Microbial Next Generation DNA Sequencing of Aspirated Synovial Fluid Shows Concordance with ICM Criteria Biomarkers for Diagnosing Periprosthetic Joint Infection in Hip and Knee Arthroplasty

Microbial Next Generation DNA Sequencing of Aspirated Synovial Fluid Shows Concordance with ICM Criteria Biomarkers for Diagnosing Periprosthetic Joint Infection in Hip and Knee Arthroplasty | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Microbial Next Generation DNA Sequencing of Aspirated Synovial Fluid Shows Concordance with ICM Criteria Biomarkers for Diagnosing Periprosthetic Joint Infection in Hip and Knee Arthroplasty Craig D. Tipton, Jacob Ancira, Saad Tarabichi, Alisina Shahi, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7410845/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The diagnosis of periprosthetic joint infection (PJI) is facilitated by consensus identification of synovial biomarkers, which may be aided by targeted microbial next generation sequencing (NGS) of synovial fluid. The primary objective of the study was to evaluate NGS performance across 3 years to ICM 2018 minor criteria for PJI. Methods Synovial fluid specimens submitted from 2020–2022 by outpatient surgical clinics to MicroGenDX for matched synovial biomarker and NGS analysis were selected for retrospective analysis. Synovial biomarkers tested included C-reactive protein (CRP), white blood cell (WBC) count, and polymorphonuclear (PMN) leukocyte percentage. Synovial fluid analysis compared NGS microbial positivity with positive incidence of PJI determined by scoring of synovial biomarkers using ICM 2018 minor criteria for infection. Results The overall sensitivity, specificity, and accuracy of NGS to ICM diagnosed PJI across 2,011 specimens was 76.4% [95% CI: 0.723–0.801], 92.3% [0.91–0.94], and 88.7% [0.87–0.90], respectively. When comparing the diagnostic performance of NGS and individual biomarkers to infection, NGS was more specific to PJI than synovial CRP (specificity = 0.894, 95% CI: 0.88–0.91), but not PMN or WBC. NGS was positive in 7.7% of ICM negative samples. NGS positive:ICM negative samples were associated with significantly elevated synovial PMN (p = 0.001) and WBC (p < 0.0001) compared to NGS negative:ICM negative samples. Across all samples, NGS positivity was associated with significantly elevated results for all tested biomarkers (p < 0.0001). Eight bacterial species dominated the composition in 68% of samples, whereas 46 different microbes were dominant in the remaining third. Conclusions Microbial targeted NGS positivity was concordant with ICM minor criteria for PJI and should be considered a useful tool for diagnosis. There is a low risk of false positive detections comparable to ICM biomarkers. Elevated biomarkers in NGS positive:ICM negative samples may indicate infection occurring that is poorly captured by the three measured synovial biomarkers. Uncommon species are collectively common to PJI and NGS is uniquely positioned to detect such species as they are frequently missed by conventional microbiological testing, including culture and quantitative PCR. These results suggest formal diagnostic schemes would benefit from the addition of NGS as a diagnostic criterion. Next Generation Sequencing NGS Periprosthetic Joint Infection PJI Diagnosis Synovial fluid International Consensus Meeting 2018 ICM-18 Diagnostic Criteria Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Periprosthetic joint infection (PJI) is a devastating complication of joint arthroplasty (JA), requiring aggressive salvage surgeries 1 , leading to increased mortality rates 2 and worsening quality of life 2 . No single test is sufficiently diagnostic for all PJI and expert panels continuously seek to improve consensus guidelines defining PJI based on combinations of clinical presentation, inflammatory biomarkers, and microbiological testing. The International Consensus Meeting 2018 (ICM-18) definition 3 , 4 is one widely accepted scheme used for diagnosis, which is composed of major and minor criteria defining PJI 4 . Major criteria include two phenotypically matching positive cultures or an open sinus tract communicating with the joint. If either criterion is observed, the joint is deemed infected, but major criteria are not met in up to 29% of cases 5 . Minor diagnostic criteria were developed to aid PJI confirmation based on a cumulative scoring index, including a single positive culture, established thresholds for serum or synovial fluid biomarkers, and histological tissue analysis at the time of debridement. The minor criteria are weighted to arrive at a diagnosis for PJI. Klement et al. reported that minor and major criteria agreed in 88% of cases for the diagnosis of PJI 6 . PJI diagnosed only by minor criteria have similar success rates when treated and are thus no less significant while lacking major criteria 6 . The integration of synovial fluid biomarkers is advantageous as synovial fluid is relatively easy to obtain during clinical visits, allowing earlier diagnosis of PJI, and can potentially identify infecting microbes from a single sample 7 , 8 . However, culture-negative PJI is a particularly challenging and common problem because the physician is given no guidance on antimicrobial therapy 9 , 10 . Thus, techniques improving preoperative detection of microbes are advantageous, and can improve the fidelity of diagnostic criteria. Molecular techniques for microbial identification continue to increase in popularity because of the ever shortening reporting time, and the ability to detect a wide range of organisms not limited by culturability 11 , 12 . A variety of molecular methods are available, with the most common being quantitative polymerase chain reaction (qPCR) and next generation sequencing (NGS). Quantitative PCR assays currently have the quickest reporting times, and can be designed to detect individual species, specific genes (e.g., antimicrobial resistance genes), or measure total bacterial or fungal load in a sample 11 – 14 . However, qPCR reports a limited set panel, identifying no more than 25–40 organisms in a test panel 15 , 16 . In contrast, NGS methods including amplicon sequencing of the bacterial 16S ribosomal gene, are designed to detect all species in a sample that meet strict reporting criteria for NGS 17 . In PJI and other orthopedic infections, NGS has shown improved sensitivity in detecting pathogenic organisms 10 , 18 – 20 and is particularly helpful identifying organisms in culture-negative PJI 10 , 21 . Appreciation for the clinical utility of NGS is evidenced by the advocacy for NGS in recent guidelines published by the Infectious Disease Society of America and the American Society for Microbiology 22 . However, a major concern regards specificity of NGS, noting improved detection of microbes may over-diagnose PJI. To address this concern, we compared NGS results in identifying PJI in aspirated synovial fluid samples to PJI diagnosis based on paired ICM-18 minor criteria synovial biomarkers, included in a commercially available laboratory developed test (LDT) which implements NGS (OrthoKEY, MicroGenDX, Lubbock, TX). METHODS Following the launch of the OrthoKEY (NGS) with synovial biomarker test (components provided in Table 1 ), all MicroGenDX records of synovial fluid specimens submitted between December 2020 and November 2022 were flagged as eligible for retrospective study inclusion. Exclusionary criteria included specimens from sources other than the hip or knee, specimens missing data on specimen source, specimens with insufficient quantity for testing, or specimens pooled with other intraoperative sample material. Study protocol was reviewed by Advarra Center for IRB Intelligence and certified as IRB exempt (Pro#00077239). For each sample, matched NGS profiling and synovial biomarker measurements were available. Synovial biomarkers measured included c-reactive protein (CRP), white blood cell count (WBC), and polymorphonuclear neutrophil percentage (PMN%) that estimated the probability of infection, using ICM-18 minor criteria thresholds for determining elevated biomarkers 4 . Specimens with a cumulative minor criterion score 4 or greater were considered high probability for infection, though for ICM-18, scores 4–5 are considered inconclusive and scores \(\:\ge\:\) 6 definitive for PJI diagnosis. Limited metadata available for each patient included joint location, reported sex, and age. Table 1 Components of MDX OrthoKEY Plus Biomarkers Test Assay Targets Reports Cutoff ICM 2018 Score qPCR Panel Universal 16S Bacterial Cell Estimate n/a Common Species Estimated count/ul n/a Antimicrobial resistance Presence/Absence n/a Targeted NGS V1-V2 16S rRNA Bacterial Profile (%) n/a ITS3-4 Fungal Profile (%) n/a Biomarkers CRP mg/L CRP > 6.9 mg/L 1 WBC cells/uL WBC > 3000 cells/uL 3 PMN% % PMN > 80% 2 Synovial Biomarker Profiling Synovial fluid was vortexed and 1 mL was aliquoted for biomarker analysis, with remaining synovial fluid reserved for microbial NGS processing. The synovial fluid aliquot was run on the Sysmex XN-350 (Sysmex America, Mundelein, IL) following manufacturer’s protocol quantifying WBC count and PMN from body fluids. CRP was quantified on the Pentra C400 (Horiba, Irvine, CA) platform following manufacturer’s protocol for body fluids. Microbial Next Generation Sequencing Samples were processed via the OrthoKEY LDT (MicroGenDX, Lubbock, TX), which includes bacterial profiling via targeted amplification and sequencing of the V1-V2 regions of 16S rRNA gene, and fungal profiling based on ITS 3–4 locus sequencing as previously reported 23 . Briefly, the central laboratory performs DNA extraction, PCR amplification, library preparation for sequencing on the Illumina Miseq (Illumina, Inc. San Diego, USA) platform, and bioinformatic processing of sequence data into microbial taxonomy and reporting to clinicians. Although partial 16s analysis cannot fully resolve all bacterial lineages, the underlying V1-V2 region used here is among the most informative for classifying to the species level 17 . Further, species-level identification is generally reproducible when using appropriately curated reference databases 24 . Microbial taxa are aggregated and reported to the species level where possible, as reported in real time to clinicians and similar to prior work 10 , 23 , 25 . The laboratory protocol includes a standardized approach for determining whether a sample is NGS positive or negative, intended to reduce false positive reporting and focus on dominant organisms detected as previously described 17 . To be reported positive, a sample must first pass through broad-range PCR amplification of the partial 16s rRNA (bacterial) and ITS (fungal) genes using 35 cycles and producing an amplicon product that can be visualized by gel electrophoresis (i.e., end point PCR). Samples with a positive product are barcoded and prepared for sequencing. To improve sensitivity, samples failing to produce a band on first attempt were re-evaluated further at 3X higher input volume. Samples fail if a band is not produced after second PCR attempt. After sequencing, samples are evaluated for contamination by a proprietary process comparing specimens to negative extraction controls (extraction without patient specimen) and no template PCR controls (PCR amplification without DNA extract), and positive controls. Contaminant signals are removed from results along with any species detected under 2% relative proportion of each sample’s total read count. Samples may fail if the Quality Control (QC) process flags a high enough amount of a sample’s composition as contamination. The determination of a contaminant signal is based on mutual detection rates and abundance values between patient samples and concurrently run negative controls. This LDT is currently in good standing with the College of American Pathologists (CAP) accreditation, Clinical Laboratory Improvement Amendments (CLIA) licensing, and New York state’s Clinical Laboratory Evaluation Program (CLEP). Statistical Analysis Statistical analysis was performed using R programming environment. Tables were generated using kable or gtsummary 26 libraries. Descriptive analyses were first performed to report on NGS positivity and ICM-18 minor criteria infected rates across hips and knee samples, as well as distributions of samples across interactions of positive NGS with positive ICM criteria. A kappa test was used to evaluate the agreement between NGS positivity and PJI. To compare diagnostic performance of individual synovial markers against PJI, the ‘epi.test’ R function (epiR library) was used to estimate sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV). Wilson’s 95% confidence limits were estimated for each test value. RESULTS A total of 2011 synovial fluid specimens were included with matched NGS profiling and biomarkers (Table 2 ). A greater proportion of specimens originated from the knee (86%) compared to the hip (14%). There was no difference in the distribution of patients by sex or age between the two joints (p > 0.05). The median time to report results was 4 days (Q1 = 3, Q3 = 5) from the time samples were received at the central lab. Overall, the observed NGS positivity was 23% in both hips and knees, whereas 24% of hips and 22% of knees met the ICM-18 minor