Dataset Information

Metagenomic Next-Generation Sequencing for Pathogens in Bronchoalveolar Lavage Fluid Improves the Survival of Patients with Pulmonary Complications After Allogeneic Hematopoietic Stem Cell Transplantation.

ABSTRACT:

Introduction

Unbiased metagenomic next-generation sequencing (mNGS) has been used for infection diagnosis. In this study, we explored the clinical diagnosis value of mNGS for pulmonary complications after allogeneic hematopoietic stem cell transplantation (allo-HSCT).

Methods

From August 2019 to June 2021, a prospective study was performed to comparatively analyze the pathogenic results of mNGS and conventional tests for bronchoalveolar lavage fluid (BALF) from 134 cases involving 101 patients with pulmonary complications after allo-HSCT.

Results

More pathogens were identified by mNGS than with conventional tests (226 vs 120). For bacteria, the diagnostic sensitivity (P = 0.144) and specificity (P = 0.687) were similar between the two methods. For fungus except Pneumocystis jirovecii (PJ), conventional tests had a significantly higher sensitivity (P = 0.013) with a similarly high specificity (P = 0.109). The sensitivities for bacteria and fungi could be increased with the combination of the two methods. As for PJ, both the sensitivity (100%) and specificity (99.12%) of mNGS were very high. For viruses, the sensitivity of mNGS was significantly higher (P = 0.021) and the negative predictive value (NPV) was 95.74% (84.27-99.26%). Pulmonary infection complications accounted for 90.30% and bacterium was the most common pathogen whether in single infection (63.43%) or mixed infection (81.08%). The 6-month overall survival (OS) of 88.89% in the early group (mNGS ≤ 7 days) was significantly higher than that of 65.52% (HR 0.287, 95% CI 0.101-0.819, P = 0.006) in the late group (mNGS > 7 days).

Conclusions

mNGS for BALF could facilitate accurate and fast diagnosis for pulmonary complications. Early mNGS could improve the prognosis of patients with pulmonary complications after allo-HSCT.

Trial registration

ClinicalTrials.gov identifier, NCT04051372.

SUBMITTER: Shen Z

PROVIDER: S-EPMC10505113 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Publications

Metagenomic Next-Generation Sequencing for Pathogens in Bronchoalveolar Lavage Fluid Improves the Survival of Patients with Pulmonary Complications After Allogeneic Hematopoietic Stem Cell Transplantation.

Shen Zaihong Z Wang Ying Y Bao Aihua A Yang Jun J Sun Xi X Cai Yu Y Wan Liping L Huang Chongmei C Xu Xiaowei X Niu Jiahua J Xia Xinxin X Shen Chang C Wei Yu Y Qiu Huiying H Zhou Kun K Zhang Min M Tong Yin Y Song Xianmin X

Infectious diseases and therapy 20230804 8

<h4>Introduction</h4>Unbiased metagenomic next-generation sequencing (mNGS) has been used for infection diagnosis. In this study, we explored the clinical diagnosis value of mNGS for pulmonary complications after allogeneic hematopoietic stem cell transplantation (allo-HSCT).<h4>Methods</h4>From August 2019 to June 2021, a prospective study was performed to comparatively analyze the pathogenic results of mNGS and conventional tests for bronchoalveolar lavage fluid (BALF) from 134 cases involving ...[more]

