A retrospective analysis of bile duct injuries treated in a tertiary center: the utility of a universal classification-the ATOM classification.

BACKGROUND: Bile duct injuries (BDI) are the most feared complications that can occur after laparoscopic cholecystectomy (LC). BDI have a high variability and complexity, several classifications being developed along the years in order to correctly assess and divide BDI. The EAES ATOM classification encompasses all the important details of a BDI: A (for anatomy), To (for time of), and M (for mechanism) but have not gained universal acceptance yet. Our study intents to analyze the cases of BDI treated in our institution with a focus on the clinical utility of the ATOM classification. METHODS: We conducted a retrospective study, on a 10-year period (2011-2020), including patients diagnosed with BDI after LC, with their definitive treatment performed in our tertiary center. All injuries were retrospectively classified using the Strasberg, Hannover, and ATOM classifications. RESULTS: We included in our study 100 patients; 15% of the BDI occurred in our center. No classification system was used in 73% of patients; 23% of the BDI were classified by the Strasberg system, 3% were classified by the Bismuth classification, 1% being classified by the ATOM classification. After retrospectively assessing all BDI, we observed that especially the Strasberg classification, as well as Hannover, over-simplifies the characteristics of the injury, many types of BDI according to ATOM being included in the same Strasberg or Hannover category. Most main bile duct injuries underwent a bilio-digestive anastomosis (60%), as a definitive treatment. An important percentage of cases (31%) underwent a primary treatment in the hospital of origin, reintervention with definitive treatment being done in our department. CONCLUSION: The ATOM classification is the best suited for accurately describing the complexity of a BDI, serving as a template for discussing the correct management for each lesion. Efforts should be made toward increasing the use of this classification in day-to-day clinical practice.

The role of artificial intelligence in pancreatic surgery: a systematic review.

Artificial intelligence (AI), including machine learning (ML), is being slowly incorporated in medical practice, to provide a more precise and personalized approach. Pancreatic surgery is an evolving field, which offers the only curative option for patients with pancreatic cancer. Increasing amounts of data are available in medicine: AI and ML can help incorporate large amounts of information in clinical practice. We conducted a systematic review, based on PRISMA criteria, of studies that explored the use of AI or ML algorithms in pancreatic surgery. To our knowledge, this is the first systematic review on this topic. Twenty-five eligible studies were included in this review; 12 studies with implications in the preoperative diagnosis, while 13 studies had implications in patient evolution. Preoperative diagnosis, such as predicting the malignancy of IPMNs, differential diagnosis between pancreatic cystic lesions, classification of different pancreatic tumours, and establishment of the correct management for each of these lesions, can be facilitated through different AI or ML algorithms. Postoperative evolution can also be predicted, and some studies reported prediction models for complications, including postoperative pancreatic fistula, while other studies have analysed the implications for prognosis evaluation (from predicting a textbook outcome, the risk of metastasis or relapse, or the mortality rate and survival). One study discussed the possibility of predicting an intraoperative complication-massive intraoperative bleeding. Artificial intelligence and machine learning models have promising applications in pancreatic surgery, in the preoperative period (high-accuracy diagnosis) and postoperative setting (prognosis evaluation and complication prediction), and the intraoperative applications have been less explored.