criteria for high probability PJI (Table 3 , Fig. 1 A). Of positive results, 448 were 16S bacterial and 26 ITS fungal positive results. Table 2 Patient Demographics and primary results by joint Characteristic Hip, N = 287 1 Knee, N = 1,724 1 Mean Age (SD) 65 (11) 67 (11) Sex (Male) 146 (51%) 931 (54%) NGS Positivity 66 (23%) 401 (23%) PJI (High probability) 68 (24%) 386 (22%) Table 3 High/Low Probabilities per NGS Positivity in each joint Knee Hip Characteristic High PJI Probability N = 386 (22%) Low PJI Probability N = 1338 (78%) High PJI Probability N = 68 (24%) Low PJI Probability N = 219 (76%) NGS Result Overall NEG 88 (23%) 1,235 (92%) 19 (28%) 202 (92%) POS 298 (77%) 103 (7.7%) 49 (72%) 17 (7.8%) 16s NEG 94 (24%) 1,247 (93%) 19 (28%) 203 (93%) POS 292 (76%) 91 (6.8%) 49 (72%) 16 (7.3%) ITS NEG 376 (97%) 1,323 (99%) 68 (100%) 218 (100%) POS 10 (2.6%) 15 (1.1%) 0 (0%) 1 (0.5%) Broad-Range NGS Positivity Comparisons to Synovial Biomarkers For most of the analysis presented herein, the synovial biomarker results were analyzed according to their pooled finding of low or high probability infection for PJI following ICM-18. However, a brief analysis also investigated if NGS positivity was correlated with the ICM minor criteria score, noting a score \(\:\ge\:\) 4 was considered high probability for infection. A highly significant linear relationship was observed between aggregated NGS positivity rate and cumulative biomarker score (p = 0.0028, R2 = 0.93, Fig. 1 b). For example; Within ICM-negative samples, samples with an ICM score of zero had the lowest NGS positivity rate of 6.6% which increased to 24.6% at an ICM score of 3. In these low probability of infection samples, NGS positivity was found to be significantly associated with both elevated WBC (p < 0.0001) and PMN values (p = 0.0011), but not CRP (p = 0.14, Figs. 1 c-e). NGS positivity was next compared to the three measured synovial biomarkers to evaluate the concordance to raw lab values of all samples. In each case, NGS positivity was found to be associated with increased values (p < 0.001; Fig. 1 f-h), particularly WBC and PMN%. Though NGS positivity was still largely discriminatory for CRP, the effect size is smaller and there is relatively more overlap. This led to the question of whether synovial NGS positivity was more concordant with PJI than synovial CRP. Indeed, the specificity of NGS positivity was observed at 92.3% (95% CI: 0.909–0.935, Table 4 ), greater than CRP at 89.4% (0.878–0.908) but lower than WBC at 97.9% (0.97–0.985) and PMN% at 94.7% (0.935–0.957). Table 4 Comparing diagnostic performance of individual synovial fluid test results to ICM-18 minor criteria for overall diagnosis by synovial markers Synovial Diagnostic Sensitivity Specificity Accuracy PPV NPV CRP 0.802 (.763-.836) 0.894 (.878-.908) 0.873 (.858-.887) 0.688 (.647-.726) 0.939 (.926-.95) WBC 1 (.992-1) 0.979 (.97-.985) 0.984 (.977-.988) 0.932 (.906-.951) 1 (.997-1) PMN(%) 0.936 (.91-.955) 0.947 (.935-.957) 0.945 (.934-.954) 0.838 (.804-.868) 0.981 (.972-.987) NGS Positivity 0.764 (.723-.801) 0.923 (.909-.935) 0.887 (.873-.9) 0.743 (.702-.781) 0.931 (.917-.942) Abbreviations; PPV = Positive Predictive Value; NPV = Negative Predictive Value NGS Positivity is Concordant with High Probability PJI NGS positivity among ICM positive specimens was 77% in knees and 72% in hips with 7.7% and 7.8% potential false positives, respectively (Table 3 ). Kappa testing was used to evaluate the chance corrected agreement between NGS and PJI. Overall, NGS positivity was substantially concordant (kappa = 0.68) and 88.71% accurate (95% CI 87.25–90.06) to PJI (Table 5 ). Concordance for hips and knees were each very similar to the overall findings, though with a higher sensitivity for knees (77.2%) as compared to hips (72.06%). Much of the observed sensitivity was due to the bacterial 16S rather than fungal ITS. For ICM positive knees, bacterial 16S and fungal ITS positivity were 76% and 2.6%, respectively. ICM positive hips had lower detection rates at 72% 16S positivity and 0% ITS positivity (Table 3 ). Table 5 Concordance of broad-range NGS positivity with Biomarkers Overall, and within each Joint Joint N Accuracy 95% CI Sensitivity Specificity PPV NPV Kappa Overall 2011 88.71% 87.25% − 90.06% 76.43% 92.29% 74.3% 93.07% 0.6803 Knee 1,724 88.92% 87.34% − 90.36% 77.2% 92.3% 74.31% 93.35% 0.6856 Hip 287 87.46% 83.06% − 91.06% 72.06% 92.24% 74.24% 91.4% 0.6495 Microbial NGS Profile A total of 160 unique bacterial species were detected with 69 being the dominant organism (> 50% relative abundance) in at least one sample versus 16 unique fungal species and 13 dominant fungi. There was a median 1 species reported per NGS positive sample and 104 (22.3%) were polymicrobial. Staphylococcal species were the most frequent dominant species, reported in 51% of samples and including S. epidermidis , S. aureus , S. lugdunensis , and S. capitis . Additional species with more than 2% dominance included Streptococcus agalactiae (2.9%), Pseudomonas aeruginosa (2.9%), Cutibacterium acnes (4.0%) and Enterococcus faecalis (2.9%). These 8 species composed 64% of the dominant species reported with 61 relatively less common species being dominant in the remaining 160 specimens (36%). Fungal detections were generally rare, observed in 2.2% (10) of all PJI specimens and in 1.0% (16) of PJI-negative specimens. Species included several Candida species ( C. albicans , C. glabrata , C. parapsilosis , etc…), Aureobasidium pullulans , Malassezia globosa , and Naganishia diffluens (Fig. 3 , Supplementary Table 2). Consistent with the high rate of NGS positivity associated with high probability infection samples, incidence and mean relative abundance of the most common species heavily favored specimens scored as high probability infection (Figs. 3 , 4). A few of these top species were noted to be generally consistent with hips and knees such as S. epidermidis and S. aureus , however others can be seen to appear more distinct between the two joints. For example, Fig. 4 separates species according to their joint-wise distributions and notes a few lineages including S. capitis and Streptococcus parasanguinis , among others, more abundant in hips. Cutibacterium acnes was a notable departure from all other species in that C. acnes appeared less discriminatory by infection probability. Logistic regression was used to estimate the relative risk of PJI based on each species, whereas C. acnes detection still indicated more than 2X greater relative risk for PJI, this risk was lower than for other species detected as abundantly (Fig. 4). Of the fungal species detected, Candida albicans , C. glabrata , and A. pullulans were generally detected at higher rates in PJI samples and dominated the fungal composition of at least two samples, whereas other Candida species ( C. parapsilosis , C. orthopsilosis , C. metapsilosis ) were more frequent and dominant among PJI-negative specimens (Fig. 3 , Supplementary Table 2). DISCUSSION This study was conducted to benchmark the performance of a commercially available NGS technology compared to synovial fluid biomarkers used to diagnose PJI using ICM-18 criterion. We hypothesized that positive DNA signals in synovial fluid signifies the presence of clinically relevant microbes, and that positive NGS detection will associate with biomarkers indicative of infection. This study shows NGS positivity is concordant with diagnostic PJI ICM minor criteria biomarkers, evidenced by the substantial agreement observed by kappa testing and performance statistics, and had greater specificity than CRP (Tables 3 , 4 ). Further, NGS positivity was associated with elevated levels for each tested biomarker (Fig. 1 ) . A concern with incorporating NGS as a standard of care for PJI diagnosis is that increased sensitivity will lead to a high false positive rate and unnecessary treatment 11 . This study refutes this concern. NGS positivity demonstrated a comparable and non-inferior specificity to prior estimates at 92.3% (95% CI: 0.91–0.94, Tables 4 , 5 ). This is supported by a recent meta-analysis including 18 studies examining the diagnostic performance of synovial fluid cultures against PJI which observed 96% specificity (95% CI: 0.93–0.98) 27 . Moreover, we report a superior increase in sensitivity for NGS at 76.4% (95% CI: 0.72–0.80) compared to the prior pooled sensitivity for culture at 63% (95% CI: 0.56–0.70). While we intend future studies with matched culture and NGS results to corroborate this finding, the present data show that NGS, using synovial fluid singularly, is both significantly more sensitive than prior expectations for culture and comparably specific to other validated biomarkers for diagnosing PJI. The comparisons of NGS positivity to biomarkers in samples deemed low probability of infection emphasizes the complementary role that NGS can provide in diagnosing PJI (Fig. 1 b-e). First, a highly significant relationship was observed between NGS positivity and cumulative ICM score, suggesting that samples scored as a three with 1–2 elevated biomarkers also show higher NGS positivity rates when compared to other ICM negative samples. Further, NGS positivity was highly significantly related to both elevated PMN and WBC when considering only ICM negative samples. Synovial WBC and PMN are considered among the most reliable synovial biomarkers for PJI 28 , 29 . These findings support that NGS based detection of microbial DNA in synovial fluid may be capturing a unique component of PJI that is missed by only assessing the more commonly measured biomarkers. More specifically, we hypothesize that some of these discrepant NGS positive results may represent infection from microbes failing to elicit a strong immune response, a sub-clinical infection, or perhaps early detection of an infection that has not yet fully manifested clinically. Future work examining longitudinal trends in microbiological findings, synovial biomarkers, and clinical findings will be critical in further understanding the value of these discrepant results. Nevertheless, the strong relationship of PMN and WBC with NGS positivity in low probability infection samples supports the potential for NGS positivity as a diagnostic criterion to improve sensitivity in diagnosing PJI, especially in difficult cases involving fastidious microbes associated with ambiguous biomarker values according to ICM-18 definitions. In addition to the role in diagnosing PJI as well as identifying microbes in culture-negative infection 10 , 12 , NGS is capable of detecting a wide variety of organisms which may be missed by culture-dependent identification as well as other molecular methods limited by species specific probes or antibodies 21 . Consistent with prior literature, we report a high prevalence of Staphyloccocus aureus , coagulase negative Staphylococcus ( S. epidermidis , S. capitis ), S. lugdunensis , Streptococcus agalactiae (i.e., GBS), Pseudomonas , and Cutibacterium acnes . Organisms underreported by culture-dependent methods were also observed, often as the dominant organism, particularly anaerobes such Anaerococcus , Bacteroides , and Finegoldia magna (Figs. 2 , 3 ). Similarly, Cutibacterium acnes is underreported compared to molecular methods 13 , 30 . Eight bacterial species dominated the composition in 68% of samples, though the remaining 32% consisted of 46 different dominant species (Supplementary Table 1). This result highlights that although any given species such as Enterococcus faecium or Streptococcus dysgalactiae may be individually uncommon, uncommon species are a common aspect in PJI which may be overlooked by routine microbiological testing. For example, the broader detection range enabled by NGS is more informative than the commonly used qPCR BioFire Joint Infection Panel (bioMerieux,Marcy-l’Etoile, FR) which, in comparison, would only detect 56% of the 69 dominant species identified in this current work. While such panels are useful in being able to deliver rapid results of common pathogens, the limited qPCR panels have shown lower overall sensitivity to PJI ranging 41%-56% 16,20,31 and are known to underreport polymicrobial instances of PJI 20 . Of the more prevalent organisms reported by NGS, C. acnes was uniquely abundant overall and reported more frequently in ICM low probability infection samples according to minor criteria (Fig. 2 ). This finding was not unexpected, as C. acnes is frequently reported as a causative pathogen in PJI 32 , yet it has also been shown to elicit a variable response from the host, relegating its positivity as a potential contaminant 33 . Reliable detection of C. acnes via culture can be a challenge requiring anaerobic isolation and extended 14-day incubation time 34 , 35 . Further, C. acnes does not always elicit a strong host response and has been associated with PJI manifesting later in the implant life cycle 34 , 36 , 37 . Thus, early confirmation via NGS is useful, providing impetus towards treatment rather than neglecting equivocal synovial biomarker data in patients with a painful JA. When detected, C. acnes JA infections have high treatment success and 2-year survival rates 32 , 33 , 38 , indicating that C. acnes associated PJI is manageable if detected. Further, current results may support treating NGS detected C. acnes in