Development and Validation of an Evidence-Based Algorithm for Diagnosing Periprosthetic Joint Infection

Noam Shohat, Timothy L. Tan, Craig J. Della Valle, Tyler E. Calkins, Jaiben George, Carlos Higuera, Javad Parvizi

Research output: Contribution to journalArticlepeer-review

Abstract

Background: The guidelines for diagnosis of periprosthetic joint infection (PJI) introduced by the American Academy of Orthopaedic Surgeons served the orthopedic community well. However, they have never been validated and do not account for newer diagnostic modalities. Our aim was to update current guidelines and develop an evidence-based and validated diagnostic algorithm. Methods: This multi-institutional study examined total joint arthroplasty patients from 3 institutions. Patients fulfilling major criteria for infection as defined by Musculoskeletal Infection Society were considered infected (n = 684). Patients undergoing aseptic revision for a noninfective indication and did not show evidence of PJI or undergo reoperation within 2 years served as a noninfected control group (n = 820). The algorithm was validated on a separate cohort of 422 cases. Results: The first step in evaluating PJI should include a physical examination, followed by serum C-reactive protein, erythrocyte sedimentation rate, and D-dimer. If at least one of these tests are elevated, or if high clinical suspicion exists, joint aspiration should be performed, sending the fluid for a white blood cell count, leukocyte esterase, polymorphonuclear percentage, and culture. Alpha defensin did not show added benefit as a routine diagnostic test. In inconclusive cases, intraoperative findings including gross purulence, histology, and next-generation sequencing or a single positive culture can aid in making the diagnosis. The proposed algorithm demonstrated a high sensitivity (96.9%) and specificity (99.5%). Conclusion: This validated, evidence-based algorithm for diagnosing PJI should guide clinicians in the workup of patients undergoing revision arthroplasty and improve clinical practice. It also has the potential to reduce cost.

Original languageEnglish
Pages (from-to)2730-2736.e1
JournalJournal of Arthroplasty
Volume34
Issue number11
DOIs
StatePublished - Nov 2019

Keywords

  • algorithm
  • diagnosis
  • evidence-based
  • periprosthetic joint infection
  • total joint arthroplasty
  • validated

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