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The Nuffield Department of Surgical Sciences is the academic department of surgery at the University of Oxford, and hosts a multidisciplinary team of senior clinical academic surgeons, senior scientists, junior clinicians and scientists in training.
Pancreas transplantation (PTx) is the only current treatment to replace completely the missing function of the pancreas in patients with diabetes. However, this is a high-risk procedure with complications that include vascular thrombosis and pancreatitis, both of which are clinical sequelae of ischemia-reperfusion (IR). The several factors that increase allograft susceptibility to IR include donor age, as well as warm and cold ischemia times. To improve the safety profile of PTx while also expanding organ utilization, future developments must minimize the occurrence and impact of ischemia-reperfusion injury (IRI) through improved preservation strategies and development of platforms to facilitate organ assessment and repair. These objectives are starting to be realized clinically in other solid organ groups through the emergence of machine perfusion and other novel technologies. Similar developments in the whole pancreas have not yet progressed to the same degree. A small number of experimental studies have investigated novel approaches to allograft preservation and these are summarized. Progress in this field requires understanding of the mechanisms that lead to ischemia-reperfusion injury, leading to innovative clinical trials. The field may then move toward a future where machine perfusion is used routinely as a means to facilitate viability assessment, as well as improve pancreas allograft preservation.
The ischaemic preconditioning paradox and its implications for islet isolation from heart-beating and non heart-beating donors.
The impact of ischaemia can severely damage procured donor organs for transplantation. The pancreas, and pancreatic islets in particular, is one of the most sensitive tissues towards hypoxia. The present study was aimed to assess the effect of hypoxic preconditioning (HP) performed ex-vivo in islets isolated from heart-beating donor (HBD) and non heart-beating donor (NHBD) rats. After HP purified islets were cultured for 24 h in hypoxia followed by islet characterisation. Post-culture islet yields were significantly lower in sham-treated NHBD than in HBD. This difference was reduced when NHBD islets were preconditioned. Similar results were observed regarding viability, apoptosis and in vitro function. Reactive oxygen species generation after hypoxic culture was significantly enhanced in sham-treated NHBD than in HBD islets. Again, this difference could be diminished through HP. qRT-PCR revealed that HP decreases pro-apoptotic genes but increases HIF-1 and VEGF. However, the extent of reduction and augmentation was always substantially higher in preconditioned NHBD than in HBD islets. Our findings indicate a lower benefit of HBD islets from HP than NHBD islets. The ischaemic preconditioning paradox suggests that HP should be primarily applied to islets from marginal donors. This observation needs evaluation in human islets.
Natural history of small asymptomatic kidney and residual stones over a long-term follow-up: systematic review over 25 years.
OBJECTIVE: To systematically review the natural history of small asymptomatic kidney and residual stones, as the incidental identification of small, asymptomatic renal calculi has risen with increasing use of high-resolution imaging. MATERIALS AND METHODS: We reviewed the natural history of small asymptomatic kidney and residual stones using the Cochrane and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. We searched MEDLINE, Scopus, EMBASE, EBSCO, Cochrane library and Clinicaltrials.gov using themes of 'asymptomatic', 'nephrolithiasis', 'observation', 'symptoms', 'admission', 'intervention' and similar allied terms for all English language articles from 1996 to 2020 (25 years). Inclusion criteria were studies with ≥50 patients, stones ≤10 mm, and a mean follow-up of ≥24 months. Primary outcomes were occurrence of symptoms, emergency admission, and interventions. RESULTS: Our literature search returned 2247 results of which 10 papers were included in the final review. Risk of symptomatic episodes ranged from 0% to 59.4%. Meta-analysis did not identify any significant difference in the likelihood of developing symptoms when comparing stones <5 mm to those >5 mm, nor those <10 mm to those >10 mm. Risk of admission varied from 14% to 19% and the risk of intervention from 12% to 35%. Meta-analysis showed a significantly decreased likelihood of intervention for stones <5 vs >5 mm and <10 vs >10 mm. Studies had variable risk of bias due to heterogeneous reporting of outcome measures with significant likelihood that observed differences in results were compatible with chance alone (Symptoms: I2 =0%, Cochran's Q = 3.09, P = 0.69; Intervention: I2 =0%, Cochran's Q = 1.76, P = 0.88). CONCLUSIONS: The present systematic review indicates that stone size is not a reliable predictor of symptoms; however, risk of intervention is greater for stones >5mm vs <5 mm and >10 vs <10 mm. This review will inform urologists as they discuss management strategies with patients who have asymptomatic renal stones and offer insight to committees during the development of evidence-based guidelines.
Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI.
A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico evaluation, but few have yet demonstrated real benefit to patient care. Early-stage clinical evaluation is important to assess an AI system's actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use and pave the way to further large-scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multi-stakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two-round, modified Delphi process to collect and analyze expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 pre-defined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. In total, 123 experts participated in the first round of Delphi, 138 in the second round, 16 in the consensus meeting and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI-specific reporting items (made of 28 subitems) and ten generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we developed a guideline comprising key items that should be reported in early-stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings.
Failure to rescue following emergency surgery: A FRAM analysis of the management of the deteriorating patient.
BACKGROUND: Failure to rescue (FTR) denotes mortality from post-operative complications after surgery with curative intent. High-volume, low-mortality units have similar complication rates to others, but have lower FTR rates. Effective response to the deteriorating post-operative patient is therefore critical to reducing surgical mortality. Resilience Engineering might afford a useful perspective for studying how the management of deterioration usually succeeds and how resilience can be strengthened. METHODS: We studied the response to the deteriorating patient following emergency abdominal surgery in a large surgical emergency unit, using the Functional Resonance Analysis Method (FRAM). FRAM focuses on the conflicts and trade-offs inherent in the process of response, and how staff adapt to them, rather than on identifying and eliminating error. 31 semi-structured interviews and two workshops were used to construct a model of the response system from which conclusions could be drawn about possible ways to strengthen system resilience. RESULTS: The model identified 23 functions, grouped into five clusters, and their respective variability. The FRAM analysis highlighted trade-offs and conflicts which affected decisions over timing, as well as strategies used by staff to cope with these underlying tensions. Suggestions for improving system resilience centred on improving team communication, organisational learning and relationships, rather than identifying and fixing specific system faults. CONCLUSION: FRAM can be used for analysing surgical work systems in order to identify recommendations focused on strengthening organisational resilience. Its potential value should be explored by empirical evaluation of its use in systems improvement.
Identifying research waste from surgical research: a protocol for assessing compliance with the IDEAL framework and recommendations.
INTRODUCTION: Approximately £1130 billion was invested in research worldwide in 2016, and 9.6% of this was on biomedical research. However, about 85% of biomedical research investment is wasted. The Lancet published a series to identify five categories relating to research waste and in 2014. Some categories of research waste in surgery are avoidable by complying with the Idea, Development, Exploration, Assessment, Long-term follow-up (IDEAL) framework for it enables researchers to design, conduct and report surgical studies robustly and transparently. This review aims to examine the extent to which surgical studies adhered to the IDEAL framework and estimate the amount of overall research waste that could be avoided if compliance was improved. METHODS: We will search for potential studies published in English and between 1 January 2018 and 31 December 2018 via PubMed. Teams of paired reviewers will screen titles, abstracts and full texts independently. Two researchers will extract data from each paper. Data will be collected about general information and specialised information in each stage, and our IDEAL Compliance Appraisal tool will be used to analyse included studies. Descriptive statistics and χ2 or Fisher's exact tests for comparisons will be presented. DISCUSSION: Our study will provide important information about whether compliance with the specific IDEAL Recommendations has reduced research waste in surgical and therapeutic device studies. And we will identify particular key aspects that are worse and need to focus on improving those in future education.
