Weng Shuwei, Ding Chen, Hu Die, Huang Likui, Peng Daoquan
Department of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
Research Institute of Blood Lipid and Atherosclerosis, Changsha, Hunan, China.
Sci Rep. 2025 Aug 13;15(1):29663. doi: 10.1038/s41598-025-14960-7.
Deep vein thrombosis (DVT) is a serious complication following gastrointestinal surgery. While D-dimer is a widely used biomarker for thrombosis, its postoperative specificity is limited due to inflammatory interference. This study introduces a novel cumulative metric-7-day D-dimer exposure (7dDDE)-to quantify perioperative coagulation burden. We retrospectively analyzed 525 patients undergoing gastrointestinal surgery, performed propensity score matching, and constructed a multivariable logistic regression model incorporating 7dDDE and other clinical variables. Model performance was evaluated using ROC curves, decision curve analysis, calibration plots, SHAP values, and a nomogram. Additionally, a linear mixed-effects model assessed D-dimer trajectories over time. The results demonstrated that 7dDDE was independently associated with postoperative DVT and was the most influential predictor in the model. The model showed good discrimination and clinical utility. Longitudinal analysis further revealed significant differences in D-dimer dynamics between DVT and non-DVT groups, even after adjustment for confounders. These findings support the use of 7dDDE as a robust biomarker for thrombotic risk stratification and highlight the importance of integrating temporal biomarker patterns into perioperative DVT prediction.
深静脉血栓形成(DVT)是胃肠手术后一种严重的并发症。虽然D - 二聚体是一种广泛用于血栓形成的生物标志物,但其术后特异性因炎症干扰而受限。本研究引入了一种新的累积指标——7天D - 二聚体暴露量(7dDDE),以量化围手术期凝血负担。我们回顾性分析了525例接受胃肠手术的患者,进行倾向评分匹配,并构建了一个纳入7dDDE和其他临床变量的多变量逻辑回归模型。使用ROC曲线、决策曲线分析、校准图、SHAP值和列线图评估模型性能。此外,线性混合效应模型评估了D - 二聚体随时间的轨迹。结果表明,7dDDE与术后DVT独立相关,并且是模型中最具影响力的预测因子。该模型显示出良好的辨别力和临床实用性。纵向分析进一步揭示,即使在调整混杂因素后,DVT组和非DVT组之间的D - 二聚体动态仍存在显著差异。这些发现支持将7dDDE用作血栓形成风险分层的可靠生物标志物,并强调了将时间性生物标志物模式纳入围手术期DVT预测的重要性。