Multiplex PCR in septic arthritis and periprosthetic joint infections microorganism identification: Results from the application of a new molecular testing diagnostic algorithm

Abstract

Purpose

Pathogen identification is key in the treatment of septic arthritis (SA) and periprosthetic joint infections (PJI). This study evaluates the outcome of the application of a new, score-based SA and PJI diagnostic algorithm, which includes the execution of molecular testing on synovial fluid.

Methods

A score-based diagnostic algorithm, which includes serologic and synovial fluid markers determination using multiplex PCR (mPCR) and Next Generation Sequencing (NGS) molecular testing, has been applied to a consecutive series of patients with clinically suspected SA or PJI. Patients with a score ≥6 underwent synovial fluid molecular testing, together with traditional culture, to identify the pathogen and its genetically determined antibiotic resistance.

Results

One hundred and seventeen joints in 117 patients (62.5% women; average age 73 years) met the criteria for possible SA/PJI. The affected joint was the knee in 87.5% (joint replacement 66.5%; native joint 21%) and the hip in 12.5% (all replaced joints). 43/117 patients (36.7%) were ultimately diagnosed with SA/PJI. Among the various testing technologies applied, mPCR was the main determinant for pathogen identification in 63%, standard culture in 26%, and mNGS in 11%. Staphylococcus aureus and Enterococcus faecalis were the top two microorganisms identified by mPCR, while Staphylococcus epidermidis was the prevalent organism identified by NGS. mPCR detected the presence/absence of the genetically determined antibiotic resistance of all identified microorganisms. The average timeframe for pathogen identification was 3.13 h for mPCR, 4.5 days for culture, and 3.2 days for NGS.

Conclusions

Molecular diagnostic technologies represent an innovative screening for fast microorganism identification when a joint infection is clinically suspected.

Level of Evidence

Level IV, case series.