The symptom phenotype of oncology outpatients remains relatively stable from prior to through 1 week following chemotherapy
- PMID: 26777053
- PMCID: PMC7233145
- DOI: 10.1111/ecc.12437
The symptom phenotype of oncology outpatients remains relatively stable from prior to through 1 week following chemotherapy
Abstract
Some oncology outpatients experience a higher number of and more severe symptoms during chemotherapy (CTX). However, little is known about whether this high risk phenotype persists over time. Latent transition analysis (LTA) was used to examine the probability that patients remained in the same symptom class when assessed prior to the administration of and following their next dose of CTX. For the patients whose class membership remained consistent, differences in demographic and clinical characteristics, and quality of life (QOL) were evaluated. The Memorial Symptom Assessment Scale (MSAS) was used to evaluate symptom burden. LTA was used to identify subgroups of patients with distinct symptom experiences based on the occurrence of the MSAS symptoms. Of the 906 patients evaluated, 83.9% were classified in the same symptom occurrence class at both assessments. Of these 760 patients, 25.0% were classified as Low-Low, 44.1% as Moderate-Moderate and 30.9% as High-High. Compared to the Low-Low class, the other two classes were younger, more likely to be women and to report child care responsibilities, and had a lower functional status and a higher comorbidity scores. The two higher classes reported lower QOL scores. The use of LTA could assist clinicians to identify higher risk patients and initiate more aggressive interventions.
Keywords: cancer; chemotherapy; latent transition analysis; predictive risk modelling; quality of life; symptoms.
© 2016 John Wiley & Sons Ltd.
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References
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- Bastanlar Y & Ozuysal M (2014) Introduction to machine learning. Methods in Molecular Biology 1107, 105–128. - PubMed
-
- Celeux G & Soromenho G (1996) An entropy criterion for assessing the number of clusters in a mixture model. Journal of Classification 13, 195–212.
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