Targeted next-generation sequencing reveals pathogen spectrum, drug resistance characteristics, and clinical determinants in children with community-acquired pneumonia

Introduction

Acute respiratory tract infections (ARTIs) represent a major cause of morbidity and mortality among young children in developing countries. These infections are highly contagious, widely prevalent, and pose substantial risks to children, the elderly, and immunocompromised individuals (1). Community-acquired pneumonia (CAP), defined as an acute infection of the lung parenchyma acquired outside hospitals or healthcare settings, is one of the leading causes of hospitalization and death worldwide (2) and remains a major contributor to childhood mortality in developing regions. The diagnosis of CAP is challenging because its clinical presentation varies with age and is often nonspecific in young children.

Mycoplasma pneumoniae (MP) is a prominent pathogen, accounting for approximately 20–40% of CAP cases. Some children with M. pneumoniae pneumonia (MPP) progress to severe disease, and the incidence of severe MPP has been increasing in recent years (3). Moreover, MPP is difficult to distinguish from bacterial pneumonia based solely on clinical and radiological findings (4). With the widespread use of macrolide antibiotics, resistance rates in MPP have risen steadily (5); however, whether resistant strains lead to more severe disease remains debated.

Targeted next-generation sequencing (tNGS) employs a highly multiplexed polymerase chain reaction (PCR)-based library preparation system to enrich and amplify target sequences, followed by high-throughput sequencing for simultaneous detection of amplified products. This approach enables rapid and objective identification of a broad range of pathogens, including bacteria, fungi, viruses, and atypical organisms, while also enabling the detection of resistance genes, thereby facilitating early clinical recognition of potential pathogens. Compared with metagenomic NGS (mNGS), tNGS offers advantages in speed, sensitivity, and specificity (6) and can identify genetic variants (7). Its application in clinical diagnosis and management has expanded considerably in recent years (8); however, studies evaluating tNGS for respiratory pathogen detection in oropharyngeal swabs from children remain limited.

This study utilized tNGS to analyze oropharyngeal swab specimens from children with ARTIs, aiming to characterize the pathogen distribution, clinical features of MPP, factors influencing bronchoalveolar lavage (BAL), the impact of macrolide resistance on disease severity, and the effect of tetracyclines on outcomes in MPP. The results are intended to provide data supporting infection control strategies in pediatric populations. We present this article in accordance with the STROBE reporting checklist (available at https://tp.amegroups.com/article/view/10.21037/tp-2026-1-0177/rc).

Methods

Study population

Clinical data were collected from 472 children with ARTIs who underwent tNGS testing between November 2023 and February 2025, which were retrieved from the laboratory information system of Nanjing University Medical School Affiliated Suzhou Hospital (Figure 1). This retrospective study was approved by the Ethics Committee of Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University(Suzhou Science and Technology Town Hospital) (approval No. IRB2025061). Patient data were anonymized. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Written informed consent was obtained from the parents of all patients.

Figure 1 Flow chart of sample screening. A total of 472 patients were enrolled for pathogen distribution analysis based on tNGS testing. Among them, 427 pediatric patients were included in the study investigating infection types. A total of 216 patients with complete MP-IgM (colloidal gold assay) results were used to compare detection rates. After applying the exclusion criteria, 186 patients comprised the final cohort for clinical comparisons (MP-positive vs. MP-negative; mild vs. severe disease) and treatment efficacy analyses (BAL, tetracycline antibiotics). A separate subset of 41 patients aged ≥8 years with detected MP drug resistance genes was analyzed for resistance patterns. BAL, bronchoalveolar lavage; CAP, community-acquired pneumonia; hsCRP, high-sensitivity C-reactive protein; IgM, immunoglobulin M; MP, Mycoplasma pneumoniae; Neut%, neutrophil percentage; tNGS, targeted next-generation sequencing; WBC, white blood cell.

Inclusion and exclusion criteria

The inclusion criteria were as follows: (I) meeting the diagnostic criteria for ARTIs and CAP; (II) age ≤13 years; and (III) tNGS sample collected on the day of admission. CAP diagnosis was established according to the 2019 Guidelines for the Diagnosis and Treatment of Community-Acquired Pneumonia. The exclusion criteria were as follows: (I) underlying conditions such as asthma, chronic cardiopulmonary disease, rheumatologic disorders, or immunodeficiency; (II) treatment at our hospital within the preceding 3 months, especially if the MP-immunoglobulin M (IgM) (colloidal gold assay) result was positive; (III) incomplete clinical data; and (IV) use of macrolide or tetracycline antibiotics before enrollment due to prolonged cough.

