Department of Anesthesiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
J Orthop Surg Res. 2023 Jun 27;18(1):463. doi: 10.1186/s13018-023-03931-1.
The implementation of more active anticoagulant prevention and treatment measures has indeed led to a significant reduction in the incidence of perioperative deep vein thrombosis (DVT) among patients with bone trauma. However, it is important to note that despite these efforts, the incidence of DVT still remains relatively high. According to the Caprini score, all patients undergoing major orthopedic surgery were defined as the high-risk group for DVT. Stratifying the risk further within high-risk groups for DVT continues to present challenges. As a result, the commonly used Caprini score during the perioperative period is not applicable to orthopedic patients. We attempt to establish a specialized model to predict postoperative DVT risk in patients with femoral fracture.
We collected the clinical data of 513 patients undergoing femoral fracture surgery in our hospital from May 2018 to December 2019. According to the independent risk factors of DVT obtained by univariate and multivariate logistic regression analysis, the corresponding nomogram model was established and verified internally. The discriminative capacity of nomogram was evaluated by receiver operating characteristic (ROC) curve and area under the curve (AUC). The calibration curve used to verify model consistency was the fitted line between predicted and actual incidences. The clinical validity of the nomogram model was assessed using decision curve analysis (DCA) which could quantify the net benefit of different risk threshold probabilities. Bootstrap method was applied to the internal validation of the nomogram model. Furthermore, a comparison was made between the Caprini score and the developed nomogram model.
The Caprini scores of subjects ranged from 5 to 17 points. The incidence of DVT was not positively correlated with the Caprini score. The predictors of the nomogram model included 10 risk factors such as age, hypoalbuminemia, multiple trauma, perioperative red blood cell infusion, etc. Compared with the Caprini scale (AUC = 0.571, 95% CI 0.479-0.623), the calibration accuracy and identification ability of nomogram were higher (AUC = 0.865,95% CI 0.780-0.935). The decision curve analysis (DCA) indicated the clinical effectiveness of nomogram was higher than the Caprini score.
The nomogram was established to effectively predict postoperative DVT in patients with femoral fracture. To further reduce the incidence, more specialized risk assessment models for DVT should take into account the unique risk factors and characteristics associated with specific patient populations.
更积极的抗凝预防和治疗措施的实施确实导致骨外伤患者围手术期深静脉血栓形成（DVT）的发生率显著降低。然而，需要注意的是，尽管做出了这些努力，DVT 的发生率仍然相对较高。根据卡普里尼评分，所有接受大型骨科手术的患者均被定义为 DVT 的高危人群。在 DVT 的高危人群中进一步分层风险仍然存在挑战。因此，围手术期常用的卡普里尼评分并不适用于骨科患者。我们试图建立一种专门的模型来预测股骨骨折患者术后 DVT 的风险。
我们收集了 2018 年 5 月至 2019 年 12 月在我院接受股骨骨折手术的 513 例患者的临床资料。根据单因素和多因素 logistic 回归分析得到的 DVT 独立危险因素，建立相应的列线图模型并进行内部验证。通过受试者工作特征（ROC）曲线和曲线下面积（AUC）评估列线图的判别能力。用于验证模型一致性的校准曲线是预测发生率与实际发生率之间的拟合线。通过决策曲线分析（DCA）评估列线图模型的临床有效性，该分析可以量化不同风险阈值概率的净收益。使用 bootstrap 方法对列线图模型进行内部验证。此外，还比较了卡普里尼评分和开发的列线图模型。
受试者的卡普里尼评分范围为 5 至 17 分。DVT 的发生率与卡普里尼评分无正相关关系。列线图模型的预测因素包括年龄、低白蛋白血症、多发伤、围手术期红细胞输注等 10 个危险因素。与卡普里尼量表（AUC=0.571，95%CI 0.479-0.623）相比，列线图的校准精度和识别能力更高（AUC=0.865，95%CI 0.780-0.935）。决策曲线分析（DCA）表明，列线图的临床效果高于卡普里尼评分。
建立了列线图以有效预测股骨骨折患者术后 DVT。为了进一步降低发生率，应考虑与特定患者群体相关的独特风险因素和特征，制定更专门的 DVT 风险评估模型。
