Introduction

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.

Introduction. Recent focus has queried whether of deoxyribonucleic acid (DNA) sequencing modalities of bacterial DNA found in periarticular fluid and tissues will improve in periprosthetic joint infection (PJI) diagnosis and organism identification diagnostic accuracy for periprosthetic joint infection The purpose of this study was to compare the diagnostic accuracy of next generation sequencing (NGS) to polymerase chain reaction (PCR) multiplex, and culture, the Musculoskeletal Infection Society (MSIS) criteria, and the recently proposed criteria by Parvizi et al. [1] in the diagnosis of periprosthetic knee infections. Methods. In this retrospective study, aspirate or tissue samples were collected in 70 revision and 58 primary knee arthroplasties for routine diagnostic workup for PJI and sent to the laboratory for NGS and PCR multiplex. Concordance along with statistical differences between diagnostic studies were calculated using Chi-squared test for categorical data. Results. When comparing to the MSIS criteria, concordance was 78.1% for NGS, 66.4% for PCR, and 85.9% for culture (p<0.001). There was no significant difference based on prior infection (p=0.825), or sample collection method (tissue swab or synovial fluid) (p=0.986). Fifteen samples were culture positive and NGS negative, of which 10 (66.7%) met both criteria for PJI. Thirteen patients were culture negative but NGS positive, of which 2 (15.4%) met both criteria. Concordance was 100% between the MSIS criteria and criteria proposed by Parvizi et al. [1]. Conclusion. In this initial cohort NGS was more accurate than 16s subunit PCR techniques, but less accurate than culture in the diagnosis of PJI determining the presence or absence of PJI. What is not clear is how NGS will perform against culture in terms of identifying the specific bacterial strain. Currently, laboratory tests used for either criteria for PJI diagnosis should be obtained regardless of NGS along with the overall clinical picture to help guide decision making for PJI treatment. For figures, tables, or references, please contact authors directly

Introduction. 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. Methods. 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. Results. The overall predictive accuracy achieved in the model was 75.9% (Figure 1). There was a high accuracy in true-negative and false-negative classification of patients using this predictive model (Figure 2), which has previously been a criticism of NGS interpretation and reporting. Specificity was 97.1%, PPV was 75.0%, and NPV was 76.2%. On comparison of the distribution of abundances between ICM-positive and ICM-negative patients, Staphylococcus aureus was the strongest contributor (F=0.99) to the predictive power of the model (Figure 3). In contrast, Cutibacterium acnes was less predictive (F=0.309) and noted to be abundant across both infected and noninfected revision TJA samples. Discussion. This study is the first to utilize predictive modeling algorithms on a large prospective multicenter database in order to transform analytic NGS data into a clinically relevant diagnostic signal. Our collaborative findings suggest the microbial DNA signal identified on NGS may be an independent useful adjunct for the diagnosis of PJI, as well as help identify causative organisms. Further work applying artificial intelligence tools will improve accuracy, predictive power and clinical utility of high-throughput sequencing technology. For figures, tables, or references, please contact authors directly