• Support Care Cancer · Aug 2021

    Distinct profiles of multiple co-occurring symptoms in patients with gastrointestinal cancers receiving chemotherapy.

    • Yufen Lin, Donald E Bailey, Sharron L Docherty, Laura S Porter, Bruce A Cooper, Steven M Paul, Kord M Kober, Marilyn J Hammer, Fay Wright, Laura B Dunn, Yvette P Conley, Jon D Levine, and Christine Miaskowski.
    • School of Nursing, Duke University, Durham, NC, USA.
    • Support Care Cancer. 2021 Aug 1; 29 (8): 4461-4471.

    PurposeIdentify subgroups of gastrointestinal (GI) cancer patients with distinct multiple co-occurring symptom profiles and evaluate for differences among these subgroups in demographic and clinical characteristics and quality of life (QOL) outcomes.MethodsPatients with GI cancers (n = 399) completed the Memorial Symptom Assessment Scale (MSAS) that was used to assess for multiple co-occurring symptoms. Latent class analysis (LCA) was used to identify subgroups of patients with distinct symptom profiles using symptom occurrence ratings. Differences in demographic and clinical characteristics and QOL outcomes among the subgroups were evaluated using parametric and nonparametric tests.ResultsAll Low (36.6%), Moderate (49.4%), and All High (14.0%) classes were identified. Compared to the All Low class, patients in the other two classes were significantly younger and were more likely to report depression and back pain. Compared to the other two classes, patients in the All High class had fewer years of education and a higher number of comorbidities. Significant differences were found among the three classes for comorbidity burden and total number of MSAS symptoms (i.e., All Low < Moderate < All High), as well as for performance status (i.e., All Low > Moderate > All High). A higher symptom burden was associated with poorer QOL outcomes.ConclusionsThe first study to identify subgroups of patients with GI cancers based on distinct symptom profiles. LCA allowed for the identification of risk factors associated with a higher symptom burden. Clinicians can use this information to identify high-risk patients and develop personalized symptom management interventions.

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