Department of Traumatology and Hand Surgery, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, Guangxi, China.
Guangxi Key Laboratory of Regenerative Medicine, Orthopaedic Department, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, Guangxi, China.
BMC Musculoskelet Disord. 2023 Aug 9;24(1):640. doi: 10.1186/s12891-023-06746-7.
Amputation is a serious complication of acute compartment syndrome (ACS), and predicting the risk factors associated with amputation remains a challenge for surgeons. The aim of this study was to analyze the risk factors for amputation in patients with ACS and develop a nomogram to predict amputation risk more accurately.
The study population consisted of 143 patients (32 in the amputation group and 111 in the limb preservation group) diagnosed with ACS. LASSO and multivariate logistic regression were used to screen predictors and create a nomogram. The model's accuracy was assessed by receiver operating characteristic (ROC) curves, C-index, calibration curves, and decision curve analysis (DCA).
The predictors included cause of injury, vascular damage, shock, and fibrinogen in the nomogram. The C-index of the model was 0.872 (95% confidence interval: 0.854-0.962), and the C-index calculated by internal validation was 0.838. The nomogram's area under the curve (AUC) was 0.849, and the calibration curve demonstrated a high degree of agreement between the nomogram's predictions and actual observations. Additionally, the DCA indicated good clinical utility for the nomogram.
The risk of amputation in ACS patients is associated with the cause of injury, vascular damage, shock, and fibrinogen. Our nomogram integrating clinical factors and biochemical blood markers enables doctors to more conveniently predict the risk of amputation in patients with ACS.
截肢是急性间隔综合征（ACS）的严重并发症，预测与截肢相关的危险因素仍然是外科医生面临的挑战。本研究旨在分析 ACS 患者截肢的危险因素，并开发一个列线图以更准确地预测截肢风险。
研究人群包括 143 名被诊断为 ACS 的患者（截肢组 32 例，保肢组 111 例）。使用 LASSO 和多变量逻辑回归筛选预测因素并创建列线图。通过受试者工作特征（ROC）曲线、C 指数、校准曲线和决策曲线分析（DCA）评估模型的准确性。
列线图中的预测因素包括损伤原因、血管损伤、休克和纤维蛋白原。模型的 C 指数为 0.872（95%置信区间：0.854-0.962），内部验证计算的 C 指数为 0.838。列线图的曲线下面积（AUC）为 0.849，校准曲线显示列线图的预测与实际观察之间具有高度一致性。此外，DCA 表明该列线图具有良好的临床实用性。
ACS 患者截肢的风险与损伤原因、血管损伤、休克和纤维蛋白原有关。我们的列线图整合了临床因素和生化血液标志物，使医生能够更方便地预测 ACS 患者的截肢风险。
