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Arthroplasty

Better choice of the type of specimen used for untargeted metagenomic sequencing in the diagnosis of periprosthetic joint infections



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Abstract

Aims

As a proven and comprehensive molecular technique, metagenomic next-generation sequencing (mNGS) has shown its potential in the diagnosis of pathogens in patients with periprosthetic joint infection (PJI), using a single type of specimen. However, the optimal use of mNGS in the management of PJI has not been explored. In this study, we evaluated the diagnostic value of mNGS using three types of specimen with the aim of achieving a better choice of specimen for mNGS in these patients.

Methods

In this prospective study, 177 specimens were collected from 59 revision arthroplasties, including periprosthetic tissues, synovial fluid, and prosthetic sonicate fluid. Each specimen was divided into two, one for mNGS and one for culture. The criteria of the Musculoskeletal Infection Society were used to define PJI (40 cases) and aseptic failure (19 cases).

Results

The sensitivity and specificity of mNGS in the diagnosis of PJI were 95% and 94.7%, respectively, for all types of specimen. The sensitivity and specificity were 65% and 100%, respectively, for periprosthetic tissues, 87.5% and 94.7%, respectively, for synovial fluid, and 92.5% and 94.7%, respectively, for prosthetic sonicate fluid. The mNGS of prosthetic sonicate fluid outperformed that for other types of specimen in the rates of detection of pathogens (84.6%), sequencing reads (> ten-fold) and the rate of genome coverage (> five-fold).

Conclusion

mNGS could serve as an accurate diagnostic tool in the detection of pathogens in patients with a PJI using three types of specimen. Due to its superior perfomance in identifying a pathogen, mNGS of prosthetic sonicate fluid provides the most value and may partly replace traditional tests such as bacteriological culture in these patients.

Cite this article: Bone Joint J 2021;103-B(5):923–930.


Correspondence should be sent to Xianlong Zhang. E-mail:

R. He and Q. Wang contributed equally to this work.


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