Τίτλος:
Congruence Between Latent Class and K-Modes Analyses in the Identification of Oncology Patients With Distinct Symptom Experiences
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
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: The objective of this study was to evaluate the concordance between the number and types of subgroups of patients with distinct symptom profiles using latent class analysis and K-modes analysis. Methods: Using data on the occurrence of 25 symptoms from the Memorial Symptom Assessment Scale, 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 latent class analysis 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, and 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 Memorial Symptom Assessment Scale 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 provide the most sensitive and specific risk profiles. © 2017 American Academy of Hospice and Palliative Medicine
Συγγραφείς:
Papachristou, N.
Barnaghi, P.
Cooper, B.A.
Hu, X.
Maguire, R.
Apostolidis, K.
Armes, J.
Conley, Y.P.
Hammer, M.
Katsaragakis, S.
Kober, K.M.
Levine, J.D.
McCann, L.
Patiraki, E.
Paul, S.M.
Ream, E.
Wright, F.
Miaskowski, C.
Περιοδικό:
Journal of Pain and Symptom Management
Εκδότης:
ELSEVIER SCIENCE INC 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA
Λέξεις-κλειδιά:
adult; age; analytic method; Article; backache; breast cancer; cancer chemotherapy; cancer patient; clinical assessment tool; clinical evaluation; clinical feature; comorbidity; controlled study; correlation analysis; demography; depression; digestive system cancer; female; female genital tract cancer; human; K mode analysis; kappa statistics; latent class analysis; longitudinal study; lung cancer; major clinical study; male; Memorial Symptom Assessment Scale; mental disease; middle aged; nonparametric test; palliative therapy; patient identification; physical disease; quality of life; Quality of Life Scale Patient Version; rating scale; Short Form 12; statistical analysis; symptom assessment; classification; computer assisted diagnosis; latent class analysis; machine learning; neoplasm; procedures; psychology; risk assessment, antineoplastic agent, Age Factors; Antineoplastic Agents; Comorbidity; Diagnosis, Computer-Assisted; Female; Humans; Latent Class Analysis; Longitudinal Studies; Machine Learning; Male; Middle Aged; Neoplasms; Quality of Life; Risk Assessment
DOI:
10.1016/j.jpainsymman.2017.08.020
