BACKGROUND: Hepatectomy for huge hepatocellular carcinoma (HCC) (diameter ≥10 cm) is characterized by high mortality. This study aimed to establish a preoperative model to evaluate the risk of postoperative 90-day mortality for huge HCC patients. METHODS: We retrospectively enrolled 1,127 consecutive patients and prospectively enrolled 93 patients with huge HCC who underwent hepatectomy (training cohort, n=798; validation cohort, n=329; prospective cohort, n=93) in our institute. Based on independent preoperative predictors of 90-day mortality, we established a logistic regression model and visualized the model by nomogram. RESULTS: The 90-day mortality rates were 9.6%, 9.2%, and 10.9% in the training, validation, and prospective cohort. The α-fetoprotein (AFP) level, the prealbumin levels, and the presence of portal vein tumor thrombosis (PVTT) were preoperative independent predictors of 90-day mortality. A logistic regression model, AFP-prealbumin-PVTT score (APP score), was subsequently established and showed good performance in predicting 90-day mortality (training cohort, AUC =0.87; validation cohort, AUC =0.91; prospective cohort, AUC =0.93). Using a cut-off of -1.96, the model could stratify patients into low risk (≤-1.96) and high risk (>-1.96) with different 90-day mortality rates (~30% vs. ~2%). Furthermore, the predictive performance for 90-day mortality and overall survival was significantly superior to the Child-Pugh score, the model of end-stage liver disease (MELD) score, and the albumin-bilirubin (ALBI) score. CONCLUSIONS: The APP score can precisely predict postoperative 90-day mortality as well as long-term survival for patients with huge HCC, assisting physician selection of suitable candidates for liver resection and improving the safety and efficacy of surgical treatment.
CITATION STYLE
Yin, Y., Cheng, J.-W., Chen, F.-Y., Chen, X.-X., Zhang, X., Huang, A., … Yang, X.-R. (2021). A novel preoperative predictive model of 90-day mortality after liver resection for huge hepatocellular carcinoma. Annals of Translational Medicine, 9(9), 774–774. https://doi.org/10.21037/atm-20-7842
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