Machine Learning in Predicting Wound Healing and Limb Salvage Outcomes Following Lower Limb Revascularization: A...

Chronic limb-threatening ischemia (CLTI) and peripheral arterial disease (PAD) sometimes lead to non-healing lesions and amputations, despite revascularization efforts. Current clinical instruments for prognostication exhibit insufficient personalized precision. This systematic research sought to assess the predictive efficacy of machine learning models in forecasting wound healing and limb preservation after lower limb revascularization. A comprehensive literature search was conducted in PubMed, Web of Science, Embase, Scopus, and IEEE Xplore from January 2018 to March 2025. Studies were considered if they utilized machine learning techniques to forecast outcomes following surgical or endovascular lower limb revascularization. The inclusion criteria adhered to the Population, Index model, Comparator, and Outcome (PICO) framework. The Prediction model Risk Of Bias ASsessment Tool (PROBAST) was utilized to evaluate the risk of bias. Only studies that presented quantifiable performance measurements (e.g., area under the receiver operating characteristic curve [AUROC], calibration, sensitivity) were included. Data extraction and risk evaluation were performed separately by two reviewers. Out of 450 records reviewed, five studies satisfied the inclusion criteria. The majority of studies utilized extensive registry data, with sample sizes varying from 392 to 235,677 patients. Machine learning techniques, such as XGBoost, neural networks, and Bayesian algorithms, surpassed standard logistic regression in prognostic accuracy (AUROC 0.78-0.95). Three studies exhibited a little risk of bias in all domains. Nevertheless, two investigations indicated a high or ambiguous risk owing to restricted sample size or absence of external validation. The variability in outcome definitions and model inputs prevented meta-analysis. External validation was infrequent, and practical applicability remains unsubstantiated. Machine learning models exhibit significant predictive capability in forecasting wound healing and limb salvage results following revascularization, frequently surpassing conventional clinical instruments. Nonetheless, extensive validation and prospective assessment are necessary prior to clinical application.