UPDRS Label Assignment by Analyzing Accelerometer Sensor Data Collected from Conventional Smartphones
© 2020, Springer Nature Switzerland AG. The study of the characteristics of hand tremors of the patients suffering from Parkinson’s disease (PD) offers an effective way to detect and assess the stage of the disease’s progression. During the semi-quantitative evaluation, neurologists label the PD patients with any of the (0–4) Unified Parkinson’s Diseases Rating Scale (UPDRS) score based on the intensity and prevalence of these tremors. This score can be bolstered by some other modes of assessment as like gait analysis to increase the reliability of PD detection. With the availability of conventional smartphones with a built-in accelerometer sensor, it is possible to acquire the 3-axes tremor and gait data very easily and analyze them by a trained algorithm. Thus we can remotely examine the PD patients from their homes and connect them to trained neurologists if required. The objective of this study was to investigate the usability of smartphones for assessing motor impairments (i.e. tremors and gait) that can be analyzed from accelerometer sensor data. We obtained 98.5% detection accuracy and 91% UPDRS labeling accuracy for 52 PD patients and 20 healthy subjects. The result of this study indicates a great promise for developing a remote system to detect, monitor, and prescribe PD patients over long distances. It will be a tremendous help for the older population in developing countries where access to a trained neurologist is very limited. Also, in a pandemic situation like COVID-19, patients from developed countries can be benefited from such a home-oriented PD detection and monitoring system.
Isolated leptomeningeal carcinomatosis and possible fungal meningitis as late sequelae of oesophageal adenocarcinoma.
We describe a case of a 67-year-old man with known chronic obstructive pulmonary disease, type 2 diabetes mellitus, hypertension, osteoarthritis, previous history of excess alcohol intake, and oesophagectomy 3 years earlier for T3N0 adenocarcinoma, referred by his general practitioner with confusion, weight loss and several recent falls. CT of the chest, abdomen and pelvis revealed a right middle-lobe pulmonary embolism, while CT of the head revealed a communicating hydrocephalus. Lumbar puncture was performed, and empirical treatment for tuberculous and fungal meningitis was commenced. Unfortunately, he suffered a rapid neurological deterioration with markedly elevated cerebrospinal fluid (CSF) pressures, leading to an external ventricular drain. Cytological analysis of a CSF sample revealed a cellular infiltrate consistent with leptomeningeal carcinomatosis (adenocarcinoma), with the previous oesophageal malignancy the likely primary. He passed away 17 days after hospital admission. Prolonged culture of CSF later produced evidence of two distinct phaeomycotic moulds (Cladosporium sp and Exophiala sp), suggesting that fungal meningitis may also have contributed to the clinical picture.
Nomogram Predicting the Risk of Postoperative Major Wound Complication in Soft Tissue Sarcoma of the Trunk and Extremities after Preoperative Radiotherapy.
Preoperative radiotherapy increases the risk of postoperative wound complication in the treatment of soft tissue sarcoma (STS). This study aims to develop a nomogram for predicting major wound complication (MaWC) after surgery. Using the Oxford University Hospital (OUH) database, a total of 126 STS patients treated with preoperative radiotherapy and surgical resection between 2007 and 2021 were retrospectively reviewed. MaWC was defined as a wound complication that required secondary surgical intervention. Univariate and multivariate regression analyses on the association between MaWC and risk factors were performed. A nomogram was formulated and the areas under the Receiver Operating Characteristic Curves (AUC) were adopted to measure the predictive value of MaWC. A decision curve analysis (DCA) determined the model with the best discriminative ability. The incidence of MaWC was 19%. Age, tumour size, diabetes mellitus and metastasis at presentation were associated with MaWC in the univariate analysis. Age, tumour size, and metastasis at presentation were independent risk factors in the multivariate analysis. The sensitivity and specificity of the predictive model is 0.90 and 0.76, respectively. The AUC value was 0.86. The nomogram constructed in the study effectively predicts the risk of MaWC after preoperative radiotherapy and surgery for STS patients.
Surgical Outcome and Oncological Survival of Osteofibrous Dysplasia-Like and Classic Adamantinomas: An International Multicenter Study of 318 Cases.
