The Utility of Frozen Section Histology in Diagnosing Periprosthetic Joint Infection in Revision Total Joint Arthroplasty

A rare case of periprosthetic joint infection of the hip due to Kocuria spp

BACKGROUND

Kocuria spp. are ubiquitous bacteria that have gained recent attention as potential infectious agents. The most common bacteria in PJI are S. aureus und S. epidermidis.

CASE PRESENTATION

We present the case of a 72-year-old woman who received total hip arthroplasty after a traumatic medial femoral neck fracture. Postoperatively, due to the clinical presentation of periprosthetic joint infection (PJI) revision surgery was performed twice. The microbiological tissue samples were positive for Kocuria spp. Initially, this was considered contamination and the patient was treated with various antibiotic regimens as well as prednisolone due to the differential diagnosis of pyoderma gangraenosum. However, a specialized histopathology lab performed further testing which substantiated the suspicion of a rare case of PJI due to Kocuria spp.

CONCLUSIONS

To our knowledge, this is the first reported case of a PJI caused by Kocuria spp. Further clinical research is necessary to assess whether Kocuria spp. are an underestimated cause of PJI.

The Fate of the Inconclusive Periprosthetic Joint Infection Workup and Reliability of Data Points

In 2018, periprosthetic joint infection (PJI) criteria were revised to include a new category labeled "inconclusive." The purpose of this study was to characterize and describe the fate of the inconclusive PJI workup and to analyze preoperative factors associated with outcomes. We reviewed all PJI workups at our institution during a 3-year period (426 patients). Patients were labeled "infected," "not infected," or "inconclusive" according to 2018 PJI preoperative criteria. In addition to standard diagnostic variables, the presence or absence of clinical elements that increase the pretest probability of infection were collected. Patients with any missing preoperative diagnostic test results and those with clinical follow-up less than 30 days were excluded. Logistic regression was used to identify the factors associated with infection. Two hundred ninety-six workups remained after exclusion criteria were applied, consisting of 66 (22.2%) with a preoperative score of 6 or greater defined as infected, 52 (17.6%) inconclusive (score 2-5), and 178 (60.1%) not infected (score 0-1). Postoperative re-scoring of the inconclusive group based on intraoperative findings as per the 2018 criteria identified 6 of 52 (11.5%) as infected, 12 (23.1%) inconclusive, and 34 (65.4%) not infected. Among those preoperatively scored as inconclusive, variables statistically correlated with the presence of infection included history of PJI, factors that increase skin barrier penetration (eg, psoriasis and venous stasis), and presence of comorbidities predisposing to infection. For patients labeled inconclusive, clinical elements of the pretest probability for infection (eg, history of prior PJI) were as reliable as any diagnostic test, including alpha-defensin, in the diagnosis of PJI. [Orthopedics. 2023;46(5):e291-e297.].

What is the Accuracy of a Rapid Strip Test That Detects D-lactic Acid in Synovial Fluid for the Diagnosis of Periprosthetic Joint Infections?

BACKGROUND

D-lactic acid is a specific marker produced almost exclusively by bacterial species; thus, the appearance of this marker in synovial fluid may indicate periprosthetic joint infection (PJI). Recently, studies have investigated the accuracy of enzyme-linked laboratory tests that detect D-lactic acid in synovial fluid to diagnose PJI. However, to our knowledge, no studies have determined the usefulness of rapid strip tests that detect D-lactic acid in synovial fluid in the diagnosis of PJI.

QUESTIONS/PURPOSES

(1) What is the best cutoff value for the rapid D-lactic acid strip test for diagnosing PJI? (2) What are the diagnostic accuracies (sensitivity, specificity, positive predictive value [PPV], and negative predictive value [NPV]) of the rapid D-lactic acid strip test and two different rapid leukocyte esterase (LE) strip tests?

