuyang City 14th Five-Year Plan Key Specialty Development Program (Department of Hematology).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN/A\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePeriselneris J, Brown JS. A clinical approach to respiratory disease in patients with hematological malignancy, with a focus on respiratory infection. Med Mycol. 2019;57(Supplement_3):S318-s27.\u003c/li\u003e\n\u003cli\u003eChemaly RF, Shah DP, Boeckh MJ. Management of respiratory viral infections in hematopoietic cell transplant recipients and patients with hematologic malignancies. Clin Infect Dis. 2014;59 Suppl 5(Suppl 5):S344-51.\u003c/li\u003e\n\u003cli\u003eYe J, Huang K, Xu Y, Chen N, Tu Y, Huang J, et al. Clinical application of nanopore-targeted sequencing technology in bronchoalveolar lavage fluid from patients with pulmonary infections. Microbiol Spectr. 2024;12(6):e0002624.\u003c/li\u003e\n\u003cli\u003eLi C, Wu J, Feng Y. Prospective study on diagnostic efficacy of targeted NGS vs. traditional testing for respiratory infections in myelosuppressed hematology patients. Front Med (Lausanne). 2025;12:1488652.\u003c/li\u003e\n\u003cli\u003eLi D, Li Q, Huang Z, Wu W, Fan X, Liu J, et al. Comparison of the Impact of tNGS with mNGS on Antimicrobial Management in Patients with LRTIs: A Multicenter Retrospective Cohort Study. Infect Drug Resist. 2025;18:93-105.\u003c/li\u003e\n\u003cli\u003eWaites KB, Balish MF, Atkinson TP. New insights into the pathogenesis and detection of Mycoplasma pneumoniae infections. Future Microbiol. 2008;3(6):635-48.\u003c/li\u003e\n\u003cli\u003eXie G, Zhao B, Wang X, Bao L, Xu Y, Ren X, et al. Exploring the Clinical Utility of Metagenomic Next-Generation Sequencing in the Diagnosis of Pulmonary Infection. Infect Dis Ther. 2021;10(3):1419-35.\u003c/li\u003e\n\u003cli\u003eDeng Z, Li C, Wang Y, Wu F, Liang C, Deng W, et al. Targeted next-generation sequencing for pulmonary infection diagnosis in patients unsuitable for bronchoalveolar lavage. Front Med (Lausanne). 2023;10:1321515.\u003c/li\u003e\n\u003cli\u003eZheng YR, Chen XH, Chen Q, Cao H. Comparison of targeted next-generation sequencing and metagenomic next-generation sequencing in the identification of pathogens in pneumonia after congenital heart surgery: a comparative diagnostic accuracy study. Ital J Pediatr. 2024;50(1):174.\u003c/li\u003e\n\u003cli\u003eWei M, Mao S, Li S, Gu K, Gu D, Bai S, et al. Comparing the diagnostic value of targeted with metagenomic next-generation sequencing in immunocompromised patients with lower respiratory tract infection. Ann Clin Microbiol Antimicrob. 2024;23(1):88.\u003c/li\u003e\n\u003cli\u003eMa H, Wang H, Han X, Fei J. Efficacy of targeted next generation sequencing for pathogen detection in lower respiratory tract infections. Am J Transl Res. 2024;16(8):3637-45.\u003c/li\u003e\n\u003cli\u003eLi S, Tong J, Li H, Mao C, Shen W, Lei Y, et al. L. pneumophila Infection Diagnosed by tNGS in a Lady with Lymphadenopathy. Infect Drug Resist. 2023;16:4435-42.\u003c/li\u003e\n\u003cli\u003eXu JH, Cui YB, Wang LJ, Nan HJ, Yang PY, Bai YL, et al. Pathogen detection by targeted next-generation sequencing test in adult hematological malignancies patients with suspected infections. Front Med (Lausanne). 2024;11:1443596.\u003c/li\u003e\n\u003cli\u003eZhang X, Wang F, Yu J, Jiang Z. Clinical application value of metagenomic second-generation sequencing technology in hematologic diseases with and without transplantation. Front Cell Infect Microbiol. 2023;13:1135460.\u003c/li\u003e\n\u003cli\u003eCordonnier C, Alanio A, Cesaro S, Maschmeyer G, Einsele H, Donnelly JP, et al. Pneumocystis jirovecii pneumonia: still a concern in patients with haematological malignancies and stem cell transplant recipients-authors\u0026apos; response. J Antimicrob Chemother. 2017;72(4):1266-8.\u003c/li\u003e\n\u003cli\u003eMilano F, Campbell AP, Guthrie KA, Kuypers J, Englund JA, Corey L, et al. Human rhinovirus and coronavirus detection among allogeneic hematopoietic stem cell transplantation recipients. Blood. 2010;115(10):2088-94.\u003c/li\u003e\n\u003cli\u003eBlennow O, Ljungman P. Infections in Hematology Patients. In: Lazarus HM, Schmaier AH, editors. Concise Guide to Hematology. Cham: Springer International Publishing; 2019. p. 503-18.\u003c/li\u003e\n\u003cli\u003eGhajari A, Lotfali E, Azari M, Fateh R, Kalantary S. Fungal Airborne Contamination as a Serious Threat for Respiratory Infection in the Hematology Ward. Tanaffos. 2015;14(4):257-61.