• Guro Lindviksmoen Astrup, Kristin Hofsø, Kristin Bjordal, Marianne Grønlie Guren, Ingvild Vistad, Bruce Cooper, Christine Miaskowski, and Tone Rustøen.
• a Department of Oncology, Division of Cancer Medicine , Oslo University Hospital , Oslo , Norway.
• Acta Oncol. 2017 Mar 1; 56 (3): 462-470.

ContextReviews of the literature on symptoms in oncology patients undergoing curative treatment, as well as patients receiving palliative care, suggest that they experience multiple, co-occurring symptoms and side effects.ObjectivesThe purposes of this study were to determine if subgroups of oncology patients could be identified based on symptom occurrence rates and if these subgroups differed on a number of demographic and clinical characteristics, as well as on quality of life (QoL) outcomes.MethodsLatent class analysis (LCA) was used to identify subgroups (i.e. latent classes) of patients with distinct symptom experiences based on the occurrence rates for the 13 most common symptoms from the Memorial Symptom Assessment Scale.ResultsIn total, 534 patients with breast, head and neck, colorectal, or ovarian cancer participated. Four latent classes of patients were identified based on probability of symptom occurrence: all low class [i.e. low probability for all symptoms (n = 152)], all high class (n = 149), high psychological class (n = 121), and low psychological class (n = 112). Patients in the all high class were significantly younger compared with patients in the all low class. Furthermore, compared to the other three classes, patients in the all high class had lower functional status and higher comorbidity scores, and reported poorer QoL scores. Patients in the high and low psychological classes had a moderate probability of reporting physical symptoms. Patients in the low psychological class reported a higher number of symptoms, a lower functional status, and poorer physical and total QoL scores.ConclusionDistinct subgroups of oncology patients can be identified based on symptom occurrence rates. Patient characteristics that are associated with these subgroups can be used to identify patients who are at greater risk for multiple co-occurring symptoms and diminished QoL, so that these patients can be offered appropriate symptom management interventions.

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