Open Access

Clinical Features and Value of Tracheal Aspirate Metagenomic Next-Generation Sequencing for Severe Pneumonia in Children in Pediatric Intensive Care Unit

XINYAN YU

XINYAN YU

Department of Critical Care Medicine, The Heilongjiang Hospital of Beijing Children’s Hospital, Capital Medical UniversityHarbin, China
Search for this author on
Sciendo|Google Scholar
YU, XINYAN
, 
JIUCHAO LIANG

JIUCHAO LIANG

Department of Critical Care Medicine, The Heilongjiang Hospital of Beijing Children’s Hospital, Capital Medical UniversityHarbin, China
Search for this author on
Sciendo|Google Scholar
LIANG, JIUCHAO
, 
RUI YANG

RUI YANG

Department of Critical Care Medicine, The Heilongjiang Hospital of Beijing Children’s Hospital, Capital Medical UniversityHarbin, China
Search for this author on
Sciendo|Google Scholar
YANG, RUI
, 
WEI GAI

WEI GAI

WillingMed Technology Beijing Co., LtdBeijing, China
Search for this author on
Sciendo|Google Scholar
GAI, WEI
 and 
YAFENG ZHENG

YAFENG ZHENG

WillingMed Technology Beijing Co., LtdBeijing, China
Search for this author on
Sciendo|Google Scholar
ZHENG, YAFENG
  
Jun 18, 2025
Clinical Features and Value of Tracheal Aspirate Metagenomic Next-Generation Sequencing for Severe Pneumonia in Children in Pediatric Intensive Care Unit's Cover Image
About this article
Previous Article
Next Article
Cite
Download Cover
Article Category: Original Paper
Published Online: Jun 18, 2025
Page range: 192 - 205
Received: Jan 20, 2025
Accepted: Apr 25, 2025
DOI: https://doi.org/10.33073/pjm-2025-016
Keywords
© 2025 XINYAN YU et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Fig. 1.

Flow diagram of patient inclusion and exclusion.
Flow diagram of patient inclusion and exclusion.

Fig. 2.

The pathogen detected results of mNGS and CMT. A) The types of infection identified in the children with severe pneumonia. The solid pie chart represents the proportion of single and mixed infections, and the other two hollow pie charts represent the specific pathogen types of single and mixed infections. B) The coincidence between mNGS and CMT for pathogen identification.
The pathogen detected results of mNGS and CMT. A) The types of infection identified in the children with severe pneumonia. The solid pie chart represents the proportion of single and mixed infections, and the other two hollow pie charts represent the specific pathogen types of single and mixed infections. B) The coincidence between mNGS and CMT for pathogen identification.

Fig. 3.

The distribution of the identified pathogen spectrum. A) The distribution of pathogens detected by mNGS and CMT. B) The distribution of pathogens for children in the < 12 months group and the 12~144 months group.
The distribution of the identified pathogen spectrum. A) The distribution of pathogens detected by mNGS and CMT. B) The distribution of pathogens for children in the < 12 months group and the 12~144 months group.

Fig. 4.

Analysis of microbiome alpha and beta diversity between patients < 12 months and 12~144 months. A) Alpha diversity differences of microbiota communities at the species level between groups. Each point represents one sample from each group. B) PCoA plot is based on Bray–Curtis’s distance between the children in groups. The x-axis is for grouping information, the y-axis is for distance information, the R-value is between (–1, 1) and there are significant differences between groups when the R-value is greater than zero and the P value is less than 0.05.
Analysis of microbiome alpha and beta diversity between patients < 12 months and 12~144 months. A) Alpha diversity differences of microbiota communities at the species level between groups. Each point represents one sample from each group. B) PCoA plot is based on Bray–Curtis’s distance between the children in groups. The x-axis is for grouping information, the y-axis is for distance information, the R-value is between (–1, 1) and there are significant differences between groups when the R-value is greater than zero and the P value is less than 0.05.

Fig. 5.

The top 20 microorganisms and their correlation with patient clinical data. A) Top 20 most abundant species and their relative abundance. B) Differences between age groups for the top 20 most abundant microbes. * p-adjust < 0.05; ** p-adjust < 0.01; *** padjust < 0.001. Metastats analysis, p-value was adjusted using the Benjamini and Hochberg method. C) LEfSe analysis of respiratory microbiome between patients < 12 months and 12~144 months. Bar chart showing the log-transformed LDA scores of pathogen species identified by LEfSe analysis (the log-transformed LDA score of 4 as the threshold).
The top 20 microorganisms and their correlation with patient clinical data. A) Top 20 most abundant species and their relative abundance. B) Differences between age groups for the top 20 most abundant microbes. * p-adjust < 0.05; ** p-adjust < 0.01; *** padjust < 0.001. Metastats analysis, p-value was adjusted using the Benjamini and Hochberg method. C) LEfSe analysis of respiratory microbiome between patients < 12 months and 12~144 months. Bar chart showing the log-transformed LDA scores of pathogen species identified by LEfSe analysis (the log-transformed LDA score of 4 as the threshold).

