Cellular analysis and metagenomic next-generation sequencing of bronchoalveolar lavage fluid in the distinction between pulmonary non-infectious and infectious disease

Pan, Yilin; Zhang, Xue; Sun, Yi; Zhang, Yingying; Bao, Wuping; Yin, Dongning; Zhang, Pengyu; Zhang, Min

doi:10.3389/fcimb.2022.1023978

Cited by 3 publications

(1 citation statement)

References 47 publications

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“…The collection procedure involved several steps as follows: In cases of localized lesions, the segment containing the lesion was chosen. For diffuse lesions, the most severe segment was selected ( Pan et al., 2022 ). The bronchoscope tip was positioned in the target bronchial segment or sub-end opening.…”

Section: Methodsmentioning

confidence: 99%

Deciphering the microbial landscape of lower respiratory tract infections: insights from metagenomics and machine learning

Li,

Xiong,

Wang

et al. 2024

Front. Cell. Infect. Microbiol.

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BackgroundLower respiratory tract infections represent prevalent ailments. Nonetheless, current comprehension of the microbial ecosystems within the lower respiratory tract remains incomplete and necessitates further comprehensive assessment. Leveraging the advancements in metagenomic next-generation sequencing (mNGS) technology alongside the emergence of machine learning, it is now viable to compare the attributes of lower respiratory tract microbial communities among patients across diverse age groups, diseases, and infection types.MethodWe collected bronchoalveolar lavage fluid samples from 138 patients diagnosed with lower respiratory tract infections and conducted mNGS to characterize the lung microbiota. Employing various machine learning algorithms, we investigated the correlation of key bacteria in patients with concurrent bronchiectasis and developed a predictive model for hospitalization duration based on these identified key bacteria.ResultWe observed variations in microbial communities across different age groups, diseases, and infection types. In the elderly group, Pseudomonas aeruginosa exhibited the highest relative abundance, followed by Corynebacterium striatum and Acinetobacter baumannii. Methylobacterium and Prevotella emerged as the dominant genera at the genus level in the younger group, while Mycobacterium tuberculosis and Haemophilus influenzae were prevalent species. Within the bronchiectasis group, dominant bacteria included Pseudomonas aeruginosa, Haemophilus influenzae, and Klebsiella pneumoniae. Significant differences in the presence of Pseudomonas phage JBD93 were noted between the bronchiectasis group and the control group. In the group with concomitant fungal infections, the most abundant genera were Acinetobacter and Pseudomonas, with Acinetobacter baumannii and Pseudomonas aeruginosa as the predominant species. Notable differences were observed in the presence of Human gammaherpesvirus 4, Human betaherpesvirus 5, Candida albicans, Aspergillus oryzae, and Aspergillus fumigatus between the group with concomitant fungal infections and the bacterial group. Machine learning algorithms were utilized to select bacteria and clinical indicators associated with hospitalization duration, confirming the excellent performance of bacteria in predicting hospitalization time.ConclusionOur study provided a comprehensive description of the microbial characteristics among patients with lower respiratory tract infections, offering insights from various perspectives. Additionally, we investigated the advanced predictive capability of microbial community features in determining the hospitalization duration of these patients.

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Section: Methodsmentioning

confidence: 99%

Deciphering the microbial landscape of lower respiratory tract infections: insights from metagenomics and machine learning

Li,

Xiong,

Wang

et al. 2024

Front. Cell. Infect. Microbiol.

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Blood plasma metagenomic next-generation sequencing for identifying pathogens of febrile neutropenia in acute leukemia patients

