Lower respiratory tract infections (LRTIs) represent a major global health challenge, significantly contributing to morbidity and mortality worldwide, particularly among children (GBD 2019 LRI Collaborators, 2022). In 2016, LRTIs were associated with 652,572 deaths among children under 5 (GBD 2016 Lower Respiratory Infections Collaborators, 2018). LRTIs were caused by diverse pathogens, including bacteria, viruses, mycoplasmas, fungi, and parasites, with respiratory viruses accounting for a significant proportion of cases (Liu et al., 2018; Wang et al., 2020). Major causative agents include influenza, respiratory syncytial virus (RSV), human metapneumovirus (hMPV), enterovirus, rhinovirus, parainfluenza virus (PIV) 1–4, adenovirus, and more recently SARS-CoV-2 being the most prevalent (Burrel et al., 2021; Chu et al., 2022). Since 2021, shifts have occurred in the epidemiology and seasonal patterns of these pathogens (Contes and Liu, 2025). For instance, RSV peaked 1–2 months earlier, and the Southern Hemisphere experienced a longer interval between peaks, with the first wave peaking earlier than usual (Memish et al., 2020). Clinically, viral LRTIs often present with more gradual symptom onset compared to bacterial infections, so in the absence of accurate pathogen identification, clinicians are frequently compelled to resort to empirical antimicrobial therapy. This practice not only increases the risk of inappropriate antibiotic use but may also contribute to the emergence and spread of resistant bacterial strains, adversely affecting clinical outcomes. Pathogen-specific infection control and treatment are required, highlighting the importance of identification in pediatric LRTIs.

Bronchoalveolar lavage fluid (BALF) and sputum are clinically relevant samples for detecting pathogenic microorganisms in pediatric LRTIs (Wei et al., 2023). Conventional detection methods typically include microbial culture, smear microscopy, and qPCR. Microbial culture remains the gold standard for microbiological identification. However, this method suffers from being time-consuming, insensitive, and ineffective, especially when applied to viruses or difficult-to-culture organisms (Millot et al., 2017). While qPCR is fast and precise, its application is limited by the need for pathogen-specific primer/probe design and low multiplexing capability in a single assay (Gauthier et al., 2023).

Metagenomic next-generation sequencing (mNGS) technology is increasingly employed for detecting, identifying, and analyzing human pathogens (Gu et al., 2019). By enabling simultaneous sequencing of billions of DNA fragments, mNGS provides an unbiased method to identify both known and novel pathogens, potentially even discovering previously unknown organisms (Shi et al., 2022). In recent years, mNGS has provided an even more efficient and accurate means of pathogen detection, revolutionizing the clinical diagnostic approach to LRTIs (Diao et al., 2022; Lv et al., 2023). While capable of simultaneously conducting DNA and RNA tests, mNGS remains cost-prohibitive for clinical deployment (Gu et al., 2019). Its diagnostic sensitivity is constrained by both low pathogen reads and overwhelming host DNA background noise, rendering it suboptimal for longitudinal disease surveillance applications (Han, 2022). Unlike mNGS, targeted next-generation sequencing (tNGS) necessitates panel construction through the design of specific primers or probes for preselected pathogens, offering a cost-effective, highly specific, and efficient approach with minimal sample input and reduced human DNA interference (Gaston et al., 2022). And the tNGS method covers the overwhelming majority of respiratory pathogens, ranging from dozens to hundreds (Shi et al., 2023; Zhang et al., 2024). Thus far, the systematic evaluation of tNGS application for pediatric LRTIs has not been sufficiently investigated. This study evaluated the diagnostic utility of tNGS technology in children with LRTIs by assessing its pathogen detection rate and comparing its performance to culture and qPCR methods.

Lower respiratory tract infection (LRTI) is a leading cause of death worldwide, particularly among pediatric populations in low-income regions (Jackson et al., 2023). Microbial infections commonly associated with LRTIs include bacterial, fungal, and viral pathogens, atypical pathogens, and multiple concurrent infections (Liu et al., 2018). This study shows that bacteria, fungi, and viruses are responsible for LRTIs in children, and 68.3% of these infections involve multiple pathogens, aligning with prior research. Therefore, early and accurate pathogen identification can reduce morbidity and mortality (Zhang et al., 2022).

