ABSTRACT
Because of their vague and subtle indications and symptoms, pancreatic injuries are frequently misdiagnosed. It's crucial to have a high level of clinical suspicion. The presence of other organ solid lesions and vascular injuries, as well as the patient's hemodynamic condition, will determine how these injuries are treated. A surgical approach is mandatory when a ductal disruption occurs. The case of a 32-year-old man who experienced an upper abdominal blunt trauma is presented. He was admitted to our hospital with an acute abdomen 48 hours later. A complete transection of the major pancreatic duct was discovered during surgical investigation, and a distal pancreatectomy with en bloc splenectomy was performed. Even in a delayed context, distal pancreatectomy can be safely performed and is the best option.
ABSTRACT
INTRODUCTION: Pancreas transplantation is currently the only treatment that can re-establish normal endocrine pancreatic function. Despite all efforts, pancreas allograft survival and rejection remain major clinical problems. The purpose of this study was to identify features that could signal patients at risk of pancreas allograft rejection. METHODS: We collected 74 features from 79 patients who underwent simultaneous pancreas-kidney transplantation (SPK) and used two widely-applicable classification methods, the Naive Bayesian Classifier and Support Vector Machine, to build predictive models. We used the area under the receiver operating characteristic curve and classification accuracy to evaluate the predictive performance via leave-one-out cross-validation. RESULTS: Rejection events were identified in 13 SPK patients (17.8%). In feature selection approach, it was possible to identify 10 features, namely: previous treatment for diabetes mellitus with long-term Insulin (U/I/day), type of dialysis (peritoneal dialysis, hemodialysis, or pre-emptive), de novo DSA, vPRA_Pre-Transplant (%), donor blood glucose, pancreas donor risk index (pDRI), recipient height, dialysis time (days), warm ischemia (minutes), recipient of intensive care (days). The results showed that the Naive Bayes and Support Vector Machine classifiers prediction performed very well, with an AUROC and classification accuracy of 0.97 and 0.87, respectively, in the first model and 0.96 and 0.94 in the second model. CONCLUSION: Our results indicated that it is feasible to develop successful classifiers for the prediction of graft rejection. The Naive Bayesian generated nomogram can be used for rejection probability prediction, thus supporting clinical decision making.