Integrating systemic inflammation biomarker and clinical predictors for surgical site infection risk assessment in closed pilon fractures: A risk prediction model

Lin Jin, Yanci Zhang, Jiale Li, Lianxin Song, Yang Luo, Tianhua Dong and Xuebin Zhang

PLOS ONE, 2026, vol. 21, issue 4, 1-19

Abstract: Background: Systemic inflammation biomarkers have emerged as promising tools for predicting infection-related complications in orthopedic surgery. However, its predictive value for surgical site infection (SSI) after closed pilon fractures remains underexplored. This study aimed to develop and validate a nomogram that integrates systemic inflammation biomarkers and conventional clinical predictors to estimate the risk of SSI after closed pilon fracture surgery. Methods: We retrospectively analyzed data from patients aged ≥18 years with closed pilon fractures treated surgically at a tertiary orthopedic center between January 2020 and December 2023. Systemic inflammation response index (SIRI) and other candidate biomarkers were calculated from peripheral blood samples collected upon admission. The diagnosis of SSI was based on CDC criteria, determined through inpatient records and routine 12-month postoperative follow-up. Restricted cubic spline (RCS) curves were used to assess dose-response relationships between biomarkers and SSI. Multivariable logistic regression was performed to identify independent predictors and construct a nomogram. Model performance was evaluated using discrimination, calibration, and decision curve analysis (DCA). Temporal validation was performed in an independent cohort from the same center (January 2024 to December 2024), and external validation was conducted in an independent cohort from another institution (August 2024 to September 2025) using identical eligibility criteria. Results: Among 1314 patients included in model development, 57 cases (4.34%) of SSI were recorded. RCS analysis revealed a near-linear association between SIRI and SSI risk, with a threshold of 2.01 used for stratification. Multivariable analysis identified SIRI ≥ 2.01, BMI, surgical delay ≥ 6 days, Tscherne classification grade 3, prolonged surgical duration, and elevated fasting blood glucose (FBG) as independent predictors. The nomogram demonstrated good discrimination in the development cohort (AUC = 0.765) and maintained performance in temporal validation (AUC = 0.788) and external validation (AUC = 0.779). Conclusions: This study identified SIRI as a novel and independent systemic inflammation biomarker associated with SSI after closed pilon fracture. We further developed a nomogram combining SIRI and conventional clinical factors and validated it in both temporal and external cohorts, which may support individualized perioperative decision-making after further prospective multicenter validation.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0346298

DOI: 10.1371/journal.pone.0346298

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