Integrating traditional Chinese medicine syndromes with clinical variables to predict lower extremity arterial disease in type 2 diabetes

Abstract

BACKGROUND

Lower extremity arterial disease (LEAD) is common macrovascular complication of type 2 diabetes mellitus (T2DM) that can lead to intermittent claudication, rest pain, and gangrene. Due to its insidious onset, LEAD is frequently diagnosed, missing the optimal window for intervention. Therefore, the construction of an accurate and convenient early risk prediction model is of significant clinical value.

AIM

To investigate the risk factors for T2DM-associated LEAD and to develop prediction models using machine learning algorithms based on integrated traditional Chinese medicine (TCM) and Western medicine features.

METHODS

Clinical data of 1018 inpatients with T2DM admitted to the Department of Endocrinology, The First Affiliated Hospital of Anhui University of Chinese Medicine, were retrospectively analyzed. The dataset was randomly divided into a training set (n = 712) and a validation set (n = 306) at a 7:3 ratio. LASSO regression was used for dimensionality reduction to identify core predictors. Five machine learning models, namely, logistic regression, XGBoost, random forest, gradient boosting decision tree (GBDT), and LightGBM, were constructed based on eight predictors: Age ≥ 60 years, hypertension, reduced hemoglobin, reduced red blood cell count, elevated blood urea nitrogen, emaciation, dark tongue, and fatigue. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1 score. Ten-fold cross-validation was performed to assess model stability. A nomogram was constructed based on logistic regression coefficients for individualized risk visualization. Decision curve analysis (DCA) was used to evaluate clinical utility.

RESULTS

Among the 1018 patients with T2DM, 647 (63.56%) had LEAD. LASSO regression identified eight core predictors from 26 candidate variables (P < 0.05). In the validation set, the XGBoost model achieved the highest AUC of 0.763 (95% confidence interval: 0.707-0.815), with an accuracy of 0.712, sensitivity of 0.651, and F1 score of 0.743. The AUCs of the remaining models were: Random forest (0.762), logistic regression (0.758), LightGBM (0.754), and GBDT (0.744). Ten-fold cross-validation showed mean AUCs of 0.746 ± 0.006 and 0.736 ± 0.043 in the training and validation sets, respectively, with a minimal difference (ΔAUC = 0.010), indicating favourable stability without obvious overfitting. DCA indicated positive clinical benefit for all five models across a wide threshold probability range, significantly outperforming the “treat all” and “treat none” extreme strategies.

CONCLUSION

The XGBoost model built on dual-dimensional TCM and Western medicine features is effective for the early identification of LEAD in T2DM patients, providing support for individualized treatment strategies.

Keywords: Machine learning; Diabetic lower extremity arterial disease; Integrated Chinese and Western medicine; Prediction model; XGBoost; Nomogram

Core Tip: This study integrated Western medicine demographic and clinical characteristics, laboratory indicators, and traditional Chinese medicine syndrome features to construct prediction models for lower extremity arterial disease (LEAD) in type 2 diabetes mellitus using multiple machine learning algorithms. The XGBoost model based on integrated Western and Chinese medicine features exhibited the highest efficacy and has potential for the early identification and treatment of LEAD.