For clinical follow-up, features of SDHB mutation-associated disease include a later age of onset, extraadrenal (abdominal or thoracic) tumors, and a higher rate of malignancy. In contrast, SDHD mutation carriers, in addition to head and neck paragangliomas, should be observed for multifocal tumors, infrequent malignancy, and the possibility of extraadrenal pheochromocytoma.

Background Surgical audit, sometimes including public reporting, is an important foundation of high quality health care. We aimed to assess the validity of a novel outcome metric, days at home up to 30 days after surgery , as a surgical outcome measure in clinical trials and quality assurance. Methods This was a multicentre, registry-based cohort study. We used prospectively collected hospital and national healthcare registry data obtained from patients aged 18 years or older undergoing a broad range of surgeries in Sweden over a 10-year period. The association between days at home up to 30 days after surgery and patient (older age, poorer physical status, comorbidity) and surgical (elective or non-elective, complexity, duration) risk factors, process of care outcomes (re-admissions, discharge destination), clinical outcomes (major complications, 30-day mortality) and death up to 1 year after surgery were measured. Findings From January, 2005, to December, 2014, we obtained demographic and perioperative data on 636,885 patients from 21 Swedish hospitals. Mortality at 30 days and one year was 1.8% and 7.3%, respectively. The median (IQR) days at home up to 30 days after surgery was 27 (23–29), being significantly lower among high-risk patients, those recovering from more complex surgical procedures, and suffering serious postoperative complications (all p < 0.0001). Patients with 8 days or less at home up to 30 days after surgery had a nearly 7-fold higher risk of death up to 1 year postoperatively when compared with those with 29 or 30 days at home (adjusted HR 6.78 [95% CI: 6.44–7.13]). Interpretation Days at home up to 30 days after surgery is a valid, easy to measure patient-centred outcome metric. It is highly sensitive to changes in surgical risk and impact of complications, and has prognostic importance; it is therefore a valuable endpoint for perioperative clinical trials and quality assurance. Funding Swedish National Research Council Medicine and Stockholm County Council ALF-project grant (LE), and the Australian National Health and Medical Research Council (PM).

No abstract

Background Risk prediction tools can be used in the perioperative setting to identify high‐risk patients who may benefit from increased surveillance and monitoring in the postoperative period, to aid shared decision‐making, and to benchmark risk‐adjusted hospital performance. We evaluated perioperative risk prediction tools relevant to an Australian context. Methods A systematic review of perioperative mortality risk prediction tools used for adults undergoing inpatient noncardiac surgery, published between 2011 and 2019 (following an earlier systematic review). We searched Medline via OVID using medical subject headings consistent with the three main areas of risk, surgery and mortality/morbidity. A similar search was conducted in Embase. Tools predicting morbidity but not mortality were excluded, as were those predicting a composite outcome that did not report predictive performance for mortality separately. Tools were also excluded if they were specifically designed for use in cardiac or other highly specialized surgery, emergency surgery, paediatrics or elderly patients. Results Literature search identified 2568 studies for screening, of which 19 studies identified 21 risk prediction tools for inclusion. Conclusion Four tools are candidates for adapting in the Australian context, including the Surgical Mortality Probability Model (SMPM), Preoperative Score to Predict Postoperative Mortality (POSPOM), Surgical Outcome Risk Tool (SORT) and NZRISK. SORT has similar predictive performance to POSPOM, using only six variables instead of 17, contains all variables of the SMPM, and the original model developed in the UK has already been successfully adapted in New Zealand as NZRISK. Collecting the SORT and NZRISK variables in a national surgical outcomes study in Australia would present an opportunity to simultaneously investigate three of our four shortlisted models and to develop a locally valid perioperative mortality risk prediction model with high predictive performance.

