Combined serum biomarker analysis shows no benefit in the diagnosis of periprosthetic joint infection

• Published in: International orthopaedics Vol. 44; no. 12; pp. 2515 - 2520
• Main Authors: Klim, S. M., Amerstorfer, F., Glehr, G., Hauer, G., Smolle, M. A., Leitner, L., Leithner, A., Glehr, M.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2020
• Subjects: Arthroplasty, Replacement, Hip - adverse effects; Arthroplasty, Replacement, Knee - adverse effects; Biomarkers; C-Reactive Protein - analysis; Humans; Medicine; Medicine & Public Health; Original Paper; Orthopedics; Prosthesis-Related Infections - diagnosis; Sensitivity and Specificity; Synovial Fluid - chemistry; Biomarker; Periprosthetic joint infection; Diagnostics; Revision joint arthroplasty
• ISSN: 0341-2695; 1432-5195
• DOI: 10.1007/s00264-020-04731-6

Purpose In many cases, the diagnosis of a periprosthetic joint infection (PJI) consisting of the clinical appearance, laboratory tests, and other diagnostic tools remains a difficult task. Single serum biomarkers are easy to collect, are suitable for periodical assessment, and are a crucial tool in PJI diagnosis, but limited sensitivity or specificity is reported in literature. The aim of this study was to combine the best-performing single serum biomarkers into a multi-biomarker model aiming to improve the diagnostic properties. Methods Within a 27-month period, 124 surgical procedures (aseptic or septic revision total knee arthroplasty (TKA) or total hip arthroplasty (THA)) were prospectively included. The serum leukocyte count, C-reactive protein (CRP), interleukin-6, procalcitonin, interferon alpha, and fibrinogen were assessed 1 day prior to surgery. Logistic regression with lasso-regularization was used for the biomarkers and all their ratios. After randomly splitting the data into a training (75%) and a test set (25%), the multi-biomarker model was calculated and validated in a cross-validation approach. Results CRP (AUC 0.91, specificity 0.67, sensitivity 0.90, p value 0.03) and fibrinogen (AUC 0.93, specificity 0.73, sensitivity 0.94, p value 0.02) had the best single-biomarker performances. The multi-biomarker model including fibrinogen, CRP, the ratio of fibrinogen to CRP, and the ratio of serum thrombocytes to CRP showed a similar performance (AUC 0.95, specificity 0.91, sensitivity 0.72, p value 0.01). Conclusion In this study, multiple biomarkers were tested for their diagnostic performance, with CRP and fibrinogen showing the best results regarding the AUC, accuracy, sensitivity, and specificity. It was not possible to further increase the diagnostic accuracy by combining multiple biomarkers using sophisticated statistical methods.