circumstances of ICM lower probability of PJI, acknowledging further study is required in understand treating C. acnes when isolated in the setting of a diminished inflammatory response 35 , 36 . Although many studies contrast the utility of culture and NGS, we believe that consensus guidelines will benefit by considering information from both techniques, rather than one or the other, noting improved sensitivity of microbiological testing for PJI when both are used 39 . A recent comprehensive review of NGS in PJI reports strong sensitivity and specificity 40 . We advocate the adoption of NGS, especially in culture-negative cases, when rapid pathogen identification is needed, in patients with a high-pretest probability of infection, or when rare pathogens are suspected. We emphasize this study shows rare pathogens to be collectively common, including microbes not included in common multiplex qPCR panels and fastidious organisms likely to be missed by culture. There is concern that NGS may lead to increased false positive detection, but the specificities shown here and the accuracies reported in the review by Martinazzi et al. 40 refute that concern. We recommend that NGS as implemented herein is a useful tool that will assist with identifying PJI causative organisms, confirming PJI diagnosis, and should be considered as a criterion in PJI diagnosis. The current study supports the use of NGS in guiding PJI diagnosis, however not without limitations. We only compared NGS to biomarkers of ICM-18 minor criteria which did not include culture and other ICM criteria for infection. However, when using synovial biomarkers, NGS showed superior sensitivity to prior expectations for culture and non-inferior or comparable specificity to currently accepted indicators for infection. While culture data could have helped to define PJI, there is a reported 88% concordance between minor and major criteria in diagnosing PJI 6 . The NGS testing platform is a highly refined technology to identify the presence of microbes within a sample. Similarly, culture identification requires specific expertise, and it is not readily feasible to perform both tests with similar alacrity at the same facility. We plan a future study comparing synovial fluid sent to both an NGS testing center and a culture testing center. Also, NGS results were compared only to synovial fluid biomarkers, absent alpha defensin, and not to ICM-18 serum biomarkers that include CRP, D-dimer, and sedimentation rate. The current work did not consider intraoperative targeted NGS results, which has shown superior sensitivity in microbial detection for PJI up to 89.3% by the same comparator lab 41 . We emphasize the focus of this study is synovial fluid analysis for preoperative diagnosis of PJI. A positive diagnosis provides surgeon clarity and a direction of treatment before surgery, rather than modifying post-operative treatment when the joint of interest is determined PJI positive based on intra-operative ICM-18 criteria. We believe a positive NGS test is commensurate with existing ICM-18 minor criteria diagnosing PJI. Based on this data, we feel that NGS testing should be a primary diagnostic tool, performed early as opposed to a secondary diagnostic when other testing has already failed. Moreover, a positive NGS should be weighted similar to a positive culture when restructuring future pji diagnostic criterion. Further, we believe NGS will close the gap in culture negative infection as NGS enables detection of fastidious microbes thus allowing fidelity in antimicrobial stewardship. The enhanced sensitivity for microbial detection can be used to prepare targeted treatment of PJI causative organisms. We look forward to seeing and conducting further research on the implementation of NGS into the management of PJI. CONCLUSION Microbial NGS testing of synovial fluid showed concordance to biomarkers used in ICM-18 PJI minor criteria. NGS showed a low risk of false positive detections in synovial fluid. NGS has greater sensitivity and broader discovery power than other available methods for microbial diagnosis. We advocate diagnostic schemes include NGS as a diagnostic criterion when restructuring future diagnostic criteria defining PJI. Declarations Ethics approval and consent to participate Study protocol was reviewed by Advarra Center for IRB Intelligence and certified as IRB exempt (Pro#00077239). Consent for publication Not applicable. Availability of data and materials De-identified data sufficient to reproduce analysis are available in Supplementary File 1. Competing interests Authors CDT, JA, NS, NAT, KO, KJ, and JW are or were employees of MicroGen DX. CDP reports consulting fees from MicroGen DX. JP reports personal fees and stock option from Corentec, personal fees from Data Trace, Elsevier, Jaypee Publishers, SLACK Inc., Wolters Kluwer, Becton Dickensen, Zimmer Biomet, Ethicon, Tenor, KCI/3M (Acelity), MicroGen DX, Jointstem, and Cardinal Health. JP reports stock option from Parvizi Surgical Innovation, Hip Innovation Technology, Alphaeon/Strathsby Crown, Elute, Ceribell, Acumed, PRN-Veterinary, Illuminus, Intellijoint, Osteal, nanooxygenic, Sonata, Molecular Surface Technologies, and Peptilogic. All other authors declare no financial or non-financial competing interests. Funding There is no specific source of funding for this work. Informed consent was not required for this retrospective study as it utilized de-identified laboratory data generating during routine testing. References Bozic, K. J. et al. The epidemiology of revision total hip arthroplasty in the United States. J. Bone Joint Surg. Am. 91 , 128–133 (2009). Xu, Y., Huang, T. B., Schuetz, M. A. & Choong, P. F. M. Mortality, patient-reported outcome measures, and the health economic burden of prosthetic joint infection. EFORT Open Rev. 8 , 690–697 (2023). Parvizi, J. & Gehrke, T. Definition of Periprosthetic Joint Infection. J. Arthroplasty 29 , 1331 (2014). Parvizi, J. et al. The 2018 Definition of Periprosthetic Hip and Knee Infection: An Evidence-Based and Validated Criteria. J. Arthroplasty 33 , 1309-1314.e2 (2018). Sigmund, I. K., Luger, M., Windhager, R. & McNally, M. A. Diagnosing periprosthetic joint infections. Bone Jt. Res. 11 , 608–618 (2022). Klement, M. R. et al. Are All Periprosthetic Joint Infections the Same? Evaluating Major vs Minor Criteria. J. Arthroplasty 33 , 1515–1519 (2018). Solarino, G. et al. What’s New in the Diagnosis of Periprosthetic Joint Infections: Focus on Synovial Fluid Biomarkers. Trop. Med. Infect. Dis. 7 , 355 (2022). Cipriano, C. A. et al. Serum and synovial fluid analysis for diagnosing chronic periprosthetic infection in patients with inflammatory arthritis. J. Bone Joint Surg. Am. 94 , 594–600 (2012). Mortazavi, S. M. J., Vegari, D., Ho, A., Zmistowski, B. & Parvizi, J. Two-stage exchange arthroplasty for infected total knee arthroplasty: predictors of failure. Clin. Orthop. 469 , 3049–3054 (2011). Goswami, K. et al. An Enhanced Understanding of Culture-Negative Periprosthetic Joint Infection with Next-Generation Sequencing: A Multicenter Study. J. Bone Joint Surg. Am. 104 , 1523–1529 (2022). McClure, E. A., Werth, P., Ross, B. & Gitajn, I. L. Application of Nucleic Acid-Based Strategies to Detect Infectious Pathogens in Orthopaedic Implant-Related Infection. J. Bone Joint Surg. Am. 105 , 556–568 (2023). Kullar, R. et al. Next-Generation Sequencing Supports Targeted Antibiotic Treatment for Culture Negative Orthopedic Infections. Clin. Infect. Dis. Off. Publ. Infect. Dis. Soc. Am. 76 , 359–364 (2022). Fink, B. et al. Preoperative PCR analysis of synovial fluid has limited value for the diagnosis of periprosthetic joint infections of total knee arthroplasties. Arch. Orthop. Trauma Surg. 138 , 871–878 (2018). Lleo, M. M. et al. Detecting the presence of bacterial DNA by PCR can be useful in diagnosing culture-negative cases of infection, especially in patients with suspected infection and antibiotic therapy. FEMS Microbiol. Lett. 354 , 153–160 (2014). Esteban, J. et al. Multicenter evaluation of the BIOFIRE Joint Infection Panel for the detection of bacteria, yeast, and AMR genes in synovial fluid samples. J. Clin. Microbiol. 61 , e00357-23. Gardete-Hartmann, S. et al. The role of BioFire Joint Infection Panel in diagnosing periprosthetic hip and knee joint infections in patients with unclear conventional microbiological results. Bone Jt. Res. 13 , 353–361 (2024). Kullar, R. et al. Next-Generation Sequencing in Periprosthetic Joint Infections: Clinicians’ Guide to Its Diagnostic Role. Infect. Dis. Clin. Pract. 33 , e1448 (2025). Huang, Z. et al. Metagenomic next-generation sequencing of synovial fluid demonstrates high accuracy in prosthetic joint infection diagnostics: mNGS for diagnosing PJI. Bone Jt. Res. 9 , 440–449 (2020). Flurin, L. et al. Targeted Metagenomic Sequencing-based Approach Applied to 2146 Tissue and Body Fluid Samples in Routine Clinical Practice. Clin. Infect. Dis. Off. Publ. Infect. Dis. Soc. Am. 75 , 1800–1808 (2022). Azad, M. A. et al. Comparison of the BioFire Joint Infection Panel to 16S Ribosomal RNA Gene-Based Targeted Metagenomic Sequencing for Testing Synovial Fluid from Patients with Knee Arthroplasty Failure. J. Clin. Microbiol. 60 , e0112622 (2022). Chowdhry, M. et al. Next generation sequencing identifies an increased diversity of microbes in post lavage specimens in infected TKA using a biofilm disrupting irrigant. The Knee 51 , 231–239 (2024). Miller, J. M. et al. Guide to Utilization of the Microbiology Laboratory for Diagnosis of Infectious Diseases: 2024 Update by the Infectious Diseases Society of America (IDSA) and the American Society for Microbiology (ASM). Clin. Infect. Dis. Off. Publ. Infect. Dis. Soc. Am. ciae104 (2024) doi:10.1093/cid/ciae104. Liss, M. A., Reveles, K. R., Tipton, C. D., Gelfond, J. & Tseng, T. Comparative Effectiveness Randomized Clinical Trial Using Next-generation Microbial Sequencing to Direct Prophylactic Antibiotic Choice Before Urologic Stone Lithotripsy Using an Interprofessional Model. Eur. Urol. Open Sci. 57 , 74–83 (2023). Hoffman, C. et al. Species-Level Resolution of Female Bladder Microbiota from 16S rRNA Amplicon Sequencing. mSystems 6 , e0051821 (2021). Whitfield, R., Tipton, C. D., Diaz, N., Ancira, J. & Landry, K. S. Clinical Evaluation of Microbial Communities and Associated Biofilms with Breast Augmentation Failure. Microorganisms 12 , 1830 (2024). Sjoberg, D. D., Whiting, K., Curry, M., Lavery, J. A. & Larmarange, J. Reproducible Summary Tables with the gtsummary Package. R J. 13 , 570–580 (2021). Watanabe, S. et al. Differences in Diagnostic Sensitivity of Cultures Between Sample Types in Periprosthetic Joint Infections: A Systematic Review and Meta-Analysis. J. Arthroplasty 39 , 1939–1945 (2024). Kuo, F.-C. et al. Which Minor Criteria is the Most Accurate Predictor for the Diagnosis of Hip and Knee Periprosthetic Joint Infection in the Asian Population? J. Arthroplasty 37 , 2076–2081 (2022). McNally, M. et al. The EBJIS definition of periprosthetic joint infection. Bone Jt. J. 103-B , 18–25 (2021). Morgenstern, C., Cabric, S., Perka, C., Trampuz, A. & Renz, N. Synovial fluid multiplex PCR is superior to culture for detection of low-virulent pathogens causing periprosthetic joint infection. Diagn. Microbiol. Infect. Dis. 90 , 115–119 (2018). Lee, R. A. Clinical performance evaluation of the BioFire Joint Infection Panel. J. Clin. Microbiol. 62 , e0102224 (2024). Hedlundh, U., Zacharatos, M., Magnusson, J., Gottlander, M. & Karlsson, J. Periprosthetic hip infections in a Swedish regional hospital between 2012 and 2018: is there a relationship between &lt;i&gt;Cutibacterium acnes&lt;/i&gt; infections and uncemented prostheses? J. Bone Jt. Infect. 6 , 219–228 (2021). Hoch, A. et al. Treatment outcomes of patients with Cutibacterium acnes-positive cultures during total joint replacement revision surgery: a minimum 2-year follow-up. Arch. Orthop. Trauma Surg. 143 , 2951–2958 (2023). Renz, N., Mudrovcic, S., Perka, C. & Trampuz, A. Orthopedic implant-associated infections caused by Cutibacterium spp. – A remaining diagnostic challenge. PLOS ONE 13 , e0202639 (2018). Nodzo, S. R. et al. Propionibacterium acnes Host Inflammatory Response During Periprosthetic Infection Is Joint Specific. HSS Journal® Musculoskelet. J. Hosp. Spec. Surg. 13 , 159–164 (2017). Liew-Littorin, C., Davidsson, S., Nilsdotter-Augustinsson, Å., Brueggemann, H. & S\" oderquist, B. GENOMIC CHARACTERIZATION AND CLINICAL EVALUATION OF PERIPROSTHETIC JOINT INFECTIONS CAUSED BY CUTIBACTERIUM ACNES. Orthop. Proc. 106-B , 15–15 (2024). Vilchez, H. H. et al. Prosthetic Shoulder Joint Infection by Cutibacterium acnes: Does Rifampin Improve Prognosis? A Retrospective, Multicenter, Observational Study. Antibiotics 10 , 475 (2021). Warne, C. N. et al. Cutibacterium acnes periprosthetic joint infections. Bone Amp Jt. J. 106-B , 1426–1430 (2024). Flurin, L. et al. Clinical Use of a 16S Ribosomal RNA Gene-Based Sanger and/or Next Generation Sequencing Assay to Test Preoperative Synovial Fluid for Periprosthetic Joint Infection Diagnosis. mBio 13 , e01322-22. Martinazzi, B. et al. HK35: Is there a role for the use of molecular techniques in isolation of infective organism(s) causing periprosthetic joint infection (PJI)? (2025). Tarabichi, M., Shohat, N. & Goswami, K. Diagnosis of periprosthetic joint infection: The potential of next-generation sequencing. J Bone Jt Surg - Am Vol , 147–154 (2018). Additional Declarations Competing interest reported. Authors C.D.T., J.A., N.S., N.A.T., K.O., K.J., and J.W. are or were employees of MicroGen DX. C.D.P. reports consulting fees from MicroGen DX. J.P. reports personal fees and stock option from Corentec, personal fees from Data Trace, Elsevier, Jaypee Publishers, SLACK Inc., Wolters Kluwer, Becton Dickensen, Zimmer Biomet, Ethicon, Tenor, KCI/3M (Acelity), MicroGen DX, Jointstem, and Cardinal Health. J.P. reports stock option from Parvizi Surgical Innovation, Hip Innovation Technology, Alphaeon/Strathsby Crown, Elute, Ceribell, Acumed, PRN-Veterinary, Illuminus, Intellijoint, Osteal, nanooxygenic, Sonata, Molecular Surface Technologies, and Peptilogic. All other authors declare no financial or non-financial competing interests. Supplementary Files SupplementalFile1.xlsx Supplementary.