Risk factors associated with reoperation following major lower limb amputations for peripheral vascular disease: A single centre prospective observational study.

Abstract

BackgroundMajor lower limb amputations, often performed as lifesaving procedures in patients with advanced peripheral vascular disease (PVD), are associated with high rates of postoperative complications and reoperations. Identification of risk factors associated with reoperation is crucial for targeted perioperative management. The objective of the study is to identify the factors associated with increased risk of reoperation following major amputation and to develop and evaluate a predictive model for the same.MethodsA prospective observational study was conducted at our tertiary care centre between October 2022 and December 2023. After taking institutional ethical committee clearance, 83 patients undergoing major lower limb amputations (above or below the knee) for PVD were included in study. Demographic, clinical, and radiological variables were collected and analyzed. Reoperation was defined as any additional procedure requiring revision of the stump within 30 days, excluding minor wound interventions. Multivariable binomial logistic regression analysis was performed to identify independent risk factors associated with reoperation.ResultsThe reoperation rate following major amputation was 44.57%, the two common causes were stump necrosis (40.54%) and wound infections (29.72%). On multivariable analysis, age >60 years (OR: 8.26, p = .001) and arterial blockage above the common femoral artery (CFA) (OR: 4.32, p = .038) emerged as significant independent risk factors. The regression model demonstrated moderate predictive strength with a Nagelkerke R² value of 40.9% and good model fit (p = .724).ConclusionAdvanced age and proximal arterial occlusion are significant predictors of reoperation after major amputation. The proposed model offers a practical tool for risk stratification, patient counselling, and surgical planning. Multicentre validation is needed to refine its predictive accuracy.