Prediction of amputation risk of patients with diabetic foot using classification algorithms: A clinical study from a tertiary center.

Diabetic foot ulcers can have vital consequences, such as amputation for patients. The primary purpose of this study is to predict the amputation risk of diabetic foot patients using machine-learning classification algorithms. In this research, 407 patients treated with the diagnosis of diabetic foot between January 2009-September 2019 in Istanbul University Faculty of Medicine in the Department of Undersea and Hyperbaric Medicine were retrospectively evaluated. Principal Component Analysis (PCA) was used to identify the key features associated with the amputation risk in diabetic foot patients within the dataset. Thus, various prediction/classification models were created to predict the "overall" risk of diabetic foot patients. Predictive machine-learning models were created using various algorithms. Additionally to optimize the hyperparameters of the Random Forest Algorithm (RF), experimental use of Bayesian Optimization (BO) has been employed. The sub-dimension data set comprising categorical and numerical values was subjected to a feature selection procedure. Among all the algorithms tested under the defined experimental conditions, the BO-optimized "RF" based on the hybrid approach (PCA-RF-BO) and "Logistic Regression" algorithms demonstrated superior performance with 85% and 90% test accuracies, respectively. In conclusion, our findings would serve as an essential benchmark, offering valuable guidance in reducing such hazards.

Analysis of risk factors for amputation in patients with diabetic foot ulcers: a cohort study from a tertiary center.

OBJECTIVE: This study aimed to analyze risk factors for amputation (overall, minor and major) in patients with diabetic foot ulcers (DFUs). METHODS: 407 patients with DFUs (286 male, 121 female; mean age = 60, age range = 32-92) who were managed in a tertiary care centre from 2009 to 2019 were retrospectively identified and included in the study. DFUs were categorized based on the Meggit-Wagner, PEDIS, S(AD)SAD, and University of Texas (UT) classification systems. To identify amputation risk-related factors, results of patients with DFUs who underwent amputations (minor or major) were compared to those who received other adjunctive treatments using Chi-Square, oneway analysis of variance (ANOVA) and Spearman correlation analysis. RESULTS: The mean C-reactive protein (CRP) and White Blood Cell (WBC) values were significantly higher in patients with major or minor amputation than in those without amputation. The mean Neutrophil (PNL), Platelets (PLT), wound width, creatinine and sedimentation (ESR) values were significantly higher in patients with major amputation compared to other groups of patients. Elevated levels of Highdensity lipoprotein (HDL), Hemoglobin (HGB) and albumin were determined to be protective factors against the risk of amputation. Spearman correlation analysis revealed a positive-sided, strong-levelled, significant relation between Wagner grades and amputation status of patients. CONCLUSION: This study has identified specific factors for major and minor amputation risk of patients with DFUs. Especially infection markers such as CRP, WBC, ESR and PNL were higher in the amputation group. Most importantly, Meggit Wagner, one of the four different classification systems used in the DFUs, was determined to be highly associated with patients' amputation risk. LEVEL OF EVIDENCE: Level IV, Prognostic Study.