Novel and simple preoperative score predicting complications after liver resection in noncirrhotic patients

Machine learning approaches for the prediction of postoperative complication risk in liver resection patients

Background

For liver cancer patients, the occurrence of postoperative complications increases the difficulty of perioperative nursing, prolongs the hospitalization time of patients, and leads to large increases in hospitalization costs. The ability to identify influencing factors and to predict the risk of complications in patients with liver cancer after surgery could assist doctors to make better clinical decisions.

Objective

The aim of the study was to develop a postoperative complication risk prediction model based on machine learning algorithms, which utilizes variables obtained before or during the liver cancer surgery, to predict when complications present with clinical symptoms and the ways of reducing the risk of complications.

Methods

The study subjects were liver cancer patients who had undergone liver resection. There were 175 individuals, and 13 variables were recorded. 70% of the data were used for the training set, and 30% for the test set. The performance of five machine learning models, logistic regression, decision trees-C5.0, decision trees-CART, support vector machines, and random forests, for predicting postoperative complication risk in liver resection patients were compared. The significant influencing factors were selected by combining results of multiple methods, based on which the prediction model of postoperative complications risk was created. The results were analyzed to give suggestions of how to reduce the risk of complications.

Results

Random Forest gave the best performance from the decision curves analysis. The decision tree-C5.0 algorithm had the best performance of the five machine learning algorithms if ACC and AUC were used as evaluation indicators, producing an area under the receiver operating characteristic curve value of 0.91 (95% CI 0.77–1), with an accuracy of 92.45% (95% CI 85–100%), the sensitivity of 87.5%, and specificity of 94.59%. The duration of operation, patient’s BMI, and length of incision were significant influencing factors of postoperative complication risk in liver resection patients.

Conclusions

To reduce the risk of complications, it appears to be important that the patient's BMI should be above 22.96 before the operation, and the duration of the operation should be minimized.

Transhepatic hilar approach for Bismuth types III and IV perihilar cholangiocarcinoma with long-term outcomes

Objective

To compare the outcomes of the transhepatic hilar approach and conventional approach for surgical treatment of Bismuth types III and IV perihilar cholangiocarcinoma.

Methods

We retrospectively reviewed the medical records of 82 patients who underwent surgical resection of Bismuth types III and IV perihilar cholangiocarcinoma from 2008 to 2016. The transhepatic hilar approach and conventional approach was used in 36 (43.9%) and 46 (56.1%) patients, respectively. Postoperative complications and overall survival were compared between the two approaches.

Results

Similar clinical features were observed between the patients treated by the conventional approach and those treated by the transhepatic hilar approach. The transhepatic hilar approach was associated with less intraoperative bleeding and a lower percentage of Clavien grade 0 to II complications than the conventional approach. However, the transhepatic hilar approach was associated with a higher R0 resection rate and better overall survival. Multivariate analysis showed that using the transhepatic hilar approach, the Memorial Sloan-Kettering Cancer Center classification, and R0 resection were independent risk factors for patient survival.

Conclusion

The transhepatic hilar approach might be the better choice for surgical resection of Bismuth types III and IV perihilar cholangiocarcinoma because it is associated with lower mortality and improved survival.