Accurately measuring the incidence of major postoperative complications is essential for funding and reimbursement of healthcare providers, for internal and external benchmarking of hospital performance and for valid and reliable public reporting of outcomes. Actual or surrogate outcomes data are typically obtained by one of three methods: clinical quality registries, clinical audit, or administrative data. In 2017 a perioperative registry was developed at the Alfred Hospital and mapped to administrative and clinical data. This study investigated the statistical agreement between administrative data (International Statistical Classification of Diseases and Related Health Problems (10th edition) Australian Modification codes) and clinical audit by anaesthetists in identifying major postoperative complications. The study population included 482 high-risk surgical patients referred to the Alfred Hospital anaesthesia postoperative service over two years. Clinical audit was conducted to determine the presence of major complications and these data were compared to administrative data. The main outcome was statistical agreement between the two methods, as defined by Cohen’s kappa statistic. Substantial agreement was observed for five major complications, moderate agreement for three, fair agreement for six and poor agreement for two. Sensitivity and positive predictive value ranged from 0 to 100%. Specificity was above 90% for all complications. There was important variation in inter-rater agreement. For four of the five complications with substantial agreement between administrative data and clinical audit, sensitivity was only moderate (61.5%–75%). Using International Statistical Classification of Diseases and Related Health Problems (10th edition) Australian Modification codes to identify postoperative complications at our hospital has high specificity but is likely to underestimate the incidence compared to clinical audit. Further, retrospective clinical audit itself is not a highly reliable method of identifying complications. We believe a perioperative clinical quality registry is necessary to validly and reliably measure major postoperative complications in Australia for benchmarking of hospital performance and before public reporting of outcomes should be considered.

Prolonged fasting leads to a shift from carbohydrate to fat as the primary energy source, resulting in the production of ketones such as beta-hydroxybutyrate. Hyperketonaemia and ketoacidosis have been observed in young children fasting for surgery. The aim of this study was to investigate ketonaemia in adults fasted for surgery. One hundred non-diabetic adults presenting for elective or emergency surgery were assessed for the presence of hyperketonaemia (beta-hydroxybutyrate levels more than 1 mmol/l), and the relationship between beta-hydroxybutyrate, blood glucose and fasting duration was investigated. Three of 100 patients demonstrated hyperketonaemia, one of whom had ingested a ketogenic supplement the evening prior to surgery. No patient demonstrated beta-hydroxybutyrate levels suggestive of ketoacidosis (above 3 mmol/l). No relationship between fasting duration and ketone or glucose levels was observed. We found no evidence that prolonged preoperative fasting led to beta-hydroxybutyrate levels consistent with ketoacidosis.

In Australia, 2.7 million surgical procedures are performed annually. Historically, a lack of perioperative data standardisation and infrastructure has limited pooling of routinely collected data across institutions. We surveyed Australian and New Zealand College of Anaesthetists (ANZCA) Clinical Trials Network hospitals to investigate current and potential uses of perioperative electronic medical record data for research and quality assurance. A targeted survey was sent to 131 ANZCA Clinical Trials Network–affiliated hospitals in Australia. The primary aim was to map current electronic data collection methods and data utilisation in six domains of the perioperative pathway. The survey response rate was 32%. Electronic data recording in the six domains ranged from 19% to 85%. Where electronic data exist, the ability of anaesthesiology departments to export them for analysis ranged from 27% to 100%. The proportion of departments with access to data exports that are regularly exporting the data for quality assurance or research ranged from 13% to 58%. The existence of a perioperative electronic medical record does not automatically lead to the data being used to measure and improve clinical outcomes. The first barrier is clinician access to data exports. Even when this barrier is overcome, a large gap remains between the proportion of departments able to access data exports and those using the data regularly to inform and improve clinical practice. We believe this gap can be addressed by establishing a national perioperative outcomes registry to lead high-quality multicentre registry research and quality assurance in Australia.

Clinical quality registries (CQRs) systematically collect data on pre‐agreed markers of quality of care for a given procedure, that can be reliably and reproducibly defined and collected across multiple sites. Data is then risk adjusted, and comparisons may be used to benchmark performance. These data then inform quality improvement initiatives. CQRs require an overarching independent governance structure and surety of funding. CQRs rely upon whole of population enrolment to minimize the risk of selection bias, and often rely on the secondary use of sensitive health information, meaning that the processes for ethical review and consent to participation are different to clinical trials. Despite several local examples of CQR improving practice in Australia and Aotearoa New Zealand, providing substantial cost–benefit to the community, there remain significant barriers to CQR implementation and functions. These include the difficulty of accurate data capture, lack of a fit for purpose ethical review system, the constraints of existing Qualified Privilege legislations and the need for protected funding. Whilst the Australian Government has released a 10‐year strategy for CQR reform, and the Aotearoa New Zealand Government has included registries in the planned Health New Zealand reforms for the public sector, we believe more urgent implementation of strategies to overcome these barriers is needed if CQRs are to have the impact on quality of care our Communities deserve.