Prognostic factors and predictive nomogram models for early death in elderly patients with hepatocellular carcinoma: a population-based study

Frontiers in Molecular Biosciences

Molecular Biosciences

Hongji Xu¹  Kai Liu²  Junhong Chen²  Hao Zhou² 

[1] Department of Abdominal Surgery, Guiqian International General Hospital, Guiyang, Guizhou, China;Department of Hepatobiliary and Pancreatic Surgery II, General Surgery Center, The First Hospital of Jilin University, Changchun, China;

关键词: hepatocellular carcinoma;    elderly;    early death;    nomogram;    SEER database;   

DOI  :  10.3389/fmolb.2023.1275791

 received in 2023-08-10, accepted in 2023-10-05,  发布年份 2023

来源: Frontiers

【 摘 要 】

Background: Owing to an aging society, there has been an observed increase in the average age of patients diagnosed with hepatocellular carcinoma (HCC). Consequently, this study is centered on identifying the prognostic factors linked with early death among this elderly demographic diagnosed with HCC. Additionally, our focus extends to developing nomograms capable of predicting such outcomes.Methods: The Surveillance, Epidemiology and End Results (SEER) database underpinned this study, showcasing participants aged 75 and above diagnosed with HCC within the timeframe from 2010 to 2015. These participants were divided randomly, at a 7:3 ratio, into training and validation cohorts. Univariable and multivariable logistic regressions were applied to the training cohort in the identification of prognostic indicators of early death, forming the basis for nomogram development. To measure the efficacy of these nomograms within both cohorts, we resorted to Receiver Operating Characteristic (ROC) curves, along with GiViTI calibration belt and Decision Curve Analysis (DCA).Results: The study involved 1,163 elderly individuals diagnosed with HCC, having reported instances of 397 all-cause early deaths and 356 HCC-specific early deaths. The sample group was divided into two cohorts: a training group consisting of 815 individuals, and a validation cohort, comprised of 348 individuals. Multifactorial analysis identified grade, T-stage, surgery, radiation, chemotherapy, bone and lung metastasis as significant predictors of mortality from all causes. Meanwhile, race, grade, T-stage, surgery, radiation, chemotherapy, and bone metastasis were revealed to be estimative factors for cancer-specific mortality. Subsequently, these factors were used to develop nomograms for prediction. GiViTI calibration belt corroborated the acceptable coherence of the nomograms, DCA confirmed their valuable clinical applicability, and ROC curves evidenced satisfactory discriminative capacity within both training and validation cohorts.Conclusion: The nomograms utilized in this study proved instrumental in detecting early death among elderly individuals afflicted with HCC. This tool could potentially assist physicians in formulating individualized treatment strategies.

【 授权许可】

Unknown   
Copyright © 2023 Zhou, Chen, Liu and Xu.