Metagenomic Next-Generation Sequencing Enhances Pathogen Detection and Prognostic Stratification in Hematopoietic Stem Cell Transplantation Recipients
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Published on: September 20, 2016
Ying Liu1, Xintong Bian1, Hongxu Wang1
1Department of Laboratory Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People's Republic of China.
Background:
As an emerging detection technique, metagenomic next-generation sequencing (mNGS) provides superior genomic resolution and enhances detection precision relative to conventional pathogen detection methods. Patients with malignant hematological disorders are susceptible to infections following hematopoietic stem cell transplantation (HSCT). mNGS demonstrates a significantly higher positivity rate compared with conventional testing.
Methods:
A total of 100 patients who had undergone HSCT were retrospectively enrolled in this study. The detection rates and patient mortality rates associated with conventional diagnostic methods and mNGS were analyzed and compared. Additionally, the impact of mNGS results on the adjustment of antibacterial therapy was investigated, and the receiver operating characteristic (ROC) curve was utilized to assess the predictive value of mNGS for mortality.
Results:
mNGS detected pathogens in 81% of patients (viruses 71.0%, with herpes simplex virus type 1 [HSV-1] at 26.7%; bacteria 25.0%, predominantly Enterobacteriaceae at 61.5%), outperforming conventional testing ( P < 0.001). The results indicated that C-reactive protein (CRP), age, length of hospital stay, transplantation modality, and granulocyte reconstitution time were independent predictors of mNGS-only positive results (P < 0.05). Antibiotic adjustment strategies aligned with mNGS findings were implemented in 59% of patients. The area under the curve (AUC) values for procalcitonin (PCT), CRP, granulocyte reconstitution time, megakaryocyte reconstitution time, and mNGS positivity alone in predicting mortality were 0.657, 0.756, 0.743, 0.724, and 0.539, respectively. The combined AUC of these five indicators was 0.812 (95% CI: 0.694-0.929).
Conclusion:
Compared with conventional methods, mNGS has a broader pathogen detection spectrum, which may improve the overall detection rate of unknown, uncommon, and mixed infectious pathogens. The selection of diagnostic modalities may be guided by patient-specific factors including CRP levels, age, length of hospital stay, transplantation modality, and granulocyte reconstitution time. mNGS informs antibiotic adjustment, while combined biomarkers (AUC = 0.812) provide prognostic value.
