Congruence Between Latent Class and K-modes Analyses in the Identification of Oncology Patients with Distinct Symptom Experiences

Nikoloas Papachristou, Payam Barnaghi, Bruce A. Cooper, Xiao Hu, Roma Maguire, Kathi Apostolidis, Jo Armes, Yvette P. Conley, Marilyn Hammer, Stylianos Katsaragakis, Kord M. Kober, Jon D. Levine, Lisa McCann, Elisabeth Patiraki, Steven M. Paul, Emma Ream, Fay Wright, Christine Miaskowski

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)
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Abstract

Context: Risk profiling of oncology patients based on their symptom experience assists clinicians to provide more personalized symptom management interventions. Recent findings suggest that oncology patients with distinct symptom profiles can be identified using a variety of analytic methods.

Objectives: To evaluate the concordance between the number and types of subgroups of patients with distinct symptom profiles using latent class analysis (LCA) and K-modes analysis.

Methods: Using data on the occurrence of 25 symptoms from the Memorial Symptom Assessment Scale (MSAS), that 1329 patients completed prior to their next dose of chemotherapy (CTX), Cohen’s kappa coefficient was used to evaluate for concordance between the two analytic methods. For both LCA and K-modes, differences among the subgroups in demographic, clinical, and symptom characteristics, as well as quality of life outcomes were determined using parametric and nonparametric statistics.

Results: Using both analytic methods, four subgroups of patients with distinct symptom profiles were identified (i.e., All Low, Moderate Physical and Lower Psychological, Moderate Physical and Higher Psychological, All High). The percent agreement between the two methods was 75.32% which suggests a moderate level of agreement. In both analyses, patients in the All High group were significantly younger and had a higher comorbidity profile, worse MSAS subscale scores, and poorer QOL outcomes.

Conclusion: Both analytic methods can be used to identify subgroups of oncology patients with distinct symptom profiles. Additional research is needed to determine which analytic methods and which dimension of the symptom experience provides the most sensitive and specific risk profiles.
Original languageEnglish
JournalJournal of Pain and Symptom Management
Early online date30 Aug 2017
DOIs
Publication statusE-pub ahead of print - 30 Aug 2017

Keywords

  • symptom clusters
  • cancer
  • latent class analysis
  • machine learning
  • clustering
  • chemotherapy
  • k-modes analysis

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