Guo Yuhui, Li Chengsi, Guo Haichuan, Wang Peiyuan, Zhang Xuebin
Department of Orthopedic Oncology, The 3rd Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050051, P.R. China.
Department of Orthopedic Surgery, The 3rd Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050051, P.R. China.
J Orthop Surg Res. 2025 Jan 13;20(1):43. doi: 10.1186/s13018-024-05446-9.
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.
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.
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.
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.
全身炎症生物标志物已被广泛证明与感染有关。本研究旨在构建一种基于全身炎症生物标志物和传统预后因素的列线图，以评估老年髋部骨折术后手术部位感染（SSI）的风险。
回顾性收集2017年6月至2022年6月期间在一家三级转诊医院接受手术且随访超过12个月的60岁以上急性髋部骨折患者的数据。生物标志物通过入院时采集的外周静脉血计算得出。采用疾病控制与预防中心（CDC）对SSI的定义，通过住院期间的医疗和病原体培养记录以及术后常规电话随访来确定SSI。多变量逻辑回归确定了SSI的独立危险因素，并开发了预测列线图。使用2022年7月至2023年6月期间治疗的一组外部患者验证模型稳定性。
共有1430例患者纳入模型开发，其中浅表SSI 41例（2.87%），深部SSI 6例（0.42%）。多变量分析确定传统预后因素年龄较大（OR = 1.08，95%CI 1.04 - 1.12）、美国麻醉医师协会（ASA）分级III - IV级（OR = 2.46，95%CI 1.32 - 4.56）、手术延迟≥6天（OR = 3.59，95%CI 1.36 - 9.47）、手术持续时间>180分钟（OR = 2.72，95%CI 1.17 - 6.35）以及全身炎症生物标志物血小板与淋巴细胞比值（PAR）≥6.6（OR = 2.25，95%CI 1.17 - 4.33）和全身免疫炎症指数（SII）≥541.1（OR = 2.24，95%CI 1.14 - 4.40）为SSI的独立预测因素。通过内部验证和对307例患者的外部验证证明了模型的稳定性，并生成了在线动态列线图（https://brooklyn99.shinyapps.io/DynNomapp/）。
本研究结合全身炎症生物标志物开发了一种在线动态列线图，用于预测老年髋部骨折患者的SSI，可用于指导对SSI高风险患者的早期筛查，并为围手术期管理提供参考工具。
