C-reactive protein/albumin and C-reactive protein/fibrinogen ratios for the diagnosis of periprosthetic joint infection in revision total joint arthroplasty

Serum albumin-to-globulin ratio and CRP-to-albumin ratio did not outperform serum CRP in diagnosing periprosthetic joint infections

Aims

Serum inflammatory parameters are widely used to aid in diagnosing a periprosthetic joint infection (PJI). Due to their limited performances in the literature, novel and more accurate biomarkers are needed. Serum albumin-to-globulin ratio (AGR) and serum CRP-to-albumin ratio (CAR) have previously been proposed as potential new parameters, but results were mixed. The aim of this study was to assess the diagnostic accuracy of AGR and CAR in diagnosing PJI and to compare them to the established and widely used marker CRP.

Methods

From 2015 to 2022, a consecutive series of 275 cases of revision total hip (n = 129) and knee arthroplasty (n = 146) were included in this retrospective cohort study. Based on the 2021 European Bone and Joint Infection Society (EBJIS) definition, 144 arthroplasties were classified as septic. Using receiver operating characteristic curve (ROC) analysis, the ideal thresholds and diagnostic performances were calculated. The areas under the curve (AUCs) were compared using the z-test.

Results

AGR, CAR, and CRP were associated with PJI (p < 0.001). Sensitivities were 62.5% (95% CI 54.3 to 70.0), 73.6% (95% CI 65.8 to 80.1), and 71.5% (95% CI 63.6 to 78.3), respectively. Specificities were calculated with 84.7% (95% CI 77.5 to 89.9), 86.3% (95% CI 79.2 to 91.2), and 87.8% (95% CI 80.9 to 92.4), respectively. The AUC of CRP (0.797 (95% CI 0.750 to 0.843)) was significantly higher than the AUC of AGR (0.736 (95% CI 0.686 to 0.786), p < 0.001), and similar to AUC of CAR (0.799 (95% CI 0.753 to 0.846), p = 0.832). Decreased sensitivities were observed in PJIs caused by low-virulence organisms (AGR: 60%, CAR: 78%) compared to high-virulence pathogens (AGR: 80%, p = 0.042; CAR: 88%, p = 0.158). Higher sensitivities were seen in acute haematogenous (AGR: 83%, CAR: 96%) compared to chronic PJIs (AGR: 54%, p = 0.001; CAR: 65%, p < 0.001).

Conclusion

Serum AGR and CAR showed limited diagnostic accuracy (especially in low-grade and chronic infections) and did not outperform the established marker CRP in our study. Hence, neither parameter can be recommended as an additional tool for diagnosing PJI.

The Development and Evaluation of a Prediction Model for Kidney Transplant-Based Pneumocystis carinii Pneumonia Patients Based on Hematological Indicators

BACKGROUND

This study aimed to develop a simple predictive model for early identification of the risk of adverse outcomes in kidney transplant-associated Pneumocystis carinii pneumonia (PCP) patients.

METHODS

This study encompassed 103 patients diagnosed with PCP, who received treatment at our hospital between 2018 and 2023. Among these participants, 20 were categorized as suffering from severe PCP, and, regrettably, 13 among them succumbed. Through the application of machine learning techniques and multivariate logistic regression analysis, two pivotal variables were discerned and subsequently integrated into a nomogram. The efficacy of the model was assessed via receiver operating characteristic (ROC) curves and calibration curves. Additionally, decision curve analysis (DCA) and a clinical impact curve (CIC) were employed to evaluate the clinical utility of the model. The Kaplan-Meier (KM) survival curves were utilized to ascertain the model's aptitude for risk stratification.

RESULTS

Hematological markers, namely Procalcitonin (PCT) and C-reactive protein (CRP)-to-albumin ratio (CAR), were identified through machine learning and multivariate logistic regression. These variables were subsequently utilized to formulate a predictive model, presented in the form of a nomogram. The ROC curve exhibited commendable predictive accuracy in both internal validation (AUC = 0.861) and external validation (AUC = 0.896). Within a specific threshold probability range, both DCA and CIC demonstrated notable performance. Moreover, the KM survival curve further substantiated the nomogram's efficacy in risk stratification.