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7410845","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504553611,"identity":"66cb6613-06ff-4ca5-b3e5-0cd4cc1889a4","order_by":0,"name":"Craig D. Tipton","email":"data:image/png;base64,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","orcid":"","institution":"MicroGen DX","correspondingAuthor":true,"prefix":"","firstName":"Craig","middleName":"D.","lastName":"Tipton","suffix":""},{"id":504553612,"identity":"9292c36b-e789-421e-83e0-f22cbee028e5","order_by":1,"name":"Jacob Ancira","email":"","orcid":"","institution":"MicroGen DX","correspondingAuthor":false,"prefix":"","firstName":"Jacob","middleName":"","lastName":"Ancira","suffix":""},{"id":504553613,"identity":"c98be710-f922-46c6-8d43-224ac8e2f5cc","order_by":2,"name":"Saad Tarabichi","email":"","orcid":"","institution":"Mayo Clinic Arizona","correspondingAuthor":false,"prefix":"","firstName":"Saad","middleName":"","lastName":"Tarabichi","suffix":""},{"id":504553614,"identity":"59c7b15e-8d9a-412b-9eeb-c5a4a6232867","order_by":3,"name":"Alisina Shahi","email":"","orcid":"","institution":"University of Texas Health Science Center at Houston","correspondingAuthor":false,"prefix":"","firstName":"Alisina","middleName":"","lastName":"Shahi","suffix":""},{"id":504553615,"identity":"5ea498da-aa59-47e4-8406-403c7a80e4da","order_by":4,"name":"Kayla Jarvis","email":"","orcid":"","institution":"MicroGen DX","correspondingAuthor":false,"prefix":"","firstName":"Kayla","middleName":"","lastName":"Jarvis","suffix":""},{"id":504553616,"identity":"8c88bdae-7db9-448a-b651-856a5adb6ea4","order_by":5,"name":"Khalid Omeir","email":"","orcid":"","institution":"MicroGen DX","correspondingAuthor":false,"prefix":"","firstName":"Khalid","middleName":"","lastName":"Omeir","suffix":""},{"id":504553617,"identity":"84a3082e-7084-4783-aa83-303dd42c8e77","order_by":6,"name":"Nicholas Sanford","email":"","orcid":"","institution":"MicroGen DX","correspondingAuthor":false,"prefix":"","firstName":"Nicholas","middleName":"","lastName":"Sanford","suffix":""},{"id":504553618,"identity":"37de6eb9-d9dc-452e-82c6-36c93d226e66","order_by":7,"name":"Nick A. Tallman","email":"","orcid":"","institution":"MicroGen DX","correspondingAuthor":false,"prefix":"","firstName":"Nick","middleName":"A.","lastName":"Tallman","suffix":""},{"id":504553619,"identity":"44a6e8fd-cf2e-4ec5-bbd3-ae780b35e225","order_by":8,"name":"Jennifer White","email":"","orcid":"","institution":"MicroGen DX","correspondingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"","lastName":"White","suffix":""},{"id":504553620,"identity":"379a1329-d8b8-481b-b095-163792999b05","order_by":9,"name":"Caleb D. Phillips","email":"","orcid":"","institution":"Texas Tech University","correspondingAuthor":false,"prefix":"","firstName":"Caleb","middleName":"D.","lastName":"Phillips","suffix":""},{"id":504553621,"identity":"98fbdee2-626e-48e6-a476-bd9e18e957d8","order_by":10,"name":"Javad Parvizi","email":"","orcid":"","institution":"Acibadem University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Javad","middleName":"","lastName":"Parvizi","suffix":""},{"id":504553622,"identity":"b0ea59bc-c6ea-45af-997b-5f22a1ade712","order_by":11,"name":"Edward J. McPherson","email":"","orcid":"","institution":"University of California Los Angeles","correspondingAuthor":false,"prefix":"","firstName":"Edward","middleName":"J.","lastName":"McPherson","suffix":""}],"badges":[],"createdAt":"2025-08-19 17:23:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7410845/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7410845/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89988948,"identity":"bfcbb9ba-531c-4672-bf2a-5923f33a67d4","added_by":"auto","created_at":"2025-08-27 07:07:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":260773,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparisons of NGS positivity to ICM synovial biomarkers.\u003c/strong\u003e (a) Histogram shows the distribution of specimens across each possible ICM score. A vertical dashed line indicates the threshold used in the current study for high probability infection adapted from the 2018 ICM minor criteria. (b) The percentage of NGS positive samples was calculated for each possible ICM score. A direct linear relationship was observed between cumulative ICM scores and aggregated NGS positivity. (c-h) Box plots show the relationship between NGS positivity and raw biomarker score distributions for (c, f) CRP, (d, g) WBC count, and (e, h) PMN%. For the first set (c-e), NGS positivity was compared to biomarkers in uninfected samples whereas (f-h) compares biomarker values in all samples by NGS positivity. In the box plots, box indicates the 1st and 3rd quartiles, the thicker banded line within the box indicates the median, and the whiskers indicate the bounds of the distribution. A violin plot layer visualizes the full data distribution. A two-tailed t-test was used to determine statistical significance by NGS positivity and each biomarker, with p value shown in plot. A dashed horizontal line indicates ICM thresholds for elevated biomarkers.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7410845/v1/17fb259a37ad5016b65224cb.png"},{"id":89990663,"identity":"7f0d39c5-99b1-4b97-bad4-06c8582069f1","added_by":"auto","created_at":"2025-08-27 07:15:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":143254,"visible":true,"origin":"","legend":"\u003cp\u003eComparing the percentage detection rate by setting and probability of infection across top species by incidence.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7410845/v1/f8b230d8bc63136d6656528b.png"},{"id":89988954,"identity":"0cc35362-82f1-40de-963c-e6447a3f36b0","added_by":"auto","created_at":"2025-08-27 07:07:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":188603,"visible":true,"origin":"","legend":"\u003cp\u003eMean relative abundance by joint location and infection probability. Right annotation references the Relative Risk of Higher probability of infection per species. Values over one, signifies an increased risk of having a higher probabilty of infection with exposure to species.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7410845/v1/86aed796acfcd90420713444.png"},{"id":89992289,"identity":"d4685de2-1b91-44ee-8a4b-62ce07c7083c","added_by":"auto","created_at":"2025-08-27 07:31:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1428945,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7410845/v1/6aa8326b-ecda-465e-8569-6e7adcbeafb1.pdf"},{"id":89988952,"identity":"c3f21c5a-ede8-455c-afb3-668d281f4f74","added_by":"auto","created_at":"2025-08-27 07:07:38","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":201683,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7410845/v1/7f99f4b456616df43a2373c3.xlsx"},{"id":89988953,"identity":"bd3f2567-6b16-45d8-8787-42d9fd64a414","added_by":"auto","created_at":"2025-08-27 07:07:39","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":26363,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-7410845/v1/b0057eee42676fea3a1a7d4b.docx"}],"financialInterests":"Competing interest reported. Authors C.D.T., J.A., N.S., N.A.T., K.O., K.J., and J.W. are or were employees of MicroGen DX. C.D.P. reports consulting fees from MicroGen DX. J.P. reports personal fees and stock option from Corentec, personal fees from Data Trace, Elsevier, Jaypee Publishers, SLACK Inc., Wolters Kluwer, Becton Dickensen, Zimmer Biomet, Ethicon, Tenor, KCI/3M (Acelity), MicroGen DX, Jointstem, and Cardinal Health. J.P. reports stock option from Parvizi Surgical Innovation, Hip Innovation Technology, Alphaeon/Strathsby Crown, Elute, Ceribell, Acumed, PRN-Veterinary, Illuminus, Intellijoint, Osteal, nanooxygenic, Sonata, Molecular Surface Technologies, and Peptilogic. All other authors declare no financial or non-financial competing interests.","formattedTitle":"Microbial Next Generation DNA Sequencing of Aspirated Synovial Fluid Shows Concordance with ICM Criteria Biomarkers for Diagnosing Periprosthetic Joint Infection in Hip and Knee Arthroplasty","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003ePeriprosthetic joint infection (PJI) is a devastating complication of joint arthroplasty (JA), requiring aggressive salvage surgeries\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, leading to increased mortality rates\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and worsening quality of life\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. No single test is sufficiently diagnostic for all PJI and expert panels continuously seek to improve consensus guidelines defining PJI based on combinations of clinical presentation, inflammatory biomarkers, and microbiological testing. The International Consensus Meeting 2018 (ICM-18) definition\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e is one widely accepted scheme used for diagnosis, which is composed of major and minor criteria defining PJI\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Major criteria include two phenotypically matching positive cultures or an open sinus tract communicating with the joint. If either criterion is observed, the joint is deemed infected, but major criteria are not met in up to 29% of cases\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMinor diagnostic criteria were developed to aid PJI confirmation based on a cumulative scoring index, including a single positive culture, established thresholds for serum or synovial fluid biomarkers, and histological tissue analysis at the time of debridement. The minor criteria are weighted to arrive at a diagnosis for PJI. Klement et al. reported that minor and major criteria agreed in 88% of cases for the diagnosis of PJI\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. PJI diagnosed only by minor criteria have similar success rates when treated and are thus no less significant while lacking major criteria\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The integration of synovial fluid biomarkers is advantageous as synovial fluid is relatively easy to obtain during clinical visits, allowing earlier diagnosis of PJI, and can potentially identify infecting microbes from a single sample\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. However, culture-negative PJI is a particularly challenging and common problem because the physician is given no guidance on antimicrobial therapy\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Thus, techniques improving preoperative detection of microbes are advantageous, and can improve the fidelity of diagnostic criteria.\u003c/p\u003e\u003cp\u003eMolecular techniques for microbial identification continue to increase in popularity because of the ever shortening reporting time, and the ability to detect a wide range of organisms not limited by culturability\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. A variety of molecular methods are available, with the most common being quantitative polymerase chain reaction (qPCR) and next generation sequencing (NGS). Quantitative PCR assays currently have the quickest reporting times, and can be designed to detect individual species, specific genes (e.g., antimicrobial resistance genes), or measure total bacterial or fungal load in a sample\u003csup\u003e\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. However, qPCR reports a limited set panel, identifying no more than 25\u0026ndash;40 organisms in a test panel\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In contrast, NGS methods including amplicon sequencing of the bacterial 16S ribosomal gene, are designed to detect all species in a sample that meet strict reporting criteria for NGS\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In PJI and other orthopedic infections, NGS has shown improved sensitivity in detecting pathogenic organisms\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e and is particularly helpful identifying organisms in culture-negative PJI\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Appreciation for the clinical utility of NGS is evidenced by the advocacy for NGS in recent guidelines published by the Infectious Disease Society of America and the American Society for Microbiology\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. However, a major concern regards specificity of NGS, noting improved detection of microbes may over-diagnose PJI. To address this concern, we compared NGS results in identifying PJI in aspirated synovial fluid samples to PJI diagnosis based on paired ICM-18 minor criteria synovial biomarkers, included in a commercially available laboratory developed test (LDT) which implements NGS (OrthoKEY, MicroGenDX, Lubbock, TX).