Background:The demand for liver transplantation far outstrips the supply of deceased donor organs, and so listing and allocation decisions aim to maximise utility. Most existing methods for predicting transplant outcomes utilise basic methods such as regression modellingnewer artificial intelligence techniques have the potential to improve predictive accuracy. Aims:To systematically review studies predicting graft outcomes following deceased liver transplantation using Artificial Intelligence (AI) techniques and comparing these to linear regression and standard predictive modelling (donor risk index, DRI; Model for end-stage liver disease, MELD; survival outcome following liver transplantation, SOFT).Methods: A systematic review was performed. PubMed, Cochrane, MEDLINE, Science Direct, Springer Link, Elsevier, and reference lists were analysed for appropriate inclusion.Results: A total of 52 papers were reviewed for inclusion. Of these papers, 9 met the inclusion criteria, reporting outcomes from 18,771 liver transplants. Artificial neural networks (ANN) were the most commonly utilised methodology, being reported in 7 studies. Two studies directly compared Machine Learning (ML) techniques to liver scoring modalities (i.e. DRI, SOFT, BAR). Both of these studies showed better prediction of individual organ survival with the optimal ANN model reporting AUC ROC 0.82 compared with BAR: 0.62 and SOFT: 0.57; and the other ANN model showing an AUC ROC: 0.84 compared to DRI: 0.68 and SOFT: 0.64. Conclusion:AI techniques can provide high accuracy in predicting graft survival based on donors and recipient variables. AI approaches need to be integrated into current organ allocation methodologies to ultimately create better individual patient outcomes and will personalise decisions to organ acceptance.

BackgroundThere is currently conflicting evidence surrounding the effects of obesity on postoperative outcomes. Previous studies have found obesity to be associated with adverse events, but others have found no association. The aim of this study was to determine whether increasing body mass index (BMI) is an independent risk factor for development of major postoperative complications.MethodsThis was a multicentre prospective cohort study across the UK and Republic of Ireland. Consecutive patients undergoing elective or emergency gastrointestinal surgery over a 4‐month interval (October–December 2014) were eligible for inclusion. The primary outcome was the 30‐day major complication rate (Clavien–Dindo grade III–V). BMI was grouped according to the World Health Organization classification. Multilevel logistic regression models were used to adjust for patient, operative and hospital‐level effects, creating odds ratios (ORs) and 95 per cent confidence intervals (c.i.).ResultsOf 7965 patients, 2545 (32·0 per cent) were of normal weight, 2673 (33·6 per cent) were overweight and 2747 (34·5 per cent) were obese. Overall, 4925 (61·8 per cent) underwent elective and 3038 (38·1 per cent) emergency operations. The 30‐day major complication rate was 11·4 per cent (908 of 7965). In adjusted models, a significant interaction was found between BMI and diagnosis, with an association seen between BMI and major complications for patients with malignancy (overweight: OR 1·59, 95 per cent c.i. 1·12 to 2·29, P = 0·008; obese: OR 1·91, 1·31 to 2·83, P = 0·002; compared with normal weight) but not benign disease (overweight: OR 0·89, 0·71 to 1·12, P = 0·329; obese: OR 0·84, 0·66 to 1·06, P = 0·147).ConclusionOverweight and obese patients undergoing surgery for gastrointestinal malignancy are at increased risk of major postoperative complications compared with those of normal weight.

CTA has been shown to be a more effective training tool when compared with traditional methods of surgical training. There is a need for the introduction of CTA into surgical curriculums as this can improve surgical skill and ultimately create better patient outcomes.