Li Zhi, Li Kun, Li Nan, Zhao Dingding, Ma Jianqing, Li Jinlong, Qin Baoju
Department of Infection Management, North China Healthcare Group Xingtai General Hospital, Xingtai, China.
Operating Room, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Ann Jt. 2025 Jul 15;10:21. doi: 10.21037/aoj-25-14. eCollection 2025.
Surgical site infections (SSIs) are common complications after joint arthroplasty, leading to increased morbidity and healthcare costs. Traditional models, like the National Nosocomial Infections Surveillance (NNIS) system, have limitations in predicting SSI risk due to a lack of patient-specific factors. This study aimed to create and validate a predictive model focusing on hypoproteinemia to enhance SSI risk assessment in joint surgery patients.
A retrospective cohort study of 726 patients undergoing joint arthroplasty between 2020 and 2022 was conducted. Data included demographics, laboratory values, and surgical details. Univariate and multivariate analyses identified key predictors, including hypoproteinemia, to develop a predictive nomogram. Model validation was performed using receiver operating characteristic curves, calibration, and decision curve analysis (DCA), comparing it to the NNIS model.
Hypoproteinemia was a significant independent predictor of SSI, with the new model outperforming the NNIS system (area under the curve: 0.829 . 0.534). Calibration analysis showed excellent agreement between predicted and observed probabilities, with a mean absolute error of 0.009. DCA further confirmed the model's clinical utility, showing a higher net benefit across various thresholds compared to traditional approaches.
Hypoproteinemia is a critical risk factor for SSI in joint arthroplasty. The new predictive model offers improved risk stratification, supporting a more personalized approach to perioperative management in orthopedic surgery.
手术部位感染（SSIs）是关节置换术后常见的并发症，会导致发病率增加和医疗成本上升。传统模型，如国家医院感染监测（NNIS）系统，由于缺乏患者特异性因素，在预测SSI风险方面存在局限性。本研究旨在创建并验证一个聚焦低蛋白血症的预测模型，以加强对关节手术患者SSI风险的评估。
对2020年至2022年间接受关节置换术的726例患者进行了一项回顾性队列研究。数据包括人口统计学信息、实验室检查值和手术细节。单因素和多因素分析确定了包括低蛋白血症在内的关键预测因素，以建立一个预测列线图。使用受试者工作特征曲线、校准和决策曲线分析（DCA）对模型进行验证，并与NNIS模型进行比较。
低蛋白血症是SSI的一个重要独立预测因素，新模型的表现优于NNIS系统（曲线下面积：0.829对0.534）。校准分析显示预测概率与观察概率之间具有良好的一致性，平均绝对误差为0.009。DCA进一步证实了该模型的临床实用性，与传统方法相比，在各种阈值下均显示出更高的净效益。
低蛋白血症是关节置换术中SSI的一个关键危险因素。新的预测模型提供了更好的风险分层，支持在骨科手术围手术期管理中采用更个性化的方法。
