Objective: Preoperative identification of infectious nonunion remains challenging, particularly in patients without overt clinical signs of infection. Conventional inflammatory markers alone often provide insufficient diagnostic accuracy, creating uncertainty in surgical planning. This study aimed to develop and internally evaluate a predictive model combining novel and conventional biomarkers for preoperative diagnosis of infectious nonunion in long bone fractures. We hypothesized that integrating procalcitonin (PCT) with classical inflammatory markers would enhance diagnostic accuracy.
Methods: We retrospectively analyzed 276 patients treated for femoral or tibial nonunion after internal fixation (2018–2023). Patients were randomly divided into a model-development cohort (n = 221) and a hold-out test cohort (n = 55). A multivariable logistic regression model was built using serum levels of PCT, interleukin-6 (IL-6), C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR). Model performance was assessed by AUC, calibration plots, and decision curve analysis.
Results: In the model-development cohort, 110 patients had infectious nonunion and 111 had aseptic nonunion. Infected cases had significantly higher biomarker levels (p < 0.001). Among single markers, ESR had the best performance (AUC ~0.79), followed by PCT (~0.71), IL-6 (~0.62), and CRP (~0.61). The best multivariable model included PCT, CRP, and ESR, achieving an AUC of ~0.85 (95% CI ~0.80–0.90). Bootstrapping confirmed minimal overfitting (corrected AUC ~0.84). In the hold-out test cohort, this model maintained strong performance (AUC ~0.85, 95% CI ~0.72–0.96), with ~75% sensitivity and 85% specificity—superior to any single biomarker. Calibration plots showed good agreement, and decision analysis demonstrated higher clinical benefit than using individual markers or treating all patients.
Conclusion: The PCT + CRP + ESR model reliably distinguishes infectious from aseptic nonunion, outperforming traditional markers alone. It offers a practical, accurate preoperative tool for infection screening, supporting more informed surgical decision-making. In clinical use, interpretation should be based on the predicted probability generated by the combined model rather than isolated elevation of individual biomarkers. Further prospective and external validation is warranted.
| [1] |
E. Gomez-Barrena and C. Ehrnthaller, “Long Bone Uninfected Non-Union: Grafting Techniques,” EFORT Open Reviews 9, no. 5 (2024): 329–338.
|
| [2] |
A. K. Jain and S. Sinha, “Infected Nonunion of the Long Bones,” Clinical Orthopaedics and Related Research 431 (2005): 57–65.
|
| [3] |
D. Bose, R. Kugan, D. Stubbs, and M. McNally, “Management of Infected Nonunion of the Long Bones by a Multidisciplinary Team,” Bone & Joint Journal 97-B, no. 6 (2015): 814–817.
|
| [4] |
C. Stucken, D. C. Olszewski, W. R. Creevy, A. M. Murakami, and P. Tornetta, “Preoperative Diagnosis of Infection in Patients With Nonunions,” Journal of Bone and Joint Surgery. American Volume 95, no. 15 (2013): 1409–1412.
|
| [5] |
B. Wildemann, A. Ignatius, F. Leung, et al., “Non-Union Bone Fractures,” Nature Reviews. Disease Primers 7, no. 1 (2021): 57.
|
| [6] |
L. S. Phieffer and J. A. Goulet, “Delayed Unions of the Tibia,” Journal of Bone and Joint Surgery. American Volume 88, no. 1 (2006): 206–216.
|
| [7] |
R. A. Gosselin, I. Roberts, and W. J. Gillespie, “Antibiotics for Preventing Infection in Open Limb Fractures,” Cochrane Database of Systematic Reviews 2004, no. 1 (2004): CD003764.
|
| [8] |
K. Okike and T. Bhattacharyya, “Trends in the Management of Open Fractures. A Critical Analysis,” Journal of Bone and Joint Surgery. American Volume 88, no. 12 (2006): 2739–2748.
|
| [9] |
T. H. Tosounidis, C. Holton, V. P. Giannoudis, N. K. Kanakaris, R. M. West, and P. V. Giannoudis, “Can CRP Levels Predict Infection in Presumptive Aseptic Long Bone Non-Unions? A Prospective Cohort Study,” Journal of Clinical Medicine 10, no. 3 (2021): 425.
|
| [10] |
L. Simon, F. Gauvin, D. K. Amre, P. Saint-Louis, and J. Lacroix, “Serum Procalcitonin and C-Reactive Protein Levels as Markers of Bacterial Infection: A Systematic Review and Meta-Analysis,” Clinical Infectious Diseases 39, no. 2 (2004): 206–217.
