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Related Experiment Video

Updated: Oct 13, 2025

The Use of Mixed Reality in Custom-Made Revision Hip Arthroplasty: A First Case Report
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Predicting the Exception-CRP and Primary Hip Arthroplasty.

Marc-Pascal Meier1, Ina Juliana Bauer1, Arvind K Maheshwari2

  • 1Department of Trauma, Orthopedics and Reconstructive Surgery, Georg-August-University of Goettingen, 37075 Göttingen, Germany.

Journal of Clinical Medicine
|November 13, 2021
PubMed
Summary

Predicting early acute periprosthetic joint infection after hip arthroplasty is crucial. A multinominal logistic regression model using five parameters can predict infection by day 5 with 87.5% sensitivity and 78.9% specificity.

Keywords:
C-reactive proteinCRPorthopedic surgeryperiprosthetic joint infectionprimary hip arthroplastyrevision surgery

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Area of Science:

  • Orthopedic Surgery
  • Infectious Disease
  • Biostatistics

Background:

  • Periprosthetic joint infection is a severe complication of primary hip arthroplasty.
  • Early detection and prediction of these infections are critical for patient outcomes.
  • C-reactive protein (CRP) is a key biomarker for monitoring postoperative inflammation.

Purpose of the Study:

  • To evaluate the predictive value of postoperative C-reactive protein (CRP) levels.
  • To develop a predictive formula for early acute periprosthetic joint infection (PJI) following hip arthroplasty.
  • To identify key parameters for accurate PJI prediction.

Main Methods:

  • Retrospective evaluation of 708 patients undergoing primary hip arthroplasty.
  • Assessment of C-reactive protein (CRP), white blood cell count (WBC), and patient characteristics for 20 days postoperatively.
  • Application of binary and multinominal logistic regression models for infection prediction.

Main Results:

  • Eight patients developed early acute periprosthetic joint infections.
  • Maximum CRP showed 75% sensitivity and 56.9% specificity for infection prediction.
  • Multinominal logistic regression achieved 87.5% sensitivity and 78.9% specificity in predicting early infection.
  • A one-phase decay model explained 71.6% of the postoperative CRP variance.

Conclusions:

  • Multinominal logistic regression is the most effective method for predicting early acute PJI after hip arthroplasty.
  • A model incorporating five parameters can predict infection by postoperative day 5 with high sensitivity (87.5%).
  • The model also offers significant specificity (78.9%) for excluding infection.