Background: Chronic venous insufficiency (CVI) can be evaluated using Duplex ultrasonography (US) and air plethysmography (PG), yet comparative performance remains unclear. Methods: We retrospectively identified patients who underwent venous insufficiency US and PG within a 90-day interval from March 1, 2015 through July 31, 2024. CVI severity was classified by the clinical (C) component of the Clinical, Etiologic, Anatomic, and Pathophysiologic (CEAP) classification system and categorized as mild (C0–2), moderate (C3), or severe (C4–6). PG and insufficiency US findings were compared across categories and machine learning models were trained to predict severe CVI. Results: We analyzed 1478 limbs from 839 patients who were predominantly women (62%) with a mean age of 61 years (± 14 years). Severe CVI was present in 32.3% of limbs. US detected venous incompetence in 67.4% of limbs overall and in 75.3% of severe CVI limbs. Abnormal PG findings occurred in 72.9% of limbs overall and in 88.5% of severe CVI limbs. PG parameters in machine learning models outperformed US parameters in predicting severe CVI (area under the receiver operating characteristic curve AUROC 0.82 vs 0.65). The top PG model (multilayer perceptron MLP) achieved an AUROC of 0.82, versus the best US model (gradient boosting) with an AUROC of 0.65. Limiting PG data to incompetence and obstruction parameters modestly reduced performance but remained higher than US (logistic regression AUROC 0.72). Conclusion: Our findings indicate that PG assessment in CVI offers superior performance compared to US. Overall, these results validate the quantitative whole-limb hemodynamic approach using PG, which provides a more complete understanding of the pathology behind CVI.
Elbenawi et al. (Tue,) studied this question.