149 research outputs found

    Cancelled operations: a 7-day cohort study of planned adult inpatient surgery in 245 UK National Health Service hospitals.

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    BACKGROUND: Cancellation of planned surgery impacts substantially on patients and health systems. This study describes the incidence and reasons for cancellation of inpatient surgery in the UK NHS. METHODS: We conducted a prospective observational cohort study over 7 consecutive days in March 2017 in 245 NHS hospitals. Occurrences and reasons for previous surgical cancellations were recorded. Using multilevel logistic regression, we identified patient- and hospital-level factors associated with cancellation due to inadequate bed capacity. RESULTS: We analysed data from 14 936 patients undergoing planned surgery. A total of 1499 patients (10.0%) reported previous cancellation for the same procedure; contemporaneous hospital census data indicated that 13.9% patients attending inpatient operations were cancelled on the day of surgery. Non-clinical reasons, predominantly inadequate bed capacity, accounted for a large proportion of previous cancellations. Independent risk factors for cancellation due to inadequate bed capacity included requirement for postoperative critical care [odds ratio (OR)=2.92; 95% confidence interval (CI), 2.12-4.02; P CONCLUSIONS: A significant proportion of patients presenting for surgery have experienced a previous cancellation for the same procedure. Cancer surgery is relatively protected, but bed capacity, including postoperative critical care requirements, are significant risk factors for previous cancellations

    A novel inpatient complex pain team: protocol for a mixed-methods evaluation of a single-centre pilot study

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    Introduction Complex pain is a debilitating condition that is responsible for low quality of life and significant economic impacts. Although best practice in the treatment of complex pain employs a multidisciplinary team, many patients do not have access to this care, leading to poor outcomes

    Timing of elective surgery and risk assessment after SARS‐CoV ‐2 infection:an update: A multidisciplinary consensus statement on behalf of the Association of Anaesthetists, Centre for Perioperative Care, Federation of Surgical Specialty Associations, Royal College of Anaesthetists, Royal College of Surgeons of England

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    The impact of vaccination and new SARS‐CoV‐2 variants on peri‐operative outcomes is unclear. We aimed to update previously published consensus recommendations on timing of elective surgery after SARS‐CoV‐2 infection to assist policymakers, administrative staff, clinicians and patients. The guidance remains that patients should avoid elective surgery within 7 weeks of infection, unless the benefits of doing so exceed the risk of waiting. We recommend individualised multidisciplinary risk assessment for patients requiring elective surgery within 7 weeks of SARS‐CoV‐2 infection. This should include baseline mortality risk calculation and assessment of risk modifiers (patient factors; SARS‐CoV‐2 infection; surgical factors). Asymptomatic SARS‐CoV‐2 infection with previous variants increased peri‐operative mortality risk three‐fold throughout the 6 weeks after infection, and assumptions that asymptomatic or mildly symptomatic omicron SARS‐CoV‐2 infection does not add risk are currently unfounded. Patients with persistent symptoms and those with moderate‐to‐severe COVID‐19 may require a longer delay than 7 weeks. Elective surgery should not take place within 10 days of diagnosis of SARS‐CoV‐2 infection, predominantly because the patient may be infectious, which is a risk to surgical pathways, staff and other patients. We now emphasise that timing of surgery should include the assessment of baseline and increased risk, optimising vaccination and functional status, and shared decision‐making. While these recommendations focus on the omicron variant and current evidence, the principles may also be of relevance to future variants. As further data emerge, these recommendations may be revised

    Perioperative Care Pathways in Low- and Lower-Middle-Income Countries: Systematic Review and Narrative Synthesis

