Clinical utility of metagenomic next-generation sequencing in the diagnosis of severe influenza complicated by invasive pulmonary aspergillosis.

The incidence and mortality of severe influenza complicated by invasive pulmonary aspergillosis (IPA) have risen markedly in recent years. This study aimed to evaluate the diagnostic performance of metagenomic next-generation sequencing (mNGS) for detecting IPA in patients with severe influenza.

Severe influenza patients with suspected of having IPA admitted to Xinxiang Central Hospital, Henan Province, China, from March 2020 to September 2025 were retrospectively enrolled. Bronchoalveolar lavage fluid (BALF) and blood were collected for fungal culture, galactomannan (GM) assay, and mNGS. Final classification into IPA and non-IPA groups was based on composite clinical and microbiological criteria. Sensitivity, specificity, and receiver operating characteristic curves were used to compare the diagnostic performance of the three methods.

Comparison with traditional fungal culture and GM testing, mNGS provided significantly faster results. Among 189 patients suspected of severe influenza-associated IPA, mNGS demonstrated a sensitivity of 72.1% and a specificity of 80.2%. Its sensitivity was higher than that of fungal culture (28.6%), serum GM testing (37.6%), and BALF GM testing (44.1%); however, its specificity was slightly lower than that of fungal culture (89.5%), serum GM testing (84.3%), and BALF GM testing (81.3%). The area under the ROC curve (AUC) for mNGS was 0.76, which is higher than that for BALF GM testing (0.63), serum GM testing (0.61), and fungal culture (0.59). The combined diagnostic approach yielded an AUC of 0.83.

mNGS offers a rapid, sensitive and accurate solution for invasive pulmonary aspergillosis in severe influenza patients. It outperforms conventional fungal culture and galactomannan assays. Integrating mNGS with traditional diagnostic methods could substantially improve early detection and overall yield of IPA.
Chronic respiratory disease
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Care/Management
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Authors

Niu Niu, Guo Guo, Li Li, Liu Liu, Zhao Zhao
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