Data collection and grouping

Demographic and clinical parameters recorded included age, sex, white blood cell (WBC) count, neutrophil percentage (Neut%), high-sensitivity C-reactive protein (hs-CRP), pathogen profile, macrolide resistance genes, and MP-IgM (colloidal gold assay) results. For hospitalized children, additional data were collected, including procalcitonin (PCT), clinical presentation (pneumonia type, lung involvement, lung auscultation, wheezing), hospital stay, fever duration, medication use [macrolides, tetracyclines, cephalosporins, penicillins, corticosteroids, and intravenous immunoglobulin (IVIG)], and BAL therapy. Subjects who tested positive for MP by tNGS were assigned to the MP-detected group, whereas those with negative tNGS results were assigned to the MP-non-detected group. Detection of a single pathogen was classified as monoinfection, while detection of more than one pathogen was classified as co-infection. Patients who met any of the following criteria were assigned to the severe group: administration of IVIG, requirement of BAL, presence of pulmonary consolidation, presence of wheezing, or fever lasting ≥7 days. Patients who met none of these criteria were assigned to the mild group. Based on the pathogenicity classification provided in the tNGS report, the detected microorganisms were classified as pathogenic, opportunistic, or colonizing flora.

Laboratory methods

MP-IgM (colloidal gold assay)

Fingertip capillary blood was tested using colloidal gold kits (Beijing Innover and Beijing Beier Biotechnology Co., Ltd.), and results were reported within 20 minutes.

Routine laboratory tests

WBC, Neut%, and hs-CRP were measured using a Mindray BC-7500 hematology analyzer. PCT was determined by chemiluminescence (Changguang Huayi).

tNGS

Oropharyngeal swab samples were sent to KingMed Diagnostics (Guangzhou) for tNGS analysis. Data analysis was performed using an independently developed pathogen bioinformatics pipeline (Guangzhou King-Key Biotechnology Co., Ltd.). Quality thresholds included Q30 >75%, minimum raw reads ≥50k, and internal control normalized reads ≥200 or target pathogen normalized reads ≥3,000. A pathogen was considered positive if at least one target showed normalized reads ≥20. Macrolide resistance was defined by detection of known resistance mutations (A2063G, A2064G, A2067G, C2617G) in the MP 23S rRNA gene.

Statistical analysis

All data were analyzed using SPSS version 22.0 and GraphPad Prism version 10. Continuous variables with skewed distributions were expressed as median [interquartile range (IQR)] and compared between two groups using the Mann-Whitney U test. Categorical variables were described as percentages and analyzed using the chi-square test. Binary logistic regression analysis was performed to identify independent factors influencing the outcome variable. Variables with statistically significant differences in univariate analysis were entered into the model as independent variables, and potential confounders were adjusted for in the regression model. A two-tailed P value of less than 0.05 was considered statistically significant.

Results

Pathogen distribution

Among the 472 children with ARTIs [age range: 3 months to 13 years; median 7 years (IQR 4–8 years); 222 males, 250 females], there was no statistically significant difference in age between males and females (P=0.89). A total of 1,182 pathogens were detected, including 501 viruses (42%, 16 types), 528 bacteria (45%, 12 species), and 153 atypical pathogens (13%, 2 species). The most frequently detected agents were adenovirus (116/1,182) among viruses, Haemophilus influenzae (202/1,182) among bacteria, and MP (152/1,182) among atypical pathogens. Of these, 683 were classified as pathogenic, 414 as opportunistic, and 85 as colonizers (Figure 2).

Figure 2 Pathogen detection results. (A) Distribution of pathogen types (bacteria, viruses, and atypical pathogens) among the 1,182 detected pathogens. (B) Classification of all pathogens based on pathogenic potential. (C) Proportional composition of individual pathogens within the atypical pathogen category. (D) Relative abundance of specific bacterial pathogens within the bacterial category. (E) Relative abundance of specific viral pathogens within the viral group; EB, Epstein-Barr.