This study demonstrates that viral infections are the most prevalent in single pathogen infections, whereas bacterial-viral co-infections predominate in polymicrobial infections. Of particular clinical significance were the recognition that viral infections may predispose to secondary bacterial pneumonia through mechanisms of epithelial damage and immune modulation (Hanada et al., 2018; Manohar et al., 2020). These highlight that rapid viral identification and early initiation of antiviral therapy significantly influence disease progression in respiratory viral infections. The diagnosis of respiratory viruses has evolved significantly since the introduction of multiplex PCR panels and other molecular techniques, which allow rapid simultaneous detection of multiple respiratory viruses (Clark et al., 2023). For example, mNGS, tNGS, and hybridization have a short TAT, high sensitivity and specificity, and high accuracy compared with conventional phenotypic methods (Wang et al., 2019).

The application of tNGS technologies for pathogen identification is a burgeoning field, with optimal practices still under active investigation. Recently, tNGS has been applied to detect respiratory tract pathogens (Ma et al., 2024; Qin et al., 2025). This study found that tNGS results showed a concordance rate of >65% with clinical diagnoses in pediatric patients with LRTIs. Consistent with previous studies, tNGS showed a concordance rate of more than 70% with clinical diagnoses in pneumonia patients (Zhang et al., 2024). Additionally, the study demonstrates that tNGS shows the highest diagnostic concordance among available methods in detecting Mycoplasma pneumoniae, viral, and fungal pathogens. Notably, since Mycoplasma pneumoniae and viruses are the most prevalent causes of pediatric LRTIs (Liu et al., 2018); tNGS proves particularly valuable for diagnosing such infections in children.

While our study demonstrates superior concordance between tNGS and clinical diagnoses, discordant cases warrant careful consideration, as they may reflect either technical limitations or clinically meaningful discrepancies. tNGS may detect low-abundance nucleic acids that do not represent true infection from colonizers such as Streptococcus pneumoniae in the nasopharynx (Chen et al., 2024). This scenario is particularly relevant in pediatric populations where asymptomatic carriage rates are high. Furthermore, even after clinical recovery, non-infectious pathogen fragments or residual genetic material may persist, potentially causing tNGS positivity in the absence of active infection. This aligns with studies on Adenovirus detection, where tNGS may overcall positives in some cases (Huang et al., 2024). Therefore, to mitigate misinterpretation, we highlight the importance of integrating tNGS results into the clinical context—for example, through correlation with white blood cell counts, procalcitonin levels, imaging data, and clinical manifestations.

The targeted enrichment process enhances the sensitivity of tNGS, enabling the detection of pathogens that are undetectable by qPCR methods (Zhao et al., 2021). Examples include Adenovirus, human rhinovirus, Mycoplasma pneumoniae, Streptococcus pneumoniae, and Haemophilus influenzae. The superior detection capability of tNGS over qPCR methods for pathogenic bacteria may originate from their distinct limits of detection and varying genome coverage rates for different pathogens (Chen et al., 2024). Our data indicate a high concordance rate (≥94.39%) for the detection of Influenza A, Influenza B, and RSV, which is consistent with previous researches (Zhang et al., 2024; Wang et al., 2025). The near-perfect concordance in detecting Influenza A, Influenza B, and RSV likely stems from the high sensitivity of tNGS to elevated viral loads during acute infections, combined with the robustness of amplification-based methods for these RNA viruses. However, the lower concordance for Adenovirus demands careful interpretation. The lack of significant RPM difference between tNGS+/qPCR+ and tNGS+/qPCR- samples suggests tNGS may detect low-level Adenovirus DNA below the clinical sensitivity threshold of qPCR or from non-replicative virus fragments (Chen et al., 2024). This highlights a critical challenge: distinguishing clinically relevant infection from incidental carriage or residual nucleic acid, particularly for persistent viruses like Adenovirus.

