A Validated Model for the Diagnosis of Periprosthetic Joint Infection with Coagulation-related Markers: TGT and NETs

J. Oto¹, Á. Fernández-Pardo¹, M. Fuertes², M.J. Solmoirago¹, E. Plana¹, D. Hervás³, F. Cana¹, C. De la Calva², M. Angulo², I. Baixauli², F. Baixauli², F. España¹, S. Navarro¹, J.V. Amaya², P. Medina¹

¹Medical Research Institute Hospital La Fe, Haemostasis, Thrombosis, Atherosclerosis and Vascular Biology Research Group, Valencia, Spain, ²La Fe University and Polytechnic Hospital, Orthopaedics and Traumatology Service, Valencia, Spain, ³Medical Research Institute Hospital La Fe, Biostatistics Unit, Valencia, Spain

Abstract Number: PB1777

Meeting: ISTH 2020 Congress

Theme: Role of Hemostatic System in Cancer, Inflammation and Immunity » Coagulation Proteins Beyond Hemostasis

Background: Bacterial infection activates coagulation through immunothrombosis. Neutrophils are the first line of defense against bacterial infection, but its mechanism in periprosthetic joint infections (PJI) is unknown. Maybe through the release of neutrophil extracellular traps (NETs), since they are present in the bacterial biofilm of PJIs. There are no diagnostic tests of PJI with 100% sensitivity/specificity and often the diagnosis is uncertain due to lack of compliance with some criteria. We previously obtained a model to estimate the pre-surgical PJI risk using markers of NETs and thrombin generation test (TGT).

Aims: To validate the diagnostic capacity of PJI of our model.

Methods: We obtained citrated plasma, in the preoperative period of prosthetic surgery, from 32 patients prospectively recruited. The PJI was diagnosed in 9 of these patients. The diagnosis of 9 patients was uncertain, which is relatively frequent. We validate our predictive model of PJI that includes markers of NETs (DNA and calprotectin) and TGT variables (lagtime and starttail), with a multivariable logistic regression model with R (v3.5.0).

Results: All parameters of NETs and TGT were significantly increased in patients with PJI and with uncertain diagnosis compared with patients undergoing prosthetic surgery due to non-septic causes. The area under the ROC curve of our model was 0.79 (P=0.019, 95% CI[0.58,1]). With our model, the presurgical probability of PJI for each patient was higher in patients with a confirmed diagnosis of PJI than without PJI (P=0.009).

Conclusions: Markers of NETs and TGT seem to have diagnostic utility of PJI before surgery. Our model could reinforce the clinical criteria currently available in order to reduce the number of uncertain diagnoses and thus be able to make an early and effective diagnosis and treatment to minimize side effects of the PJI such as tissue damage, bone degradation and replacement surgery. ISCIII-FEDER(PI14/00079, PI14/00512, FI14/00269, CPII15/00002, PI17/00495), GV(ACIF/2017/138),SETH.

To cite this abstract in AMA style:

Oto J, Fernández-Pardo Á, Fuertes M, Solmoirago MJ, Plana E, Hervás D, Cana F, De la Calva C, Angulo M, Baixauli I, Baixauli F, España F, Navarro S, Amaya JV, Medina P. A Validated Model for the Diagnosis of Periprosthetic Joint Infection with Coagulation-related Markers: TGT and NETs [abstract]. Res Pract Thromb Haemost. 2020; 4 (Suppl 1). https://abstracts.isth.org/abstract/a-validated-model-for-the-diagnosis-of-periprosthetic-joint-infection-with-coagulation-related-markers-tgt-and-nets/. Accessed January 26, 2022.

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