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. 2023 Jul 22;31(8):485.
doi: 10.1007/s00520-023-07954-6.

Identification of distinct symptom profiles in patients with gynecologic cancers using a pre-specified symptom cluster

Marilyn J Hammer  1 , Bruce A Cooper  2 , Lee-May Chen  3 , Alexi A Wright  1 , Rachel Pozzar  1 , Stephanie V Blank  4 , Bevin Cohen  5 , Laura Dunn  6 , Steven Paul  2 , Yvette P Conley  7 , Jon D Levine  3 , Christine Miaskowski  8   9
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Identification of distinct symptom profiles in patients with gynecologic cancers using a pre-specified symptom cluster

Marilyn J Hammer et al. Support Care Cancer. .

Abstract

Purpose: Pain, fatigue, sleep disturbance, and depression are four of the most common symptoms in patients with gynecologic cancer. The purposes were to identify subgroups of patients with distinct co-occurring pain, fatigue, sleep disturbance, and depression profiles (i.e., pre-specified symptom cluster) in a sample of patients with gynecologic cancer receiving chemotherapy and assess for differences in demographic and clinical characteristics, as well as the severity of other common symptoms and QOL outcomes among these subgroups.

Methods: Patients completed symptom questionnaires prior to their second or third cycle of chemotherapy. Latent profile analysis was used to identify subgroups of patients using the pre-specified symptom cluster. Parametric and nonparametric tests were used to evaluate for differences between the subgroups.

Results: In the sample of 233 patients, two distinct latent classes were identified (i.e., low (64.8%) and high (35.2%)) indicating lower and higher levels of symptom burden. Patients in high class were younger, had child care responsibilities, were unemployed, and had a lower annual income. In addition, these women had a higher body mass index, a higher comorbidity burden, and a lower functional status. Patients in the high class reported higher levels of anxiety, as well as lower levels of energy and cognitive function and poorer quality of life scores.

Conclusions: This study identified a number of modifiable and non-modifiable risk factors associated with membership in the high class. Clinicians can use this information to refer patients to dieticians and physical therapists for tailored interventions.

Keywords: Depression; Fatigue; Gynecologic cancer; Pain; Sleep disturbance; Symptom cluster.

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