Constructing a risk prediction model for hepatocellular carcinoma in patients with chronic liver disease based on aMAP score combined with RAR and PIV
- VernacularTitle:aMAP评分联合RAR及PIV构建慢性肝病患者肝细胞癌发生风险的预测模型
- Author:
Xiaohan JIANG
1
;
Jie CAO
1
;
Dandan LIU
1
;
Dan XUE
1
;
Zhiguo GUO
1
Author Information
- Publication Type:Journal Article
- Keywords: carcinoma,hepatocellular; nomograms; ROC curve; aMAP score; chronic liver disease; RAR; PIV
- From: Tianjin Medical Journal 2025;53(1):42-46
- CountryChina
- Language:Chinese
- Abstract: Objective To construt and validate a risk prediction model for hepatocellular carcinoma(HCC)in patients with chronic liver disease based on age-male-ALBI-platelets(aMAP)score combined with RAR and PIV.Methods A total of 143 patients with chronic liver disease were divided into the HCC group(32 cases)and the non-HCC group(111 cases)according to whether HCC occurred.General clinical data,aMAP score and peripheral blood indicator level were compared between two groups.Multivariate Logistic regression was used to analyze influencing factors of HCC in inpatients with chronic liver disease.A nomogram risk prediction model was constructed and validated.Results Compared with the non-HCC group,there were higher age,higher proportion of males,higher levels of total bilirubin(TBIL),red blood cell distribution width(RDW),neutrophil count(NEU)and monocyte count(MON),lower levels of albumin(ALB)and lymphocyte count(LYM),higher levels of aMAP score,RDW to ALB(RAR)and pan-immune inflammation value(PIV)in the HCC group(P＜0.05).Multivariate Logistic regression showed that higher levels of aMAP score,RAR and PIV were independent risk factors for HCC in inpatients with chronic liver disease(P＜0.05).The area under receiver operator characteristic(ROC)curve(AUC)of the nomogram risk prediction model constructed based on above factors was 0.823(95%CI:0.747-0.899).The calibration curve showed that the predicted value was basically consistent with the actual observed value,and the Brier score was 0.125.The decision curve showed that the model had a clear positive net benefit.The AUC of internal validation of the prediction model by Bootstrap method was 0.823(95%CI:0.820-0.825),indicating that the model had a good degree of differentiation.Conclusion The nomogram risk prediction model based on aMAP score,RAR and PIV showed a good predictive performance of HCC in patients with chronic liver disease,which could benefits the individualized treatment and follow-up.