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    BACKGROUND: Safe and effective care for surgical patients requires high-quality perioperative care. In high-income countries (HICs), care pathways have been shown to be effective in standardizing clinical practice to optimize patient outcomes. Little is known about their use in low- and middle-income countries (LMICs) where perioperative mortality is substantially higher. METHODS: Systematic review and narrative synthesis to identify and describe studies in peer-reviewed journals on the implementation or evaluation of perioperative care pathways in LMICs. Searches were conducted in MEDLINE, EMBASE, CINAHL Plus, WHO Global Index, Web of Science, Scopus, Global Health and SciELO alongside citation searching. Descriptive statistics, taxonomy classifications and framework analyses were used to summarize the setting, outcome measures, implementation strategies, and facilitators and barriers to implementation. RESULTS: Twenty-seven studies were included. The majority of pathways were set in tertiary hospitals in lower-middle-income countries and were focused on elective surgery. Only six studies were assessed as high quality. Most pathways were adapted from international guidance and had been implemented in a single hospital. The most commonly reported barriers to implementation were cost of interventions and lack of available resources. CONCLUSIONS: Studies from a geographically diverse set of low and lower-middle-income countries demonstrate increasing use of perioperative pathways adapted to resource-poor settings, though there is sparsity of literature from low-income countries, first-level hospitals and emergency surgery. As in HICs, addressing patient and clinician beliefs is a major challenge in improving care. Context-relevant and patient-centered research, including qualitative and implementation studies, would make a valuable contribution to existing knowledge. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00268-022-06621-x

    American Society for Enhanced Recovery (ASER) and Perioperative Quality Initiative (POQI) joint consensus statement on measurement to maintain and improve quality of enhanced recovery pathways for elective colorectal surgery.

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    BACKGROUND: This article sets out a framework for measurement of quality of care relevant to enhanced recovery pathways (ERPs) in elective colorectal surgery. The proposed framework is based on established measurement systems and/or theories, and provides an overview of the different approaches for improving clinical monitoring, and enhancing quality improvement or research in varied settings with different levels of available resources. METHODS: Using a structure-process-outcome framework, we make recommendations for three hierarchical tiers of data collection. DISCUSSION: Core, Quality Improvement, and Best Practice datasets are proposed. The suggested datasets incorporate patient data to describe case-mix, process measures to describe delivery of enhanced recovery and clinical outcomes. The fundamental importance of routine collection of data for the initiation, maintenance, and enhancement of enhanced recovery pathways is emphasized

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

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    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

    Development and internal validation of a model for postoperative morbidity in adults undergoing major elective colorectal surgery: the peri-operative quality improvement programme (PQIP) colorectal risk model

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    Over 1.5 million major surgical procedures take place in the UK NHS each year and approximately 25% of patients develop at least one complication. The most widely used risk-adjustment model for postoperative morbidity in the UK is the physiological and operative severity score for the enumeration of mortality and morbidity. However, this model was derived more than 30 years ago and now overestimates the risk of morbidity. In addition, contemporary definitions of some model predictors are markedly different compared with when the tool was developed. A second model used in clinical practice is the American College of Surgeons National Surgical Quality Improvement Programme risk model; this provides a risk estimate for a range of postoperative complications. This model, widely used in North America, is not open source and therefore cannot be applied to patient populations in other settings. Data from a prospective multicentre clinical dataset of 118 NHS hospitals (the peri-operative quality improvement programme) were used to develop a bespoke risk-adjustment model for postoperative morbidity. Patients aged ≥ 18 years who underwent colorectal surgery were eligible for inclusion. Postoperative morbidity was defined using the postoperative morbidity survey at postoperative day 7. Thirty-one candidate variables were considered for inclusion in the model. Death or morbidity occurred by postoperative day 7 in 3098 out of 11,646 patients (26.6%). Twelve variables were incorporated into the final model, including (among others): Rockwood clinical frailty scale; body mass index; and index of multiple deprivation quintile. The C-statistic was 0.672 (95%CI 0.660–0.684), with a bootstrap optimism corrected C-statistic of 0.666 at internal validation. The model demonstrated good calibration across the range of morbidity estimates with a mean slope gradient of predicted risk of 0.959 (95%CI 0.894–1.024) with an index-corrected intercept of −0.038 (95%CI −0.112–0.036) at internal validation. Our model provides parsimonious case-mix adjustment to quantify risk of morbidity on postoperative day 7 for a UK population of patients undergoing major colorectal surgery. Despite the C-statistic of < 0.7, our model outperformed existing risk-models in widespread use. We therefore recommend application in case-mix adjustment, where incorporation into a continuous monitoring tool such as the variable life adjusted display or exponentially-weighted moving average-chart could support high-level monitoring and quality improvement of risk-adjusted outcome at the population level
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