A practical nomogram for predicting amputation rates in acute compartment syndrome patients based on clinical factors and biochemical blood markers

Abstract

Background: 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.

Methods: 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).

Results: 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.

Conclusion: 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.

Keywords: Acute compartment syndrome; Amputation; Nomogram; Predictors.