\u003c/li\u003e\n\u003cli\u003eCheng Q, Tang Y, Liu J, Liu F, Li X. The Differential Diagnostic Value of Chest Computed Tomography for the Identification of Pathogens Causing Pulmonary Infections in Patients with Hematological Malignancies. Infect Drug Resist. 2024;17:4557-66.\u003c/li\u003e\n\u003cli\u003eQin L, Liang M, Song J, Chen P, Zhang S, Zhou Y, et al. Utilizing Targeted Next-Generation Sequencing for Rapid, Accurate, and Cost-Effective Pathogen Detection in Lower Respiratory Tract Infections. Infect Drug Resist. 2025;18:329-40.\u003c/li\u003e\n\u003cli\u003eLi Y, Jiang Y, Liu H, Fu Y, Lu J, Li H, et al. Targeted next-generation sequencing for antimicrobial resistance detection in ventilator-associated pneumonia. Front Cell Infect Microbiol. 2025;15:1526087.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"respiratory infections, immunocompromised hematology patients, Infectious Disease Diagnostics, Next-Generation Sequencing (NGS) Technologies, Clinical Pathogen Detection","lastPublishedDoi":"10.21203/rs.3.rs-6951707/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6951707/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eRespiratory infections, involving both upper and lower respiratory tracts, represent a common and potentially life-threatening complication in patients with hematologic disorders. These patients exhibit compromised immunity and require highly sensitive diagnostic methods for both timing and capability. Traditional microbiological techniques often demonstrate inadequate diagnostic performance and delayed results, necessitating the exploration of alternative approaches.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eSputum samples were collected from patients with hematologic diseases and concurrent respiratory infections (as the majority could not undergo bronchoalveolar lavage). The samples underwent Targeted Next-Generation Sequencing (tNGS), and the results were compared with those from traditional microbiological testing (TMT). The study aimed to evaluate the diagnostic performance, economic benefits, and pathogen prevalence\u0026mdash;including fungi, viruses, atypical pathogens, and bacteria\u0026mdash;along with the clinical impact of tNGS-guided treatment modifications.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003etNGS demonstrated superior pathogen detection (98.6%) compared to TMT (47.2%), identifying bacteria in 79.7%, viruses in 87.8%, fungi in 58.1%, and atypical pathogens in 0.02% of cases. Common bacterial pathogens included Haemophilus influenzae, Streptococcus pyogenes, Klebsiella pneumoniae, and Stenotrophomonas maltophilia. Predominant fungal pathogens included Aspergillus flavus, Aspergillus niger, Aspergillus fumigatus, and Candida glabrata, while viral pathogens included Epstein-Barr virus, novel coronavirus, Herpes simplex virus type 1, and rhinovirus type A. The mean turnaround time for tNGS was 24 hours, significantly faster than TMT (3\u0026ndash;5 days), and tNGS proved more cost-effective. Treatment modifications based on tNGS results were implemented in 55.6% of cases.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003etNGS offers notable improvements in pathogen detection, especially for fungi and atypical pathogens, enabling timely, targeted antimicrobial therapy for immunocompromised hematologic malignancy patients. Additionally, tNGS demonstrates excellent cost-effectiveness, rapid result turnaround, and extensive pathogen coverage, emphasizing its potential to enhance clinical outcomes. These findings advocate for the integration of tNGS into clinical practice, particularly for hematologic patients requiring more precise diagnostic capabilities.\u003c/p\u003e","manuscriptTitle":"Enhanced Pathogen Detection in Hematology Patients with Respiratory Infections Using Targeted Next-Generation Sequencing (tNGS) ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-25 07:27:28","doi":"10.21203/rs.3.rs-6951707/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ef2d420e-c10a-4f46-93c6-e49c2549ae28","owner":[],"postedDate":"June 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50471929,"name":"Health sciences/Oncology/Cancer/Haematological cancer"},{"id":50471930,"name":"Biological sciences/Cancer/Haematological cancer"}],"tags":[],"updatedAt":"2026-02-06T10:27:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-25 07:27:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6951707","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6951707","identity":"rs-6951707","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}