Predictive value of NLR, Fib4, and APRI in the occurrence of liver failure after hepatectomy in patients with hepatocellular carcinoma

ARTICLE HIGHLIGHTS

Research background

Hepatectomy is a common surgical procedure for hepatocellular carcinoma, but liver failure can occur after surgery, which is a serious complication that can be life-threatening to some extent. Therefore, predicting the occurrence of liver failure is very important for postoperative management and patient care. Neutrophil-lymphocyte ratio (NLR), fibrosis index based on four factors (Fib4), aspartate aminotransferase-to-platelet ratio index (APRI) are indicators derived from a simple blood test that reflect liver function and degree of fibrosis. By analyzing the relationship between these indicators and the occurrence of liver failure, we can evaluate their potential value in predicting liver failure and provide a basis for clinical practice.

Research motivation

Hepatectomy is an important treatment for hepatocellular carcinoma, but the occurrence of postoperative liver failure may bring serious complications and risks to patients. Abnormal expressions of NLR, Fib4, and APRI are common in patients with liver failure. However, there are few studies on the predictive value and changes of these indicators in the occurrence of postoperative liver failure.

Research objectives

To analyze the expression differences of NLR, Fib4, and APRI in hepatocellular carcinoma patients with liver failure after hepatectomy and their predictive value in postoperative liver failure, and establish and verify their nomogram prediction models.

Research methods

A total of 220 patients with hepatocellular carcinoma who received treatment in our hospital from January 2022 to January 2023 were retrospectively selected as research objects, and were divided into a modeling cohort of 154 patients and a model validation cohort of 66 patients according to a ratio of 7:3. At the same time, according to whether the patients developed liver failure after hepatectomy, The model group was divided into liver failure group (n = 53 cases) and no liver failure group (n = 101 cases). The model validation cohort was divided into a group with liver failure (n = 21 cases) and a group without liver failure (n = 45 cases). By comparing the general data of patients, binary logistic regression analysis was conducted to analyze the factors affecting the occurrence of liver failure in patients with hepatocellular carcinoma after hepatectomy, the road map prediction model was established and verified, the predictive efficacy of the model was evaluated by patient operating characteristic curve (ROC), the consistency of predicted events with actual events was evaluated by calibration curve, and the effectiveness of the model was evaluated by decision curve analysis.

Research results

Child-Pugh grade, surgical site, NLR, Fib4, and APRI were all risk factors for liver failure in patients with hepatocellular carcinoma after hepatectomy. In addition, in this study, the deviation between the actual result curve and the calibration curve of the nomogram generated by the modeling queue and the verification queue is small, and the consistency between the predicted event and the actual event is high. The validity of the nomogram prediction model is further confirmed in the decision analysis curve of modeling queue and verifying queue prediction model.

Research conclusions

NLR, Fib4, and APRI were all independent factors influencing the occurrence of liver failure in hepatocellular carcinoma patients after hepatectomy, and the further constructed nomogram prediction model of liver failure in hepatocellular carcinoma patients after hepatectomy showed good prediction ability, with high consistency between predicted events and actual events. This model has broad potential as a tool to prevent liver failure in patients with hepatocellular carcinoma after hepatectomy.

Research perspectives

This study is a retrospective analysis with limited clinical data of subjects, and the selection of indicators may not be comprehensive enough. At the same time, there are initial differences in the modeling and validation of groups of patients, which may lead to differences in study results. Therefore, more clinical indicators need to be added for further comprehensive evaluation and a more comprehensive prediction model needs to be established.