Mitigating Concerns Over Transport Delays: An Analysis of Synovial Fluid Culture Results in Arthroplasty.

INTRODUCTION:  There is a concern in the field of arthroplasty that synovial fluid transport delays may reduce the accuracy of synovial fluid culture. However, synovial fluid samples collected in the office, and sometimes in a hospital setting, often require transport to a third-party central or specialty laboratory, causing delays in the initiation of culture incubation. This study investigated the impact of transportation delays on synovial fluid culture results. METHODS:  A retrospective review of prospectively collected data at one clinical laboratory, from 2016 to 2022, was conducted. A total of 125,270 synovial fluid samples from knee arthroplasties, from 2,858 different US institutions, were transported to a single clinical laboratory for diagnostic testing including synovial fluid culture (blood culture bottles). Synovial fluid to be cultured was transported in red top tubes without additives. Samples were grouped into six-time cohorts based on the number of days between aspiration and culture initiation (1-day-delay to 6-day-delay). Metrics such as culture positivity, false-positive culture rate, culture sensitivity, and proportional growth of top genera of organisms were assessed across the cohorts. RESULTS:  Of the 125,270 samples in this study, 71.2% were received the day after aspiration (1-day-delay), with an exponential decrease in samples received on each subsequent day. Culture-positive rates for synovial fluid samples received after 1, 2, 3, 4, 5, and 6 days of transport time were 12.2%, 13.3%, 13.5%, 13.1%, 11.6%, and 11.0%, respectively. The maximum absolute difference in culture-positive rate compared to the 1-day-delay cohort was an increase of 1.3% in the 3-day-delay cohort, which was not considered a clinically meaningful difference. The estimated false-positive culture rate remained relatively consistent across time cohorts, with values of 0.3%, 0.4%, 0.3%, 0.2%, 0.5%, and 0.5% for 1, 2, 3, 4, 5, and 6 days of transport time, respectively. None of the cohorts showed a statistically significant difference after adjustment for multiplicity compared to the 1-day-delay cohort. Culture sensitivity was estimated at 68.2%, 67.2%, 70.5%, 70.7%, 65.9%, and 70.7% for 1, 2, 3, 4, 5, and 6 days of transport time, respectively. None of the cohorts showed a statistically significant difference after adjustment for multiplicity compared to the 1-day-delay cohort. Organism proportions were consistent across time cohorts, with Staphylococcus being the most commonly identified organism. No statistically significant differences were found in the proportional contribution of major genera across the cohorts. CONCLUSIONS:  Synovial fluid culture exhibited surprisingly consistent results despite variable transport time to the destination laboratory, with differences that have minimal clinical importance. While the authors of this study advocate for short transport times as a best practice to expedite diagnosis, it appears that concerns regarding the rapid degradation of culture results due to synovial fluid transportation is unwarranted.

Achieving High Accuracy in Predicting the Probability of Periprosthetic Joint Infection From Synovial Fluid in Patients Undergoing Hip or Knee Arthroplasty: The Development and Validation of a Multivariable Machine Learning Algorithm.

Background and objective The current periprosthetic joint infection (PJI) diagnostic guidelines require clinicians to interpret and integrate multiple criteria into a complex scoring system. Also, PJI classifications are often inconclusive, failing to provide a clinical diagnosis. Machine learning (ML) models could be leveraged to reduce reliance on these complex systems and thereby reduce diagnostic uncertainty. This study aimed to develop an ML algorithm using synovial fluid (SF) test results to establish a PJI probability score. Methods We used a large clinical laboratory's dataset of SF samples, aspirated from patients with hip or knee arthroplasty as part of a PJI evaluation. Patient age and SF biomarkers [white blood cell count, neutrophil percentage (%PMN), red blood cell count, absorbance at 280 nm wavelength, C-reactive protein (CRP), alpha-defensin (AD), neutrophil elastase, and microbial antigen (MID) tests] were used for model development. Data preprocessing, principal component analysis, and unsupervised clustering (K-means) revealed four clusters of samples that naturally aggregated based on biomarker results. Analysis of the characteristics of each of these four clusters revealed three clusters (n=13,133) with samples having biomarker results typical of a PJI-negative classification and one cluster (n=4,032) with samples having biomarker results typical of a PJI-positive classification. A decision tree model, trained and tested independently of external diagnostic rules, was then developed to match the classification determined by the unsupervised clustering. The performance of the model was assessed versus a modified 2018 International Consensus Meeting (ICM) criteria, in both the test cohort and an independent unlabeled validation set of 5,601 samples. The SHAP (SHapley Additive exPlanations) method was used to explore feature importance. Results The ML model showed an area under the curve of 0.993, with a sensitivity of 98.8%, specificity of 97.3%, positive predictive value (PPV) of 92.9%, and negative predictive value (NPV) of 99.8% in predicting the modified 2018 ICM diagnosis among test set samples. The model maintained its diagnostic accuracy in the validation cohort, yielding 99.1% sensitivity, 97.1% specificity, 91.9% PPV, and 99.9% NPV. The model's inconclusive rate (diagnostic probability between 20-80%) in the validation cohort was only 1.3%, lower than that observed with the modified 2018 ICM PJI classification (7.4%; p<0.001). The SHAP analysis found that AD was the most important feature in the model, exhibiting dominance among >95% of "infected" and "not infected" diagnoses. Other important features were the sum of the MID test panel, %PMN, and SF-CRP. Conclusions Although defined methods and tools for diagnosis of PJI using multiple biomarker criteria are available, they are not consistently applied or widely implemented. There is a need for algorithmic interpretation of these biomarkers to enable consistent interpretation of the results to drive treatment decisions. The new model, using clinical parameters measured from a patient's SF sample, renders a preoperative probability score for PJI which performs well compared to a modified 2018 ICM definition. Taken together with other clinical signs, this model has the potential to increase the accuracy of clinical evaluations and reduce the rate of inconclusive classification, thereby enabling more appropriate and expedited downstream treatment decisions.