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