Fungal and mycobacterial periprosthetic joint infections (PJI) are rare events. Clinicians are wary of missing these diagnoses, often leading to the routine ordering of fungal and mycobacterial cultures on periprosthetic specimens. Our goal was to examine the utility of these cultures and explore a modern bacterial culture technique using bacterial blood culture bottles (BCBs) as an alternative. We performed a retrospective review of patients diagnosed with hip or knee PJI between 1 January 2010 and 31 December 2019, at the Mayo Clinic in Rochester, Minnesota, USA. We included patients aged 18 years or older who had fungal, mycobacterial, or both cultures performed together with bacterial cultures. Cases with positive fungal or mycobacterial cultures were reviewed using the electronic medical record to classify the microbiological findings as representing true infection or not.Aims
Methods
The aim of this study was to determine the diagnostic accuracy of α defensin (AD) lateral flow assay (LFA) and enzyme-linked immunosorbent assay (ELISA) tests for periprosthetic joint infection (PJI) in comparison to conventional synovial white blood cell (WBC) count and polymorphonuclear neutrophil percentage (PMN%) analysis. Patients undergoing joint aspiration for evaluation of pain after total knee arthroplasty (TKA) or total hip arthroplasty (THA) were considered for inclusion. Synovial fluids from 99 patients (25 THA and 74 TKA) were analyzed by WBC count and PMN% analysis, AD LFA, and AD ELISA. WBC and PMN% cutoffs of ≥ 1,700 cells/mm3 and ≥ 65% for TKA and ≥ 3,000 cells/mm3 and ≥ 80% for THA were used, respectively. A panel of three physicians, all with expertise in orthopaedic infections and who were blinded to the results of AD tests, independently reviewed patient data to diagnose subjects as with or without PJI. Consensus PJI classification was used as the reference standard to evaluate test performances. Results were compared using McNemar’s test and area under the receiver operating characteristic curve (AUC) analysis.Aims
Methods