Differences in WBC, Neut%, and hs-CRP levels among different types of infections

After excluding colonizers flora (reported as normal respiratory flora), 427 children with complete data on WBC count, Neut%, and hs-CRP were stratified by age. Co-infection accounted for 74.94% of cases. A significant difference in WBC count among infection types was observed only in the 6–13 years age group (P<0.05), with higher values noted in co-infected children (Table 1).

Comparison between tNGS and MP-IgM (colloidal gold assay)

Among the 216 children tested using both methods, paired chi-square analysis revealed a statistically significant difference in MP detection rates (P<0.001; Table 2).

Comparative analysis of clinical features in patients with and without MP detection

MP was detected by tNGS in 152 of 472 children (32.20%); of these MP-positive children, 132 (86.30%) carried a macrolide resistance gene. From the 152 MP-positive and 320 MP-negative children, 186 with radiologically confirmed pneumonia and complete clinical data were further analyzed. Significant differences (P<0.05) were observed in age, viral co-detection, pneumonia type, lung involvement, wheezing, and fever duration. MP-positive children were older (median 7 vs. 5 years). Lobar pneumonia was predominantly associated with MP, accounting for 90.63% of lobar pneumonia cases in the MP-positive group. In contrast, bronchopneumonia was more common in MP-negative children (59.84%). MP-negative cases had higher rates of bilateral lung involvement (70.89% vs. 42.06% in MP-positive cases), viral co-detection (86.10% vs. 46.73%), and wheezing (75% of wheezing cases occurred in MP-negative children) (Table 3).

Impact of tetracyclines on clinical outcomes

Among 41 children aged ≥8 years with tNGS-confirmed macrolide resistance genes in MP, no significant differences were observed in fever duration, hospital stay, or severe disease classification between those who received tetracyclines and those who did not (P>0.05; Table 4).

Factors associated with BAL

Comparison of 186 children with CAP (with vs. without BAL) revealed significant differences (P<0.05) in pneumonia type, use of cephalosporins, tetracyclines, IVIG, Neut%, and fever duration. BAL-treated children were more likely to have lobar pneumonia, higher Neut% (73.00% vs. 63.45%), longer fever duration (median 6 vs. 5 days), and greater use of tetracyclines and IVIG, but lower use of cephalosporins (Table 5). A binary logistic regression analysis was performed with BAL as the dependent variable (BAL performed =1, not performed =0). Variables that showed statistically significant differences in Table 5 were entered as independent variables, including lobar pneumonia, tetracycline use, cephalosporin use, IVIG use, Neut%, and fever duration. In addition, potential confounders including sex, age, and co-infection status were adjusted for in the model. The results showed that Neut%, lobar pneumonia, cephalosporin use, and IVIG use were independent factors associated with BAL (P<0.05). The regression model demonstrated satisfactory goodness-of-fit (Hosmer-Lemeshow test, P>0.99), and the overall prediction accuracy of the model was 91.9% (Figure 3).

Figure 3 Independent influencing factors of BAL. Binary logistic regression was performed on factors with P<0.05 from Table 5 (lobar pneumonia, tetracycline use, cephalosporin use, IVIG, Neut%, and fever duration) to identify independent predictors for BAL. Neut%, the presence of lobar pneumonia, and the use of IVIG were identified as risk factors associated with an increased need for BAL. In contrast, cephalosporin use was associated with a reduced likelihood of BAL requirement. Among children with lobar pneumonia, 16 underwent BAL. BAL, bronchoalveolar lavage; CI, confidence interval; IVIG, intravenous immunoglobulin; Neut%, neutrophil percentage; OR, odds ratio.

Comparison of clinical characteristics between severe and mild groups

A total of 186 children with CAP were stratified into severe and mild groups. Severe CAP (as defined above) was associated with higher MP detection (68.49% vs. 50.44%) and more frequent lobar pneumonia (45.21% vs. 27.43%) compared with mild cases (P<0.05; Table 6).

Discussion

Upper and lower respiratory tract infections contribute substantially to childhood morbidity and mortality (9); therefore, early pathogen identification and severity assessment are crucial. Previous studies have suggested good concordance between upper and lower respiratory pathogen profiles (10,11). Using tNGS of oropharyngeal swabs, we found that viruses were the most common pathogens in pediatric ARTIs, with pathogenic viruses accounting for 70.57% of viral detections, followed by bacteria. It is recognized that viruses can enhance bacterial colonization of the upper respiratory tract (12), which may subsequently facilitate bacterial invasion of the lungs (13). Accordingly, early antiviral therapy should be emphasized.

