Scoping review: The terminology used to describe major abdominal surgical procedures.

BACKGROUND: Major abdominal surgery (MAS) can have a profound impact on the patient but there is currently no consensus as to which surgical procedures constitute MAS. The main objective of this work is to ascertain the terminology used to describe MAS procedures and to apply these findings in order to propose a definition of MAS. METHODS: The following databases were searched: Ovid MEDLINE (R) ALL, Embase Classic and Embase (via OvidSP), Global Health (via OvidSP), Health Management Information Consortium (via OvidSP), APA PsycInfo (via OvidSP), PubMed and Web of Science. Original research articles, published between 1980 and April 26, 2022 that contained a description of MAS procedure were included in this study. Article screening and data extraction was undertaken independently by 3 authors. Content analysis was performed to identify key terminology used to describe MAS. RESULTS: Five thousand six hundred and sixty three articles were identified, of which 767 underwent full-text review and 312 were included in the scoping review. Content analysis resulted in 4 main categories: (1) pre-operative factors, (2) intraoperative factors, (3) operation-related factors, (4) post-operative factors. Operation-related factors was the predominant category (1137 references coded). The gastrointestinal resection made the vast majority of the references coded (591). CONCLUSIONS: Based on these results, the term "major abdominal surgery" should be defined as an intra-peritoneal operation with no primary involvement of the thorax, involving either luminal resection and/or resection of a solid organ associated with the gastrointestinal tract. However, further work is required to verify this definition using real world data.

Postoperative critical care and high-acuity care provision in the United Kingdom, Australia, and New Zealand.

BACKGROUND: Decisions to admit high-risk postoperative patients to critical care may be affected by resource availability. We aimed to quantify adult ICU/high-dependency unit (ICU/HDU) capacity in hospitals from the UK, Australia, and New Zealand (NZ), and to identify and describe additional 'high-acuity' beds capable of managing high-risk patients outside the ICU/HDU environment. METHODS: We used a modified Delphi consensus method to design a survey that was disseminated via investigator networks in the UK, Australia, and NZ. Hospital- and ward-level data were collected, including bed numbers, tertiary services offered, presence of an emergency department, ward staffing levels, and the availability of critical care facilities. RESULTS: We received responses from 257 UK (response rate: 97.7%), 35 Australian (response rate: 32.7%), and 17 NZ (response rate: 94.4%) hospitals (total 309). Of these hospitals, 91.6% reported on-site ICU or HDU facilities. UK hospitals reported fewer critical care beds per 100 hospital beds (median=2.7) compared with Australia (median=3.7) and NZ (median=3.5). Additionally, 31.1% of hospitals reported having high-acuity beds to which high-risk patients were admitted for postoperative management, in addition to standard ICU/HDU facilities. The estimated numbers of critical care beds per 100 000 population were 9.3, 14.1, and 9.1 in the UK, Australia, and NZ, respectively. The estimated per capita high-acuity bed capacities per 100 000 population were 1.2, 3.8, and 6.4 in the UK, Australia, and NZ, respectively. CONCLUSIONS: Postoperative critical care resources differ in the UK, Australia, and NZ. High-acuity beds may have developed to augment the capacity to deliver postoperative critical care.

Risk stratification tools for predicting morbidity and mortality in adult patients undergoing major surgery: qualitative systematic review.

Risk stratification is essential for both clinical risk prediction and comparative audit. There are a variety of risk stratification tools available for use in major noncardiac surgery, but their discrimination and calibration have not previously been systematically reviewed in heterogeneous patient cohorts.Embase, MEDLINE, and Web of Science were searched for studies published between January 1, 1980 and August 6, 2011 in adult patients undergoing major noncardiac, nonneurological surgery. Twenty-seven studies evaluating 34 risk stratification tools were identified which met inclusion criteria. The Portsmouth-Physiology and Operative Severity Score for the enUmeration of Mortality and the Surgical Risk Scale were demonstrated to be the most consistently accurate tools that have been validated in multiple studies; however, both have limitations. Future work should focus on further evaluation of these and other parsimonious risk predictors, including validation in international cohorts. There is also a need for studies examining the impact that the use of these tools has on clinical decision making and patient outcome.

