关键词: 2型糖尿病, 糖尿病肾脏疾病, 系统性免疫炎症指数, 系统性炎症反应指数, 机器学习算法

Abstract:

Objective To construct an optimal risk prediction model for diabetic kidney disease （DKD） in patients with type 2 diabetes mellitus （T2DM） based on routine blood indicators combined with the systemic immune-inflammation index （SII） and systemic inflammation response index （SIRI）， and to compare the predictive performance of different machine learning models， so as to provide an effective tool for the early screening of DKD. Methods A total of 658 T2DM patients hospitalized in the Affiliated Hospital of Traditional Chinese Medicine of Guangzhou Medical University from January 2023 to November 2024 were retrospectively selected as the research subjects. The patient data were divided into a training set （460 cases） and a test set （198 cases） at a ratio of 7∶3 using the computer simple random sampling method. First， LASSO regression was used to screen 12 potential predictive features including SII and SIRI. Then， based on the screened variables， four machine learning algorithms， namely logistic regression （LR）， support vector machine （SVM）， random forest （RF）， and extreme gradient boosting （XGBoost）， were applied to construct risk prediction models for DKD in T2DM patients. Indicators such as AUC value， sensitivity， accuracy， and F1 score were used to comprehensively evaluate the discrimination of the models， and the calibration curve and decision curve analysis （DCA） were used to evaluate the calibration and clinical utility of the models respectively. Finally， the SHAP method was used to conduct interpretability analysis of the optimal model. Results Ten predictive features were screened out by LASSO regression. SHAP values showed that creatinine had high importance in all four risk prediction models. The AUC values of the LR， RF， SVM， and XGBoost models in the test set were 0.914， 0.943， 0.929， and 0.917 respectively， and the F1 scores were 0.627， 0.737， 0.474， and 0.772 respectively. The overall accuracies obtained from the confusion matrix heat maps were 90.4%， 92.4%， 89.9%， and 93.4% respectively. The prediction accuracies of RF and XGBoost for DKD occurrence in the confusion matrix heat maps were relatively high， at 72.4% and 75.9% respectively. DCA showed that all four models had positive clinical net benefits at most threshold probabilities. Conclusion The RF and XGBoost models can accurately predict the risk of DKD in T2DM patients， which is helpful for clinicians to identify high-risk T2DM patients with DKD at an early stage.

Key words: type 2 diabetes mellitus, diabetic kidney disease, SII, SIRI, machine learning algorithm