Surgical management of PJI remains challenging with patients failing treatment despite the best efforts. An important question is whether these later failures reflect reinfection or the persistence of infection. Proponents of reinfection believe hosts are vulnerable to developing infection and new organisms emerge. The alternative hypothesis is that later failure is a result of an organism that was present in the joint but was not picked up by initial culture or was not a pathogen initially but became so under antibiotic pressure. This multicenter study explores the above dilemma. Utilizing next-generation sequencing (NGS), we hypothesize that failures after two stage exchange arthroplasty can be caused by an organism that was present at the time of initial surgery but not isolated by culture. This prospective study involving 15 institutions collected samples from 635 revision total hip (n=310) and knee (n=325) arthroplasties. Synovial fluid, tissue and swabs were obtained intraoperatively for NGS analysis. Patients were classified per 2018 Consensus definition of PJI. Treatment failure was defined as reoperation for infection that yielded positive cultures, during minimum 1-year follow-up. Concordance of the infecting pathogen cultured at failure with NGS analysis at initial revision was determined.Introduction
Methods
The purpose of this multi-center, randomized clinical trial was to compare static and articulating spacers in the treatment of PJI complicating total knee arthroplasty TKA. 68 Patients treated with two-stage exchange arthroplasty were randomized to either a static (32 patients) or an articulating (36 patients) spacer. A power analysis determined that 28 patients per group were necessary to detect a 13º difference in range of motion between groups. Six patients were excluded after randomization, six died, and seven were lost to follow-up prior to two years.Background
Methods
Next generation sequencing (NGS) has been shown to facilitate detection of microbes in a clinical sample, particularly in the setting of culture-negative periprosthetic joint infection (PJI). However, it is unknown whether every microbial DNA signal detected by NGS is clinically relevant. This multi-institutional study was conceived to 1) identify species detected by NGS that may predict PJI, then 2) build a predictive model for PJI in a developmental cohort; and 3) validate the predictive utility of the model in a separate multi-institutional cohort. This multicenter investigation involving 15 academic institutions prospectively collected samples from 194 revision total knee arthroplasties (TKA) and 184 revision hip arthroplasties (THA) between 2017–2019. Patients undergoing reimplantation or spacer exchange procedures were excluded. Synovial fluid, deep tissue and swabs were obtained at the time of surgery and shipped to MicrogenDx (Lubbock, TX) for NGS analysis. Deep tissue specimens were also sent to the institutional labs for culture. All patients were classified per the 2018 Consensus definition of PJI. Microbial DNA analysis of community similarities (ANCOM) was used to identify 17 candidate bacterial species out of 294 (W-value >50) for differentiating infected vs. noninfected cases. Logistic Regression with LASSO model selection and random forest algorithms were then used to build a model for predicting PJI. For this analysis, ICM classification was the response variable (gold standard) and the species identified through ANCOM were the predictor variables. Recruited cases were randomly split in half, with one half designated as the training set, and the other half as the validation set. Using the training set, a model for PJI diagnosis was generated. The optimal resulting model was then tested for prediction ability with the validation set. The entire model-building procedure and validation was iterated 1000 times. From the model set, distributions of overall assignment rate, specificity, sensitivity, positive predictive value (PPV) and negative predicative value (NPV) were assessed.Introduction
Methods