Patient-satisfaction measures in anesthesia: qualitative systematic review.

Patient satisfaction is an important measure of the quality of health care and is used as an outcome measure in interventional and quality improvement studies. Previous studies have found that there are few appropriately developed and validated questionnaires available. The authors conducted a systematic review to identify all tools used to measure patient satisfaction with anesthesia, which have undergone a psychometric development and validation process, appraised the quality of these processes, and made recommendations of tools that may be suitable for use in different clinical and academic settings. There are a number of robustly developed and subsequently validated instruments, however, there are still many studies using nonvalidated instruments or poorly developed tools, claiming to accurately assess satisfaction with anesthesia. This can lead to biased and inaccurate results. Researchers in this field should be encouraged to use available validated tools, to ensure that patient satisfaction is measured and reported fairly and accurately.

Publishing quality improvement studies: learning to share and sharing to learn.

This editorial welcomes the decision of BJA Open to publish quality improvement (QI) studies. It summarises the current problems with conducting, evaluating, and publishing QI studies. It highlights existing guidance for prospective authors to follow regarding the reporting of QI interventions, their context(s), underlying theories, and evaluation. In so doing, we hope to encourage the publication of more QI studies of sufficient quality to facilitate learning or replication elsewhere.

Mixed methods study protocol for combining stakeholder-led rapid evaluation with near real-time continuous registry data to facilitate evaluations of quality of care in intensive care units.

Background: Improved access to healthcare in low- and middle-income countries (LMICs) has not equated to improved health outcomes. Absence or unsustained quality of care is partly to blame. Improving outcomes in intensive care units (ICUs) requires delivery of complex interventions by multiple specialties working in concert, and the simultaneous prevention of avoidable harms associated with the illness and the treatment interventions. Therefore, successful design and implementation of improvement interventions requires understanding of the behavioural, organisational, and external factors that determine care delivery and the likelihood of achieving sustained improvement. We aim to identify care processes that contribute to suboptimal clinical outcomes in ICUs located in LMICs and to establish barriers and enablers for improving the care processes. Methods: Using rapid evaluation methods, we will use four data collection methods: 1) registry embedded indicators to assess quality of care processes and their associated outcomes; 2) process mapping to provide a preliminary framework to understand gaps between current and desired care practices; 3) structured observations of processes of interest identified from the process mapping and; 4) focus group discussions with stakeholders to identify barriers and enablers influencing the gap between current and desired care practices. We will also collect self-assessments of readiness for quality improvement. Data collection and analysis will be led by local stakeholders, performed in parallel and through an iterative process across eight countries: Kenya, India, Malaysia, Nepal, Pakistan, South Africa, Uganda and Vietnam. Conclusions: The results of our study will provide essential information on where and how care processes can be improved to facilitate better quality of care to critically ill patients in LMICs; thus, reduce preventable mortality and morbidity in ICUs. Furthermore, understanding the rapid evaluation methods that will be used for this study will allow other researchers and healthcare professionals to carry out similar research in ICUs and other health services.

A scoping review of local quality improvement using data from UK perioperative National Clinical Audits.

