학술논문
Integrated host-microbe plasma metagenomics for sepsis diagnosis in a prospective cohort of critically ill adults
Document Type
article
Author
Kalantar, Katrina L; Neyton, Lucile; Abdelghany, Mazin; Mick, Eran; Jauregui, Alejandra; Caldera, Saharai; Serpa, Paula Hayakawa; Ghale, Rajani; Albright, Jack; Sarma, Aartik; Tsitsiklis, Alexandra; Leligdowicz, Aleksandra; Christenson, Stephanie A; Liu, Kathleen; Kangelaris, Kirsten N; Hendrickson, Carolyn; Sinha, Pratik; Gomez, Antonio; Neff, Norma; Pisco, Angela; Doernberg, Sarah B; Derisi, Joseph L; Matthay, Michael A; Calfee, Carolyn S; Langelier, Charles R
Source
Nature Microbiology. 7(11)
Subject
Language
Abstract
We carried out integrated host and pathogen metagenomic RNA and DNA next generation sequencing (mNGS) of whole blood (n = 221) and plasma (n = 138) from critically ill patients following hospital admission. We assigned patients into sepsis groups on the basis of clinical and microbiological criteria. From whole-blood gene expression data, we distinguished patients with sepsis from patients with non-infectious systemic inflammatory conditions using a trained bagged support vector machine (bSVM) classifier (area under the receiver operating characteristic curve (AUC) = 0.81 in the training set; AUC = 0.82 in a held-out validation set). Plasma RNA also yielded a transcriptional signature of sepsis with several genes previously reported as sepsis biomarkers, and a bSVM sepsis diagnostic classifier (AUC = 0.97 training set; AUC = 0.77 validation set). Pathogen detection performance of plasma mNGS varied on the basis of pathogen and site of infection. To improve detection of virus, we developed a secondary transcriptomic classifier (AUC = 0.94 training set; AUC = 0.96 validation set). We combined host and microbial features to develop an integrated sepsis diagnostic model that identified 99% of microbiologically confirmed sepsis cases, and predicted sepsis in 74% of suspected and 89% of indeterminate sepsis cases. In summary, we suggest that integrating host transcriptional profiling and broad-range metagenomic pathogen detection from nucleic acid is a promising tool for sepsis diagnosis.