Diagnosis of Periprosthetic Joint Infection: An Algorithmic Approach to Patients



Identification of periprosthetic joint infection (PJI) is critical, as the treatment between infected and noninfected arthroplasties is fundamentally different and missing the diagnosis will lead to recurrent failure. Yet in the absence of a true gold standard, the diagnosis of PJI can often be elusive. Although every clinical scenario requires clinical judgment, by maintaining an algorithmic approach to the diagnosis of PJI, the clinician can most effectively use the battery of tests that are available for diagnosis. Relying on a patient’s risk factors, physical exam, and plain radiographs allows a particular patient to be stratified into either a high-probability or a low-probability category for PJI, which will allow better interpretation of subsequent tests and guide how aggressively the diagnosis of PJI is pursued. Erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) should be obtained in all patients where a revision is planned or if a source for pain is being sought. If positive or if the clinical suspicion for PJI is high, a joint aspiration should be performed and the fluid sent for a synovial fluid white blood cell (WBC) count, differential, and culture. Intraoperative testing can be performed in cases where the diagnosis of PJI has not been confirmed or excluded preoperatively. Given the rising incidence of this problem and the growing infection burden anticipated in coming years, much effort is being put toward improving diagnostic tests for PJI. Emerging technology, such as synovial fluid biomarker analysis, PCR, and sonication, as well as the increasing availability of serum markers, such as IL-6, may improve our ability to identify PJI in the future.


Total Knee Arthroplasty Early Postoperative Period Periprosthetic Joint Infection Positive Likelihood Ratio Prosthetic Joint Infection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Orthopaedic SurgeryLenox Hill HospitalNew YorkUSA
  2. 2.Department of Orthopaedic SurgeryRush University Medical CenterChicagoUSA

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