Nomogram for predicting the risk of post hepatectomy liver failure was established based on preoperative routine test indexes
10.3760/cma.j.cn114452-20240205-00071
- VernacularTitle:基于术前常规检验指标建立预测肝癌肝切除术后肝功能衰竭风险的模型
- Author:
Guoping DONG
1
;
Chen CHEN
;
Xudong LU
;
Jiali WU
;
Wenhao ZHENG
;
Lin TONG
Author Information
1. 上海东方肝胆外科医院实验诊断科，上海 200438
- Keywords:
Liver cancer;
Liver resection;
Liver failure;
Platelet;
Prealbumin;
Alkaline phosphatase;
Logistic regression;
Nomogram
- From:
Chinese Journal of Laboratory Medicine
2024;47(8):895-901
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish a risk prediction model of liver failure after liver resection for hepatocellular carcinoma.Method:A retrospective case-control study was designed. Clinical data and laboratory results, including gender, age, and preoperative 18 laboratory indicators, were collected from 320 patients with hepatocellular carcinoma undergoing liver resection in Eastern Hepatobiliary Surgery Hospital Affiliated to Naval Medical University from January 1, 2013 to December 31, 2023. According to the surgical time, 252 cases in the training cohort were divided into 62 and 190 cases with and without postoperative liver failure, respectively. Of the 68 cases in validation cohort, 34 developed postoperative liver failure and 34 did not. Binary Logistic regression analysis was used to conduct univariate analysis of gender, age, and 18 preoperative laboratory indicators, and multivariate analysis was carried out for significant results to determine the influencing factors of liver failure after liver resection for hepatocellular carcinoma, and Logistic regression model was established.Result:In the training cohort, indicators significantly associated with liver failure after liver resection for hepatocellular carcinoma included age ( P=0.016), platelets ( P=0.005), prealbumin ( P<0.001), and alkaline phosphatase ( P<0.001). Logistic regression was used to construct a nomogram model and draw a calibration curve by combining these four indicators. In the training cohort, the nomogram model showed good discriminability in predicting the risk of liver failure after hepatectomy for hepatocellular carcinoma. The area under the curve of was 0.82 (95% CI 0.76-0.88), and the sensitivity was 73% and specificity was 80% when the optimal cut-off value was 0.2646. In the validation cohort, the predictive performance of the nomogram model was comparable to that of the training cohort, with an area under the curve of 0.81 (95% CI 0.71-0.92), sensitivity of 82%, and specificity of 77%. Conclusion:Preoperative platelet and prealbumin decreases, alkaline phosphatase increases, and elderly patients are prone to liver failure after liver resection. The nomogram model constructed with preoperative test data has shows good discriminatory ability and accuracy in predicting liver failure after liver resection for hepatocellular carcinoma.
