Development and Validation of a Nomogram Based on Perioperative Factors to Predict Post-hepatectomy Liver Failure

Abstract

Background and Aims: Post-hepatectomy liver failure (PHLF) is a severe complication and main cause of death in patients undergoing hepatectomy. The aim of this study was to build a predictive model of PHLF in patients undergoing hepatectomy. Methods: We retrospectively analyzed patients undergoing hepatectomy at Zhongshan Hospital, Fudan University from July 2015 to June 2018, and randomly divided them into development and internal validation cohorts. External validation was performed in an independent cohort. Least absolute shrinkage and selection operator (commonly referred to as LASSO) logistic regression was applied to identify predictors of PHLF, and multivariate binary logistic regression analysis was performed to establish the predictive model, which was visualized with a nomogram. Results: A total of 492 eligible patients were analyzed. LAS-SO and multivariate analysis identified three preoperative variables, total bilirubin (p=0.001), international normalized ratio (p<0.001) and platelet count (p=0.004), and two intraoperative variables, extent of resection (p=0.002) and blood loss (p=0.004), as independent predictors of PHLF. The area under receiver operating characteristic curve (referred to as AUROC) of the predictive model was 0.838 and outperformed the model for end-stage liver disease score, albumin-bilirubin score and platelet-albumin-bilirubin score (AUROCs: 0.723, 0.695 and 0.663, respectively; p<0.001 for all). The optimal cut-off value of the predictive model was 14.7. External validation showed the model could predict PHLF accurately and distinguish high-risk patients. Conclusions: PHLF can be accurately predicted by this model in patients undergoing hepatectomy, which may significantly contribute to the postoperative care of these patients.

Machine learning prediction model for post- hepatectomy liver failure in hepatocellular carcinoma: A multicenter study

“…Patients with a perioperative PLT count< 150/nL (four studies, 817 patients; odds ratio [OR]: 4.79; 95% CI, 2.89-7.94) and those with a PLT count< 100/nL (four studies, 949 patients; OR: 4.65; 95% CI: 2.608.31) had a high risk of developing PHLF ( 37 ). As shown in previous studies, PLT count ( 38 ), age ( 39 ), Cr ( 40 ), INR ( 41 ), and AFP ( 42 ) are all predictors of PHLF.…”

Nomogram to Predict the Survival of Chinese Patients with Alcohol-Related Liver Disease

“…At the time of this study, there are many predictive models for surgical treatment, radiotherapy, and chemotherapy of ALD [ 36 , 37 ], but there is a lack of predictive models for medical treatment of ALD patients. This study included most of the baseline indicators that were found to be meaningful in previous studies and constructed a nomogram using the variables of relapse, cirrhosis, cancer, and an MELD score of ≥21.…”

A Novel Nomogram for Predicting Postoperative Liver Failure After Major Hepatectomy for Hepatocellular Carcinoma

“…Previously, several models were established to predict PHLF but exhibited obvious heterogeneity. Some nomograms were derived from data of patients with benign and malignant lesions ( 15 ), while some nomograms incorporated both preoperative and intraoperative variables ( 15 , 25 ), and some nomograms were based on analysis of both minor and major hepatectomies ( 17 , 18 , 26 ). Recently, Chin and colleagues ( 27 ) created a PHLF prognostic nomogram for major hepatectomy; however, the study involved patients with either HCC or colorectal liver metastasis.…”

“…Recently, a few predictive models have been established for PHLF in HCC patients and showed certain predictive ability (15)(16)(17)(18)(19). However, most of these models incorporated both preand postoperative predictors which might reduce the validity of preoperative prediction.…”