| ObjectiveThis study aims to conduct a longitudinal tracking investigation of cancer-related fatigue(CRF)and its influencing factors during chemotherapy in newly diagnosed lymphoma patients in China.The study will analyze the occurrence,changes,and influencing factors of CRF in patients during chemotherapy,explore whether there are heterogeneous subgroups in the trajectory of CRF in lymphoma patients,and analyze the predictive factors for different subgroups.The findings will provide a theoretical basis for clinical management of CRF and improving the quality of life of lymphoma patients.MethodA longitudinal study design will be used to conveniently select 131 newly diagnosed lymphoma patients who will undergo chemotherapy at Jiangnan University Affiliated Hospital between April 2021 and October 2022 as study subjects.The initial survey will be conducted before the patient’s first chemotherapy(T0),and follow-up surveys will be conducted at three time points: after the first cycle of chemotherapy(T1),after the third cycle(T2),and after the sixth cycle(T3).General demographic data will be collected using a general demographic questionnaire at T0,while the Cancer Fatigue Scale(CFS),Herth Hope Index(HHI),Insomnia Severity Index(ISI),and Social Support Rating Scale(SSRS)will be used to assess the fatigue level,hope level,sleep status,and social support status of lymphoma patients at T0,T1,T2,and T3.Descriptive statistics such as mean ± standard deviation,median(upper quartile,lower quartile),frequency,and percentage will be used to describe the general demographic and disease-related data of lymphoma patients.Mann-Whitney U test and Kruskal-Wallis test will be used to analyze differences in CRF levels among lymphoma patients with different demographic and disease characteristics.Spearman correlation analysis will be used to analyze the correlation between hope level,insomnia severity,social support,and CRF at different time points.The Generalized Estimated Equation(GEE)will be used to compare the differences in CRF,hope level,insomnia severity,and social support between T0 and T3 and analyze the influencing factors of CRF in lymphoma patients.The Latent Growth Curve Model(LGCM)will be used to fit the longitudinal trajectory of CRF from T0 to T3,and the Latent Class Growth Model(LCGM)will be used to determine the subgroups of CRF change trends.The unordered multiclass logistic regression will be used to analyze the predictive role of patient demographics,disease-related data,hope level,insomnia severity,and social support status before the first chemotherapy in predicting the patient’s CRF trajectory.Results1.A total of 150 patients were included,with 19 lost to follow-up.Ultimately,131 patients fully participated in the study,with a sample loss rate of 12.7%.The general characteristics of the lost to follow-up patients were comparable to those who fully participated(P > 0.05).2.The CRF scores of lymphoma chemotherapy patients at T0,T1,T2,and T3 were 17(13,30),31(17,37),32(24,38),and 27(18,36)points,respectively.GEE results showed that the total CRF score showed an upward trend followed by a downward trend(χ~2 =143.436,P < 0.001),reaching its peak in the third cycle of chemotherapy,and gradually decreasing thereafter.However,the CRF score in the sixth cycle of chemotherapy was still higher than before chemotherapy.The incidence rates of CRF at the four time points were45.8%,71.0%,87.0%,and 74.8%,respectively.There were also statistically significant differences in the scores of physical fatigue,emotional fatigue,and cognitive fatigue at different time points(P < 0.05).3.Lymphoma chemotherapy patients showed differences in levels of hope,sleep quality,and social support at T0,T1,T2,and T3(P < 0.05).With an increase in the number of chemotherapy cycles,the levels of hope and social support gradually increased(χ~2 = 27.390,P < 0.001;χ~2 = 274.147,P < 0.001),and sleep quality improved(χ~2 = 11.777,P = 0.008).4.GEE analysis was used to determine the factors influencing CRF scores at T0-T3.The results showed that age(B = 2.445,P = 0.032),disease stage(B =-2.718,P = 0.005),Eastern Cooperative Oncology Group(ECOG)performance status(B =-2.769,P = 0.013),hemoglobin(B = 4.218,P = 0.005),hope level(B =-0.150,P < 0.001),severity of insomnia(B = 0.428,P < 0.001),and social support(B =-1.121,P = 0.005)were all factors influencing CRF.5.LGCM was used to fit the longitudinal trend of CRF,and four models were constructed,including a linear unconditional model,a quadratic growth model,a time parameter free estimation growth model,and a segmented growth model.The results showed that both the quadratic growth model and the segmented growth model could fit well,with the segmented growth model having a better fit.According to the segmented growth model,CRF showed a positive growth trend from T0 to T2(s1 = 7.404,P < 0.001),and a negative growth trend from T2 to T3(s2 =-4.142,P < 0.001).The intercept variance of the model was significant(σ2 = 116.105,P < 0.001),and the slope variance from T0 to T2 was also significant(σ2 = 30.501,P = 0.021),indicating significant individual differences in initial fatigue levels and the rate of change.6.LCGM was used to analyze whether there was heterogeneity in the trend of CRF in lymphoma chemotherapy patients.The results showed that there were three heterogeneous subgroups of patients with CRF,named the "persistent high fatigue group," the "moderate fatigue growth group," and the "low fatigue growth group," with 47 patients(35.9%),46patients(35.1%),and 38 patients(29%),respectively.7.Chi-square tests and unordered multinomial logistics regression were used to analyze the factors influencing the heterogeneity of fatigue trajectories in lymphoma patients.The results showed that age,hope level,severity of insomnia,and social support were predictive factors for the "persistent high fatigue group"(P < 0.05),while age and severity of insomnia were predictive factors for the "moderate fatigue growth group"(P < 0.05).Conclusion1.Lymphoma patients commonly experience CRF,and their CRF shows a dynamic trend of increasing and then decreasing from before chemotherapy to the sixth chemotherapy session(T0～T3).Healthcare professionals should strengthen the dynamic monitoring and assessment of patients’ CRF.2.The CRF of lymphoma patients is influenced by physiological,psychological,and social environmental factors.In this study,age,disease stage,ECOG score,hemoglobin level,hope level,sleep disorders,and social support were all factors influencing CRF.Healthcare professionals should take preventive measures early based on risk factors to reduce the distress caused by CRF to patients.3.There are three different fatigue trajectories in lymphoma patients,and younger patients with lower hope levels,sleep disorders,and poor social support are more likely to develop a "persistent high fatigue" state.Healthcare professionals should pay special attention to these patients,take timely intervention measures,improve patient hope levels,improve patient sleep quality,and promote patient social support to reduce their fatigue levels. |
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