Longitudinal Change in Symptom Clusters in Patients With Ovarian Cancer

Dan, Xin; Tian, Y.; Zhou, Li; Du, Juan; He, Yalin

doi:10.1097/jnr.0000000000000478

Cited by 8 publications

(2 citation statements)

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“…The prevalence rates for fatigue, pain, and sleep disturbance in ovarian cancer patients during chemotherapy varied widely. Specifically, the prevalence ranged from 69.78% to 85.26% for fatigue, 50.93% to 79.35% for pain, and 67.37% to 74.07% for sleep disturbance 8,18 . Previous studies of patients with gynecological cancer have used LPA to identify subgroups of single symptoms such as anxiety 19 and sleep disturbance 20 during chemotherapy and explored the risk factors of the susceptible to symptom burden.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, the prevalence ranged from 69.78% to 85.26% for fatigue, 50.93% to 79.35% for pain, and 67.37% to 74.07% for sleep disturbance. 8 , 18 Previous studies of patients with gynecological cancer have used LPA to identify subgroups of single symptoms such as anxiety 19 and sleep disturbance 20 during chemotherapy and explored the risk factors of the susceptible to symptom burden. However, few studies assessed the profiles for the occurring symptoms only in patients with ovarian cancer receiving chemotherapy.…”

Section: Introductionmentioning

confidence: 99%

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A longitudinal study of symptom cluster latent profiles in ovarian cancer patients undergoing chemotherapy

Feng,

Lin,

Liu

et al. 2024

Cancer Medicine

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BackgroundThis study aimed to identify distinct patterns within the symptom cluster of fatigue, pain, and sleep disturbance among ovarian cancer patients receiving chemotherapy, to determine the factors predicting these patterns and their impact on quality of life.MethodsThe longitudinal study collected data from 151 ovarian cancer patients at three time points: before chemotherapy (T0), after the first chemotherapy cycle (T1), and following the completion of four cycles of chemotherapy (T2). Latent profile analysis and latent transition analysis were used to identify symptom patterns and evaluate changes in symptom patterns. A bias‐adjusted three‐step approach was utilized to examine predictor variables and distal outcomes associated with latent class membership.ResultsThree symptom patterns emerged: “All Low,” “Moderate” (T0)/“Low pain and high sleep disturbance” (T1 and T2), and “All High.” Patients with lower educational attainment and higher levels of anxiety and depression were found to be at an elevated risk of belonging to the “All High” class. All quality‐of‐life domains showed significant differences among the three subgroups, following an “All Low” > “All High” pattern (p < 0.05). Membership in three classes remained relatively stable over time, with probabilities of 0.749 staying within their groups from T0 to T2.ConclusionsThis study underscores the existence of a diverse and heterogeneous experience within the symptom cluster of fatigue, pain, and sleep disturbance among ovarian cancer patients. Importantly, these patterns were stable throughout chemotherapy. Recognizing and understanding these patterns can inform the development of targeted interventions to alleviate the burden of symptom clusters in this population.

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Section: Introductionmentioning

confidence: 99%