\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eFollowing the launch of the OrthoKEY (NGS) with synovial biomarker test (components provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), all MicroGenDX records of synovial fluid specimens submitted between December 2020 and November 2022 were flagged as eligible for retrospective study inclusion. Exclusionary criteria included specimens from sources other than the hip or knee, specimens missing data on specimen source, specimens with insufficient quantity for testing, or specimens pooled with other intraoperative sample material. Study protocol was reviewed by Advarra Center for IRB Intelligence and certified as IRB exempt (Pro#00077239). For each sample, matched NGS profiling and synovial biomarker measurements were available. Synovial biomarkers measured included c-reactive protein (CRP), white blood cell count (WBC), and polymorphonuclear neutrophil percentage (PMN%) that estimated the probability of infection, using ICM-18 minor criteria thresholds for determining elevated biomarkers\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Specimens with a cumulative minor criterion score 4 or greater were considered high probability for infection, though for ICM-18, scores 4\u0026ndash;5 are considered inconclusive and scores \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e6 definitive for PJI diagnosis. Limited metadata available for each patient included joint location, reported sex, and age.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComponents of MDX OrthoKEY Plus Biomarkers\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTest\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAssay Targets\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReports\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCutoff\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eICM 2018 Score\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eqPCR Panel\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUniversal 16S\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBacterial Cell Estimate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003en/a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCommon Species\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEstimated count/ul\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003en/a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAntimicrobial resistance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePresence/Absence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003en/a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTargeted NGS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eV1-V2 16S rRNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBacterial Profile (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003en/a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eITS3-4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFungal Profile (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003en/a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBiomarkers\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCRP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003emg/L CRP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;6.9 mg/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ecells/uL WBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;3000 cells/uL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePMN%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e% PMN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSynovial Biomarker Profiling\u003c/p\u003e\u003cp\u003eSynovial fluid was vortexed and 1 mL was aliquoted for biomarker analysis, with remaining synovial fluid reserved for microbial NGS processing. The synovial fluid aliquot was run on the Sysmex XN-350 (Sysmex America, Mundelein, IL) following manufacturer\u0026rsquo;s protocol quantifying WBC count and PMN from body fluids. CRP was quantified on the Pentra C400 (Horiba, Irvine, CA) platform following manufacturer\u0026rsquo;s protocol for body fluids.\u003c/p\u003e\u003cp\u003eMicrobial Next Generation Sequencing\u003c/p\u003e\u003cp\u003eSamples were processed via the OrthoKEY LDT (MicroGenDX, Lubbock, TX), which includes bacterial profiling via targeted amplification and sequencing of the V1-V2 regions of 16S rRNA gene, and fungal profiling based on ITS 3\u0026ndash;4 locus sequencing as previously reported\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Briefly, the central laboratory performs DNA extraction, PCR amplification, library preparation for sequencing on the Illumina Miseq (Illumina, Inc. San Diego, USA) platform, and bioinformatic processing of sequence data into microbial taxonomy and reporting to clinicians. Although partial 16s analysis cannot fully resolve all bacterial lineages, the underlying V1-V2 region used here is among the most informative for classifying to the species level\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Further, species-level identification is generally reproducible when using appropriately curated reference databases\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Microbial taxa are aggregated and reported to the species level where possible, as reported in real time to clinicians and similar to prior work\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe laboratory protocol includes a standardized approach for determining whether a sample is NGS positive or negative, intended to reduce false positive reporting and focus on dominant organisms detected as previously described\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. To be reported positive, a sample must first pass through broad-range PCR amplification of the partial 16s rRNA (bacterial) and ITS (fungal) genes using 35 cycles and producing an amplicon product that can be visualized by gel electrophoresis (i.e., end point PCR). Samples with a positive product are barcoded and prepared for sequencing. To improve sensitivity, samples failing to produce a band on first attempt were re-evaluated further at 3X higher input volume. Samples fail if a band is not produced after second PCR attempt. After sequencing, samples are evaluated for contamination by a proprietary process comparing specimens to negative extraction controls (extraction without patient specimen) and no template PCR controls (PCR amplification without DNA extract), and positive controls. Contaminant signals are removed from results along with any species detected under 2% relative proportion of each sample\u0026rsquo;s total read count. Samples may fail if the Quality Control (QC) process flags a high enough amount of a sample\u0026rsquo;s composition as contamination. The determination of a contaminant signal is based on mutual detection rates and abundance values between patient samples and concurrently run negative controls. This LDT is currently in good standing with the College of American Pathologists (CAP) accreditation, Clinical Laboratory Improvement Amendments (CLIA) licensing, and New York state\u0026rsquo;s Clinical Laboratory Evaluation Program (CLEP).\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analysis was performed using R programming environment. Tables were generated using kable or gtsummary\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e libraries. Descriptive analyses were first performed to report on NGS positivity and ICM-18 minor criteria infected rates across hips and knee samples, as well as distributions of samples across interactions of positive NGS with positive ICM criteria. A kappa test was used to evaluate the agreement between NGS positivity and PJI. To compare diagnostic performance of individual synovial markers against PJI, the \u0026lsquo;epi.test\u0026rsquo; R function (epiR library) was used to estimate sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV). Wilson\u0026rsquo;s 95% confidence limits were estimated for each test value.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 2011 synovial fluid specimens were included with matched NGS profiling and biomarkers (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). A greater proportion of specimens originated from the knee (86%) compared to the hip (14%). There was no difference in the distribution of patients by sex or age between the two joints (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The median time to report results was 4 days (Q1\u0026thinsp;=\u0026thinsp;3, Q3\u0026thinsp;=\u0026thinsp;5) from the time samples were received at the central lab. Overall, the observed NGS positivity was 23% in both hips and knees, whereas 24% of hips and 22% of knees met the ICM-18 minor criteria for high probability PJI (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). Of positive results, 448 were 16S bacterial and 26 ITS fungal positive results.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePatient Demographics and primary results by joint\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHip, N\u0026thinsp;=\u0026thinsp;287\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKnee, N\u0026thinsp;=\u0026thinsp;1,724\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Age (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex (Male)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e146 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e931 (54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNGS Positivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e401 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePJI (High probability)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e386 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eHigh/Low Probabilities per NGS Positivity in each joint\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eKnee\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHip\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh PJI\u003c/p\u003e\n \u003cp\u003eProbability\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;386 (22%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLow PJI\u003c/p\u003e\n \u003cp\u003eProbability\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1338 (78%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh PJI\u003c/p\u003e\n \u003cp\u003eProbability\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;68 (24%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLow PJI\u003c/p\u003e\n \u003cp\u003eProbability\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;219 (76%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNGS Result Overall\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,235 (92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e202 (92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e298 (77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e16s\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,247 (93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e203 (93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e292 (76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91 (6.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (7.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eITS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e376 (97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,323 (99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e218 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eBroad-Range NGS Positivity Comparisons to Synovial Biomarkers\u003c/p\u003e\n\u003cp\u003eFor most of the analysis presented herein, the synovial biomarker results were analyzed according to their pooled finding of low or high probability infection for PJI following ICM-18. However, a brief analysis also investigated if NGS positivity was correlated with the ICM minor criteria score, noting a score \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e4 was considered high probability for infection. A highly significant linear relationship was observed between aggregated NGS positivity rate and cumulative biomarker score (p\u0026thinsp;=\u0026thinsp;0.0028, R2\u0026thinsp;=\u0026thinsp;0.93, Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb). For example; Within ICM-negative samples, samples with an ICM score of zero had the lowest NGS positivity rate of 6.6% which increased to 24.6% at an ICM score of 3. In these low probability of infection samples, NGS positivity was found to be significantly associated with both elevated WBC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and PMN values (p\u0026thinsp;=\u0026thinsp;0.0011), but not CRP (p\u0026thinsp;=\u0026thinsp;0.14, Figs. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ec-e).