Development and Validation of a Multivariable Prediction Model of Peri-operative Mortality in Vascular Surgery: The New Zealand Vascular Surgical Risk Tool (NZRISK-VASC).

Abstract

<h4>Objective</h4>Risk calculators and prediction models are available to assist clinicians and patients with peri-operative decision making to optimise outcomes. In a vascular surgical setting, the majority of these models is based on open AAA repair outcomes, and in general their clinical use is limited. The objective of this study was to develop and validate a simple and accurate vascular surgical risk prediction model.<h4>Methods</h4>A national administrative database was accessed to collect information on all adult patients undergoing vascular surgery between 1 July 2011 and 30 June 2016 in New Zealand. The primary outcomes were mortality at 30 days, one year, and two years. Previously established covariables including American Society of Anaesthesiologists (ASA) physical status score, sex, surgical urgency, cancer status and ethnicity were tested, and other covariables such as smoking status, presence of renal failure, diabetes, anatomical site of operation, structure operated, and type of procedures (open or endovascular) were explored. LASSO regression was used to select variables for inclusion in the model.<h4>Results</h4>A total of 21 597 cases formed the final risk prediction models, with covariables including ASA score, gender, surgical urgency, cancer status, presence of renal failure, diabetes, anatomical site, structure operated, and endovascular procedure. The area under the receiver operating curve (AUROC) for 30 day, one year, and two year mortality using L-min model was 0.869, 0.833, and 0.824, respectively, demonstrating very good discrimination. Calibration with the validation dataset was also excellent, with slopes of 0.971, 1.129, and 1.011, respectively, and McFadden's pseudo-R² statistics of 0.250, 0.227, and 0.227, respectively.<h4>Conclusion</h4>A simple and accurate multivariable risk calculator for vascular surgical patients was developed and validated using the New Zealand national dataset, with excellent discrimination and calibration for 30 day, one year, and two year mortality.