517-P: Machine Learning Algorithms Using Electronic Medical Record and Ultrasonography of Lower Extremities for 10-Year Risk Assessment of Diabetic Foot Ulcer in China

Abstract

Objective: Accurate prediction of diabetic foot ulcer (DFU) occurrence in patients with type 2 diabetes mellitus (T2DM) is crucial for determining appropriate treatment strategy and avoiding amputation and infection. Here, we explored machine learning algorithms (ML) of electronic medical record (EMR) for predicting the incident of DFU. Methods: In this retrospective longitudinal observational cohort, 26579 cases from T2DM database of The Third Affiliated Hospital of Sun Yatsen University from Jan 2011 to Dec 2019 were screened and finally 2770 patients without DFU were enrolled. During 5.6 years of follow-up, 178 patients occurred DFU. 44 clinical parameters including EMR and imaging finding were used to develop a DFU risk prediction model using XGBoost, RF, and LR. Results: ML models were established in 178 patients with DFU (110 male, mean 61.1±12.4 years), and 2591 patients without DFU (1424 male, mean 59.0±12.1 years). XGBoost model achieved the best performance in predicting DFU occurrence with an AUC of 0.94, which is better than the performance of RF with AUC of 0.91and LR with AUC of 0.89. A DFU incident significantly increased in high-risk patients (>10%) in the next 10 years. Conclusion: Our study provided a simple and effective risk tool for the prediction of 10-year DFU risk, which can help professionals clinical screening and decision-making for T2DM patients. Disclosure L.Wu: None. Y.Liang: None. X.He: None. Y.Chen: None.