Co-infection is often associated with more severe disease progression (14); therefore, early assessment of infection type is of considerable importance. WBC count, Neut%, and hs-CRP are basic indicators used to differentiate bacterial infection and evaluate disease severity. In the present study, no significant differences in Neut% or hs-CRP were observed across infection types in any age group. In contrast, WBC count demonstrated discriminative ability only in children aged 6–13 years, suggesting that this parameter retains clinical value in this older subgroup.

The MP-IgM colloidal gold assay is a rapid and convenient method commonly used for clinical screening of MP. In the present study, 53.90% of patients who tested positive by MP-IgM were negative by tNGS. This discordance may lead to unnecessary use of macrolide antibiotics. Given the absence of a reference standard method for MP detection in this study, the comparative diagnostic performance of the two methods could not be reliably determined, and conclusions regarding their diagnostic accuracy are therefore limited. Consequently, a positive MP-IgM result should be interpreted with caution, and antibiotic therapy should be initiated only after comprehensive evaluation in conjunction with other clinical indicators and manifestations. Macrolide resistance in MP remains a growing concern (15). In this study, the observed resistance rate (86.3%) was lower than that reported by Xu et al. [96% (16)] but higher than that reported by Zhan et al. [64.6% (17)]. All resistant strains carried 23S rRNA mutations, exclusively at the A2063G site, which is consistent with a 2023 surveillance report from Beijing (18).

Previous studies have suggested that viral infection may increase susceptibility to MP (19). Interestingly, in the present study, the MP-negative group had higher rates of viral detection. This finding may be explained by a stronger immune response triggered by MP infection (20), which could interfere with or suppress viral co-infection. MP often presents as lobar pneumonia (21), a finding that is consistent with the present results. MP-positive children had longer fever duration (5.25 vs. 4.00 days), whereas MP-negative children more frequently presented with bronchopneumonia. Notably, bilateral infiltrates and wheezing were more common in the MP-negative group, which consisted of younger children (median age 5 years) with a high rate of bacterial-viral co-infection (94.14%). It is known that viruses promote upper respiratory bacterial colonization (22), and such colonization has been linked to lower respiratory tract infections in young children (11). Therefore, we speculate that secondary bacterial infection may contribute to bilateral infiltrates and wheezing in MP-negative patients. These clinical distinctions may assist clinicians in assessing the likelihood of MP infection.

The use of tetracyclines as second-line agents for macrolide-resistant MPP remains controversial. Some authors recommend switching to doxycycline if fever persists for more than 48 hours after macrolide therapy (23), whereas others advocate reserving alternative antibiotics for severe cases (24). In addition, Cai et al. (25) raised concerns regarding the dermatological effects of doxycycline in children. In the present study, no significant impact of tetracyclines on clinical outcomes was observed, which aligns with the findings of Ha et al. (26). Therefore, for patients showing a slow response to macrolides, further evaluation and individualized management are warranted.

BAL is an effective intervention for severe or refractory pneumonia. The higher Neut% observed in the BAL group (73.00% vs. 63.45%) suggests an enhanced neutrophil-driven immune response, which is consistent with the findings of Zhu et al. (20). In the present cohort, all children who received IVIG and 88.89% of those who underwent BAL were MP-positive. Regression analysis revealed that children with lobar pneumonia had 43.8-fold higher odds of requiring BAL, and IVIG use increased the odds by 18.7-fold. In contrast, cephalosporin use was associated with a reduced likelihood of BAL requirement. The predictive model demonstrated 91.9% accuracy, providing a useful tool for individualized treatment planning.

Notably, the presence of resistance genes did not correlate with more severe disease, whereas MP positivity and lobar pneumonia were identified as independent risk factors for severe CAP, underscoring the need for close monitoring and active management in these patients. In the present study, 46.73% (50/107) of MPP cases had viral co-detection, which is higher than the 27.27% reported by Zhao et al. (27) but lower than the 56.07% reported by Zhou et al. (23), indicating that viruses continue to pose a concurrent threat in MP infections.

