Influencing Factors of Overall Survival of Elderly Patients with Hepatocellular Carcinoma and Construction of Prediction Model of Prognosis Nomogram: A Population-Based Study
10.3971/j.issn.1000-8578.2024.24.0009
- VernacularTitle:老年肝细胞癌患者总生存期的影响因素及预后列线图预测模型构建：一项基于人群的研究
- Author:
Yang WU
1
,
2
;
Tian LI
3
;
Tingting SHI
1
,
2
;
Lingling ZHU
1
,
2
;
Yani ZHANG
1
,
2
;
Peipei GUO
2
;
Runbing ZHANG
1
,
2
;
Shunna WANG
1
,
2
;
Chun GAO
2
;
Xiaohui YU
2
;
Jiucong ZHANG
2
Author Information
1. The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou 730000, China
2. Department of Gastroenterology, The 940th Hospital of Joint Logistic Support Force of PLA, Lanzhou 730050, China.
3. The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou 730000, China.
- Publication Type:CLINICALRESEARCH
- Keywords:
Hepatocellular carcinoma;
SEER database;
Elderly;
Prognostic factors;
Nomogram
- From:
Cancer Research on Prevention and Treatment
2024;51(9):756-763
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the independent risk factors that affect the overall survival (OS) of elderly patients with hepatocellular carcinoma (HCC, ≥60 years old) and build a nomogram prediction model. Methods Clinical data of all elderly patients with HCC from the SEER database from 2005 to 2020 were downloaded from SEER database. In accordance with the inclusion and exclusion criteria, the screened patients were randomly assigned to a training group (70%) and a validation group (30%). The independent risk factors of elderly patients with HCC were determined by univariate and multivariate Cox regression analyses and further validated by Kaplan-Meier survival analysis. On the basis of the determined variables, nomograms were developed and verified to predict the OS of elderly patients with HCC at 6, 12, and 24 months. The consistency index (C index), calibration curve, receiver’s operating characteristic (ROC) curve, and area under curve (AUC) were used to evaluate the prediction efficiency and discrimination ability of the prediction model, and decision curve analysis (DCA) was used to evaluate the potential clinical application value of the nomogram. Results A total of 1134 elderly patients with HCC were included, with 793 in the training group and 341 in the validation group. Seven variables, including age, clinical grade, clinical stage, M stage, tumor size classification, and radiotherapy, were identified as independent prognostic factors of this population. The constructed nomogram shows excellent prediction performance, with C indices of 0.745 in the training group and 0.704 in the validation group. The AUC values of the training group at 6, 12, and 24 months were 0.785, 0.788, and 0.798, respectively, and those of the validation group were 0.780, 0.725, and 0.607, respectively. The calibration curve shows good consistency from the predicted survival probability to the actual probability. The ROC curve and DCA show that the nomogram proposed in this study has good prediction ability. Conclusion Age, clinical grade, clinical stage, M stage, tumor size classification, and radiotherapy are important influencing factors for the survival of elderly patients with HCC. The prediction model of prognosis nomogram constructed in this study has good predictive value, and it can be used to predict the OS of elderly patients with HCC, which could be helpful for individualized survival assessment and clinical management of these patients.