\u003c/p\u003e\n\u003cp\u003eNGS positivity was next compared to the three measured synovial biomarkers to evaluate the concordance to raw lab values of all samples. In each case, NGS positivity was found to be associated with increased values (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ef-h), particularly WBC and PMN%. Though NGS positivity was still largely discriminatory for CRP, the effect size is smaller and there is relatively more overlap. This led to the question of whether synovial NGS positivity was more concordant with PJI than synovial CRP. Indeed, the specificity of NGS positivity was observed at 92.3% (95% CI: 0.909\u0026ndash;0.935, Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), greater than CRP at 89.4% (0.878\u0026ndash;0.908) but lower than WBC at 97.9% (0.97\u0026ndash;0.985) and PMN% at 94.7% (0.935\u0026ndash;0.957).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparing diagnostic performance of individual synovial fluid test results to ICM-18 minor criteria for overall diagnosis by synovial markers\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSynovial\u003c/p\u003e\n \u003cp\u003eDiagnostic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAccuracy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePPV\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNPV\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.802 (.763-.836)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.894 (.878-.908)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.873 (.858-.887)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.688 (.647-.726)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.939 (.926-.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (.992-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.979 (.97-.985)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.984 (.977-.988)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.932 (.906-.951)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (.997-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePMN(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.936 (.91-.955)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.947 (.935-.957)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.945 (.934-.954)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.838 (.804-.868)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.981 (.972-.987)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNGS Positivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.764 (.723-.801)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.923 (.909-.935)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.887 (.873-.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.743 (.702-.781)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.931 (.917-.942)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eAbbreviations; PPV\u0026thinsp;=\u0026thinsp;Positive Predictive Value; NPV\u0026thinsp;=\u0026thinsp;Negative Predictive Value\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNGS Positivity is Concordant with High Probability PJI\u003c/p\u003e\n\u003cp\u003eNGS positivity among ICM positive specimens was 77% in knees and 72% in hips with 7.7% and 7.8% potential false positives, respectively (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Kappa testing was used to evaluate the chance corrected agreement between NGS and PJI. Overall, NGS positivity was substantially concordant (kappa\u0026thinsp;=\u0026thinsp;0.68) and 88.71% accurate (95% CI 87.25\u0026ndash;90.06) to PJI (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Concordance for hips and knees were each very similar to the overall findings, though with a higher sensitivity for knees (77.2%) as compared to hips (72.06%). Much of the observed sensitivity was due to the bacterial 16S rather than fungal ITS. For ICM positive knees, bacterial 16S and fungal ITS positivity were 76% and 2.6%, respectively. ICM positive hips had lower detection rates at 72% 16S positivity and 0% ITS positivity (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eConcordance of broad-range NGS positivity with Biomarkers Overall, and within each Joint\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eJoint\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAccuracy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePPV\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNPV\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKappa\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.71%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.25% \u0026minus;\u0026thinsp;90.06%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.43%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.29%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93.07%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6803\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKnee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.92%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.34% \u0026minus;\u0026thinsp;90.36%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.31%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93.35%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6856\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHip\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.46%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.06% \u0026minus;\u0026thinsp;91.06%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.06%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.24%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.24%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6495\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eMicrobial NGS Profile\u003c/p\u003e\n\u003cp\u003eA total of 160 unique bacterial species were detected with 69 being the dominant organism (\u0026gt;\u0026thinsp;50% relative abundance) in at least one sample versus 16 unique fungal species and 13 dominant fungi. There was a median 1 species reported per NGS positive sample and 104 (22.3%) were polymicrobial. Staphylococcal species were the most frequent dominant species, reported in 51% of samples and including \u003cem\u003eS. epidermidis\u003c/em\u003e, \u003cem\u003eS. aureus\u003c/em\u003e, \u003cem\u003eS. lugdunensis\u003c/em\u003e, and \u003cem\u003eS. capitis\u003c/em\u003e. Additional species with more than 2% dominance included \u003cem\u003eStreptococcus agalactiae\u003c/em\u003e (2.9%), \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e (2.9%), \u003cem\u003eCutibacterium acnes\u003c/em\u003e (4.0%) and \u003cem\u003eEnterococcus faecalis\u003c/em\u003e (2.9%). These 8 species composed 64% of the dominant species reported with 61 relatively less common species being dominant in the remaining 160 specimens (36%). Fungal detections were generally rare, observed in 2.2% (10) of all PJI specimens and in 1.0% (16) of PJI-negative specimens. Species included several \u003cem\u003eCandida\u003c/em\u003e species (\u003cem\u003eC. albicans\u003c/em\u003e, \u003cem\u003eC. glabrata\u003c/em\u003e, \u003cem\u003eC. parapsilosis\u003c/em\u003e, etc\u0026hellip;), \u003cem\u003eAureobasidium pullulans\u003c/em\u003e, \u003cem\u003eMalassezia globosa\u003c/em\u003e, and \u003cem\u003eNaganishia diffluens\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary Table 2).\u003c/p\u003e\n\u003cp\u003eConsistent with the high rate of NGS positivity associated with high probability infection samples, incidence and mean relative abundance of the most common species heavily favored specimens scored as high probability infection (Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, 4). A few of these top species were noted to be generally consistent with hips and knees such as \u003cem\u003eS. epidermidis\u003c/em\u003e and \u003cem\u003eS. aureus\u003c/em\u003e, however others can be seen to appear more distinct between the two joints. For example, \u003cstrong\u003eFig.\u0026nbsp;4\u003c/strong\u003e separates species according to their joint-wise distributions and notes a few lineages including \u003cem\u003eS. capitis\u003c/em\u003e and \u003cem\u003eStreptococcus parasanguinis\u003c/em\u003e, among others, more abundant in hips. \u003cem\u003eCutibacterium acnes\u003c/em\u003e was a notable departure from all other species in that \u003cem\u003eC. acnes\u003c/em\u003e appeared less discriminatory by infection probability. Logistic regression was used to estimate the relative risk of PJI based on each species, whereas \u003cem\u003eC. acnes\u003c/em\u003e detection still indicated more than 2X greater relative risk for PJI, this risk was lower than for other species detected as abundantly (Fig. 4). Of the fungal species detected, \u003cem\u003eCandida albicans\u003c/em\u003e, \u003cem\u003eC. glabrata\u003c/em\u003e, and \u003cem\u003eA. pullulans\u003c/em\u003e were generally detected at higher rates in PJI samples and dominated the fungal composition of at least two samples, whereas other \u003cem\u003eCandida\u003c/em\u003e species (\u003cem\u003eC. parapsilosis\u003c/em\u003e, \u003cem\u003eC. orthopsilosis\u003c/em\u003e, \u003cem\u003eC. metapsilosis\u003c/em\u003e) were more frequent and dominant among PJI-negative specimens (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary Table\u0026nbsp;2).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study was conducted to benchmark the performance of a commercially available NGS technology compared to synovial fluid biomarkers used to diagnose PJI using ICM-18 criterion. We hypothesized that positive DNA signals in synovial fluid signifies the presence of clinically relevant microbes, and that positive NGS detection will associate with biomarkers indicative of infection. This study shows NGS positivity is concordant with diagnostic PJI ICM minor criteria biomarkers, evidenced by the substantial agreement observed by kappa testing and performance statistics, and had greater specificity than CRP (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Further, NGS positivity was associated with elevated levels for each tested biomarker (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. A concern with incorporating NGS as a standard of care for PJI diagnosis is that increased sensitivity will lead to a high false positive rate and unnecessary treatment\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. This study refutes this concern. NGS positivity demonstrated a comparable and non-inferior specificity to prior estimates at 92.3% (95% CI: 0.91\u0026ndash;0.94, Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e,\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This is supported by a recent meta-analysis including 18 studies examining the diagnostic performance of synovial fluid cultures against PJI which observed 96% specificity (95% CI: 0.93\u0026ndash;0.98)\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Moreover, we report a superior increase in sensitivity for NGS at 76.4% (95% CI: 0.72\u0026ndash;0.80) compared to the prior pooled sensitivity for culture at 63% (95% CI: 0.56\u0026ndash;0.70). While we intend future studies with matched culture and NGS results to corroborate this finding, the present data show that NGS, using synovial fluid singularly, is both significantly more sensitive than prior expectations for culture and comparably specific to other validated biomarkers for diagnosing PJI.\u003c/p\u003e\u003cp\u003eThe comparisons of NGS positivity to biomarkers in samples deemed low probability of infection emphasizes the complementary role that NGS can provide in diagnosing PJI (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb-e). First, a highly significant relationship was observed between NGS positivity and cumulative ICM score, suggesting that samples scored as a three with 1\u0026ndash;2 elevated biomarkers also show higher NGS positivity rates when compared to other ICM negative samples. Further, NGS positivity was highly significantly related to both elevated PMN and WBC when considering only ICM negative samples. Synovial WBC and PMN are considered among the most reliable synovial biomarkers for PJI\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. These findings support that NGS based detection of microbial DNA in synovial fluid may be capturing a unique component of PJI that is missed by only assessing the more commonly measured biomarkers. More specifically, we hypothesize that some of these discrepant NGS positive results may represent infection from microbes failing to elicit a strong immune response, a sub-clinical infection, or perhaps early detection of an infection that has not yet fully manifested clinically. Future work examining longitudinal trends in microbiological findings, synovial biomarkers, and clinical findings will be critical in further understanding the value of these discrepant results. Nevertheless, the strong relationship of PMN and WBC with NGS positivity in low probability infection samples supports the potential for NGS positivity as a diagnostic criterion to improve sensitivity in diagnosing PJI, especially in difficult cases involving fastidious microbes associated with ambiguous biomarker values according to ICM-18 definitions.