Development and Validation of a Machine Learning-Based Risk Prediction Model for Limb Amputation in Patients with Diabetic Foot Complications: A Retrospective Cohort Study

Description

This retrospective cohort study analyzed 364 consecutive patients with diabetic foot ulcers treated at three Ibb University-affiliated tertiary hospitals between March 1, 2018 and December 3, 2024. We developed a Random Forest classifier incorporating 37 clinical variables spanning demographics, comorbidities, wound characteristics, laboratory parameters, and treatment factors. Model performance was evaluated using temporal validation (2024 cohort) with discrimination assessed via AUC-ROC and calibration via Brier scores.

Steps to reproduce

This retrospective cohort study analyzed 364 adult patients with diabetes mellitus presenting with diabetic foot ulcers or infections between 2018 and 2023. Patients with Charcot neuroarthropathy, dialysis-dependent renal failure, prior major limb amputations, or those with incomplete records exceeding 20% missing core variables were excluded to ensure data integrity. Clinical and laboratory parameters, including wound severity classified by Wagner staging, were used to train a Random Forest predictive model. Missing data were addressed through multiple imputation. The dataset was partitioned into training (70%) and testing (30%) subsets using stratified sampling to maintain outcome distribution. Hyperparameters were optimized via five-fold cross-validation. Model performance was evaluated based on discrimination (area under the receiver operating characteristic curve [AUC-ROC]), calibration (Brier score), and clinical utility through risk stratification. Feature importance was interpreted using SHapley Additive exPlanations (SHAP), while logistic regression quantified the effects of key predictors.

Institutions

• Ibb University

  Ibb Governorate, Ibb

Categories

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