Construction and Validation of a Nomogram for Predicting Postoperative Complication Risk in Patients with Arteriosclerosis Obliterans Based on Preoperative Ankle-Brachial Index (ABI) and Clinical Characteristics
Discuss this preprint
Start a discussion What are Sciety discussions?Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Objective: To screen the independent risk factors for 30-day postoperative complications in patients with arteriosclerosis obliterans of the lower extremities (ASO), construct and validate a visualized nomogram prediction model, and provide a quantitative tool for perioperative risk assessment and individualized intervention. Methods: A single-center retrospective cohort study was performed to collect clinical data from ASO patients who underwent surgical treatment. Univariate and multivariate Logistic regression analyses were used to screen independent risk factors and establish the nomogram. The discrimination, calibration and clinical utility of the model were evaluated by Bootstrap internal validation, receiver operating characteristic (ROC) curve, calibration curve and optimal cutoff value analysis. Results: Advanced age, smoking history, diabetes mellitus, and preoperative ankle-brachial index (ABI) < 0.4 were independent risk factors for 30-day postoperative complications in ASO patients (P < 0.05). The C-index and area under the curve (AUC) of the model were both 0.806 (95%CI: 0.762–0.851), and the Bootstrap-corrected C-index was 0.799, indicating good discrimination. The calibration curve showed favorable overall agreement, and the Hosmer–Lemeshow test yielded χ² = 7.234 (P = 0.512), suggesting satisfactory calibration. The optimal diagnostic performance was obtained at the cutoff value of 0.29, with a sensitivity of 69.91%, specificity of 82.24%, and Youden index of 0.522. Conclusion: The nomogram model constructed in this study exhibits favorable predictive performance, is intuitive and easy to apply, and can accurately assess the risk of postoperative complications. It provides a reliable basis for perioperative risk stratification and optimized intervention strategies in clinical practice, with high value for clinical application and popularization.