|
| [11] |
E. Berbari, T. Mabry, G. Tsaras, et al., “Inflammatory Blood Laboratory Levels as Markers of Prosthetic Joint Infection: A Systematic Review and Meta-Analysis,” Journal of Bone and Joint Surgery 92, no. 11 (2010): 2102–2109.
|
| [12] |
X. Yang, J. Zeng, X. Yu, et al., “PCT, IL-6, and IL-10 Facilitate Early Diagnosis and Pathogen Classifications in Bloodstream Infection,” Annals of Clinical Microbiology and Antimicrobials 22, no. 1 (2023): 103.
|
| [13] |
P. Schuetz, “How to Best Use Procalcitonin to Diagnose Infections and Manage Antibiotic Treatment,” Clinical Chemistry and Laboratory Medicine (CCLM) 61, no. 5 (2023): 822–828.
|
| [14] |
C. J. Shen, M. S. Wu, K. H. Lin, et al., “The Use of Procalcitonin in the Diagnosis of Bone and Joint Infection: A Systemic Review and Meta-Analysis,” European Journal of Clinical Microbiology & Infectious Diseases 32, no. 6 (2013): 807–814.
|
| [15] |
H. Xu, J. W. Xie, L. Liu, D. Wang, Z. Y. Huang, and Z. K. Zhou, “Combination of CRP With NLR Is a Sensitive Tool for Screening Fixation-Related Infection in Patients Undergoing Conversion Total Hip Arthroplasty After Failed Internal Fixation for Femoral Neck Fracture,” Bone & Joint Journal 103b, no. 9 (2021): 1534–1540.
|
| [16] |
M. Sollini, N. Trenti, E. Malagoli, et al., “[18F]FDG PET/CT in Non-Union: Improving the Diagnostic Performances by Using Both PET and CT Criteria,” European Journal of Nuclear Medicine and Molecular Imaging 46, no. 8 (2019): 1605–1615.
|
| [17] |
E. Ghanem, V. Antoci, Jr., L. Pulido, A. Joshi, W. Hozack, and J. Parvizi, “The Use of Receiver Operating Characteristics Analysis in Determining Erythrocyte Sedimentation Rate and C-Reactive Protein Levels in Diagnosing Periprosthetic Infection Prior to Revision Total Hip Arthroplasty,” International Journal of Infectious Diseases 13, no. 6 (2009): e444–e449.
|
| [18] |
J. Conteduca, M. Filipponi, P. Pichierri, A. Casto, L. Meccariello, and G. Rollo, “Common Inflammatory Markers in the Screening of Knee Arthroprosthesis Infections,” Medicinski Glasnik 21, no. 1 (2024): 203–207.
|
| [19] |
G. Falzarano, A. Piscopo, P. Grubor, et al., “Use of Common Inflammatory Markers in the Long-Term Screening of Total Hip Arthroprosthesis Infections: Our Experience,” Advances in Orthopaedics 2017 (2017): 9679470.
|
| [20] |
L. Meccariello, A. Prkic, V. Campagna, et al., “Two-Stage Bone-And-Strut Technique in the Treatment of Septic Non-Unions in the Upper Limb,” Medicinski Glasnik (Zenica) 18, no. 1 (2021): 232–238.
|
| [21] |
G. Rollo, G. Logroscino, D. Stomeo, R. Cioffi, V. Calvisi, and L. Meccariello, “Comparing the Use of Preformed vs Hand-Made Antibiotic Spacer Cement in Two Stages Revision of Hip Periprosthetic Infection,” Journal of Clinical Orthopaedics and Trauma 11, no. S5 (2020): S772–S778.
|
| [22] |
H. Xu, J.-W. Xie, L. Liu, D. Wang, Z.-Y. Huang, and Z.-K. Zhou, “Combination of CRP With NLR Is a Sensitive Tool for Screening Fixation-Related Infection in Patients Undergoing Conversion Total Hip Arthroplasty After Failed Internal Fixation for Femoral Neck Fracture,” Bone & Joint Journal 103-B, no. 9 (2021): 1534–1540.
|
Rights & permissions
2026 The Author(s). Orthopaedic Surgery published by Tianjin Hospital and John Wiley & Sons Australia, Ltd.
PDF
(2861KB)
