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01-11-2013 | Uitgave 9/2013

Quality of Life Research 9/2013

Using group-based trajectory modeling to examine heterogeneity of symptom burden in patients with head and neck cancer undergoing aggressive non-surgical therapy

Tijdschrift:
Quality of Life Research > Uitgave 9/2013
Auteurs:
Qiuling Shi, Tito R. Mendoza, G. Brandon Gunn, Xin Shelley Wang, David I. Rosenthal, Charles S. Cleeland

Abstract

Purpose

Treatment-related symptom burden varies significantly among patients undergoing radiotherapy or chemoradiotherapy, yet such variation is typically not reflected in the results from single-group studies. We applied group-based trajectory modeling (GBTM) to describe the heterogeneity of symptom burden among patients with head and neck cancer and to identify subgroups with distinct symptom-development trajectories.

Methods

Patients (n = 130) were recruited pretherapy and rated multiple symptoms weekly for 10 weeks via the M. D. Anderson Symptom Inventory. With the mean of five most severe symptoms over time as an outcome measure, GBTM was used to identify patient subgroups with distinct symptom trajectories. Linear mixed-effects modeling (LMM) was applied to compare with GBTM’s ability to describe the longitudinal symptom data.

Results

The five most severe symptoms were problems with taste, difficulty swallowing or chewing, problems with mucus, fatigue, and dry mouth. A two-group GBTM model identified 68 % of patients as having high symptom burden, associated with older age, worse baseline performance status, and chemoradiotherapy treatment. A four-group GBTM model generated one stable group (4 % of patients) and three groups varying in symptom severity with both linear and quadratic functions over time. LMM revealed symptom-change patterns similar to that produced by GBTM but was inferior in identifying risk factors for high symptom burden.

Conclusions

For cancer patients undergoing aggressive therapy, GBTM is capable of identifying various symptom-burden trajectories and provides severity groupings that will aid research and may be of clinical utility. These results may be generalizable to other cancer types and treatments.

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