Latent Class Analysis Reveals Distinct Subgroups of Patients Based on Symptom Occurrence and Demographic and Clinical Characteristics

• Published in: Journal of pain and symptom management Vol. 50; no. 1; pp. 28 - 37
• Main Authors: Miaskowski, Christine, RN, PhD, Dunn, Laura, MD, Ritchie, Christine, MD, Paul, Steven M., PhD, Cooper, Bruce, PhD, Aouizerat, Bradley E., PhD, MAS, Alexander, Kimberly, RN, PhD, Skerman, Helen, PhD, Yates, Patsy, RN, PhD
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-07-2015
• Subjects: age differences; Aged; Aging - physiology; Aging - psychology; Anesthesia & Perioperative Care; Australia - epidemiology; Data Interpretation, Statistical; Female; gender differences; Humans; latent class analysis; Male; Middle Aged; Neoplasms - classification; Neoplasms - epidemiology; Neoplasms - physiopathology; Neoplasms - psychology; Pain Medicine; Self Report; Sex Characteristics; Symptom Assessment; Symptom clusters; symptom profiles; United States - epidemiology; age differences; symptom profiles; latent class analysis; Symptom clusters; gender differences
• ISSN: 0885-3924; 1873-6513
• DOI: 10.1016/j.jpainsymman.2014.12.011

Abstract Context 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. Objectives 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. Conclusion 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.