Improving outcomes for patients undergoing major lower limb amputation for complications of peripheral vascular disease

Background: Around 5000 patients undergo major lower limb amputation in the UK each year, commonly as a result of peripheral vascular disease. Around 10% of these patients die before hospital discharge, and 30% die within a year of surgery. Despite this, evidence for optimal management of these patients is weak. The aim of this thesis is to develop tools which will direct future research and quality-improvement towards key interventions and outcomes for these patients. Methods: I used data from the UK National Registry to identify risk-factors for poor mortality and morbidity outcomes using rigorous statistical tools and developed a prognostic model for in-hospital mortality. I identified important outcomes for patients undergoing major lower limb amputation through systematic review of the literature and focus groups. I then established consensus on core outcome sets for short- and medium-term studies recruiting these patients using a multi-round consensus survey followed by a face-to-face consensus meeting. Results: Independent risk-factors for in-hospital mortality were identified as emergency admission, bilateral operation, trans-femoral operation, age, American Society of Anesthesiologists grade, abnormal electrocardiogram and increased white cell count or creatinine, decreased albumin or patient weight. Previous revascularisation procedures were protective. I established consensus on 11 core outcomes for short-term studies and 11 core outcomes for medium-term studies. Stump wound infection or healing, problems with the other leg and psychological morbidity were present in both sets. Outcomes related to death, additional healthcare, communication and pain relief were core for short-term studies. Outcomes related to mobility, social re-integration, independence and quality of life were core for medium-term studies. Conclusions: I have identified contemporary risk-factors for peri-operative outcomes and defined core outcome sets for patients undergoing major lower limb amputation. Future work should adopt these in order to design interventions which modify key risk-factors and use core outcomes as their key endpoints

The Abdominal Aortic Aneurysm Statistically Corrected Operative Risk Evaluation (AAA SCORE) for predicting mortality after open and endovascular interventions

BackgroundAccurate adjustment of surgical outcome data for risk is vital in an era of surgeon-level reporting. Current risk prediction models for abdominal aortic aneurysm (AAA) repair are suboptimal. We aimed to develop a reliable risk model for in-hospital mortality after intervention for AAA, using rigorous contemporary statistical techniques to handle missing data.MethodsUsing data collected during a 15-month period in the United Kingdom National Vascular Database, we applied multiple imputation methodology together with stepwise model selection to generate preoperative and perioperative models of in-hospital mortality after AAA repair, using two thirds of the available data. Model performance was then assessed on the remaining third of the data by receiver operating characteristic curve analysis and compared with existing risk prediction models. Model calibration was assessed by Hosmer-Lemeshow analysis.ResultsA total of 8088 AAA repair operations were recorded in the National Vascular Database during the study period, of which 5870 (72.6%) were elective procedures. Both preoperative and perioperative models showed excellent discrimination, with areas under the receiver operating characteristic curve of .89 and .92, respectively. This was significantly better than any of the existing models (area under the receiver operating characteristic curve for best comparator model, .84 and .88; P < .001 and P = .001, respectively). Discrimination remained excellent when only elective procedures were considered. There was no evidence of miscalibration by Hosmer-Lemeshow analysis.ConclusionsWe have developed accurate models to assess risk of in-hospital mortality after AAA repair. These models were carefully developed with rigorous statistical methodology and significantly outperform existing methods for both elective cases and overall AAA mortality. These models will be invaluable for both preoperative patient counseling and accurate risk adjustment of published outcome data

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. The aim was 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 < .01 for all). Independent factors reducing mortality were transtibial operation, increased albumin or patient weight, and previous ipsilateral revascularisation procedures (p < .01 for all). A risk model incorporating these factors had good discrimination (C-statistic 0.79, 95% confidence interval 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). Conclusion Morbidity and mortality after lower limb amputation are high in the UK. Some potentially modifiable factors for quality improvement initiatives have been identified and accurate predictive models that could assist patient counselling and decision making have been developed