Fig. 6.

Spearman correlations between the top 20 most abundant species and clinical data. * 0.01 < p < 0.05; ** 0.001 < p ≤ 0.01; *** p ≤ 0.001.
Spearman correlations between the top 20 most abundant species and clinical data. * 0.01 < p < 0.05; ** 0.001 < p ≤ 0.01; *** p ≤ 0.001.

Demographic and clinical characteristics of patients_

Characteristics Severe pneumonia (n = 55) Children less than 12 months (n = 33) Children aged 12~144 months (n = 22) p-valuea
Age, month (M, IQR) 5.97 (3.28–30.50) 3.70 (2.80–4.63) 40.50 (24.75–51.75) < 0.0001
< 12 33 / / /
12–144 22 / / /
Gender, male, n (%) 24 (43.64%) 18 (54.55%) 6 (27.27%) 0.0457
Symptoms, n (%)
Fever 24 (43.64%) 9 (27.27%) 15 (68.18%) 0.0027
Cough 41 (74.55%) 26 (78.79%) 15 (68.18%) 0.3764
Abnormal breathing 51 (92.73%) 33 (100%) 18 (72%) 0.0012
Unconsciousness 6 (10.91%) 3 (9.09%) 3 (13.64%) 0.5963
Apathetic 43 (78.18%) 23 (69.70%) 20 (90.91%) 0.0620
Fidgety 10 (18.18%) 2 (6.06%) 8 (36.36%) 0.0043
Laboratory examination (mean ± SD)
Pulse (times per minute) 106.9 ± 28.95 172.88 ± 22.49 143 ± 28.69 <0.0001
Respiratory rate (times per minute) 42.31 ± 11.39 44.55 ± 9.95 38.96 ± 12.77 0.0742
WBC 10.42 ± 4.79 11.03 ± 4.52 9.49 ± 5.14 0.2465
Hb 105.8 ± 18.72 104.82 ± 14.19 107.27 ± 24.30 0.6382
PLT 368.13 ± 183.67 441.64 ± 187.03 257.86 ± 110.54 0.0001
NEUT% 12.38 ± 23.53 9.60 ± 17.41 16.56 ± 30.53 0.2866
LY% 12.36 ± 23.38 17.66 ± 28.21 4.42 ± 9.15 0.0386
NEUT 5.02 ± 3.80 3.82 ± 2.49 6.80 ± 4.71 0.0035
HCT 31.38 ± 7.91 31.38 ± 6.79 31.38 ± 9.52 0.9990
CRP 21.06 ± 43.04 7.35 ± 19.80 41.63 ± 58.54 0.0029
PCT 2.61 ± 6.85 0.91 ± 2.84 5.09 ± 9.78 0.0260
BUN 4.93 ± 4.61 4.67 ± 2.95 5.33 ± 6.44 0.6157
Arterial blood pH 7.38 ± 0.08 7.37 ± 0.08 7.40 ± 0.08 0.2710
PaO2 58.29 ± 17.37 57.67 ± 14.03 59.23 ± 21.77 0.7474
PaCO2 43.78 ± 12.70 47.33 ± 11.44 38.45 ± 12.88 0.0097
HCO3− 26.32 ± 8.26 26.93 ± 5.14 25.40 ± 11.55 0.5058
PaO2/FiO2 213.13 ± 64.00 221.33 ± 48.89 200.82 ± 81.41 0.2478
Premature, n 7 (12.73%) 6 (18.18%) 1 (4.55%) 0.1371
Post operation, n 3 (5.45%) 1 (3.03%) 2 (9.09%) 0.3322
Intubation, n 30 (54.55%) 19 (57.58%) 11 (50%) 0.5804
Mechanical ventilation, n 30 (54.55%) 19 (57.58%) 11 (50%) 0.5804
SOFA score 3.58 ± 2.03 3.48 ± 1.68 3.73 ± 2.51 0.6691
APACHE II score 13.45 ± 4.69 13.58 ± 3.95 13.27 ± 5.72 0.8167
LOHS (mean ± SD), day 16.49 ± 8.83 17.39 ± 9.72 15.14 ± 7.30 0.3579
ICU (mean ± SD), day 15.24 ± 8.87 16.76 ± 9.75 12.96 ± 6.97 0.1203
Outcomes, n
Cured 53 (96.36%) 32 (96.97%) 21 (95.45%) 0.7687
Died 2 (3.64%) 1 (3.03%) 1 (4.55%) 0.7687
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Life Sciences, Microbiology and Virology
Journal RSS Feed