Background: Many multivariable models to calculate mortality risk after surgery are limited by insufficient sample size at development or by application to cohorts distinct from derivation populations. The aims of this study were to validate the Surgical Outcome Risk Tool (SORT) for a New Zealand population and to develop an extended NZRISK model to calculate 1-month, 1-year and 2-year mortality after non-cardiac surgery.Methods: Data from the New Zealand National Minimum Data Set for patients having surgery between January 2013 and December 2014 were used to validate SORT. A random 75 per cent split of the data was used to develop the NZRISK model, which was validated in the other 25 per cent of the data set. 1-month, 1-year and 2-year outcomes, demonstrated excellent discrimination with AUROC values of 0⋅921, 0⋅904 and 0⋅895 respectively, and excellent calibration (McFadden's pseudo-R 2 0⋅275, 0⋅308 and 0⋅312 respectively). Calibration slopes were 1⋅12, 1⋅02 and 1⋅02 respectively. Results: External validation of SORT in the 360 140 patients who underwent surgery in the study period showed good discrimination (area under the receiver operating characteristic curve (AUROC) value of 0⋅906) but poor calibration (McFadden's pseudo-R 2 0⋅137, calibration slope 5⋅32), indicating it was invalid in this national surgical population. Internal validation of the NZRISK model, which incorporates sex and ethnicity in addition to the variables used in SORT for Conclusion:The SORT performed poorly in this national population. However, inclusion of sex and ethnicity in the NZRISK model improved performance. Calculation of mortality risk beyond 30 days after surgery adds to the utility of this tool for shared decision-making.

ObjectivesIn this manuscript, we describe broad trends in postoperative mortality in New Zealand (a country with universal healthcare) for acute and elective/waiting list procedures conducted between 2005 and 2017.Design, participants and settingWe use high-quality national-level hospitalisation data to compare the risk of postoperative mortality between demographic subgroups after adjusting for key patient-level confounders and mediators. We also present temporal trends and consider how rates in postoperative death following acute and elective/waiting list procedures have changed over this time period.Results and conclusionA total of 1 836 683 unique patients accounted for 3 117 374 admissions in which a procedure was performed under general anaesthetic over the study period. We observed an overall 30-day mortality rate of 0.5 per 100 procedures and a 90-day mortality rate of 0.9 per 100. For acute procedures, we observed a 30-day mortality rate of 1.6 per 100, compared with 0.2 per 100 for elective/waiting list procedures. In terms of procedure specialty, respiratory and cardiovascular procedures had the highest rate of 30-day mortality (age-standardised rate, acute procedures: 3–6 per 100; elective/waiting list: 0.7-1 per 100). As in other contexts, we observed that the likelihood of postoperative death was not proportionally distributed within our population: older patients, Māori patients, those living in areas with higher deprivation and those with comorbidity were at increased risk of postoperative death, even after adjusting for all available factors that might explain differences between these groups. Increasing procedure risk (measured using the Johns Hopkins Surgical Risk Classification System) was also associated with an increased risk of postoperative death. Encouragingly, it appears that risk of postoperative mortality has declined over the past decade, possibly reflecting improvements in perioperative quality of care; however, this decline did not occur equally across procedure specialties.

Objective: To examine variation in “failure to rescue” (FTR) as a driver of differences in mortality between centres and over time for patients undergoing colorectal cancer surgery. Background: Wide variation exists in postoperative mortality following colorectal cancer surgery. FTR has been identified as an important determinant of variation in postoperative outcomes. We hypothesized that differences in mortality both between hospitals and over time are driven by variation in FTR. Methods: A national population-based study of patients undergoing colorectal cancer resection from 2010 to 2019 in Aotearoa New Zealand was conducted. Rates of 90-day FTR, mortality, and complications were calculated overall, and for surgical and nonoperative complications. Twenty District Health Boards (DHBs) were ranked into quartiles using risk- and reliability-adjusted 90-day mortality rates. Variation between DHBs and trends over the 10-year period were examined. Results: Overall, 15,686 patients undergoing resection for colorectal adenocarcinoma were included. Increased postoperative mortality at high-mortality centers (OR 2.4, 95% CI 1.8–3.3) was driven by higher rates of FTR (OR 2.0, 95% CI 1.5–2.8), and postoperative complications (OR 1.4, 95% CI 1.3–1.6). These trends were consistent across operative and nonoperative complications. Over the 2010 to 2019 period, postoperative mortality halved (OR 0.5, 95% CI 0.4–0.6), associated with a greater improvement in FTR (OR 0.5, 95% CI 0.4–0.7) than complications (OR 0.8, 95% CI 0.8–0.9). Differences between centers and over time remained when only analyzing patients undergoing elective surgery. Conclusion: Mortality following colorectal cancer resection has halved over the past decade, predominantly driven by improvements in “rescue” from complications. Differences in FTR also drive hospital-level variation in mortality, highlighting the central importance of “rescue” as a target for surgical quality improvement.

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Pre-operative decision making is a crucial part of a patient's management plan, and risk calculators and prediction models are available to assist clinicians and patients during this process to optimise the outcome. There are numerous surgery specific risk prediction tools for vascular surgical patients; however, their general clinical utility is limited. A multivariable risk prediction model was developed that can predict 30 day, one year, and two year mortality in vascular surgical patients. It requires only 10 easily obtainable covariables in the pre-operative setting, making it potentially practical and easy to use.Objective: Risk calculators and prediction models are available to assist clinicians and patients with perioperative 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. Methods: 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. Results: 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-R 2 statistics of 0.250, 0.227, and 0.227, respectively. Conclusion: 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.