What question did this study set out to answer?

The study aims to develop a predictive nomogram for assessing the risk of poor healing following great saphenous vein stripping in patients with varicose ulcers.

February 11, 2026Open Access

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

Key Points

The study aims to develop a predictive nomogram for assessing the risk of poor healing following great saphenous vein stripping in patients with varicose ulcers.
Retrospective analysis of clinical data from 278 patients with venous varicose ulcers.
Patients were divided into training (166) and validation (112) sets for model development and validation.
Logistic regression analyses were performed to identify independent risk factors for poor healing at 6 months postoperatively.
The nomogram was internally validated using bootstrap resampling, and model performance was assessed with ROC and calibration curves.
40.29% of patients experienced poor healing postoperatively (112/278).
Diabetes, increased soluble transferrin receptor, decreased albumin, and elevated C-reactive protein were identified as independent risk factors.
The nomogram achieved an AUC of 0.75 in the training set and 0.74 in the validation set, indicating good predictive accuracy.
Calibration showed a strong agreement between predicted and observed outcomes.

Abstract

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.

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