肝癌电子杂志 ›› 2025, Vol. 12 ›› Issue (2): 8-13.

• 论著 • 上一篇    下一篇

晚期肝细胞癌化疗患者癌因性疲乏变化轨迹亚型分析

贾义德1, 曹志国1, 吴德平2, 常远华2,*, 项柱1   

  1. 1.皖西卫生职业学院附属医院,六安市第二人民医院普外科,安徽六安 237000;
    2.皖西卫生职业学院附属医院,六安市第二人民医院肿瘤内科,安徽六安 237000
  • 收稿日期:2024-05-04 发布日期:2025-07-29
  • 通讯作者: *常远华,E-mail: changyh1205@163.com
  • 基金资助:
    安徽省教育厅自然科学研究项目(KJ2019A1259)

A longitudinal study of the trajectory of change in cancer-related fatigue in patients treated with chemotherapy for advanced hepatocellular carcinoma

Jia Yide1, Cao Zhiguo1, Wu Deping2, Chang Yuanhua2,*, Xiang Zhu1   

  1. 1. Department of General Surgery, Affiliated Hospital of West Anhui Health Vocational College, Lu'an Second People's Hospital, Lu'an 237000, Anhui, China;
    2. Department of Medical Oncology, Affiliated Hospital of West Anhui Health Vocational College, Lu'an Second People's Hospital, Lu'an 237000, Anhui, China
  • Received:2024-05-04 Published:2025-07-29
  • Contact: *Chang Yuanhua, E-mail: changyh1205@163.com

摘要: 目的: 探讨晚期肝细胞癌(HCC)化疗患者癌因性疲乏(CRF)变化轨迹及其影响因素。
方法: 选择皖西卫生职业学院附属医院2021年10月至2023年10月间治疗的95例晚期HCC患者。通过Piper疲乏修订量表(PFS-R)评估患者6次化疗前、化疗后和化疗后1周的CRF特征。采用混合增长模型识别CRF轨迹亚型。采用Kaplan-Meier法分析不同CRF轨迹亚型总生存率。采用多因素logistic回归分析不同CRF轨迹亚型的影响因素。
结果: 共识别出4种CRF轨迹亚型,分别为高疲乏快速增长型15例(15.8%)、高疲乏中速增长型10例(10.5%)、中疲乏缓慢增长型29例(30.5%)和低疲乏缓慢增长型41例(43.2%)。4种CRF轨迹亚型的总生存率均逐渐降低(均P<0.05)。多因素logistic回归分析显示年龄、体质量指数和C反应蛋白是3种CRF增长型的共同影响因素(均P<0.05);白蛋白是中疲乏缓慢增长型的独立影响因素(P<0.05);文化程度和居住方式均是高疲乏快速增长型的独立影响因素(均P<0.05)。
结论: 晚期HCC化疗患者CRF呈现4种CRF轨迹亚型,并鉴定出不同CRF轨迹亚型的独立影响因素,为晚期HCC化疗患者CRF的个性化、全程化管理提供临床依据及新思路。

关键词: 晚期肝细胞癌, 癌因性疲乏, 化疗, 轨迹

Abstract: Objective: To investigate the trajectory of cancer-related fatigue (CRF) and its influencing factors in chemotherapy patients with advanced hepatocellular carcinoma (HCC).
Methods: Ninety-five patients with advanced HCC treated between October 2021 and October 2023 in Affiliated Hospital of West Anhui Health Vocational College were selected. Patients' CRF was assessed by Piper fatigue scale-revised (PFS-R) before, after and 1 week after 6 chemotherapy sessions. A mixed growth model was used to identify CRF trajectory subtypes. The Kaplan-Meier method was used to analyze the overall survival of different CRF trajectory subtypes. Multifactorial logistic regression was used to analyze the influencing factors of different CRF trajectory subtypes.
Results: A total of four CRF trajectory subtypes were identified in this study: high fatigue fast-growth type in 15 cases (15.8%), high fatigue moderate-growth type in 10 cases (10.5%), moderate fatigue slow-growth type in 29 cases (30.5%) and low fatigue slow-growth type in 41 cases (43.2%). The overall survival rates of high-fatigue rapid-growth, high-fatigue moderate-growth, moderate-fatigue stable, and low-fatigue slow-growth types increased gradually (all P<0.05). Multifactorial logistic regression analysis showed that age, body mass index and C-reactive protein were the common influencing factors for the three CRF growth types (P<0.05); albumin was a independent influencing factors for the moderate-fatigue slow-growth type (P<0.05); and literacy level and mode of residence were the independent influencing factors for the high-fatigue fast-growth type (all P<0.05).
Conclusion: CRF in advanced HCC chemotherapy patients presented four CRF trajectory subtypes, and the unique influencing factors of different CRF trajectory subtypes were identified, which provided clinical basis and new ideas for the personalized and holistic management of CRF in advanced HCC chemotherapy patients.

Key words: Advanced hepatocellular carcinoma, Cancer-caused fatigue, Chemotherapy, Trajectory