Yang Wenming, Lin Qitai, Li Zehao, Shan Chuanjie, Cheng Xiaoyu, Xing Yugang, Ma Yongsheng, Liu Yang, Li Meiming, Liang Ruifeng, Duan Wangping, Li Pengcui, Wei Xiaochun
Academy of Medical Sciences, Shanxi Medical University, Taiyuan, China.
Department of Orthopaedics, Second Hospital of Shanxi Medical University, Taiyuan, China.
Front Med (Lausanne). 2025 May 22;12:1528154. doi: 10.3389/fmed.2025.1528154. eCollection 2025.
Perioperative monitoring thrombosis has become more crucial due to the rising demand for arthroplasty and shorter hospital stays. We aimed to comprehensively explore immune-inflammatory and hypercoagulable states during perioperative periods patients undergoing arthroplasty to identify the risk factors for early postoperative deep vein thrombosis (DVT) and construct a nomogram prediction model for postoperative DVT.
Electronic medical records of 841 patients who underwent primary arthroplasty at a single institution were retrospectively reviewed. Patients' demographic and perioperative laboratory data were collected and divided into training (73.8%) and validation sets (26.2%) based on order of procedure date. Variables were screened from the training set using the Least Absolute Shrinkage and Selection Operator (LASSO) regression; a nomogram was constructed after multivariate logistic regression. The validation set was used to evaluate its discriminatory capacity and efficacy. The model's performance was evaluated through the Brier score, receiver operating characteristic curves, area under the curve (AUC), calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC).
We found an asymptomatic DVT incidence of 27.5% (231/841) on postoperative day three and identified seven predictors: age, chronic heart failure, stroke, tourniquet, postoperative monocyte-to-lymphocyte ratio, and postoperative alpha and D-dimer levels. The predictive model yielded an AUC of 0.737 (95% CI, 0.6933-0.7785), with an external validation AUC of 0.683 (95% CI, 0.6139-0.7716). The Brier score was 0.176, indicating the model's strong robustness in predicting perioperative DVT incidence in arthroplasty. Clinical impact and decision curve analysis revealed that using the proposed nomogram for prediction yielded a net benefit for threshold probabilities of 10-70%.
Our risk prediction model demonstrated reasonable discriminative capacity for predicting perioperative DVT risk in arthroplasty. This model may help increase the clinical benefits for patients by promptly identifying high-risk individuals early postoperatively.
由于关节置换术需求的增加以及住院时间的缩短，围手术期血栓监测变得更加关键。我们旨在全面探索接受关节置换术患者围手术期的免疫炎症和高凝状态，以确定术后早期深静脉血栓形成（DVT）的危险因素，并构建术后DVT的列线图预测模型。
回顾性分析了在单一机构接受初次关节置换术的841例患者的电子病历。收集患者的人口统计学和围手术期实验室数据，并根据手术日期顺序分为训练集（73.8%）和验证集（26.2%）。使用最小绝对收缩和选择算子（LASSO）回归从训练集中筛选变量；经过多变量逻辑回归后构建列线图。验证集用于评估其区分能力和有效性。通过Brier评分、受试者工作特征曲线、曲线下面积（AUC）、校准曲线、决策曲线分析（DCA）和临床影响曲线（CIC）评估模型的性能。
我们发现术后第三天无症状DVT的发生率为27.5%（231/841），并确定了七个预测因素：年龄、慢性心力衰竭、中风、止血带、术后单核细胞与淋巴细胞比值以及术后α和D-二聚体水平。预测模型的AUC为0.737（95%CI，0.6933-0.7785），外部验证AUC为0.683（95%CI，0.6139-0.7716）。Brier评分为0.176，表明该模型在预测关节置换术围手术期DVT发生率方面具有很强的稳健性。临床影响和决策曲线分析表明，使用所提出的列线图进行预测在阈值概率为10%-70%时产生了净效益。
我们的风险预测模型在预测关节置换术围手术期DVT风险方面显示出合理的区分能力。该模型可能有助于通过在术后早期及时识别高危个体来增加患者的临床获益。
