Combining systemic inflammation biomarkers with traditional prognostic factors to predict surgical site infections in elderly hip fracture patients: a risk factor analysis and dynamic nomogram development - Yuhui Guo; Chengsi Li; Haichuan Guo; Peiyuan Wang; Xuebin Zhang (Journal of Orthopaedic Surgery and Research)
Abstract Background Systemic inflammation biomarkers have been widely shown to be associated with infection. This study aimed to construct a nomogram based on systemic inflammation biomarkers and traditional prognostic factors to assess the risk of surgical site infection (SSI) after hip fracture in the elderly. Methods Data were retrospectively collected from patients over 60 with acute hip fractures who underwent surgery and were followed for more than 12 months between June 2017 and June 2022 at a tertiary referral hospital. Biomarkers were calculated from peripheral venous blood collected on admission. The Centers for Disease Control and Prevention (CDC) definition of SSI was applied, with SSI identified through medical and pathogen culture records during hospitalization and routine postoperative telephone follow-ups. Multivariable logistic regression identified independent risk factors for SSI and developed predictive nomograms. Model stability was validated using an external set of patients treated from July 2022 to June 2023. Results A total of 1430 patients were included in model development, with 41 cases (2.87%) of superficial SSI and 6 cases (0.42%) of deep SSI. Multivariable analysis identified traditional prognostic factors older age (OR = 1.08, 95% CI 1.04–1.12), ASA class III-IV (OR = 2.46, 95% CI 1.32–4.56), surgical delay ≥ 6 days (OR = 3.59, 95% CI 1.36–9.47), surgical duration > 180 min (OR = 2.72, 95% CI 1.17–6.35), and systemic inflammation biomarkers Platelet-to-lymphocyte ratio (PAR) ≥ 6.6 (OR = 2.25, 95% CI 1.17–4.33) and Systemic Immune-Inflammation Index (SII) ≥ 541.1 (OR = 2.24, 95% CI 1.14–4.40) as independent predictors of SSI. Model’s stability was proved by internal validation, and external validation with 307 patients, and an online dynamic nomogram ( https://brooklyn99.shinyapps.io/DynNomapp/ ) was generated. Conclusions This study combined systemic inflammatory biomarkers and developed an online dynamic nomogram to predict SSI in elderly hip fracture patients, which could be used to guide early screening of patients with high risk of SSI and provide a reference tool for perioperative management.