BACKGROUND: Significant resources are invested in the UK to collect data for National Clinical Audits (NCAs), but it is unclear whether and how they facilitate local quality improvement (QI). The perioperative setting is a unique context for QI due to its multidisciplinary nature and history of measurement. It is unclear which NCAs evaluate perioperative care, to what extent their data have been used for QI, and which factors influence this usage. METHODS: NCAs were identified from the directories held by Healthcare Quality Improvement Partnership (HQIP), Scottish Healthcare Audits and the Welsh National Clinical Audit and Outcome Review Advisory Committee. QI reports were identified by the following: systematically searching MEDLINE, CINAHL Plus, Web of Science, Embase, Google Scholar and HMIC up to December 2019, hand-searching grey literature and consulting relevant stakeholders. We charted features describing both the NCAs and the QI reports and summarised quantitative data using descriptive statistics and qualitative themes using framework analysis. RESULTS: We identified 36 perioperative NCAs in the UK and 209 reports of local QI which used data from 19 (73%) of these NCAs. Six (17%) NCAs contributed 185 (89%) of these reports. Only one NCA had a registry of local QI projects. The QI reports were mostly brief, unstructured, often published by NCAs themselves and likely subject to significant reporting bias. Factors reported to influence local QI included the following: perceived data validity, measurement of clinical processes as well as outcomes, timely feedback, financial incentives, sharing of best practice, local improvement capabilities and time constraints of clinicians. CONCLUSIONS: There is limited public reporting of UK perioperative NCA data for local QI, despite evidence of improvement of most NCA metrics at the national level. It is therefore unclear how these improvements are being made, and it is likely that opportunities are being missed to share learning between local sites. We make recommendations for how NCAs could better support the conduct, evaluation and reporting of local QI and suggest topics which future research should investigate. TRIAL REGISTRATION: The review was registered with the International Prospective Register of Systematic Reviews (PROSPERO: CRD42018092993 ).

High-risk surgery: epidemiology and outcomes.

Surgical morbidity is a significant public health issue worldwide. It is estimated that >230 million surgical procedures are performed each year, with an estimated mortality of at least 0.4% and morbidity of between 3% and 17%. Furthermore, there are potentially far-reaching consequences of a complicated perioperative course, because perioperative morbidity is associated with reduced long-term survival. In this review, we examine the factors that are associated with surgical outcomes. Issues related to the delivery of health care, such as structure, process, and resource utilization, have been shown to vary within and between institutions, leading to differences in both morbidity and mortality after surgery. Patient-related factors, in particular comorbid illness, functional capacity, and cardiovascular health, are also related to perioperative risk, and may be assessed using risk stratification models, exercise testing, and biomarker assays. The strengths and weaknesses of each of these techniques are discussed. We also review the strengths and limitations of the measures used to assess outcome after surgery, including patient-centered variables such as mortality and morbidity scores, and patient-related outcome measures. Finally, we suggest the direction of future work, which should be aimed at improving the precision of tools for describing perioperative risk, and of the measures used to assess the outcomes and quality of surgical health care. These tools are the building blocks of high-quality clinical trials, epidemiological studies, and quality improvement programs.

Clinical risk scores to guide perioperative management.

Perioperative morbidity is associated with reduced long term survival. Comorbid disease, cardiovascular illness, and functional capacity can predispose patients to adverse surgical outcomes. Accurate risk stratification would facilitate informed patient consent and identify those individuals who may benefit from specific perioperative interventions. The ideal clinical risk scoring system would be objective, accurate, economical, simple to perform, based entirely on information available preoperatively, and suitable for patients undergoing both elective and emergency surgery. The POSSUM (Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity) scoring systems are the most widely validated perioperative risk predictors currently utilised; however, their inclusion of intra- and postoperative variables precludes validation for preoperative risk prediction. The Charlson Index has the advantage of consisting exclusively of preoperative variables; however, its validity varies in different patient cohorts. Risk models predicting cardiac morbidity have been extensively studied, despite the relatively uncommon occurrence of postoperative cardiac events. Probably the most widely used cardiac risk score is the Lee Revised Cardiac Risk Index, although it has limited validity in some patient populations and for non-cardiac outcomes. Bespoke clinical scoring systems responding to dynamic changes in population characteristics over time, such as those developed by the American College of Surgeons National Surgical Quality Improvement Program, are more precise, but require considerable resources to implement. The combination of objective clinical variables with information from novel techniques such as cardiopulmonary exercise testing and biomarker assays, may improve the predictive precision of clinical risk scores used to guide perioperative management.

