背景：随着社会老龄化，肝细胞癌（HCC）患者的平均年龄明显增加。因此，本研究的重点是确定与诊断为 HCC 的老年人群中过早死亡相关的预后因素。此外，我们的重点还包括开发能够预测此类结果的列线图。方法：监测、流行病学和最终结果 (SEER) 数据库为本研究提供了基础，显示了 2010 年至 2015 年期间诊断为 HCC 的 75 岁及以上参与者。这些参与者以 7:3 的比例随机分为训练组和训练组。验证队列。将单变量和多变量逻辑回归应用于训练队列，以确定早期死亡的预后指标，形成列线图开发的基础。为了测量这些列线图在两个队列中的功效，我们采用了受试者操作特征 (ROC) 曲线以及 GiViTI 校准带和决策曲线分析 (DCA)。结果：该研究涉及 1,163 名被诊断患有 HCC 的老年人，报告了 397 例全因早期死亡和 356 例 HCC 特异性早期死亡。样本组分为两个队列：训练组由 815 人组成，验证组由 348 人组成。多因素分析确定分级、T 分期、手术、放疗、化疗、骨和肺转移是所有原因死亡率的重要预测因素。同时，种族、等级、T 分期、手术、放疗、化疗和骨转移被证明是癌症特异性死亡率的估计因素。随后，这些因素被用来开发列线图以进行预测。 GiViTI 校准带证实了列线图可接受的一致性，DCA 证实了其有价值的临床适用性，ROC 曲线证明了训练和验证队列中令人满意的判别能力。结论：本研究中使用的列线图有助于检测患有 HCC 的老年人的早期死亡。该工具可以帮助医生制定个体化治疗策略。版权所有 © 2023 Zhou、Chen、Liu 和 Xu。

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