PMID: 37541984

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Project description:ObjectivesThe aim of this study was to evaluate the diagnostic value of bronchoalveolar lavage fluid (BALF) metagenomic next-generation sequencing (mNGS) versus conventional microbiological tests (CMTs) for pediatric pneumonia.MethodsThis retrospective observational study enrolled 103 children who were diagnosed with pneumonia and hospitalized at Hubei Maternity and Child Health Care Hospital between 15 October 2020 and 15 February 2022. The pneumonia diagnosis was based on clinical manifestations, lung imaging, and microbiological tests. Pathogens in the lower respiratory tract were detected using CMTs and BALF mNGS (of DNA and RNA). The diagnostic performance of BALF mNGS was compared with that of CMTs.ResultsIn 96 patients, pathogens were identified by microbiological tests. The overall pathogen detection rate of mNGS was significantly higher than that of CMTs (91.3% vs. 59.2%, p = 0.000). The diagnostic performance of mNGS varied for different pathogens; however, its sensitivity and accuracy for diagnosing bacterial and viral infections were both higher than those of CMTs (p = 0.000). For the diagnosis of fungi, the sensitivity of mNGS (87.5%) was higher than that of CMTs (25%); however, its specificity and accuracy were lower than those of CMTs (p < 0.01). For the diagnosis of Mycoplasma pneumoniae, the specificity (98.8%) and accuracy (88.3%) of mNGS were high; however, its sensitivity (42.1%) was significantly lower than that of CMTs (100%) (p = 0.001). In 96 patients with definite pathogens, 52 cases (50.5%) were infected with a single pathogen, while 44 cases (42.7%) had polymicrobial infections. Virus-bacteria and virus-virus co-infections were the most common. Staphylococcus aureus, Haemophilus influenzae, rhinovirus, cytomegalovirus, parainfluenza virus, and fungi were more likely to be associated with polymicrobial infections.ConclusionsBALF mNGS improved the detection rate of pediatric pneumonia, especially in mixed infections. The diagnostic performance of BALF mNGS varies according to pathogen type. mNGS can be used to supplement CMTs. A combination of mNGS and CMTs may be the best diagnostic strategy.

Project description:Objective: To evaluate the diagnostic performance of metagenomic next-generation sequencing (mNGS) using bronchoalveolar lavage fluid (BALF) in patients with ventilator-associated pneumonia (VAP). Methods: BALF samples of 72 patients with VAP were collected from August 2018 to May 2020. The diagnostic performance of conventional testing (CT) and mNGS methods were compared based on bacterial and fungal examinations. The diagnostic value of mNGS for viral and mixed infections was also analyzed. Results: The percentage of mNGS positive samples was significantly higher than that estimated by the CT method [odds ratio (OR), 4.33; 95% confidence interval (CI), 1.78-10.53; p < 0.001]. The sensitivity and specificity of mNGS for bacterial detection were 97.1% (95% CI, 93.2-101.0%) and 42.1% (95 CI, 30.7-53.5%), respectively, whereas the positive predictive value (PPV) and the negative predictive value (NPV) were 60.0% (95% CI, 48.7-71.3%) and 94.1% (95% CI, 88.7-99.6%), respectively. A total of 38 samples were negative for bacterial detection as determined by the CT method, while 22 samples were positive as shown by the mNGS method. Conflicting results were obtained for three samples between the two methods of bacterial detection. However, no significant differences were noted between the mNGS and CT methods (OR, 1.42; 95% CI, 0.68-2.97; p = 0.46) with regard to fungal infections. The sensitivity and specificity of mNGS were 71.9% (95% CI, 61.5-82.3%) and 77.5% (95% CI, 67.9-87.1%), respectively. mNGS exhibited a PPV of 71.9% (95% CI, 61.5-82.3%) and an NPV of 77.5% (95% CI, 67.9-87.1%). A total of 9 out of 40 samples were found positive for fungi according to mNGS, whereas the CT method failed to present positive results in these samples. The mNGS and CT methods produced conflicting results with regard to fungal detection of the two samples. A total of 30 patients were virus-positive using mNGS. Furthermore, 42 patients (58.3%) were identified as pulmonary mixed infection cases. Conclusions: mNGS detection using BALF improved the sensitivity and specificity of bacterial identification in patients who developed VAP. In addition, mNGS exhibited apparent advantages in detecting viruses and identifying mixed infections.

Dataset Information

Metagenomic Next-Generation Sequencing for Pathogens in Bronchoalveolar Lavage Fluid Improves the Survival of Patients with Pulmonary Complications After Allogeneic Hematopoietic Stem Cell Transplantation.

Introduction

Methods

Results

Conclusions

Trial registration

Publications

Metagenomic Next-Generation Sequencing for Pathogens in Bronchoalveolar Lavage Fluid Improves the Survival of Patients with Pulmonary Complications After Allogeneic Hematopoietic Stem Cell Transplantation.

Similar Datasets

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