Artificial Intelligence for Predicting Amputation and Related Outcomes in Peripheral Artery Disease: A Systematic Review and Meta-Analysis.

Abstract

Background: Peripheral artery disease (PAD) affects over 236 million individuals globally and carries substantial risks of amputation, major adverse limb events (MALE), and mortality. Existing clinical scoring systems focus on short-term surgical outcomes and lack generalizability. Machine learning (ML) has emerged as a promising tool for PAD outcome prediction.

Objective: To conduct the first quantitative meta-analysis evaluating the discriminative performance, methodological rigor, and certainty of evidence of ML-based prognostic models for adverse limb outcomes in PAD.

Methods: Following PRISMA/PRISMA-S guidelines, PubMed, Embase, Cochrane Library, and Web of Science were searched from inception through March 15, 2026. Two reviewers independently performed study selection, data extraction, and PROBAST risk-of-bias assessment. Random-effects meta-analysis (DerSimonian-Laird with Hartung-Knapp-Sidik-Jonkman correction) and hierarchical summary ROC (HSROC) analysis were performed; certainty was rated with GRADE.

Results: Ten studies were included. The pooled AUC for amputation-related outcomes was 0.88 (95% CI, 0.83-0.94; prediction interval, 0.79-0.98) with substantial heterogeneity (I² = 95.4%). HSROC yielded pooled sensitivity 0.855 and specificity 0.836. Registry-based studies showed higher pooled AUC than non-registry studies (0.92 vs 0.80; p = 0.004). All models had high or unclear risk of bias in the analysis domain; none underwent independent external validation. GRADE certainty was very low.

Conclusions: ML models demonstrate high discriminative performance for predicting amputation-related outcomes in PAD, but very low certainty of evidence, absence of external validation, and high risk of bias currently limit clinical implementation. Prospective multicenter validation and adherence to AI reporting guidelines are essential before clinical translation.