Predicting Death or Disability after Surgery in the Older Adult.

BACKGROUND: Older patients are vulnerable to developing new or worsening disability after surgery. Despite this, patient or surgical characteristics predisposing to postoperative disability are poorly defined. The aim of the study was to develop and validate a model, subsequently transformed to point-score form, to predict 6-month death or disability in older patients after surgery. METHODS: The authors built a prospective, single-center registry to develop and validate the prediction model. The registry included patients 70 yr of age or older undergoing elective and nonelective, cardiac and noncardiac surgery between May 25, 2017, and February 11, 2021, and combined clinical data from the electronic medical record, hospital administrative data (International Classification of Diseases, Tenth Revision, Australian Modification codes) and World Health Organization (Geneva, Switzerland) Disability Assessment Schedule data collected directly from the patients. Death or disability was defined as being dead or having a World Health Organization Disability Assessment Schedule score 16% or greater. Included patients were randomly divided into model development (70%) and internal validation (30%) cohorts. Once constructed, the logistic regression and point-score models were assessed using the internal validation cohort and an external validation cohort comprising data from a separate randomized trial. RESULTS: Of 2,176 patients who completed the World Health Organization Disability Assessment Schedule immediately before surgery, 927 (43%) patients were disabled, and 413 (19%) had significant disability. By 6 months after surgery, 1,640 patients (75%) had data available for the primary outcome analysis. Of these patients, 195 (12%) patients had died, and 691 (42%) were dead or disabled. The developed point-score model included the preoperative World Health Organization Disability Assessment Schedule score, patient age, dementia, and chronic kidney disease. The point score model retained good discrimination in the internal (area under the curve, 0.74; 95% CI, 0.69 to 0.79) and external (area under the curve, 0.77; 95% CI, 0.74 to 0.80) validation data sets. CONCLUSIONS: The authors developed and validated a point score model to predict death or disability in older patients after surgery.

Hospital costs and factors associated with days alive and at home after surgery (DAH30 ).

OBJECTIVE: To assess the relationships of patient and surgical factors and hospital costs with the number of days alive and at home during the 30 days following surgery (DAH30 ). DESIGN: Retrospective cohort study; analysis of Medibank Private health insurance hospital claims data, Australia, 1 January 2016 - 31 December 2017. SETTING, PARTICIPANTS: Admissions of adults (18 years or older) to hospitals for elective or emergency inpatient surgery with anaesthesia covered by private health insurance, Australia, 1 January 2016 - 31 December 2017. MAIN OUTCOME MEASURES: Associations between DAH30 and total hospital costs, and between DAH30 and surgery risk factors. RESULTS: Complete data were available for 126 788 of 181 281 eligible patients (69.9%); their median age was 62 years (IQR, 47-73 years), 72 872 were women (57%), and 115 117 had undergone elective surgery (91%). The median DAH30 was 27.1 days (IQR, 24.2-28.8 days), the median hospital cost per patient was $10 358 (IQR, $6624-20 174). The association between DAH30 and total hospital costs was moderate (Spearman ρ = -0.60; P < 0.001). Median DAH30 declined with age, comorbidity score, ASA physical status score, and surgical severity and duration, and was also lower for women. CONCLUSIONS: DAH30 is a validated, patient-centred outcome measure of post-surgical outcomes; higher values reflect shorter hospital stays and fewer serious complications, re-admissions, and deaths. DAH30 can be used to benchmark quality of surgical care and to monitor quality improvement programs for reducing the costs of surgical and other peri-operative care.

Towards a national perioperative outcomes registry: A survey of perioperative electronic medical record utilisation to support quality assurance and research at Australian and New Zealand College of Anaesthetists Clinical Trials Network hospitals in Australia.

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: urgent reform is required to enable best practice and best care.

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.

External validity of four risk scores predicting 30-day mortality after surgery.

Background: Surgical risk prediction tools can facilitate shared decision-making and efficient allocation of perioperative resources. Such tools should be externally validated in target populations before implementation. Methods: Predicted risk of 30-day mortality was retrospectively derived for surgical patients at Royal Perth Hospital from 2014 to 2021 using the Surgical Outcome Risk Tool (SORT) and the related NZRISK (n=44 031, 53 395 operations). In a sub-population (n=31 153), the Physiology and Operative Severity Score for the enumeration of Mortality (POSSUM) and the Portsmouth variant of this (P-POSSUM) were matched from the Copeland Risk Adjusted Barometer (C2-Ai, Cambridge, UK). The primary outcome was risk score discrimination of 30-day mortality as evaluated by area-under-receiver operator characteristic curve (AUROC) statistics. Calibration plots and outcomes according to risk decile and time were also explored. Results: All four risk scores showed high discrimination (AUROC) for 30-day mortality (SORT=0.922, NZRISK=0.909, P-POSSUM=0.893; POSSUM=0.881) but consistently over-predicted risk. SORT exhibited the best discrimination and calibration. Thresholds to denote the highest and second-highest deciles of SORT risk (>3.92% and 1.52-3.92%) captured the majority of deaths (76% and 13%, respectively) and hospital-acquired complications. Year-on-year SORT calibration performance drifted towards over-prediction, reflecting a decrease in 30-day mortality over time despite an increase in the surgical population risk. Conclusions: SORT was the best performing risk score in predicting 30-day mortality after surgery. Categorising patients based on SORT into low, medium (80-90th percentile), and high risk (90-100th percentile) might guide future allocation of perioperative resources. No tools were sufficiently calibrated to support shared decision-making based on absolute predictions of risk.

Systematic review of perioperative mortality risk prediction models for adults undergoing inpatient non-cardiac surgery.

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.

Towards a national perioperative clinical quality registry: The diagnostic accuracy of administrative data in identifying major postoperative complications.

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.

Clinical presentation and penetrance of pheochromocytoma/paraganglioma syndromes.

CONTEXT: The identification of mutations in genes encoding peptides of succinate dehydrogenase (SDH) in pheochromocytoma/paraganglioma syndromes has necessitated clear elucidation of genotype-phenotype associations. OBJECTIVE: Our objective was to determine genotype-phenotype associations in a cohort of patients with pheochromocytoma/paraganglioma syndromes and succinate dehydrogenase subunit B (SDHB) or subunit D (SDHD) mutations. DESIGN, SETTING, AND PARTICIPANTS: The International SDH Consortium studied 116 individuals (83 affected and 33 clinically unaffected) from 62 families with pheochromocytoma/paraganglioma syndromes and SDHB or SDHD mutations. Clinical data were collected between August 2003 and September 2004 from tertiary referral centers in Australia, France, New Zealand, Germany, United States, Canada, and Scotland. MAIN OUTCOME MEASURES: Data were collected on patients with pheochromocytomas and/or paragangliomas with respect to onset of disease, diagnosis, genetic testing, surgery, pathology, and disease progression. Clinical features were evaluated for evidence of genotype-phenotype associations, and penetrance was determined. RESULTS: SDHB mutation carriers were more likely than SDHD mutation carriers to develop extraadrenal pheochromocytomas and malignant disease, whereas SDHD mutation carriers had a greater propensity to develop head and neck paragangliomas and multiple tumors. For the index cases, there was no difference between 43 SDHB and 19 SDHD mutation carriers in the time to first diagnosis (34 vs. 28 yr, respectively; P = 0.3). However, when all mutation carriers were included (n = 112), the estimated age-related penetrance was different for SDHB vs. SDHD mutation carriers (P = 0.008). CONCLUSIONS: 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.