Pre-operative stenting is associated with a higher prevalence of post-operative complications following pancreatoduodenectomy

AbstractObjectivesWhilst there are theoretical benefits from pre-operatively draining the biliary tree prior to pancreatoduodenectomy (PD), the current literature does not support this intervention. The aim of this study was to explore the relationship between pre-operative stenting, bactibilia and outcome in a large United Kingdom tertiary referral practice.MethodsPatients undergoing PD were identified from a prospectively maintained database. The presence or absence of a stent prior to PD, and the results of bile cultures taken at PD were related to the subsequent post-operative course and the development of complications.Results280 patients underwent PD for periampullary malignancies, all of whom presented with jaundice. 118 patients were stented prior to referral (98 ERCP, 20 PTC). Bile cultures were positive more frequently in the stent group (83% vs. 55%; p = 0.000002) and bactibilia was more common after ERCP than PTC (83% vs. 56%; p = 0.006). The overall prevalence of complications was 54% in the stented and 41% in the non-stented group (p = 0.03) with statistical significance achieved for pancreatic leak (p = 0.013) and haemorrhagic complications (p = 0.03). Comparing stent with no stent, there as no difference in the 30-day mortalities (8.5% vs. 6.8%; p = 0.6) or the 1-year mortality rates (35% vs. 28%; p = 0.21). Mortality rates in the infection versus no infection groups were comparable at 30 days (8.5% vs. 5.5%; p = 0.21), and at 1 year (30.7% vs. 26.4%; p = 0.25).ConclusionsPre-operative stent insertion prior to PD is associated with increased morbidity but not mortality and this is greatest for stents placed at ERCP

Donor insulin use predicts beta‐cell function after islet transplantation

Insulin is routinely used to manage hyperglycaemia in organ donors and during the peri-transplant period in islet transplant recipients. However, it is unknown whether donor insulin use (DIU) predicts beta-cell dysfunction after islet transplantation. We reviewed data from the UK Transplant Registry and the UK Islet Transplant Consortium; all first-time transplants during 2008-2016 were included. Linear regression models determined associations between DIU, median and coefficient of variation (CV) peri-transplant glucose levels and 3-month islet graft function. In 91 islet cell transplant recipients, DIU was associated with lower islet function assessed by BETA-2 scores (β [SE] -3.5 [1.5], P = .02), higher 3-month post-transplant HbA1c levels (5.4 [2.6] mmol/mol, P = .04) and lower fasting C-peptide levels (−107.9 [46.1] pmol/l, P = .02). Glucose at 10 512 time points was recorded during the first 5 days peri-transplant: the median (IQR) daily glucose level was 7.9 (7.0-8.9) mmol/L and glucose CV was 28% (21%-35%). Neither median glucose levels nor glucose CV predicted outcomes post-transplantation. Data on DIU predicts beta-cell dysfunction 3 months after islet transplantation and could help improve donor selection and transplant outcomes

Machine learning for prediction of postoperative complications after hepato-biliary and pancreatic surgery

Decision making in Hepatobiliary and Pancreatic Surgery is challenging, not least because of the significant complications that may occur following surgery and the complexity of interventions to treat them. Machine Learning (ML) relates to the use of computer derived algorithms and systems to enhance knowledge in order to facilitate decision making and could be of great benefit to surgical patients. ML could be employed pre- or peri-operatively to shape treatment choices prospectively, or could be utilised in the post-hoc analysis of complications in order to inform future practice. ML could reduce errors by drawing attention to known risks of complications through supervised learning, and gain greater insights by identifying previously under-appreciated aspects of care through unsupervised learning. Accuracy, validity and integrity of data are of fundamental importance if predictive models generated by ML are to be successfully integrated into surgical practice. The choice of appropriate ML models and the interface between ML, traditional statistical methodologies and human expertise will also impact the potential to incorporate data science techniques into daily clinical practice