首页|期刊导航|肿瘤预防与治疗|食管癌患者癌因性疲乏变化轨迹的潜类别增长模型分析及影响因素研究

食管癌患者癌因性疲乏变化轨迹的潜类别增长模型分析及影响因素研究OA

Change Trajectories of Cancer-Related Fatigue in Esophageal Cancer Pa-tients:A Latent Class Growth Model Analysis and Associated Factors

中文摘要英文摘要

目的:基于潜类别增长模型(latent class growth model,LCGM)分析食管癌患者癌因性疲乏(cancer-related fatigue,CRF)变化轨迹及影响因素.方法:研究对象为我院收治的 105 例不可手术局部晚期食管癌患者,选取年限为 2023 年 2 月至 2025 年 1 月期间,于患者入院时采用一般资料调查问卷、社会支持评定量表(Social Support Rating Scale,SSRS)、心理弹性量表(Connor-Davidson Resilience Scale,CD-RISC)、匹兹堡睡眠指数量表(Pittsburgh Sleep Quality Index,PSQI)、Piper 疲乏修订量表(Revised Piper Fatigue Scale,PFS-R)等进行调查,在化疗前(T0)、化疗后 1个月(T1)、化疗后 3 个月(T2)、化疗后 6 个月(T3)、化疗后 9 个月(T4)进行 PFS-R 评分调查.食管癌患者 CRF 的变化轨迹采用 LCGM 识别,并通过多因素 Logistic 回归分析其影响因素.结果:随着潜变量类别个数增加,赤池信息准则、贝叶斯信息准则、校正贝叶斯信息准则随着模型类别数增加而减小,3 类别模型熵最大,且 Bootstrap 的似然比检验均 P<0.05,表示类别分布均匀,其中第 3 类别模型拟合度为良好,模型整体呈下降趋势,最终保留 3 个类别,命名为重度疲乏慢速缓解组(22.86%)、中度疲乏中速缓解组(43.81%)及轻度疲乏快速缓解组(33.33%).3 个类别患者在化疗频率、睡眠障碍、心理弹性、SSRS 评分、汉密顿抑郁量表(Hamilton Depression Scale,HAMD)评分、汉密顿焦虑量表(Hamilton Anxiety Scale,HAMA)评分方面相比,差异有统计学意义(F/x2 值分别为 9.657、7.838、15.258、7.439、116.549、64.514,P 均<0.05).Logistic 回归分析显示,化疗频率(OR=5.937,95%CI:1.679~20.991,P<0.001)、睡眠障碍(OR=5.853,95%CI:2.319~9.387,P<0.001)、心理弹性(OR=0.894,95%CI:0.846~0.944,P<0.001)、SSRS 评分(OR=0.198,95%CI:0.072~0.549,P<0.001)、HAMD 评分(OR=1.065,95%CI:1.027~1.104,P=0.001)、HAMA 评分(OR=7.001,95%CI:3.109~10.893,P<0.001)均为 CRF 变化轨迹的影响因素.结论:食管癌患者 CRF 呈现 3 种发展轨迹,存在个体差异,并且随时间而发生变化,大部分患者 PFS-R 呈高评分状态;食管癌患者 CRF 变化轨迹变化的影响因素包含化疗频率、睡眠障碍、心理弹性、SSRS 评分、不良情绪.建议医护人员根据患者不同发展特点及影响因素提供个性化干预措施.

Objective:To identify the trajectories of cancer-related fatigue and their influencing factors in esophageal cancer patients based on a latent class growth mod-el(LCGM).Methods:The study enrolled 105 patients with unresectable locally advanced esophageal cancer who were admitted to our hospital from February 2023 to January 2025.Up-on admission,participants completed a survey comprising a general information questionnaire,the Social Support Rating Scale(SSRS),the Connor-Davidson Resilience Scale(CD-RISC),the Pittsburgh Sleep Quality Index(PSQI),the Re-vised Piper Fatigue Scale(PFS-R),and other relevant measures.PFS-R scores were assessed at the following time points:before chemotherapy(T0),1 month after chemotherapy(T1),3 months after chemotherapy(T2),6 months after chemo-therapy(T3),and 9 months after chemotherapy(T4).The trajectories of cancer-related fatigue in esophageal cancer pa-tients were identified using LCGM,and their influencing factors were analyzed via multivariable Logistic regression.Results:As the number of latent classes increased,the AIC(Akaike Information Criterion),BIC(Bayesian Information Criterion),and aBIC(adjusted BIC)values progressively decreased.The 3-class model exhibited the highest entropy value,and the Bootstrap Likelihood Ratio Test was significant for all class solutions(P<0.05),indicating good class separation and high classification accuracy.The 3-class model demonstrated the best fit,with AIC,BIC,and aBIC continuing to decline as the number of classes increased.Accordingly,three distinct trajectories were retained and labeled as:the severe fatigue with slow relief group(22.86%),the moderate fatigue with moderate relief group(43.81%),and the mild fatigue with rapid re-lief group(33.33%).There were statistically significant differences among the three patient categories in terms of chemo-therapy frequency,sleep disturbances,psychological resilience,SSRS scores,Hamilton Depression Scale(HAMD)scores,and Hamilton Anxiety Scale(HAMA)scores(F/χ2 values of 9.657,7.838,15.258,7.439,116.549,and 64.514,respectively;all P<0.05).Logistic regression analysis showed that chemotherapy frequency(OR=5.937,95%CI:1.679~20.991,P<0.001),sleep disturbances(OR=5.853,95%CI:2.319~9.387,P<0.001),and psycholog-ical resilience(OR=0.894,95%CI:0.846~0.944,P<0.001),SSRS score(OR=0.198,95%CI:0.072~0.549,P<0.001),HAMD scores(OR=1.065,95%CI:1.027~1.104,P=0.001),and HAMA scores(OR=7.001,95%CI:3.109~10.893,P<0.001)were all factors influencing the CRF trajectory.Conclusion:Esophageal cancer patients exhibit three distinct trajectories of cancer-related fatigue,demonstrating individual variability and evolving over time,with most patients maintaining high PFS-R scores.Factors influencing the trajectory of cancer-related fatigue in esophageal cancer patients include chemotherapy frequency,sleep disturbances,psychological resilience,SSRS scores,and negative affective states.Healthcare providers are advised to offer personalized interventions tailored to the distinct develop-mental characteristics and influencing factors of different trajectory groups.

李珍;张景俊;张俊

641400 成都,简阳市人民医院 肿瘤科641400 成都,简阳市人民医院 肿瘤科641400 成都,简阳市人民医院 胸外科

医药卫生

潜类别增长模型食管癌癌因性疲乏变化轨迹社会支持

Latent class growth modelEsophageal cancerCancer-related fatigueTrajectoriesSocial support

《肿瘤预防与治疗》 2026 (8)

685-692,8

This study was supported by grants from Beijing Medical Award Foundation(No.YXL-2023-0227-0130). 北京医学奖励基金会研究项目(编号:YXL-2023-0227-0130)

10.3969/j.issn.1674-0904.2026.08.008

评论