Annali italiani di chirurgia · 2026
Development and Validation of a Predictive Model for Poor Healing After Great Saphenous Vein Stripping in Patients With Varicose Ulcers: Focusing on Preoperative Iron Metabolism Markers.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Zhou Yu, Zhan Xiaorong, Qiao Tong
Аннотация
AIM: To develop and validate a nomogram for predicting the risk of poor healing after great saphenous vein stripping in patients with venous varicose ulcers, based on preoperative iron metabolism markers and other clinical indicators.
METHODS: Clinical data from 278 patients with venous varicose ulcers who underwent great saphenous vein stripping at Wujin Hospital Affiliated with Jiangsu University between July 2022 and January 2025 were retrospectively analyzed. Patients were randomly divided to a training set (n = 166) and a validation set (n = 112) at a 6:4 ratio. Based on the ulcer healing status at 6 months postoperatively, patients were categorized into a poor healing group and a good healing group. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for postoperative poor healing, and a nomogram prediction model was developed based on these factors. Internal validation of model performance was conducted using the Bootstrap resampling method. Model discrimination, calibration, and clinical utility were evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA), respectively.
RESULTS: The overall incidence of postoperative poor healing was 40.29% (112/278). Multivariate logistic regression analysis revealed that diabetes (odds ratio [OR] = 3.01, 95% confidence interval [CI]: 1.38-6.57), increased soluble transferrin receptor (sTfR) (OR = 1.78, 95% CI: 1.17-2.70), decreased albumin (OR = 0.92, 95% CI: 0.85-0.99), and elevated C-reactive protein (CRP) (OR = 1.05, 95% CI: 1.02-1.08) were independent risk factors for postoperative poor healing (all p < 0.05). The nomogram model based on these factors yielded an area under the curve (AUC) of 0.75 (95% CI: 0.68-0.83) in the training set and 0.74 (95% CI: 0.64-0.83) in the validation set. Calibration curves demonstrated good agreement between predicted and observed probabilities. Decision curve analysis indicated a favorable clinical net benefit of the model.
CONCLUSIONS: The prediction model developed in this study effectively assesses the risk of poor healing after great saphenous vein stripping in patients with venous varicose ulcers, facilitating early identification of high-risk patients and supporting targeted clinical interventions.
METHODS: Clinical data from 278 patients with venous varicose ulcers who underwent great saphenous vein stripping at Wujin Hospital Affiliated with Jiangsu University between July 2022 and January 2025 were retrospectively analyzed. Patients were randomly divided to a training set (n = 166) and a validation set (n = 112) at a 6:4 ratio. Based on the ulcer healing status at 6 months postoperatively, patients were categorized into a poor healing group and a good healing group. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for postoperative poor healing, and a nomogram prediction model was developed based on these factors. Internal validation of model performance was conducted using the Bootstrap resampling method. Model discrimination, calibration, and clinical utility were evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA), respectively.
RESULTS: The overall incidence of postoperative poor healing was 40.29% (112/278). Multivariate logistic regression analysis revealed that diabetes (odds ratio [OR] = 3.01, 95% confidence interval [CI]: 1.38-6.57), increased soluble transferrin receptor (sTfR) (OR = 1.78, 95% CI: 1.17-2.70), decreased albumin (OR = 0.92, 95% CI: 0.85-0.99), and elevated C-reactive protein (CRP) (OR = 1.05, 95% CI: 1.02-1.08) were independent risk factors for postoperative poor healing (all p < 0.05). The nomogram model based on these factors yielded an area under the curve (AUC) of 0.75 (95% CI: 0.68-0.83) in the training set and 0.74 (95% CI: 0.64-0.83) in the validation set. Calibration curves demonstrated good agreement between predicted and observed probabilities. Decision curve analysis indicated a favorable clinical net benefit of the model.
CONCLUSIONS: The prediction model developed in this study effectively assesses the risk of poor healing after great saphenous vein stripping in patients with venous varicose ulcers, facilitating early identification of high-risk patients and supporting targeted clinical interventions.