Limitations

Several limitations of this study should be acknowledged. First, the single-center retrospective design and the relatively small hospitalized cohort size may limit the generalizability of the findings. Second, the use of oropharyngeal swab samples represents a limitation, as microbial detection in the upper respiratory tract cannot reliably distinguish true infection from colonization. Although the pathogen classification provided in the test report (definite pathogens, opportunistic pathogens, or colonizing flora) was adopted to mitigate this concern, the potential for misclassification remains. Therefore, larger multicenter prospective studies using lower respiratory tract specimens are warranted to validate these findings and enable a more accurate assessment of infection status. Finally, future work should also explore the mechanisms underlying MP-viral co-infection to further guide precision medicine in pediatric respiratory infections.

Conclusions

In conclusion, tNGS of oropharyngeal swabs is effective for pathogen and macrolide resistance detection in pediatric ARTIs. Viruses are the predominant pathogens, and co-infection is common. The MP-IgM colloidal gold assay shows considerable discordance with tNGS, warranting cautious interpretation. Tetracycline therapy did not improve outcomes in children aged ≥8 years with macrolide-resistant MP, challenging current second-line practices. MP-positive children typically present with lobar pneumonia, older age, and longer fever duration, whereas MP-negative children more often have bilateral infiltrates, wheezing, and viral co-detection. Neut%, lobar pneumonia, and IVIG use independently predicted BAL requirement, while cephalosporin use was associated with lower BAL likelihood. Macrolide resistance genes did not correlate with disease severity, whereas MP positivity and lobar pneumonia were independent risk factors for severe CAP. Prospective multicenter studies using lower respiratory tract specimens are needed to validate these findings and optimize antimicrobial stewardship.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tp.amegroups.com/article/view/10.21037/tp-2026-1-0177/rc

Data Sharing Statement: Available at https://tp.amegroups.com/article/view/10.21037/tp-2026-1-0177/dss

Peer Review File: Available at https://tp.amegroups.com/article/view/10.21037/tp-2026-1-0177/prf

