Developing and validating a nomogram based on skeletal muscle index and clinical scoring system for prediction of liver failure after hepatectomy - Ding, Cong; Jia, Jianye; Han, Lei; Zhou, Wei; Liu, Ziyan; Bai, Genji; Wang, Qian (Frontiers in Oncology, 2023)
Background and objectives: Hepatectomy is the preferred treatment for patients with liver tumors. Post-hepatectomy liver failure (PHLF) remains the most dreaded complication. We aim to explore the risk factors of PHLF and create a nomogram for early prediction of PHLF. Methods: We retrospectively analyzed patients undergoing hepatectomy at the Affiliated Huaian No. 1 Hospital of Nanjing Medical University between 2015 and 2022, and randomly divided them into development and internal validation cohorts. The patients undergoing liver resection from the Affiliated Huai’an Hospital of Xuzhou Medical University work as external validation was performed. Then, established a nomogram based on multivariate analyses for predicting PHLF. The predictive accuracy and discriminative ability of the nomogram were evaluated by the area under the ROC curve (AUROC) and calibration were used Hosmer -Lemeshow test. Results: A total of 420 eligible patients were analyzed. Multivariate analysis identified four preoperative variables, ASA (American Society of Anesthesiologists) score, Child-Pugh score, SMI (Skeletal muscle index), and MELD (Model for end-stage liver disease) score as independent predictors of PHLF. The area under the ROC curve of the predictive model in the training cohort, internal and external cohort validation cohort were 0.89, 0.82, and 0.89. Hosmer -Lemeshow P values in the training cohort, internal cohort, and external validation cohort were 0.91, 0.22, and 0.15. Internal and external validation showed the model could predict PHLF accurately and distinguish high-risk patients. Conclusion: We develop a nomogram to predict the grade B/C PHLF of ISGLS (International Study Group of Liver Surgery) in patients who underwent hepatic resection based on risk factors. This tool can provide a visual and accurate preoperative prediction of the grade B/C PHLF and guide the next step of clinical decision-making.