Metagenomic next-generation sequencing of synovial fluid demonstrates high accuracy in prosthetic joint infection diagnostics: mNGS for diagnosing PJI

Abstract

Aims: The aim of this study was to evaluate the performance of metagenomic next-generation sequencing (mNGS) in detecting pathogens from synovial fluid of prosthetic joint infection (PJI) patients.

Methods: A group of 75 patients who underwent revision knee or hip arthroplasties were enrolled prospectively. Ten patients with primary arthroplasties were included as negative controls. Synovial fluid was collected for mNGS analysis. Optimal thresholds were determined to distinguish pathogens from background microbes. Synovial fluid, tissue, and sonicate fluid were obtained for culture.

Results: A total of 49 PJI and 21 noninfection patients were finally included. Of the 39 culture-positive PJI cases, mNGS results were positive in 37 patients (94.9%), and were consistent with culture results at the genus level in 32 patients (86.5%) and at the species level in 27 patients (73.0%). Metagenomic next-generation sequencing additionally identified 15 pathogens from five culture-positive and all ten culture-negative PJI cases, and even one pathogen from one noninfection patient, while yielding no positive findings in any primary arthroplasty. However, seven pathogens identified by culture were missed by mNGS. The sensitivity of mNGS for diagnosing PJI was 95.9%, which was significantly higher than that of comprehensive culture (79.6%; p = 0.014). The specificity is similar between mNGS and comprehensive culture (95.2% and 95.2%, respectively; p = 1.0).

Conclusion: Metagenomic next-generation sequencing can effectively identify pathogens from synovial fluid of PJI patients, and demonstrates high accuracy in diagnosing PJI.Cite this article: Bone Joint Res 2020;9(7):440-449.

Keywords: Diagnosis; Metagenomic next-generation sequencing; Prosthetic joint infection; Synovial fluid.

Fig. 1

General workflow of metagenomic next-generation sequencing. hg19, human reference genome.

Fig. 2

Optimal thresholds for infectious bacteria and fungi identification by metagenomic next-generation sequencing (mNGS). a) Youden index was calculated under different thresholds of relative abundance in genus level. The optimal threshold of bacterial relative abundance in genus level was determined as 15% with highest Youden index. b) Standardized number of reads stringently mapped to pathogen in genus level (SNRSMG) ≥ 3 were recognized as basic inclusive criteria. The graph shows the SNRSMG and relative abundance of bacteria genus (excluding Mycobacteria and Burkholderia), which were interpreted as potential pathogens by mNGS. Red dots indicate culture-positive bacteria identified by mNGS. Blue dots indicate bacteria genera that contained the highest relative abundance in samples of culture-negative prosthetic joint infection. Black dots indicate bacteria genera that contained the highest relative abundance in samples from the noninfection group. The vertical dotted line indicates the optimal threshold of bacterial relative abundance in genus level was determined as 15%. c) SNRSMG and relative abundance of fungal genera that were interpreted as potential pathogens by mNGS. Red dots indicate culture-positive fungi identified by mNGS. The vertical dotted line indicates the optimal threshold of fungal relative abundance in genus level was determined as 30%.

Fig. 3

a) Flowchart detailing enrolment and microbiological results of study samples. b) The concordance between metagenomic next-generation sequencing (mNGS) and culture of culture-positive prosthetic joint infection (PJI), where n = number of patients. c) The number of pathogens detected by mNGS in genus-level compared to culture in the PJI group.

Fig. 4

Comparison of median number of mapped reads among prosthetic joint infection (PJI), noninfection, and primary arthroplasty (PA) groups. Bars indicate interquartile ranges of reads. *p = 0.058