Prognostic risk modelling for patients undergoing major lower limb amputation: An analysis of the UK National Vascular Registry

Objective: Major lower limb amputation is the highest-risk lower limb procedure in Vascular Surgery. Despite this, few high-quality studies have examined factors contributing to mortality. We aimed to identify independent risk factors for peri-operative morbidity and mortality and develop reliable models for estimating risk.
Methods: All patients undergoing lower-limb amputation above the ankle entered into the UK National Vascular Registry (January 2014–December 2016) were included. Missing data were handled using multiple imputation. Models were developed to evaluate independent risk-factors for mortality (the primary outcome) and morbidity using logistic regression, minimising the Bayesian information criterion to balance complexity and model fit. Ethical approval for the study was granted (Wales REC 3 ref:16/WA/0353). 
Results: All 9549 above ankle joint amputations in the registry were included. Overall, 865 patients (9.1%) died before leaving hospital. Independent factors associated with mortality were emergency admission, bilateral operation, age, American Society of Anesthesiologists grade, abnormal electrocardiogram and increased white cell count or creatinine (P<0.01 for all). Independent factors reducing mortality were trans-tibial operation, increased albumin or patient weight, and previous ipsilateral revascularisation procedures (P<0.01 for all). A risk model incorporating these factors had good discrimination (C-statistic 0.79, 95% C.I. 0.77-0.80) and excellent calibration.Morbidity rates were high, with 6.6%, 9.7% and 4.3% of patients suffering cardiac, respiratory and renal complications respectively. The risk model was also predictive of morbidity outcomes (C-statistics 0.74, 0.69 and 0.74 respectively). 
Conclusions: Morbidity and mortality after lower limb amputation are high in the UK. We identified some potentially modifiable factors for quality improvement initiatives and developed accurate predictive models that could assist patient counselling and decision-making.