Developing and validating subjective and objective risk-assessment measures for predicting mortality after major surgery: An international prospective cohort study.

BACKGROUND: Preoperative risk prediction is important for guiding clinical decision-making and resource allocation. Clinicians frequently rely solely on their own clinical judgement for risk prediction rather than objective measures. We aimed to compare the accuracy of freely available objective surgical risk tools with subjective clinical assessment in predicting 30-day mortality. METHODS AND FINDINGS: We conducted a prospective observational study in 274 hospitals in the United Kingdom (UK), Australia, and New Zealand. For 1 week in 2017, prospective risk, surgical, and outcome data were collected on all adults aged 18 years and over undergoing surgery requiring at least a 1-night stay in hospital. Recruitment bias was avoided through an ethical waiver to patient consent; a mixture of rural, urban, district, and university hospitals participated. We compared subjective assessment with 3 previously published, open-access objective risk tools for predicting 30-day mortality: the Portsmouth-Physiology and Operative Severity Score for the enUmeration of Mortality (P-POSSUM), Surgical Risk Scale (SRS), and Surgical Outcome Risk Tool (SORT). We then developed a logistic regression model combining subjective assessment and the best objective tool and compared its performance to each constituent method alone. We included 22,631 patients in the study: 52.8% were female, median age was 62 years (interquartile range [IQR] 46 to 73 years), median postoperative length of stay was 3 days (IQR 1 to 6), and inpatient 30-day mortality was 1.4%. Clinicians used subjective assessment alone in 88.7% of cases. All methods overpredicted risk, but visual inspection of plots showed the SORT to have the best calibration. The SORT demonstrated the best discrimination of the objective tools (SORT Area Under Receiver Operating Characteristic curve [AUROC] = 0.90, 95% confidence interval [CI]: 0.88-0.92; P-POSSUM = 0.89, 95% CI 0.88-0.91; SRS = 0.85, 95% CI 0.82-0.87). Subjective assessment demonstrated good discrimination (AUROC = 0.89, 95% CI: 0.86-0.91) that was not different from the SORT (p = 0.309). Combining subjective assessment and the SORT improved discrimination (bootstrap optimism-corrected AUROC = 0.92, 95% CI: 0.90-0.94) and demonstrated continuous Net Reclassification Improvement (NRI = 0.13, 95% CI: 0.06-0.20, p < 0.001) compared with subjective assessment alone. Decision-curve analysis (DCA) confirmed the superiority of the SORT over other previously published models, and the SORT-clinical judgement model again performed best overall. Our study is limited by the low mortality rate, by the lack of blinding in the 'subjective' risk assessments, and because we only compared the performance of clinical risk scores as opposed to other prediction tools such as exercise testing or frailty assessment. CONCLUSIONS: In this study, we observed that the combination of subjective assessment with a parsimonious risk model improved perioperative risk estimation. This may be of value in helping clinicians allocate finite resources such as critical care and to support patient involvement in clinical decision-making.

SNAP-2 EPICCS: the second Sprint National Anaesthesia Project-EPIdemiology of Critical Care after Surgery: protocol for an international observational cohort study.

INTRODUCTION: The admission of high-risk patients to critical care after surgery is a recommended standard of care. Nevertheless, poor compliance against this recommendation has been repeatedly demonstrated in large epidemiological studies. It is unclear whether this is due to reasons of capacity, equipoise, poor quality clinical care or because hospitals are working creatively to create capacity for augmented care on normal surgical wards. The EPIdemiology of Critical Care after Surgery study aims to address these uncertainties. METHODS AND ANALYSIS: One-week observational cohort study in the UK and Australasia. All patients undergoing inpatient (overnight stay) surgery will be included. All will have prospective data collection on risk factors, surgical procedure and postoperative outcomes including the primary outcome of morbidity (measured using the Postoperative Morbidity Survey on day 7 after surgery) and secondary outcomes including length of stay and mortality. Data will also be collected on critical care referral and admission, surgical cancellations and critical care occupancy. The epidemiology of patient characteristics, processes and outcomes will be described. Inferential techniques (multilevel multivariable regression, propensity score matching and instrumental variable analysis) will be used to evaluate the relationship between critical care admission and postoperative outcome. ETHICS AND DISSEMINATION: The study has received ethical approval from the National Research Ethics Service in the UK and equivalent in Australasia. The collection of patient identifiable data without prior consent has been approved by the Confidentiality Advisory Group (England and Wales) and the Public Privacy and Patient Benefit Panel (Scotland). In these countries, patient identifiable data will be used to link prospectively collected data with national registers of death and inpatient administrative data. The study findings will be disseminated using a multimedia approach with the support of our lay collaborators, to patients, public, policy-makers, clinical and academic audiences.