Management of iatrogenic bile duct injuries: Multiple logistic regression analysis of predictive factors affecting morbidity and mortality

Iatrogenic Injury of Biliary Tree-Single-Centre Experience

Cholecystolithiasis is among the most prevalent gastrointestinal disorders requiring surgical intervention, and iatrogenic damage to the bile tree is a severe complication. We aimed to present the frequency of bile duct injuries and how our facility handles these complications. We retrospectively analyzed bile duct injuries in patients undergoing surgery. We concentrated on factors such as sex, age, indications for surgery, type of surgery, primary procedure, bile tree injury, repair, and timing as well as early and late complications. There were 22 cases of bile duct injury in the studied material, primarily affecting women-15 individuals (68.2%). Eleven cases (45.7%) of acute cholecystitis were the primary reason for surgery, and an injury to the common bile duct that extended up to 2 cm from the common hepatic duct was the most common complication (European Association for Endoscopic Surgery grade 2). Roux-en-Y hepaticojejunostomy was the most common repair procedure in 14 cases (63.6%). Eleven patients (50%) experienced early complications following reconstruction surgery, whereas five patients (22.7%) experienced late complications. An annual mortality rate of 22.7% (five patients) was observed. Iatrogenic bile duct injury is a severe complication of surgical treatment for cholecystolithiasis. Reconstruction procedures are characterized by high complication rates and high mortality.

Relationships of hepatic histopathological findings and bile microbiological aspects with bile duct injury repair surgical outcomes: A historical cohort

Backgrounds/Aims

To analyze relationships of hepatic histopathological findings and bile microbiological profiles with perioperative outcomes and risk of late biliary stricture in individuals undergoing surgical bile duct injury (BDI) repair.

Methods

A historical cohort study was carried out at a tertiary university hospital. Fifty-six individuals who underwent surgical BDI repair from 2014-2018 with a minimal follow-up of 24 months were enrolled. Liver biopsies were performed to analyze histopathology. Bile samples were collected during repair procedures. Hepatic histopathological findings and bile microbiological profiles were then correlated with perioperative and late outcomes through uni- and multi-variate analyses.

Results

Forty-three individuals (76.8%) were females and average age was 47.2 ± 13.2 years; mean follow-up was 38.1 ± 18.6 months. The commonest histopathological finding was hepatic fibrosis (87.5%). Bile cultures were positive in 53.5%. The main surgical technique was Roux-en-Y hepaticojejunostomy (96.4%). Overall morbidity was 35.7%. In univariate analysis, liver fibrosis correlated with the duration of the operation (R = 0.3; p = 0.02). In multivariate analysis, fibrosis (R = 0.36; p = 0.02) and cholestasis (R = 0.34; p = 0.02) independently correlated with operative time. Strasberg classification independently correlated with estimated bleeding (R = 0.31; p = 0.049). The time elapsed between primary cholecystectomy and BDI repair correlated with hepatic fibrosis (R = 0.4; p = 0.01).

Conclusions

Bacterial contamination of bile was observed in most cases. The degree of fibrosis and cholestasis correlated with operative time. The waiting time for definitive repair correlated with the severity of liver fibrosis.

Predicting in-hospital mortality in ICU patients with sepsis using gradient boosting decision tree

Sepsis is a leading cause of mortality in the intensive care unit. Early prediction of sepsis can reduce the overall mortality rate and cost of sepsis treatment. Some studies have predicted mortality and development of sepsis using machine learning models. However, there is a gap between the creation of different machine learning algorithms and their implementation in clinical practice.This study utilized data from the Medical Information Mart for Intensive Care III. We established and compared the gradient boosting decision tree (GBDT), logistic regression (LR), k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM).A total of 3937 sepsis patients were included, with 34.3% mortality in the Medical Information Mart for Intensive Care III group. In our comparison of 5 machine learning models (GBDT, LR, KNN, RF, and SVM), the GBDT model showed the best performance with the highest area under the receiver operating characteristic curve (0.992), recall (94.8%), accuracy (95.4%), and F1 score (0.933). The RF, SVM, and KNN models showed better performance (area under the receiver operating characteristic curve: 0.980, 0.898, and 0.877, respectively) than the LR (0.876).The GBDT model showed better performance than other machine learning models (LR, KNN, RF, and SVM) in predicting the mortality of patients with sepsis in the intensive care unit. This could be used to develop a clinical decision support system in the future.