Mycoplasma pneumoniae and human rhinoviruses are common pathogens causing infections in hospitalized children (Yun KW et al., 2022). tNGS demonstrated substantial clinical utility in identifying Mycoplasma pneumoniae and the human rhinoviruses frequently detected in respiratory tract infections (Fu et al., 2025a). Several studies have shown that using qPCR or tNGS to monitor pathogen dynamics continuously may help clinicians better assess patient status (Gibert et al., 2024; Zhang et al., 2024). In our study, for HRV and M. pneumoniae, the significantly higher RPM values in concordantly positive samples (tNGS+/qPCR+) than in tNGS+/qPCR− samples indicate that RPM magnitude correlates with qPCR positivity. This suggests that RPM could serve as a quantitative measure, particularly for M. pneumoniae (AUC=0.917), effectively distinguishing true positives. The lower AUC for HRV (0.759) implies greater overlap in RPM ranges between true positives and potential false positives/background, possibly reflecting that the high genetic diversity of HRV strains affects primer/probe binding efficiency in qPCR or leads to variable replication kinetics. Thus, while tNGS reliably detects viral presence, RPM thresholds may serve as necessary adjuncts for clinical interpretation.

For lower respiratory tract infections, the traditional culture method has several shortcomings, such as a low positive rate, a long turnaround time, and a limited range of detectable pathogens (Foschi et al., 2021). Studies have shown that tNGS outperforms culture methods in bacterial detection (Chen et al., 2025; Li et al., 2025). The results of this study demonstrate that although the detection concordance rate for common bacteria (e.g., Streptococcus pneumoniae and Haemophilus influenzae) exceeded 78%, tNGS exhibited a significantly higher positive detection rate compared to conventional culture methods, consistent with previous research. The superior sensitivity of tNGS in detecting more infections compared to culture methods may result from variations in detection thresholds across different pathogens. However, when designing tNGS probes that prioritize genus-level sequences and inadequately cover species-specific sequences, this approach demands higher caution. It often leads to failed species-level categorization and a higher detection limit (Gaston et al., 2022). Furthermore, although tNGS exhibits a high detection rate, it lacks the ability to perform antimicrobial susceptibility testing (AST). This gap may delay optimal antibiotic initiation, particularly with fastidious organisms or failed empirical treatments. In such cases, clinicians must rely on supplemental culture results or molecular detection of resistance genes, both of which may require additional time and resources. Future advances, such as machine learning–based prediction of resistance from genomic data or rapid phenotypic AST platforms, may help bridge this gap.

Several studies have identified polymicrobial infections as one of the key characteristics of pediatric LRTIs (Ademhan Tural et al., 2022; Li et al., 2023; Rouze et al., 2025). Several studies highlight tNGS’s potential to supplement routine diagnostic methods in cases of multiple pathogen co-infection (Tan et al., 2025; Xiao et al., 2025). Consistent with previous studies, we identified polymicrobial infections in 76 patients. In our study, tNGS outperformed other methods by achieving significantly higher detection efficiency. Furthermore, we compared the clinical characteristics between patients with single infection and those with multiple infection. The results showed that no indicators were significantly associated with multiple infection, which further demonstrates the important role of tNGS in identifying and diagnosing mixed infections.

This study had several limitations. First, since this study was a single-center retrospective investigation, its findings may be subject to selection bias. Thus, validation through future research with larger samples and prospective designs is necessary. Secondly, the detection results lack an assessment of clinical prognosis. Future prospective studies should establish a direct association between tNGS findings and patient outcomes, such as mortality rates and length of hospital stay. Thirdly, the range of commercially available PCR kits was limited, particularly as common respiratory pathogens had not been validated using PCR methodology. Furthermore, some of the patients in our study received antibiotic therapy, which affected the diagnostic performance of the culture method. And finally, it is often unclear whether microbes identified through tNGS represent contaminants, colonizers, or pathogens. These distinctions require further investigation, particularly in lower respiratory tract infections.

Although tNGS is more sensitive than both qPCR and culture in detecting pathogens causing pediatric LRTIs, it cannot replace drug sensitivity results. Our data suggest that the tNGS results demonstrated higher concordance with the clinical diagnosis. tNGS is highly applicable for diagnosing pathogens in pediatric patients with severe, mixed, or refractory infections. tNGS technology has demonstrated diagnostic potential to complement routine diagnostics, particularly for opportunistic pathogens and mixed infections, or for cases with negative qPCR and culture testing.