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

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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.

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Data availability

Data will be provided to the publisher after they obtain a material transfer agreement from the University of California, San Francisco.

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Funding

This study was funded by a grant from the National Cancer Institute (CA134900). Dr. Miaskowski is an American Cancer Society Clinical Research Professor.

Author information

Authors and Affiliations

  1. Dana-Farber Cancer Institute, Boston, MA, USA

    Marilyn J. Hammer, Alexi A. Wright & Rachel Pozzar

  2. School of Nursing, University of California, San Francisco, San Francisco, CA, USA

    Bruce A. Cooper, Steven Paul & Christine Miaskowski

  3. School of Medicine, University of California, San Francisco, San Francisco, CA, USA

    Lee-May Chen, Jon D. Levine & Christine Miaskowski

  4. Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Stephanie V. Blank

  5. The Mount Sinai Hospital, New York, NY, USA

    Bevin Cohen

  6. School of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA

    Laura Dunn

  7. School of Nursing, University of Pittsburgh, Pittsburgh, PA, USA

    Yvette P. Conley

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  1. Marilyn J. Hammer
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  2. Bruce A. Cooper
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  3. Lee-May Chen
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  11. Jon D. Levine
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Contributions

CAM and MJH designed the study. BAC conducted the statistical analyses. All of the authors contributed to revisions of the manuscript and approved the final version of the paper that was submitted for publication. All of the authors participated in the revisions to this paper and the interpretation of the results and approved the final paper.

Corresponding author

Correspondence to Christine Miaskowski.

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Ethics approval

This study was approved by the Committee on Human Research at the University of California. The study was performed in accordance with the Declaration of Helsinki.

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All of the patients provided written informed consent.

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All of the authors approved the final paper for publication.

Conflict of interest

The authors declare no competing interests.

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The funding sources were not involved in the study design; the collection, analysis, and interpretation of data; the writing of the report; or the decision to submit the article for publication.

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Hammer, M.J., Cooper, B.A., Chen, LM. et al. Identification of distinct symptom profiles in patients with gynecologic cancers using a pre-specified symptom cluster. Support Care Cancer 31, 485 (2023). https://doi.org/10.1007/s00520-023-07954-6

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