Clinical application of targeted next-generation sequencing utilizing bronchoalveolar lavage fluid in thoracic surgery ICU patients with suspected pulmonary infections
- Guo, Xiaobo
- Xie, Nianlin
- Xi, Xiaotong
- li, Pei
- Jia, Jianbo
- Chen, Lianhong
- Ren, Mingzhi
- Wang, Yaping
- Zhang, Peipei
- Deng, Wanglong
- Wang, Yan
- Jing, Pengyu
- Ding, Ran
- Gu, Zhongping
Abstract
Aims:
The aim of this prospective study was to evaluate the diagnostic value of targeted next-generation sequencing (tNGS) in identifying pathogens from bronchoalveolar lavage fluid (BALF) in thoracic surgery ICU patients, offering additional diagnostic methods for clinical practice.
Methods and results:
We collected clinical data from patients with suspected pulmonary infections in the thoracic surgery ICU of the Second Affiliated Hospital of Air Force Medical University. A total of 50 patients were enrolled in this study. Traditional pathogen detection (TPD), involving culture and loop-mediated isothermal amplification assays for 12 pathogens, along with tNGS, was employed for pathogen identification in BALF samples. Our findings demonstrated that the positive rate of tNGS was significantly greater than that of TPD (96% vs. 68%). Among the 50 samples analyzed, tNGS identified a total of 165 pathogens, whereas TPD detected only 48 pathogens. The TPD method primarily detected bacteria and fungi, whereas tNGS exhibited broader capabilities, identifying 104 cases with bacteria, 19 with fungi, 34 with DNA viruses, and 8 with RNA viruses. Notably, tNGS displayed enhanced efficiency in detecting atypical pathogens such as fungi, DNA viruses and RNA viruses. Furthermore, compared with TPD, tNGS demonstrated superior sensitivity (95.83% vs. 68.75%).
Conclusions:
tNGS technology, characterized by its high sensitivity, specificity, and cost-effectiveness, holds great promise as a reliable diagnostic tool for assessing pulmonary infections in the thoracic surgery ICU patients.
Impact Statement
This study demonstrates that targeted next-generation sequencing (tNGS) significantly outperforms traditional methods in detecting pulmonary infections, with broader pathogen identification and higher sensitivity. The adoption of tNGS has the potential to transform clinical diagnostics, improving patient outcomes in thoracic surgery ICU settings.