Management of Post‐Cholecystectomy Biliary Complications: Surgeon's Perspective

Postcholecystectomy Duodenal Injuries, Their Management, and Review of the Literature

Backgrounds

Laparoscopic cholecystectomy (LC) is the gold standard for treating gallstones; however, it is not free of complications. Postcholecystectomy duodenal injuries are rare but challenging complications after cholecystectomy. The objective of this study was to analyze the management of postcholecystectomy duodenal injuries and to review the related literature.

Materials and methods

An observational and retrospective study was conducted. We included all patients with postcholecystectomy duodenal injuries treated at a reference center, from January 2019 to December 2023. In addition, a review of the literature was carried out.

Results

Fifteen patients were found, mostly women; with gallbladder wall thickening on ultrasound (mean of 8 mm). The majority were emergency (n = 12, 80%) and LCs (n = 8, 53.33%). Cholecystectomies were reported to be associated with excessive difficulty (n = 10, 66.66%). The most injured duodenal portion was the first portion (n = 9, 60%), and blunt dissection was the most common mechanism of injury (n = 7, 46.66%). Most of these injuries were detected in the operating room (n = 9, 60%), and treated with primary closure (n = 11, 73.33%). Three patients with delayed injuries died (20%). According to the literature reviewed, 93 duodenal injuries were found, mostly detected intraoperatively, in the second portion, and treated with primary closure. A minority of patients were treated with more complex procedures, for a mortality rate of 15.38%.

Conclusion

Postcholecystectomy duodenal injuries are rare. Most of these injuries are detected and repaired intraoperatively. However, a high percentage of patients have high morbidity and mortality.

How to cite this article

Diaz-Martinez J, Pérez-Correa N. Postcholecystectomy Duodenal Injuries, Their Management, and Review of the Literature. Euroasian J Hepato-Gastroenterol 2024;14(1):44-50.

A Machine Learning-Based Method for Detecting Liver Fibrosis

Cholecystectomy and Metabolic-associated steatotic liver disease (MASLD) are prevalent conditions in gastroenterology, frequently co-occurring in clinical practice. Cholecystectomy has been shown to have metabolic consequences, sharing similar pathological mechanisms with MASLD. A database of MASLD patients who underwent cholecystectomy was analysed. This study aimed to develop a tool to identify the risk of liver fibrosis after cholecystectomy. For this purpose, the extreme gradient boosting (XGB) algorithm was used to construct an effective predictive model. The factors associated with a better predictive method were platelet level, followed by dyslipidaemia and type-2 diabetes (T2DM). Compared to other ML methods, our proposed method, XGB, achieved higher accuracy values. The XGB method had the highest balanced accuracy (93.16%). XGB outperformed KNN in accuracy (93.16% vs. 84.45%) and AUC (0.92 vs. 0.84). These results demonstrate that the proposed XGB method can be used as an automatic diagnostic aid for MASLD patients based on machine-learning techniques.