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

Papachristou, Nikolaos, Barnaghi, Payam, Hu, X, Maguire, Roma, Apostolidis, K, Armes, J, Conley, YP, Hammer, M, Katsaragakis, S, Kober, KM , Levine, JD, McCann, Lisa, Patiraki, E, Paul, SM, Ream, Emma, Wright, F and Miaskowski, C (2017) Congruence Between Latent Class and K-modes Analyses in the Identification of Oncology Patients with Distinct Symptom Experiences Journal of Pain and Symptom Management.

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Congruence Between Latent Class and K-modes Analyses in the Identification of Oncology.Submitted.pdf - Accepted version Manuscript
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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.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Papachristou, Nikolaosn.papachristou@surrey.ac.ukUNSPECIFIED
Barnaghi, PayamP.Barnaghi@surrey.ac.ukUNSPECIFIED
Hu, XUNSPECIFIEDUNSPECIFIED
Maguire, Romar.maguire@surrey.ac.ukUNSPECIFIED
Apostolidis, KUNSPECIFIEDUNSPECIFIED
Armes, JUNSPECIFIEDUNSPECIFIED
Conley, YPUNSPECIFIEDUNSPECIFIED
Hammer, MUNSPECIFIEDUNSPECIFIED
Katsaragakis, SUNSPECIFIEDUNSPECIFIED
Kober, KMUNSPECIFIEDUNSPECIFIED
Levine, JDUNSPECIFIEDUNSPECIFIED
McCann, Lisal.mccann@surrey.ac.ukUNSPECIFIED
Patiraki, EUNSPECIFIEDUNSPECIFIED
Paul, SMUNSPECIFIEDUNSPECIFIED
Ream, Emmae.ream@surrey.ac.ukUNSPECIFIED
Wright, FUNSPECIFIEDUNSPECIFIED
Miaskowski, CUNSPECIFIEDUNSPECIFIED
Date : 28 August 2017
Identification Number : 10.1016/j.jpainsymman.2017.08.020
Copyright Disclaimer : © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Uncontrolled Keywords : symptom clusters; cancer; latent class analysis; machine learning; clustering; chemotherapy, k-modes analysis
Depositing User : Melanie Hughes
Date Deposited : 22 Aug 2017 08:34
Last Modified : 07 Sep 2017 08:44
URI: http://epubs.surrey.ac.uk/id/eprint/841991

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