BACKGROUND: Osteofibrous dysplasia-like adamantinoma (OFD-AD) and classic adamantinoma (AD) are rare, neoplastic diseases with only limited data supporting current treatment protocols. We believe that our retrospective multicenter cohort study is the largest analysis of patients with adamantinoma to date. The primary purpose of this study was to describe the disease characteristics and evaluate the oncological outcomes. The secondary purpose was to identify risk factors for local recurrence after surgical treatment and propose treatment guidelines. METHODS: Three hundred and eighteen confirmed cases of OFD-AD and AD for which primary treatment was carried out between 1985 and 2015 were submitted by 22 tertiary bone tumor centers. Proposed clinical risk factors for local recurrence such as size, type, and margins were analyzed using univariable and multivariate Cox regression analysis. RESULTS: Of the 318 cases, 128 were OFD-AD and 190 were AD. The mean age at diagnosis was 17 years (median, 14.5 years) for OFD-AD and 32 years (median, 28 years) for AD; 53% of the patients were female. The mean tumor size in the OFD-AD and AD groups combined was 7.8 cm, measured histologically. Sixteen percent of the patients sustained a pathological fracture prior to treatment. Local recurrence was recorded in 22% of the OFD-AD cases and 24% of the AD cases. None of the recurrences in the OFD-AD group progressed to AD. Metastatic disease was found in 18% of the AD cases and fatal disease, in 11% of the AD cases. No metastatic or fatal disease was reported in the OFD-AD group. Multivariate Cox regression analysis demonstrated that uncontaminated resection margins (hazard ratio [HR] = 0.164, 95% confidence interval [CI] = 0.092 to 0.290, p < 0.001), pathological fracture (HR = 1.968, 95% CI = 1.076 to 3.600, p = 0.028), and sex (female versus male: HR = 0.535, 95% CI = 0.300 to 0.952, p = 0.033) impacted the risk of local recurrence. CONCLUSIONS: OFD-AD and AD are parts of a disease spectrum but should be regarded as different entities. Our results support reclassification of OFD-AD into the intermediate locally aggressive category, based on the local recurrence rate of 22% and absence of metastases. In our study, metastatic disease was restricted to the AD group (an 18% rate). We advocate wide resection with uncontaminated margins including bone and involved periosteum for both OFD-AD and AD. LEVEL OF EVIDENCE: Prognostic Level IV. See Instructions for Authors for a complete description of levels of evidence.
The Use of Machine Learning to Reduce Overtreatment of the Axilla in Breast Cancer: Retrospective Cohort Study.
BACKGROUND: Patients with early breast cancer undergoing primary surgery, who have low axillary nodal burden, can safely forego axillary node clearance (ANC). However, routine use of axillary ultrasound (AUS) leads to 43% of patients in this group having ANC unnecessarily, following a positive AUS. The intersection of machine learning with medicine can provide innovative ways to understand specific risks within large patient data sets, but this has not yet been trialed in the arena of axillary node management in breast cancer. OBJECTIVE: The objective of this study was to assess if machine learning techniques could be used to improve preoperative identification of patients with low and high axillary metastatic burden. METHODS: A single-center retrospective analysis was performed on patients with breast cancer who had a preoperative AUS, and the specificity and sensitivity of AUS were calculated. Standard statistical methods and machine learning methods, including artificial neural network, naive Bayes, support vector machine, and random forest, were applied to the data to see if they could improve the accuracy of preoperative AUS to better discern high and low axillary burden. RESULTS: The study included 459 patients; 142 (31%) had a positive AUS; among this group, 88 (62%) had 2 or fewer macrometastatic nodes at ANC. Logistic regression outperformed AUS (specificity 0.950 vs 0.809). Of all the methods, the artificial neural network had the highest accuracy (0.919). Interestingly, AUS had the highest sensitivity of all methods (0.777), underlining its utility in this setting. CONCLUSIONS: We demonstrated that machine learning improves identification of the important subgroup of patients with no palpable axillary disease, positive ultrasound, and more than 2 metastatically involved nodes. A negative ultrasound in patients with no palpable lymphadenopathy is highly indicative of low axillary burden, and it is unclear whether sentinel node biopsy adds value in this situation. Further studies with larger patient numbers focusing on specific breast cancer subgroups are required to refine these techniques in this setting.