The evaluation of risk prediction models in predicting outcomes after bariatric surgery: a prospective observational cohort pilot study.

BACKGROUND: As the prevalence of obesity is increasing, the number of patients requiring surgical intervention for obesity-related illness is also rising. The aim of this pilot study was to explore predictors of short-term morbidity and longer-term poor weight loss after bariatric surgery. METHODS: This was a single-centre prospective observational cohort pilot study in patients undergoing bariatric surgery. We assessed the accuracy (discrimination and calibration) of two previously validated risk prediction models (the Physiological and Operative Severity Score for the enumeration of Morbidity and Mortality, POSSUM score, and the Obesity Surgical Mortality Risk Score, OS-MS) for postoperative outcome (postoperative morbidity defined using the Post Operative Morbidity Survey). We then tested the relationship between postoperative morbidity and longer-term weight loss outcome adjusting for known patient risk factors. RESULTS: Complete data were collected on 197 patients who underwent surgery for obesity or obesity-related illnesses between March 2010 and September 2013. Results showed POSSUM and OS-MRS were less accurate at predicting Post Operative Morbidity Survey (POMS)-defined morbidity on day 3 than defining prolonged length of stay due to poor mobility and/or POMS-defined morbidity. Having fewer than 28 days alive and out of hospital within 30 days of surgery was predictive of poor weight loss at 1 year, independent of POSSUM-defined risk (odds ratio 2.6; 95% confidence interval 1.28-5.24). CONCLUSIONS: POSSUM may be used to predict patients who will have prolonged postoperative LOS after bariatric surgery due to morbidity or poor mobility. However, independent of POSSUM score, having less than 28 days alive and out of hospital predicted poor weight loss outcome at 1 year. This adds to the literature that postoperative complications are independently associated with poor longer-term surgical outcomes.

Design and methodology of SNAP-1: a Sprint National Anaesthesia Project to measure patient reported outcome after anaesthesia.

BACKGROUND: Patient satisfaction is an important metric of health-care quality. Accidental awareness under general anaesthesia (AAGA) is a serious complication of anaesthesia care which may go unrecognised in the immediate perioperative period but leads to long-term psychological harm for affected patients. The SNAP-1 study aimed to measure patient satisfaction with anaesthesia care and the incidence of AAGA, reported on direct questioning within 24 h of surgery, in a large multicentre cohort. A secondary aim of SNAP-1 was to test the effectiveness of a new network of Quality Audit and Research Coordinators in NHS anaesthetic departments, to achieve widespread study participation and high patient recruitment rates. This manuscript describes the study methodology. METHODS: SNAP-1 was a prospective observational cohort study. The study protocol was approved by the National Research Ethics Service. All UK NHS hospitals with anaesthetic departments were invited to participate. Adult patients undergoing any type of non-obstetric surgery were recruited in participating hospitals on 13th and 14th May 2014. Demographic data were collected by anaesthetists providing perioperative care. Patients were then approached within 24 h of surgery to complete two questionnaires-the Bauer patient satisfaction questionnaire (to measure patient reported outcome) and the modified Brice questionnaire (to detect possible accidental awareness). Completion of postoperative questionnaires was taken as evidence of implied consent. Results were recorded on a standard patient case report form, and local investigators entered anonymised data into an electronic database for later analysis by the core research team. RESULTS: Preliminary analyses indicate that over 15,000 patients were recruited across the UK, making SNAP-1 the largest NIHR portfolio-adopted study in anaesthesia to date. Both descriptive and analytic epidemiological analyses will be used to answer specific questions about the patient perception of anaesthesia care overall and in surgical sub-specialties and to determine the incidence of AAGA. CONCLUSIONS: The SNAP-1 study recruited a large number of UK hospitals and thousands of perioperative patients using newly established networks in the UK anaesthetic profession. The results will provide benchmarking information to aid interpretation of patient satisfaction data and also determine the incidence of AAGA reported on a single postoperative visit.