Textbook outcome of laparoscopic hepatectomy in the context of precision surgery: A single center experience

Use of textbook outcome as a quality metric in hepatopancreaticobiliary surgery: a systematic review and meta-analysis

BACKGROUND

Textbook outcomes (TOs) represent the optimal course after surgery. To date, no meta-analysis has assessed the pooled TOs of patients undergoing hepatopancreatobiliary (HPB) surgery and the effect of TO achievement on patient outcomes. This systematic review and meta-analysis aimed to assess TO achievement across different studies and to characterize the effect of TO achievement on patient-related outcomes, including disease-free survival (DFS) and overall survival (OS).

METHODS

PubMed, Embase, and Scopus databases were searched (1990-2024). The criteria used to define TO and the median overall TO in HPB surgery were obtained. In addition, a random-effects meta-analysis was conducted to assess the effect of TO achievement on 5-year OS and DFS.

RESULTS

A total of 27 studies involving 517,304 patients met inclusion criteria. The main criteria used to define TO included absence of readmission and mortality within 30 days after discharge, severe postoperative complications, prolonged hospital stay, and negative surgical margin (R0). Of note, the main factors related to TO achievement were younger patient age and lower American Society of Anesthesiologists score. Overall, the median rates of TOs achieved across procedures were 62.0% (IQR, 48.0%-69.0%) for hepatic procedure, 54.0% (IQR, 41.0%-68.0%) for biliary procedure, 46.0% (IQR, 42.0%-46.5%) for combined hepatopancreatic procedure, 45.0% (IQR, 30.5%-59.0%) for pancreatic procedure, 33.0% (IQR, 32.2%-34.0%) for liver transplantation, and 19.5% (IQR, 16.8%-22.3%) for combined hepatobiliary procedure. TO achievement was associated with improved odds of 5-year OS (odds ratio [OR], 1.22 [95% CI, 1.20-1.24]) and 5-year DFS (OR, 1.26 [95% CI, 1.16-1.37]).

CONCLUSION

Overall, hepatic and biliary operations had the highest TO achievement, followed by pancreatic procedures. In contrast, hepatobiliary surgery and liver transplantation had the lowest TO. There was a significant discrepancy in the definition of TO across different studies, highlighting the need for consensus on the definition of TO.

Preoperative prediction of textbook outcome in intrahepatic cholangiocarcinoma by interpretable machine learning: A multicenter cohort study

BACKGROUND

To investigate the preoperative factors influencing textbook outcomes (TO) in Intrahepatic cholangiocarcinoma (ICC) patients and evaluate the feasibility of an interpretable machine learning model for preoperative prediction of TO, we developed a machine learning model for preoperative prediction of TO and used the SHapley Additive exPlanations (SHAP) technique to illustrate the prediction process.

AIM

To analyze the factors influencing textbook outcomes before surgery and to establish interpretable machine learning models for preoperative prediction.

METHODS

A total of 376 patients diagnosed with ICC were retrospectively collected from four major medical institutions in China, covering the period from 2011 to 2017. Logistic regression analysis was conducted to identify preoperative variables associated with achieving TO. Based on these variables, an EXtreme Gradient Boosting (XGBoost) machine learning prediction model was constructed using the XGBoost package. The SHAP (package: Shapviz) algorithm was employed to visualize each variable's contribution to the model's predictions. Kaplan-Meier survival analysis was performed to compare the prognostic differences between the TO-achieving and non-TO-achieving groups.

RESULTS

Among 376 patients, 287 were included in the training group and 89 in the validation group. Logistic regression identified the following preoperative variables influencing TO: Child-Pugh classification, Eastern Cooperative Oncology Group (ECOG) score, hepatitis B, and tumor size. The XGBoost prediction model demonstrated high accuracy in internal validation (AUC = 0.8825) and external validation (AUC = 0.8346). Survival analysis revealed that the disease-free survival rates for patients achieving TO at 1, 2, and 3 years were 64.2%, 56.8%, and 43.4%, respectively.

CONCLUSION

Child-Pugh classification, ECOG score, hepatitis B, and tumor size are preoperative predictors of TO. In both the training group and the validation group, the machine learning model had certain effectiveness in predicting TO before surgery. The SHAP algorithm provided intuitive visualization of the machine learning prediction process, enhancing its interpretability.