Qi,

Lin,

Liao

et al. 2023

Sci Rep

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To investigate the value of metagenomic next-generation sequencing (mNGS) in acute leukemia (AL) patients with febrile neutropenia (FN). We retrospectively reviewed 37 AL patients with FN and compared the results of mNGS with blood culture (BC) and the clinical features of the mNGS-positive group and the mNGS-negative group. A total of 14 detected pathogens were the final clinical diagnosis, of which 9 strains were detected only by mNGS and 5 strains were detected by both mNGS and BC. The top pathogens were Klebsiella pneumoniae, Pseudomonas aeruginosa and Stenotrophomonas maltophilia. A total of 67.57% (25/37) were bacterial infections, and 2.7% (1/37) were fungal or viral infections. The diagnostic positivity rate of mNGS (25/37, 67.6%) was significantly higher than that of BC (7/37, 18.9%), and the difference was statistically significant (p < 0.05). Then, we explored the clinical distinction between the mNGS-positive group and the mNGS-negative group, and 3 features were filtered, including lymphocyte count (LY), creatinine levels (Cr), and white blood cell count (WBC). Our study demonstrated that early implementation of mNGS can effectively improve the efficacy of pathogen detection in AL patients with FN. The higher diagnostic positivity rate and the ability to detect additional pathogens compared to BC made mNGS a valuable tool in the management of infectious complications in this patient population. Furthermore, the identified clinical features associated with mNGS results provided additional insights for the clinical indication of infection in AL patients with FN.

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Diagnostic test accuracy of cellular analysis of bronchoalveolar lavage fluid in distinguishing pulmonary infectious and non-infectious diseases in patients with pulmonary shadow

Li,

Wang,

Liu

et al. 2024

Front. Med.

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PurposeThis study aims to assess the diagnostic accuracy of cellular analysis of bronchoalveolar lavage fluid (BALF) in distinguishing between pulmonary infectious and non-infectious diseases in patients with pulmonary shadows. Additionally, it will develop and validate a novel scoring system based on a nomogram for the purpose of differential diagnosis.MethodsA retrospective analysis was conducted involving data from 222 patients with pulmonary shadows, whose etiological factors were determined at our institution. The cohort was randomly allocated into a training set comprising 155 patients and a validation set of 67 patients, (ratio of 7:3), the least absolute shrinkage and selection operator (LASSO) regression model was applied to optimize feature selection for the model. Multivariable logistic regression analysis was applied to construct a predictive model. The receiver operating characteristic curve (ROC) and calibration curve were utilized to assess the prediction accuracy of the model. Decision curve analysis (DCA) and clinical impact curve (CIC) were employed to evaluate the clinical applicability of the model. Moreover, model comparison was set to evaluate the discrimination and clinical usefulness between the nomogram and the risk factors.ResultsAmong the relevant predictors, the percentage of neutrophils in BALF (BALF NP) exhibited the most substantial differentiation, as evidenced by the largest area under the ROC curve (AUC = 0.783, 95% CI: 0.713–0.854). A BALF NP threshold of ≥16% yielded a sensitivity of 72%, specificity of 70%, a positive likelihood ratio of 2.07, and a negative likelihood ratio of 0.38. LASSO and multivariate regression analyses indicated that BALF NP (p < 0.001, OR = 1.04, 95% CI: 1.02–1.06) and procalcitonin (p < 0.021, OR = 52.60, 95% CI: 1.83–1510.06) serve as independent predictors of pulmonary infection. The AUCs for the training and validation sets were determined to be 0.853 (95% CI: 0.806–0.918) and 0.801 (95% CI: 0.697–0.904), respectively, with calibration curves demonstrating strong concordance. The DCA and CIC analyses indicated that the nomogram model possesses commendable clinical applicability. In models comparison, ROC analyses revealed that the nomogram exhibited superior discriminatory accuracy compared to alternative models, with DCA further identifying the nomogram as offering the highest net benefits across a broad spectrum of threshold probabilities.ConclusionBALF NP ≥16% serves as an effective discriminator between pulmonary infectious and non-infectious diseases in patients with pulmonary shadows. We have developed a nomogram model incorporating BALF NP and procalcitonin (PCT), which has proven to be a valuable tool for predicting the risk of pulmonary infections. This model holds significant potential to assist clinicians in making informed treatment decisions.

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Cellular analysis and metagenomic next-generation sequencing of bronchoalveolar lavage fluid in the distinction between pulmonary non-infectious and infectious disease

Cited by 3 publications

References 47 publications

Deciphering the microbial landscape of lower respiratory tract infections: insights from metagenomics and machine learning

Deciphering the microbial landscape of lower respiratory tract infections: insights from metagenomics and machine learning

Blood plasma metagenomic next-generation sequencing for identifying pathogens of febrile neutropenia in acute leukemia patients

Diagnostic test accuracy of cellular analysis of bronchoalveolar lavage fluid in distinguishing pulmonary infectious and non-infectious diseases in patients with pulmonary shadow

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