title: Functional impairments as symptoms in the symptom cluster analysis of patients newly diagnosed with advanced cancer.
abstract: BACKGROUND
  Symptoms and subsequent functional impairment have been associated with the biological processes of disease, including the interaction between disease and treatment in a measurement model of symptoms. However, hitherto cluster analysis has primarily focused on symptoms.
  
  
  OBJECTIVE
  This study among patients within 100 days of diagnosis with advanced cancer explored whether self-reported physical symptoms and functional impairments formed clusters at the time of diagnosis.
  
  
  METHODS
  We applied cluster analysis to self-reported symptoms and activities of daily living of 111 patients newly diagnosed with advanced gastrointestinal (GI), gynecological, head and neck, and lung cancers. Based on content expert evaluations, the best techniques and variables were identified, yielding the best solution.
  
  
  RESULTS
  The best cluster solution used a K-means algorithm and cosine similarity and yielded five clusters of physical as well as emotional symptoms and functional impairments. Cancer site formed the predominant organizing principle of composition for each cluster. The top five symptoms and functional impairments in each cluster were Cluster 1 (GI): outlook, insomnia, appearance, concentration, and eating/feeding; Cluster 2 (GI): appetite, bowel, insomnia, eating/feeding, and appearance; Cluster 3 (gynecological): nausea, insomnia, eating/feeding, concentration, and pain; Cluster 4 (head and neck): dressing, eating/feeding, bathing, toileting, and walking; and Cluster 5 (lung): cough, walking, eating/feeding, breathing, and insomnia.
  
  
  CONCLUSIONS
  Functional impairments in patients newly diagnosed with late-stage cancers behave as symptoms during the diagnostic phase. Health care providers need to expand their assessments to include both symptoms and functional impairments. Early recognition of functional changes may accelerate diagnosis at an earlier cancer stage.
authors: Yale University, New Haven, Connecticut, USA. Electronic address: samah.fodeh@yale.edu.
authors: Samah J
authors: SJ
authors: Fodeh
authors: Mark
authors: M
authors: Lazenby
authors: Mei
authors: M
authors: Bai
authors: Elizabeth
authors: E
authors: Ercolano
authors: Terrence
authors: T
authors: Murphy
authors: Ruth
authors: R
authors: McCorkle
mesh_terms: D000203:Activities of Daily Living; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D016000:Cluster Analysis; D015897:Comorbidity; D003237:Connecticut / Q000453:epidemiology; D003371:Cough / Q000453:epidemiology; D005221:Fatigue / Q000453:epidemiology*; D005260:Female; D006801:Humans; D015994:Incidence; D008297:Male; D008875:Middle Aged; D009325:Nausea / Q000453:epidemiology*; D009369:Neoplasms / Q000175:diagnosis / Q000453:epidemiology*; D010146:Pain / Q000453:epidemiology*; D016016:Proportional Hazards Models; D011788:Quality of Life; D012307:Risk Factors; D013315:Stress, Psychological / Q000453:epidemiology*; D063189:Symptom Assessment / Q000379:methods* / Q000706:statistics & numerical data; D013577:Syndrome
keywords: Cluster analysis; activities of daily living; advanced cancer; cancer; function; newly diagnosed; symptom
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