Asian journal of surgery · 2022
A nomogram for predicting post-hepatectomy liver failure in patients with hepatocellular carcinoma based on spleen-volume-to-platelet ratio.
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Meng Xue-Qin, Miao Hui, Xia Yong, Shen Hao, Qian Yi, YanChen, Shen Feng, Guo Jia
Аннотация
BACKGROUND AND OBJECTIVES: Post-hepatectomy liver failure (PHLF) is one of the most serious complications after hepatectomy in patients with hepatocellular carcinoma (HCC), and has an association with high morbidity and mortality. This study aimed to explore the risk factors of PHLF and to establish and validate a nomogram to predict PHLF.
METHODS: We retrospectively analyzed 971 HCC patients undergoing major liver resection at the Eastern Hepatobiliary Surgery Hospital between 2011 and 2016, and established a nomogram based on multivariate analyses for predicting PHLF. The predictive accuracy and discriminative ability of the nomogram were evaluated by concordance index (C-index) and calibration curve. The predictive ability of PHLF of this nomogram was compared with conventional models using receiver operating characteristic (ROC) curves.
RESULTS: The incidence of PHLF was 18.8%. Multivariate analysis identified age, BMI, preoperative ascites, preoperative prealbumin, spleen volume-to-platelet ratio, total bilirubin, and intraoperative blood loss as independent predictors of PHLF. The area under ROC curve (AUROC) of the predictive model was 0.668 and was higher than that of the albumin-bilirubin score (ALBI). The optimal cut-off value of the model was 124.
CONCLUSIONS: We constructed a nomogram for predicting PHLF based on risk factors. The nomogram can assist clinicians in identifying patients with high-risk PHLF, eventually facilitating earlier interventions and improving clinical outcomes.
METHODS: We retrospectively analyzed 971 HCC patients undergoing major liver resection at the Eastern Hepatobiliary Surgery Hospital between 2011 and 2016, and established a nomogram based on multivariate analyses for predicting PHLF. The predictive accuracy and discriminative ability of the nomogram were evaluated by concordance index (C-index) and calibration curve. The predictive ability of PHLF of this nomogram was compared with conventional models using receiver operating characteristic (ROC) curves.
RESULTS: The incidence of PHLF was 18.8%. Multivariate analysis identified age, BMI, preoperative ascites, preoperative prealbumin, spleen volume-to-platelet ratio, total bilirubin, and intraoperative blood loss as independent predictors of PHLF. The area under ROC curve (AUROC) of the predictive model was 0.668 and was higher than that of the albumin-bilirubin score (ALBI). The optimal cut-off value of the model was 124.
CONCLUSIONS: We constructed a nomogram for predicting PHLF based on risk factors. The nomogram can assist clinicians in identifying patients with high-risk PHLF, eventually facilitating earlier interventions and improving clinical outcomes.