\u003c/p\u003e\u003cp\u003eIn addition to the role in diagnosing PJI as well as identifying microbes in culture-negative infection\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, NGS is capable of detecting a wide variety of organisms which may be missed by culture-dependent identification as well as other molecular methods limited by species specific probes or antibodies\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Consistent with prior literature, we report a high prevalence of \u003cem\u003eStaphyloccocus aureus\u003c/em\u003e, coagulase negative \u003cem\u003eStaphylococcus\u003c/em\u003e (\u003cem\u003eS. epidermidis\u003c/em\u003e, \u003cem\u003eS. capitis\u003c/em\u003e), \u003cem\u003eS. lugdunensis\u003c/em\u003e, \u003cem\u003eStreptococcus agalactiae\u003c/em\u003e (i.e., GBS), \u003cem\u003ePseudomonas\u003c/em\u003e, and \u003cem\u003eCutibacterium acnes\u003c/em\u003e. Organisms underreported by culture-dependent methods were also observed, often as the dominant organism, particularly anaerobes such \u003cem\u003eAnaerococcus\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, and \u003cem\u003eFinegoldia magna\u003c/em\u003e (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Similarly, \u003cem\u003eCutibacterium acnes\u003c/em\u003e is underreported compared to molecular methods\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Eight bacterial species dominated the composition in 68% of samples, though the remaining 32% consisted of 46 different dominant species (Supplementary Table\u0026nbsp;1). This result highlights that although any given species such as \u003cem\u003eEnterococcus faecium\u003c/em\u003e or \u003cem\u003eStreptococcus dysgalactiae\u003c/em\u003e may be individually uncommon, uncommon species are a common aspect in PJI which may be overlooked by routine microbiological testing. For example, the broader detection range enabled by NGS is more informative than the commonly used qPCR BioFire Joint Infection Panel (bioMerieux,Marcy-l\u0026rsquo;Etoile, FR) which, in comparison, would only detect 56% of the 69 dominant species identified in this current work. While such panels are useful in being able to deliver rapid results of common pathogens, the limited qPCR panels have shown lower overall sensitivity to PJI ranging 41%-56%\u003csup\u003e16,20,31\u003c/sup\u003e and are known to underreport polymicrobial instances of PJI\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOf the more prevalent organisms reported by NGS, \u003cem\u003eC. acnes\u003c/em\u003e was uniquely abundant overall and reported more frequently in ICM low probability infection samples according to minor criteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This finding was not unexpected, as \u003cem\u003eC. acnes\u003c/em\u003e is frequently reported as a causative pathogen in PJI\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, yet it has also been shown to elicit a variable response from the host, relegating its positivity as a potential contaminant\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Reliable detection of \u003cem\u003eC. acnes\u003c/em\u003e via culture can be a challenge requiring anaerobic isolation and extended 14-day incubation time\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Further, \u003cem\u003eC. acnes\u003c/em\u003e does not always elicit a strong host response and has been associated with PJI manifesting later in the implant life cycle\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Thus, early confirmation via NGS is useful, providing impetus towards treatment rather than neglecting equivocal synovial biomarker data in patients with a painful JA. When detected, \u003cem\u003eC. acnes\u003c/em\u003e JA infections have high treatment success and 2-year survival rates\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, indicating that \u003cem\u003eC. acnes\u003c/em\u003e associated PJI is manageable if detected. Further, current results may support treating NGS detected \u003cem\u003eC. acnes\u003c/em\u003e in circumstances of ICM lower probability of PJI, acknowledging further study is required in understand treating \u003cem\u003eC. acnes\u003c/em\u003e when isolated in the setting of a diminished inflammatory response\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAlthough many studies contrast the utility of culture and NGS, we believe that consensus guidelines will benefit by considering information from both techniques, rather than one or the other, noting improved sensitivity of microbiological testing for PJI when both are used\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. A recent comprehensive review of NGS in PJI reports strong sensitivity and specificity\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. We advocate the adoption of NGS, especially in culture-negative cases, when rapid pathogen identification is needed, in patients with a high-pretest probability of infection, or when rare pathogens are suspected. We emphasize this study shows rare pathogens to be collectively common, including microbes not included in common multiplex qPCR panels and fastidious organisms likely to be missed by culture. There is concern that NGS may lead to increased false positive detection, but the specificities shown here and the accuracies reported in the review by Martinazzi et al.\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e refute that concern. We recommend that NGS as implemented herein is a useful tool that will assist with identifying PJI causative organisms, confirming PJI diagnosis, and should be considered as a criterion in PJI diagnosis.\u003c/p\u003e\u003cp\u003eThe current study supports the use of NGS in guiding PJI diagnosis, however not without limitations. We only compared NGS to biomarkers of ICM-18 minor criteria which did not include culture and other ICM criteria for infection. However, when using synovial biomarkers, NGS showed superior sensitivity to prior expectations for culture and non-inferior or comparable specificity to currently accepted indicators for infection. While culture data could have helped to define PJI, there is a reported 88% concordance between minor and major criteria in diagnosing PJI\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The NGS testing platform is a highly refined technology to identify the presence of microbes within a sample. Similarly, culture identification requires specific expertise, and it is not readily feasible to perform both tests with similar alacrity at the same facility. We plan a future study comparing synovial fluid sent to both an NGS testing center and a culture testing center. Also, NGS results were compared only to synovial fluid biomarkers, absent alpha defensin, and not to ICM-18 serum biomarkers that include CRP, D-dimer, and sedimentation rate. The current work did not consider intraoperative targeted NGS results, which has shown superior sensitivity in microbial detection for PJI up to 89.3% by the same comparator lab\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. We emphasize the focus of this study is synovial fluid analysis for preoperative diagnosis of PJI. A positive diagnosis provides surgeon clarity and a direction of treatment before surgery, rather than modifying post-operative treatment when the joint of interest is determined PJI positive based on intra-operative ICM-18 criteria.\u003c/p\u003e\u003cp\u003eWe believe a positive NGS test is commensurate with existing ICM-18 minor criteria diagnosing PJI. Based on this data, we feel that NGS testing should be a primary diagnostic tool, performed early as opposed to a secondary diagnostic when other testing has already failed. Moreover, a positive NGS should be weighted similar to a positive culture when restructuring future pji diagnostic criterion. Further, we believe NGS will close the gap in culture negative infection as NGS enables detection of fastidious microbes thus allowing fidelity in antimicrobial stewardship. The enhanced sensitivity for microbial detection can be used to prepare targeted treatment of PJI causative organisms. We look forward to seeing and conducting further research on the implementation of NGS into the management of PJI.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eMicrobial NGS testing of synovial fluid showed concordance to biomarkers used in ICM-18 PJI minor criteria. NGS showed a low risk of false positive detections in synovial fluid. NGS has greater sensitivity and broader discovery power than other available methods for microbial diagnosis. We advocate diagnostic schemes include NGS as a diagnostic criterion when restructuring future diagnostic criteria defining PJI.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eStudy protocol was reviewed by Advarra Center for IRB Intelligence and certified as IRB exempt (Pro#00077239).\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eDe-identified data sufficient to reproduce analysis are available in Supplementary File 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eAuthors CDT, JA, NS, NAT, KO, KJ, and JW are or were employees of MicroGen DX. CDP reports consulting fees from MicroGen DX. JP reports personal fees and stock option from Corentec, personal fees from Data Trace, Elsevier, Jaypee Publishers, SLACK Inc., Wolters Kluwer, Becton Dickensen, Zimmer Biomet, Ethicon, Tenor, KCI/3M (Acelity), MicroGen DX, Jointstem, and Cardinal Health. JP reports stock option from Parvizi Surgical Innovation, Hip Innovation Technology, Alphaeon/Strathsby Crown, Elute, Ceribell, Acumed, PRN-Veterinary, Illuminus, Intellijoint, Osteal, nanooxygenic, Sonata, Molecular Surface Technologies, and Peptilogic. All other authors declare no financial or non-financial competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThere is no specific source of funding for this work.\u003c/p\u003e\n\u003cp\u003eInformed consent was not required for this retrospective study as it utilized de-identified laboratory data generating during routine testing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBozic, K. J. \u003cem\u003eet al.\u003c/em\u003e The epidemiology of revision total hip arthroplasty in the United States. \u003cem\u003eJ. Bone Joint Surg. Am.\u003c/em\u003e \u003cstrong\u003e91\u003c/strong\u003e, 128\u0026ndash;133 (2009).\u003c/li\u003e\n \u003cli\u003eXu, Y., Huang, T. B., Schuetz, M. A. \u0026amp; Choong, P. F. M. Mortality, patient-reported outcome measures, and the health economic burden of prosthetic joint infection. \u003cem\u003eEFORT Open Rev.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 690\u0026ndash;697 (2023).\u003c/li\u003e\n \u003cli\u003eParvizi, J. \u0026amp; Gehrke, T. Definition of Periprosthetic Joint Infection. \u003cem\u003eJ. Arthroplasty\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 1331 (2014).\u003c/li\u003e\n \u003cli\u003eParvizi, J. \u003cem\u003eet al.\u003c/em\u003e The 2018 Definition of Periprosthetic Hip and Knee Infection: An Evidence-Based and Validated Criteria. \u003cem\u003eJ. Arthroplasty\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 1309-1314.e2 (2018).\u003c/li\u003e\n \u003cli\u003eSigmund, I. K., Luger, M., Windhager, R. \u0026amp; McNally, M. A. Diagnosing periprosthetic joint infections. \u003cem\u003eBone Jt. Res.\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 608\u0026ndash;618 (2022).\u003c/li\u003e\n \u003cli\u003eKlement, M. R. \u003cem\u003eet al.\u003c/em\u003e Are All Periprosthetic Joint Infections the Same? Evaluating Major vs Minor Criteria. \u003cem\u003eJ. Arthroplasty\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 1515\u0026ndash;1519 (2018).\u003c/li\u003e\n \u003cli\u003eSolarino, G. \u003cem\u003eet al.\u003c/em\u003e What\u0026rsquo;s New in the Diagnosis of Periprosthetic Joint Infections: Focus on Synovial Fluid Biomarkers. \u003cem\u003eTrop. Med. Infect. Dis.\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 355 (2022).\u003c/li\u003e\n \u003cli\u003eCipriano, C. A. \u003cem\u003eet al.\u003c/em\u003e Serum and synovial fluid analysis for diagnosing chronic periprosthetic infection in patients with inflammatory arthritis. \u003cem\u003eJ. Bone Joint Surg. Am.\u003c/em\u003e \u003cstrong\u003e94\u003c/strong\u003e, 594\u0026ndash;600 (2012).\u003c/li\u003e\n \u003cli\u003eMortazavi, S. M. J., Vegari, D., Ho, A., Zmistowski, B. \u0026amp; Parvizi, J. Two-stage exchange arthroplasty for infected total knee arthroplasty: predictors of failure. \u003cem\u003eClin. Orthop.\u003c/em\u003e \u003cstrong\u003e469\u003c/strong\u003e, 3049\u0026ndash;3054 (2011).\u003c/li\u003e\n \u003cli\u003eGoswami, K. \u003cem\u003eet al.\u003c/em\u003e An Enhanced Understanding of Culture-Negative Periprosthetic Joint Infection with Next-Generation Sequencing: A Multicenter Study. \u003cem\u003eJ. Bone Joint Surg. Am.\u003c/em\u003e \u003cstrong\u003e104\u003c/strong\u003e, 1523\u0026ndash;1529 (2022).\u003c/li\u003e\n \u003cli\u003eMcClure, E. A., Werth, P., Ross, B. \u0026amp; Gitajn, I. L. Application of Nucleic Acid-Based Strategies to Detect Infectious Pathogens in Orthopaedic Implant-Related Infection. \u003cem\u003eJ. Bone Joint Surg. Am.\u003c/em\u003e \u003cstrong\u003e105\u003c/strong\u003e, 556\u0026ndash;568 (2023).\u003c/li\u003e\n \u003cli\u003eKullar, R. \u003cem\u003eet al.\u003c/em\u003e Next-Generation Sequencing Supports Targeted Antibiotic Treatment for Culture Negative Orthopedic Infections. \u003cem\u003eClin. Infect. Dis. Off. Publ. Infect. Dis. Soc. Am.\u003c/em\u003e \u003cstrong\u003e76\u003c/strong\u003e, 359\u0026ndash;364 (2022).\u003c/li\u003e\n \u003cli\u003eFink, B. \u003cem\u003eet al.\u003c/em\u003e Preoperative PCR analysis of synovial fluid has limited value for the diagnosis of periprosthetic joint infections of total knee arthroplasties. \u003cem\u003eArch. Orthop. Trauma Surg.