title: Latent Class Analysis Reveals Distinct Subgroups of Patients Based on Symptom Occurrence and Demographic and Clinical Characteristics.
abstract: BACKGROUND
  Cancer patients experience a broad range of physical and psychological symptoms as a result of their disease and its treatment. On average, these patients report 10 unrelieved and co-occurring symptoms.
  
  
  OBJECTIVE
  The aims were to determine if subgroups of oncology outpatients receiving active treatment (n = 582) could be identified based on their distinct experience with 13 commonly occurring symptoms; to determine whether these subgroups differed on select demographic and clinical characteristics; and to determine if these subgroups differed on quality of life (QOL) outcomes.
  
  
  METHODS
  Demographic, clinical, and symptom data from one Australian and two U.S. studies were combined. Latent class analysis was used to identify patient subgroups with distinct symptom experiences based on self-report data on symptom occurrence using the Memorial Symptom Assessment Scale.
  
  
  RESULTS
  Four distinct latent classes were identified (i.e., all low [28.0%], moderate physical and lower psych [26.3%], moderate physical and higher psych [25.4%], and all high [20.3%]). Age, gender, education, cancer diagnosis, and presence of metastatic disease differentiated among the latent classes. Patients in the all high class had the worst QOL scores.
  
  
  CONCLUSIONS
  Findings from this study confirm the large amount of interindividual variability in the symptom experience of oncology patients. The identification of demographic and clinical characteristics that place patients at risk for a higher symptom burden can be used to guide more aggressive and individualized symptom management interventions.
authors: School of Nursing, University of California, San Francisco, California, USA. Electronic address: chris.miaskowski@nursing.ucsf.edu.
authors: Christine
authors: C
authors: Miaskowski
authors: School of Medicine, University of California, San Francisco, California, USA.
authors: Laura
authors: L
authors: Dunn
authors: School of Medicine, University of California, San Francisco, California, USA.
authors: Christine
authors: C
authors: Ritchie
authors: School of Nursing, University of California, San Francisco, California, USA.
authors: Steven M
authors: SM
authors: Paul
authors: School of Nursing, University of California, San Francisco, California, USA.
authors: Bruce
authors: B
authors: Cooper
authors: School of Nursing, University of California, San Francisco, California, USA; Institute for Human Genetics, University of California, San Francisco, California, USA.
authors: Bradley E
authors: BE
authors: Aouizerat
authors: School of Nursing, Queensland University of Technology, Kelvin Grove, Queensland, Australia.
authors: Kimberly
authors: K
authors: Alexander
authors: School of Nursing, Queensland University of Technology, Kelvin Grove, Queensland, Australia.
authors: Helen
authors: H
authors: Skerman
authors: School of Nursing, Queensland University of Technology, Kelvin Grove, Queensland, Australia.
authors: Patsy
authors: P
authors: Yates
mesh_terms: D000368:Aged; D000375:Aging / Q000502:physiology / Q000523:psychology; D001315:Australia / Q000453:epidemiology; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009369:Neoplasms / Q000145:classification* / Q000453:epidemiology* / Q000503:physiopathology / Q000523:psychology; D057566:Self Report; D012727:Sex Characteristics; D063189:Symptom Assessment; D014481:United States / Q000453:epidemiology
keywords: Symptom clusters; age differences; gender differences; latent class analysis; symptom profiles
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