Independent Predictors of Major Limb Amputation: A Systematic Review and Meta-Analysis of Adjusted Risk Factors

Background/Objectives: To identify independent predictors of major amputation using adjusted effect estimates. Methods: PubMed, Scopus, Web of Science, and Cochrane Library were searched from inception to 1 March 2024, alongside grey literature. Studies reporting adjusted predictors of major amputation (at or proximal to the wrist or ankle) were eligible. Adjusted odds ratios (aORs) reported by ≥2 studies were pooled using random-effects models. Results: Forty-two studies were pooled. Markers of clinical severity showed the strongest associations: critical limb ischemia (aOR = 4.60, 95% CI 4.20-5.00), leukocytosis (aOR = 3.42, 1.72-5.13), end-stage renal disease (aOR = 3.19, 2.94-3.44), and Rutherford class ≥ IIb (aOR = 3.16, 1.87-4.45). Moderate associations included chronic kidney disease (aOR = 2.22, 1.68-2.76), current smoking (aOR = 2.21, 2.13-2.28), and several comorbidities (prior bypass surgery, chronic obstructive pulmonary disease, peripheral vascular disease; aORs 1.82-1.92). Sociodemographic factors and diabetes mellitus showed smaller but consistent effects (aORs 1.19-1.64). Lower serum albumin (aOR = 0.58, 0.45-0.70) and statin use (aOR = 0.71, 0.66-0.75) reduced odds. Hemoglobin, modelled per unit increase rather than dichotomised, was unexpectedly associated with increased odds (aOR = 1.59, 1.55-1.63), most plausibly reflecting residual confounding. Heterogeneity was substantial for most predictors (I² frequently > 80%). Conclusions: To our knowledge, this is the first meta-analysis of major amputation restricted to adjusted estimates across diverse populations and both amputation levels. Ischemic burden and systemic disease severity dominate the risk profile, whereas diabetic foot ulcer, wound depth, wound infection, coronary artery disease, and neuropathy did not retain independent significance after adjustment. Given the observational evidence base and substantial heterogeneity, these findings should inform risk stratification rather than causal inference.