\u003c/em\u003e \u003cstrong\u003e138\u003c/strong\u003e, 871\u0026ndash;878 (2018).\u003c/li\u003e\n \u003cli\u003eLleo, M. M. \u003cem\u003eet al.\u003c/em\u003e Detecting the presence of bacterial DNA by PCR can be useful in diagnosing culture-negative cases of infection, especially in patients with suspected infection and antibiotic therapy. \u003cem\u003eFEMS Microbiol. Lett.\u003c/em\u003e \u003cstrong\u003e354\u003c/strong\u003e, 153\u0026ndash;160 (2014).\u003c/li\u003e\n \u003cli\u003eEsteban, J. \u003cem\u003eet al.\u003c/em\u003e Multicenter evaluation of the BIOFIRE Joint Infection Panel for the detection of bacteria, yeast, and AMR genes in synovial fluid samples. \u003cem\u003eJ. Clin. Microbiol.\u003c/em\u003e \u003cstrong\u003e61\u003c/strong\u003e, e00357-23.\u003c/li\u003e\n \u003cli\u003eGardete-Hartmann, S. \u003cem\u003eet al.\u003c/em\u003e The role of BioFire Joint Infection Panel in diagnosing periprosthetic hip and knee joint infections in patients with unclear conventional microbiological results. \u003cem\u003eBone Jt. Res.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 353\u0026ndash;361 (2024).\u003c/li\u003e\n \u003cli\u003eKullar, R. \u003cem\u003eet al.\u003c/em\u003e Next-Generation Sequencing in Periprosthetic Joint Infections: Clinicians\u0026rsquo; Guide to Its Diagnostic Role. \u003cem\u003eInfect. Dis. Clin. Pract.\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, e1448 (2025).\u003c/li\u003e\n \u003cli\u003eHuang, Z. \u003cem\u003eet al.\u003c/em\u003e Metagenomic next-generation sequencing of synovial fluid demonstrates high accuracy in prosthetic joint infection diagnostics: mNGS for diagnosing PJI. \u003cem\u003eBone Jt. Res.\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 440\u0026ndash;449 (2020).\u003c/li\u003e\n \u003cli\u003eFlurin, L. \u003cem\u003eet al.\u003c/em\u003e Targeted Metagenomic Sequencing-based Approach Applied to 2146 Tissue and Body Fluid Samples in Routine Clinical Practice. \u003cem\u003eClin. Infect. Dis. Off. Publ. Infect. Dis. Soc. Am.\u003c/em\u003e \u003cstrong\u003e75\u003c/strong\u003e, 1800\u0026ndash;1808 (2022).\u003c/li\u003e\n \u003cli\u003eAzad, M. A. \u003cem\u003eet al.\u003c/em\u003e Comparison of the BioFire Joint Infection Panel to 16S Ribosomal RNA Gene-Based Targeted Metagenomic Sequencing for Testing Synovial Fluid from Patients with Knee Arthroplasty Failure. \u003cem\u003eJ. Clin. Microbiol.\u003c/em\u003e \u003cstrong\u003e60\u003c/strong\u003e, e0112622 (2022).\u003c/li\u003e\n \u003cli\u003eChowdhry, M. \u003cem\u003eet al.\u003c/em\u003e Next generation sequencing identifies an increased diversity of microbes in post lavage specimens in infected TKA using a biofilm disrupting irrigant. \u003cem\u003eThe Knee\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, 231\u0026ndash;239 (2024).\u003c/li\u003e\n \u003cli\u003eMiller, J. M. \u003cem\u003eet al.\u003c/em\u003e Guide to Utilization of the Microbiology Laboratory for Diagnosis of Infectious Diseases: 2024 Update by the Infectious Diseases Society of America (IDSA) and the American Society for Microbiology (ASM). \u003cem\u003eClin. Infect. Dis. Off. Publ. Infect. Dis. Soc. Am.\u003c/em\u003e ciae104 (2024) doi:10.1093/cid/ciae104.\u003c/li\u003e\n \u003cli\u003eLiss, M. A., Reveles, K. R., Tipton, C. D., Gelfond, J. \u0026amp; Tseng, T. Comparative Effectiveness Randomized Clinical Trial Using Next-generation Microbial Sequencing to Direct Prophylactic Antibiotic Choice Before Urologic Stone Lithotripsy Using an Interprofessional Model. \u003cem\u003eEur. Urol. Open Sci.\u003c/em\u003e \u003cstrong\u003e57\u003c/strong\u003e, 74\u0026ndash;83 (2023).\u003c/li\u003e\n \u003cli\u003eHoffman, C. \u003cem\u003eet al.\u003c/em\u003e Species-Level Resolution of Female Bladder Microbiota from 16S rRNA Amplicon Sequencing. \u003cem\u003emSystems\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, e0051821 (2021).\u003c/li\u003e\n \u003cli\u003eWhitfield, R., Tipton, C. D., Diaz, N., Ancira, J. \u0026amp; Landry, K. S. Clinical Evaluation of Microbial Communities and Associated Biofilms with Breast Augmentation Failure. \u003cem\u003eMicroorganisms\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 1830 (2024).\u003c/li\u003e\n \u003cli\u003eSjoberg, D. D., Whiting, K., Curry, M., Lavery, J. A. \u0026amp; Larmarange, J. Reproducible Summary Tables with the gtsummary Package. \u003cem\u003eR J.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 570\u0026ndash;580 (2021).\u003c/li\u003e\n \u003cli\u003eWatanabe, S. \u003cem\u003eet al.\u003c/em\u003e Differences in Diagnostic Sensitivity of Cultures Between Sample Types in Periprosthetic Joint Infections: A Systematic Review and Meta-Analysis. \u003cem\u003eJ. Arthroplasty\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 1939\u0026ndash;1945 (2024).\u003c/li\u003e\n \u003cli\u003eKuo, F.-C. \u003cem\u003eet al.\u003c/em\u003e Which Minor Criteria is the Most Accurate Predictor for the Diagnosis of Hip and Knee Periprosthetic Joint Infection in the Asian Population? \u003cem\u003eJ. Arthroplasty\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 2076\u0026ndash;2081 (2022).\u003c/li\u003e\n \u003cli\u003eMcNally, M. \u003cem\u003eet al.\u003c/em\u003e The EBJIS definition of periprosthetic joint infection. \u003cem\u003eBone Jt. J.\u003c/em\u003e \u003cstrong\u003e103-B\u003c/strong\u003e, 18\u0026ndash;25 (2021).\u003c/li\u003e\n \u003cli\u003eMorgenstern, C., Cabric, S., Perka, C., Trampuz, A. \u0026amp; Renz, N. Synovial fluid multiplex PCR is superior to culture for detection of low-virulent pathogens causing periprosthetic joint infection. \u003cem\u003eDiagn. Microbiol. Infect. Dis.\u003c/em\u003e \u003cstrong\u003e90\u003c/strong\u003e, 115\u0026ndash;119 (2018).\u003c/li\u003e\n \u003cli\u003eLee, R. A. Clinical performance evaluation of the BioFire Joint Infection Panel. \u003cem\u003eJ. Clin. Microbiol.\u003c/em\u003e \u003cstrong\u003e62\u003c/strong\u003e, e0102224 (2024).\u003c/li\u003e\n \u003cli\u003eHedlundh, U., Zacharatos, M., Magnusson, J., Gottlander, M. \u0026amp; Karlsson, J. Periprosthetic hip infections in a Swedish regional hospital between 2012 and 2018: is there a relationship between \u0026amp;amp;lt;i\u0026amp;amp;gt;Cutibacterium acnes\u0026amp;amp;lt;/i\u0026amp;amp;gt; infections and uncemented prostheses? \u003cem\u003eJ. Bone Jt. Infect.\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 219\u0026ndash;228 (2021).\u003c/li\u003e\n \u003cli\u003eHoch, A. \u003cem\u003eet al.\u003c/em\u003e Treatment outcomes of patients with Cutibacterium acnes-positive cultures during total joint replacement revision surgery: a minimum 2-year follow-up. \u003cem\u003eArch. Orthop. Trauma Surg.\u003c/em\u003e \u003cstrong\u003e143\u003c/strong\u003e, 2951\u0026ndash;2958 (2023).\u003c/li\u003e\n \u003cli\u003eRenz, N., Mudrovcic, S., Perka, C. \u0026amp; Trampuz, A. Orthopedic implant-associated infections caused by Cutibacterium spp. \u0026ndash; A remaining diagnostic challenge. \u003cem\u003ePLOS ONE\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, e0202639 (2018).\u003c/li\u003e\n \u003cli\u003eNodzo, S. R. \u003cem\u003eet al.\u003c/em\u003e \u003cstrong\u003e\u0026lt;i\u0026gt;Propionibacterium\u003c/strong\u003e\u0026lt;/i\u0026gt; acnes Host Inflammatory Response During Periprosthetic Infection Is Joint Specific. \u003cem\u003eHSS Journal\u0026reg; Musculoskelet. J. Hosp. Spec. Surg.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 159\u0026ndash;164 (2017).\u003c/li\u003e\n \u003cli\u003eLiew-Littorin, C., Davidsson, S., Nilsdotter-Augustinsson, \u0026Aring;., Brueggemann, H. \u0026amp; S\\\u0026quot; oderquist, B. GENOMIC CHARACTERIZATION AND CLINICAL EVALUATION OF PERIPROSTHETIC JOINT INFECTIONS CAUSED BY \u003cem\u003eCUTIBACTERIUM\u003c/em\u003e ACNES. \u003cem\u003eOrthop. Proc.\u003c/em\u003e \u003cstrong\u003e106-B\u003c/strong\u003e, 15\u0026ndash;15 (2024).\u003c/li\u003e\n \u003cli\u003eVilchez, H. H. \u003cem\u003eet al.\u003c/em\u003e Prosthetic Shoulder Joint Infection by Cutibacterium acnes: Does Rifampin Improve Prognosis? A Retrospective, Multicenter, Observational Study. \u003cem\u003eAntibiotics\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 475 (2021).\u003c/li\u003e\n \u003cli\u003eWarne, C. N. \u003cem\u003eet al.\u003c/em\u003e \u003cem\u003eCutibacterium\u003c/em\u003e acnes periprosthetic joint infections. \u003cem\u003eBone Amp Jt. J.\u003c/em\u003e \u003cstrong\u003e106-B\u003c/strong\u003e, 1426\u0026ndash;1430 (2024).\u003c/li\u003e\n \u003cli\u003eFlurin, L. \u003cem\u003eet al.\u003c/em\u003e Clinical Use of a 16S Ribosomal RNA Gene-Based Sanger and/or Next Generation Sequencing Assay to Test Preoperative Synovial Fluid for Periprosthetic Joint Infection Diagnosis. \u003cem\u003emBio\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, e01322-22.\u003c/li\u003e\n \u003cli\u003eMartinazzi, B. \u003cem\u003eet al.\u003c/em\u003e HK35: Is there a role for the use of molecular techniques in isolation of infective organism(s) causing periprosthetic joint infection (PJI)? (2025).\u003c/li\u003e\n \u003cli\u003eTarabichi, M., Shohat, N. \u0026amp; Goswami, K. Diagnosis of periprosthetic joint infection: The potential of next-generation sequencing. \u003cem\u003eJ Bone Jt Surg - Am\u003c/em\u003e \u003cstrong\u003eVol\u003c/strong\u003e, 147\u0026ndash;154 (2018).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Next Generation Sequencing NGS, Periprosthetic Joint Infection PJI, Diagnosis, Synovial fluid International Consensus Meeting 2018, ICM-18, Diagnostic Criteria","lastPublishedDoi":"10.21203/rs.3.rs-7410845/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7410845/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe diagnosis of periprosthetic joint infection (PJI) is facilitated by consensus identification of synovial biomarkers, which may be aided by targeted microbial next generation sequencing (NGS) of synovial fluid. The primary objective of the study was to evaluate NGS performance across 3 years to ICM 2018 minor criteria for PJI.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eSynovial fluid specimens submitted from 2020\u0026ndash;2022 by outpatient surgical clinics to MicroGenDX for matched synovial biomarker and NGS analysis were selected for retrospective analysis. Synovial biomarkers tested included C-reactive protein (CRP), white blood cell (WBC) count, and polymorphonuclear (PMN) leukocyte percentage. Synovial fluid analysis compared NGS microbial positivity with positive incidence of PJI determined by scoring of synovial biomarkers using ICM 2018 minor criteria for infection.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe overall sensitivity, specificity, and accuracy of NGS to ICM diagnosed PJI across 2,011 specimens was 76.4% [95% CI: 0.723\u0026ndash;0.801], 92.3% [0.91\u0026ndash;0.94], and 88.7% [0.87\u0026ndash;0.90], respectively. When comparing the diagnostic performance of NGS and individual biomarkers to infection, NGS was more specific to PJI than synovial CRP (specificity\u0026thinsp;=\u0026thinsp;0.894, 95% CI: 0.88\u0026ndash;0.91), but not PMN or WBC. NGS was positive in 7.7% of ICM negative samples. NGS positive:ICM negative samples were associated with significantly elevated synovial PMN (p\u0026thinsp;=\u0026thinsp;0.001) and WBC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) compared to NGS negative:ICM negative samples. Across all samples, NGS positivity was associated with significantly elevated results for all tested biomarkers (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Eight bacterial species dominated the composition in 68% of samples, whereas 46 different microbes were dominant in the remaining third.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eMicrobial targeted NGS positivity was concordant with ICM minor criteria for PJI and should be considered a useful tool for diagnosis. There is a low risk of false positive detections comparable to ICM biomarkers. Elevated biomarkers in NGS positive:ICM negative samples may indicate infection occurring that is poorly captured by the three measured synovial biomarkers. Uncommon species are collectively common to PJI and NGS is uniquely positioned to detect such species as they are frequently missed by conventional microbiological testing, including culture and quantitative PCR. These results suggest formal diagnostic schemes would benefit from the addition of NGS as a diagnostic criterion.\u003c/p\u003e","manuscriptTitle":"Microbial Next Generation DNA Sequencing of Aspirated Synovial Fluid Shows Concordance with ICM Criteria Biomarkers for Diagnosing Periprosthetic Joint Infection in Hip and Knee Arthroplasty","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 07:07:34","doi":"10.21203/rs.3.rs-7410845/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0b2c0fef-bed4-4897-b8c4-a327cd8be54e","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-27T07:07:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-27 07:07:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7410845","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7410845","identity":"rs-7410845","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}