Diagnostic value of metagenomic next-generation sequencing using bronchoalveolar lavage fluid samples for pathogen detection in children with severe or refractory pneumonia
Bibliographic record
Abstract
ABSTRACT Pneumonia is the leading cause of morbidity and mortality in children and needs rapid and accurate pathogenic diagnosis. The aim of this study was to evaluate the diagnostic value of bronchoalveolar lavage fluid (BALF) metagenomic next-generation sequencing (mNGS) and conventional microbiological tests (CMTs) for pathogen detection in children with severe or refractory pneumonia. In this retrospective study, the clinical data of 127 children with severe or refractory pneumonia admitted to the respiratory department from June 2021 to March 2022 were analyzed. BALF mNGS and CMTs were utilized for pathogen diagnosis and comparison of their detection performance for different pathogens. The pathogenic diagnosis rate was 95.28% (121/127) by combining mNGS and CMTs. mNGS had significantly higher overall (96.06% vs 72.44%, P < 0.001), bacterial (69.29% vs 12.60%, P < 0.001), and fungal (11.81% vs 3.15%, P = 0.009) detection rates than CMTs. However, there was no significant difference of detection rates between them for respiratory viruses (33.86% vs 33.75%, P = 0.99) and Mycoplasma pneumoniae (48.03% vs 45.67%, P = 0.71). The sensitivities of mNGS for total pathogens, bacteria, and fungi were 99.17%, 100%, and 87.50%, respectively, which were higher than those of CMTs. CMTs for M. pneumoniae had the highest sensitivity (91.23%) compared with mNGS (89.47%) and multiplex PCR (88.57%). For respiratory viruses, mNGS and mPCR had similar sensitivities (97.67% vs 96.43%). mNGS was superior to CMTs in bacterial and fungal detection, while it was comparable to multiplex PCR for the detection of M. pneumoniae and respiratory viruses. Different detection methods should be rationalized for different pathogens. IMPORTANCE This study on 127 patients with severe and refractory pneumonia showed that mNGS was significantly superior to CMTs in terms of bacterial and fungal detection. We also found that multiplex PCR assay was comparable to mNGS for the detection of Mycoplasma pneumoniae and respiratory viruses and may have greater application advantages in combination with CMTs, such as M. pneumoniae IgM. For severe and refractory pneumonia, or when empiric treatment is not effective, collecting BALF for mNGS can help to quickly identify the causative organisms at an early stage. It is also important to choose more appropriate methods or combinations for different pathogens.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".