Detection of Infectious Pathogens in Peripheral Lung Field

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Detection of Infectious Pathogens Located in the Peripheral Lung Field by Metagenomic Next-Generation Sequencing Combined with Virtual Bronchoscopic Navigation

Lower respiratory tract infections (LRTIs) are a leading cause of global mortality, particularly in low-income countries and among immunocompromised populations. Infections in the peripheral lung field (PLF), defined as lesions within the outer-third elliptical regions around the hilum on computed tomography (CT) scans, pose diagnostic challenges due to their anatomical location. Conventional bronchoscopy often fails to visualize these regions, leading to delayed or inaccurate diagnoses. This study evaluates the diagnostic utility of combining virtual bronchoscopic navigation (VBN) with metagenomic next-generation sequencing (mNGS) to improve pathogen detection in PLF infections.

Study Design and Methodology

The retrospective single-center study analyzed 136 patients with PLF lesions admitted to Tianjin Medical University General Hospital between July 2018 and February 2019. Patients were divided into two groups: the VBN group (87 patients), where bronchoscopy was guided by VBN, and the non-VBN group (49 patients), where standard bronchoscopy was performed. Specimens collected included bronchoalveolar lavage fluid (BALF) and lung tissue samples, which underwent conventional microbiological tests (cultures, histopathology, and polymerase chain reaction [PCR]) alongside mNGS.

VBN uses CT data to generate dynamic, three-dimensional virtual reconstructions of the bronchial tree, enabling precise navigation to peripheral lesions. mNGS involves high-throughput sequencing of microbial DNA directly extracted from clinical samples, followed by bioinformatics analysis to map sequencing reads against a pathogen reference database. Diagnostic thresholds for mNGS positivity varied by pathogen type:

  • Bacteria and viruses: Coverage score ≥5 times higher than other organisms, with strictly mapped reads >3.
  • Fungi: Strictly mapped reads >3, exceeding the reference range.
  • Mycobacterium tuberculosis complex (MTBC): ≥1 strictly mapped read due to low contamination risk.

Key Findings

Pathogen Distribution and Diagnostic Performance

Among 136 patients, 66.2% (90/136) were diagnosed with infectious diseases (ID group), while 33.8% (46/136) had non-infectious etiologies (NID group). Pathogens detected in the ID group included bacteria (37.8%, 34/90), fungi (37.8%, 34/90), viruses (14.4%, 13/90), and MTBC (10.0%, 9/90). Conventional diagnostic methods (cultures, PCR, histopathology) confirmed all infections, with cultures identifying 57 cases, histopathology 20, and PCR 13.

Bacterial Infections:

  • The most common bacteria were Pseudomonas aeruginosa, Acinetobacter baumannii, and Rothia mucilaginosa, with mNGS reads ranging from 3 to 14,978.
  • mNGS demonstrated superior sensitivity over cultures for BALF (81.6% vs. 31.4%; P<0.001) and tissue samples (72.9% vs. 31.4%; P<0.001). Specificity remained high for both specimen types (79.2% for BALF; 85.0% for tissue).

Fungal Infections:

  • Predominant fungi included Pneumocystis jirovecii, Aspergillus fumigatus, and Cryptococcus neoformans, with reads ranging from 3 to 95,738.
  • BALF mNGS sensitivity for fungi was 76.9% in the VBN group versus 62.5% in the non-VBN group.

Viruses and MTBC:

  • Viral pathogens and MTBC were detected in smaller subsets (16 and 8 cases, respectively, in the VBN group).

Impact of VBN on Diagnostic Accuracy

The integration of VBN significantly enhanced the diagnostic yield of mNGS:

  • BALF mNGS: Sensitivity increased from 58.6% (non-VBN) to 81.6% (VBN; P=0.023), while specificity improved from 45.0% to 79.2% (P=0.019).
  • Tissue mNGS: Sensitivity rose from 48.3% (non-VBN) to 72.9% (VBN; P=0.029), though specificity differences were not statistically significant (85.0% vs. 75.0%; P=0.429).

For bacterial infections, VBN-guided BALF mNGS achieved a sensitivity of 95.0% compared to 57.1% without VBN (P=0.012). Fungal detection showed no significant differences in sensitivity or specificity between groups, likely due to smaller sample sizes.

Clinical Implications

  1. Enhanced Sensitivity for Bacterial Pathogens: VBN’s precision in guiding bronchoscopy to PLF lesions improves specimen quality, increasing bacterial detection rates.
  2. Utility in Complex Infections: mNGS identifies polymicrobial and fastidious pathogens (e.g., MTBC) missed by conventional methods.
  3. Reduced Diagnostic Delays: Rapid, comprehensive pathogen profiling with mNGS facilitates timely, targeted antimicrobial therapy.

Limitations and Future Directions

  • Sample Size: Small cohorts for viral and MTBC infections limited statistical power.
  • Cost and Accessibility: While VBN requires only software, mNGS remains resource-intensive, restricting widespread use.
  • Contamination Risks: Despite stringent thresholds, false positives from environmental or commensal organisms require careful interpretation.

Conclusion

The combination of VBN and mNGS offers a robust diagnostic framework for PLF infections, addressing the limitations of traditional bronchoscopy and culture-based methods. By improving access to peripheral lesions and enabling unbiased pathogen detection, this approach holds promise for reducing LRTI-related morbidity and mortality. Further multicenter studies with larger cohorts are needed to validate these findings and refine implementation protocols.

doi:10.1097/CM9.0000000000001339

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