CONCLUSIONS

Based on hematological parameters, especially CAR and PCT, a simple nomogram was established to stratify prognostic risk in patients with renal transplant-related PCP.

Accuracy of Albumin, Globulin, and Albumin-Globulin Ratio for Diagnosing Periprosthetic Joint Infection: A Systematic Review and Meta-Analysis

Periprosthetic joint infection (PJI) is one of the most intractable orthopedic diseases, partly because of the difficulty in differentiating septic from aseptic conditions. We aimed to evaluate and consolidate the diagnostic accuracy of the quantitative assessment of serum albumin (Alb), globulin (Glb), and albumin-globulin ratio (AGR), alone or in combination with the inflammatory marker, C-reactive protein (CRP), for PJI. We searched the PubMed, CINAHL, and Cochrane Library databases for studies that quantitatively measured Alb, Glb, or AGR for the diagnosis of PJI up until the 30 April 2023. A total of 2339 patients were included from 10 studies, including 845 patients with a definitive diagnosis of PJI and 1494 with non-PJI. The pooled sensitivity, specificity, and area under the curve (AUC) in the summary receiver-operating characteristic curve were as follows: 0.625, 0.732, and 0.715 for Alb; 0.815, 0.857, and 0.887 for Glb; 0.753, 0.757, and 0.875 for AGR; 0.788, 0.837, and 0.876 for CRP; 0.879, 0.890, and 0.917 for the CRP-Alb ratio; and 0.845, 0.855, and 0.908 for the CRP-AGR ratio. Serum Alb, Glb, and AGR levels are feasible and accurate diagnostic markers for PJI, and the combination of these markers with CRP levels may potentially improve preoperative serum diagnostic accuracy. Future prospective studies are required to verify these findings because of the small numbers of included studies.

Serum procalcitonin has no significance in the diagnosis of periprosthesis joint infection before total hip and knee replacement

Background

Currently, there is no "gold standard" for early diagnosing PJI. The diagnosis of periprosthetic joint infection (PJI) is a challenging problem in the clinic. As we know, many serum markers have been used in the early diagnosis of PJI. The aim of this study was to validate the value of PCT in the diagnosis of PJI.

Methods

A retrospective review of 77 patients with revision arthroplasties from January 2013 to July 2020 was conducted. PJI was defined using the modified Musculoskeletal Infection Society (MSIS) criteria combined with follow-up results. Besides medical history, clinical and laboratory data was gathered. Preoperative blood was taken for serum PCT and other biomarkers measurement. Receiver operating characteristic (ROC) curves were generated to evaluate the biomarkers' diagnostic performance and optimal cut-off value.

Results

Forty-one patients were identified as the PJI group (27 hips and 14 knees), while thirty-six patients were identified as the aseptic loosening (AL) group (33 hips and 3 knees). The AUCs for C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), Platelets (PLT), Fibrinogen (FIB), and Procalcitonin (PCT) were 0.845 (95% CI 0.755-0.936, p < 0.001), 0.817 (95% CI 0.718-0.916, p < 0.001), 0.728 (95% CI 0.613-0.843, p < 0.001), 0.810 (95% CI 0.710-0.910, p < 0.001) and 0.504 (95% CI 0.373-0.635, p = 0.950), respectively. Higher Area under the Curve (AUC) values were obtained for the combinations of PCT and CRP (AUC = 0.870) (95% CI, 0.774-0.936), PCT and ESR (AUC = 0.817) (95% CI, 0.712-0.896), PCT and PLT (AUC = 0.731) (95% CI, 0.617-0.825), PCT and FIB (AUC = 0.815) (95% CI, 0.710-0.894). The serum PCT indicated a sensitivity of 19.51% and a specificity of 83.33% for diagnosing PJI. When the optimal cut-off value for PCT was set as 0.05 ng/ml, its positive and negative likelihood ratios were 57.1% and 47.6%, respectively.

Conclusion

In conclusion, serum PCT appeared to be no reliable biomarker in differentiating PJI from aseptic loosening before revision arthroplasties. However, PCT combined with other biomarkers further increases the diagnostic accuracy.