Funding: This work was supported by The Science and Technology Program of Suzhou (No. SKY2021032).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tp.amegroups.com/article/view/10.21037/tp-2026-1-0177/coif). R.H.Y. reports funding support from The Science and Technology Program of Suzhou (No. SKY2021032). The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University (Suzhou Science and Technology Town Hospital) (approval No. IRB2025061). Written informed consent was obtained from the parents of all patients.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Merera A, Asena T, Senbeta M. Bayesian multilevel analysis of determinants of acute respiratory infection in children under the age of five years in Ethiopia. BMC Pediatr 2022;22:123. [Crossref] [PubMed]
2. World Health Organization. The top 10 causes of death. Available online: https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death
3. Wang N, Wang Z, Ouyang J, et al. The impact of Mycoplasma pneumoniae infection on platelets in children with immune thrombocytopenia: a real-world study from China. Ann Hematol 2025;104:105-10. [Crossref] [PubMed]
4. Miyashita N, Nakamori Y, Ogata M, et al. Clinical Differences between Community-Acquired Mycoplasma pneumoniae Pneumonia and COVID-19 Pneumonia. J Clin Med 2022;11:964. [Crossref] [PubMed]
5. Chen YC, Hsu WY, Chang TH. Macrolide-Resistant Mycoplasma pneumoniae Infections in Pediatric Community-Acquired Pneumonia. Emerg Infect Dis 2020;26:1382-91. [Crossref] [PubMed]
6. Zhong Y, Xu F, Wu J, et al. Application of Next Generation Sequencing in Laboratory Medicine. Ann Lab Med 2021;41:25-43. [Crossref] [PubMed]
7. Trinh MP, Shin SJ, Shin M-K. Understanding recurrence in Mycobacterium avium complex pulmonary disease: genotypic strategies to support clinical decision-making. J Clin Microbiol 2026;64:e0108625. [Crossref] [PubMed]
8. Li X, Liu Y, Li M, et al. Epidemiological investigation of lower respiratory tract infections during influenza A (H1N1) pdm09 virus pandemic based on targeted next-generation sequencing. Front Cell Infect Microbiol 2023;13:1303456. [Crossref] [PubMed]
9. GBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 2020;396:1223-49. [Crossref] [PubMed]
10. Wang H, Li X, Zheng Y, et al. Concordance in pathogen identification at the upper and lower respiratory tract of children with severe pneumonia. BMC Infect Dis 2023;23:170. [Crossref] [PubMed]
11. Claassen-Weitz S, Lim KYL, Mullally C, et al. The association between bacteria colonizing the upper respiratory tract and lower respiratory tract infection in young children: a systematic review and meta-analysis. Clin Microbiol Infect 2021;27:1262-70. [Crossref] [PubMed]
12. Wren JT, Blevins LK, Pang B, et al. Influenza A virus alters pneumococcal nasal colonization and middle ear infection independently of phase variation. Infect Immun 2014;82:4802-12. [Crossref] [PubMed]
13. Ning H, Zheng W, Zhang J, et al. Analysis of Cough Factors and Quality of Life Score Among Children With Protracted Bacterial Bronchitis: Cross-Sectional Study. JMIR Pediatr Parent 2025;8:e82887. [Crossref] [PubMed]
14. Babawale PI, Guerrero-Plata A. Respiratory Viral Coinfections: Insights into Epidemiology, Immune Response, Pathology, and Clinical Outcomes. Pathogens 2024;13:316. [Crossref] [PubMed]
15. Wang X, Li M, Luo M, et al. Mycoplasma pneumoniae triggers pneumonia epidemic in autumn and winter in Beijing: a multicentre, population-based epidemiological study between 2015 and 2020. Emerg Microbes Infect 2022;11:1508-17. [Crossref] [PubMed]
16. Xu M, Li Y, Shi Y, et al. Molecular epidemiology of Mycoplasma pneumoniae pneumonia in children, Wuhan, 2020-2022. BMC Microbiol 2024;24:23. [Crossref] [PubMed]
17. Zhan XW, Deng LP, Wang ZY, et al. Correlation between Mycoplasma pneumoniae drug resistance and clinical characteristics in bronchoalveolar lavage fluid of children with refractory Mycoplasma pneumoniae pneumonia. Ital J Pediatr 2022;48:190. [Crossref] [PubMed]
18. Gong C, Huang F, Suo L, et al. Increase of respiratory illnesses among children in Beijing, China, during the autumn and winter of 2023. Euro Surveill 2024;29:2300704. [Crossref] [PubMed]
19. He J, Liu M, Ye Z, et al. Insights into the pathogenesis of Mycoplasma pneumoniae Mol Med Rep 2016;14:4030-6. (Review). [Crossref] [PubMed]
20. Zhu Y, Luo Y, Li L, et al. Immune response plays a role in Mycoplasma pneumoniae pneumonia. Front Immunol 2023;14:1189647. [Crossref] [PubMed]
21. Luo Y, Bai H, Jiao F, et al. Establishment and validation of a predictive model for Lobar pneumonia caused by Mycoplasma pneumoniae infection in children. Sci Rep 2025;15:22811. [Crossref] [PubMed]
22. Sender V, Hentrich K, Henriques-Normark B. Virus-Induced Changes of the Respiratory Tract Environment Promote Secondary Infections With Streptococcus pneumoniae. Front Cell Infect Microbiol 2021;11:643326. [Crossref] [PubMed]
23. Zhou Y, Wang J, Chen W, et al. Impact of viral coinfection and macrolide-resistant mycoplasma infection in children with refractory Mycoplasma pneumoniae pneumonia. BMC Infect Dis 2020;20:633. [Crossref] [PubMed]
24. Yen MH, Yan DC, Wang CJ, et al. The clinical significance of and the factors associated with macrolide resistance and poor macrolide response in pediatric Mycoplasma pneumoniae infection: A retrospective study. J Microbiol Immunol Infect 2023;56:634-40. [Crossref] [PubMed]
25. Cai F, Li J, Liang W, et al. Effectiveness and safety of tetracyclines and quinolones in people with Mycoplasma pneumonia: a systematic review and network meta-analysis. EClinicalMedicine 2024;71:102589. [Crossref] [PubMed]
26. Ha SG, Oh KJ, Ko KP, et al. Therapeutic Efficacy and Safety of Prolonged Macrolide, Corticosteroid, Doxycycline, and Levofloxacin against Macrolide-Unresponsive Mycoplasma pneumoniae Pneumonia in Children. J Korean Med Sci 2018;33:e268. [Crossref] [PubMed]
27. Zhao MC, Wang L, Qiu FZ, et al. Impact and clinical profiles of Mycoplasma pneumoniae co-detection in childhood community-acquired pneumonia. BMC Infect Dis 2019;19:835. [Crossref] [PubMed]