METHODS

This prospective study enrolled 157 patients who underwent revision THA or TKA from May 2021 to February 2022 at a single orthopaedic center. Seventy percent (110 of 157) were eligible for analysis; 10% of these patients (15 of 157) were excluded based on the exclusion criteria (causes of revisions and additional comorbidities that may interfere with the results), and 20% (32 of 157) of the synovial fluid samples could not be tested (dry taps and blood-contaminated samples that could not be centrifuged). We performed the following off-label diagnostic tests on synovial fluid samples collected from all patients: the D-lactic acid strip test (QuantiQuick TM , BioAssay System), two different LE strip tests (10 EA from ARKRAY and BM 10 from BioMaxima). Differently colored strips were marked with symbols (from [-] to [++++] for D-lactic acid and from [-] to [+++] for LE tests) according to the manufacturers' instructions. For the LE tests, results were different for (++), which corresponds to a minimal value of 250 leu/mL for 10 EA and 125 leu/mL for BM 10 tests. The diagnostic standard for the presence or absence of PJI in this study was the International Consensus Meeting (ICM) 2018 criteria; based on these criteria (without the application of an LE test as a minor criterion), all patients were assessed and divided into two groups. Patients who did not meet the criteria for PJI and underwent revision for aseptic loosening, implant malposition, instability, or implant damage were included in the aseptic revision total joint arthroplasty group (68 patients). Patients with a fistula penetrating the joint, those with two positive culture results of the same pathogen, or those with ≥ 6 points according to ICM 2018 minor criteria were enrolled in the PJI group (42 patients). To ascertain the best cutoff value for the rapid D-lactic acid and both LE strip tests for diagnosing PJI, we used collected results, generated a receiver operating characteristic curve, and calculated the Youden index. To determine the accuracies of the diagnostic tests, we calculated their sensitivities, specificities, PPVs, and NPVs against the diagnostic standard (the ICM 2018 criteria).

RESULTS

The best cutoff value for D-lactic acid was 22.5 mg/L, which corresponded to a reading of (+) on the test strip. For D-lactic acid, in the diagnosis of PJI, the sensitivity was 83% (95% confidence interval [CI] 68% to 92%) and specificity was 100% (95% CI 93% to 100%). For both LE strip tests, the best cutoff value was the same as that proposed in the ICM 2018 criteria. For LE (10 EA), the sensitivity was 81% (95% CI 66% to 91%) and specificity was 99% (95% CI 91% to 100%); for LE (BM 10), sensitivity was 81% (95% CI 65% to 91%) and specificity was 97% (95% CI 89% to 100%).

CONCLUSION

A rapid off-label D-lactic acid strip test is valuable for diagnosing PJI. The results of this study indicate very good accuracy with comparable sensitivity and specificity for both LE strip tests. The usefulness of the test in a group of patients with chronic inflammatory diseases and the reproducibility of the reading by different researchers were not analyzed in this study and require further investigations. Before a rapid D-lactic strip test is routinely used for diagnosing PJI, multicenter studies on a larger group of patients should be conducted.Level of Evidence Level II, diagnostic study.

A preliminary study on the application of deep learning methods based on convolutional network to the pathological diagnosis of PJI

OBJECTIVE

This study aimed to establish a deep learning method based on convolutional networks for the preliminary study of the pathological diagnosis of prosthetic joint infections (PJI).

METHODS

We enrolled 20 revision patients after joint replacement from the Department of Orthopedics, the First Medical Center, General Hospital of the People's Liberation Army, from January 2021 to January 2022 (10 of whom were confirmed to be infected against 2018 ICM criteria, and the remaining 10 were verified to be non-infected), and classified high-power field images according to 2018 ICM criteria. Then, we inputted 576 positive images and 576 negative images into a neural network by employing a resNET model, used to select 461 positive images and 461 negative images as training sets, 57 positive images and 31 negative images as internal verification sets, 115 positive images and 115 negative images as external test sets.

RESULTS

The resNET model classification was used to analyze the pathological sections of PJI patients under high magnification fields. The results of internal validation set showed a positive accuracy of 96.49%, a negative accuracy of 87.09%, an average accuracy of 93.22%, an average recall rate 96.49%, and an F1 of 0.9482. The accuracy of external test results was 97.39% positive, 93.04% negative, the average accuracy of external test set was 93.33%, the average recall rate was 97.39%, with an F1 of 0.9482. The AUC area of the intelligent image-reading diagnosis system was 0.8136.

CONCLUSIONS

This study used the convolutional neural network deep learning to identify high-magnification images from pathological sections of soft tissues around joints, against the diagnostic criteria for acute infection, and a high precision and a high recall rate were accomplished. The results of this technique confirmed that better results could be achieved by comparing the new method with the standard strategies in terms of diagnostic accuracy. Continuous upgrading of extended training sets is needed to improve the diagnostic accuracy of the convolutional network deep learning before it is applied to clinical practice.