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

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

doi:10.1016/j.jpainsymman.2017.08.020

Cited by 32 publications

(27 citation statements)

References 61 publications

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“…Because oncology patients experience an average of fifteen unrelieved symptoms that are highly variable in their occurrence, severity, and distress 1–3 , an equally important question in symptom research is to determine which symptom or symptoms is driving the other symptoms. While our NA of cross-sectional data does not demonstrate causality, the centrality indices provide some insights into the structural importance of each of the symptoms within each of the networks.…”

Section: Discussionmentioning

confidence: 99%

“…Oncology patients undergoing cancer treatment experience an average of fifteen unrelieved symptoms that are highly variable in both their severity and distress 1–3 . In order to advance symptom management science and gain a better understanding of oncology patients’ symptom experiences, research has focused on the evaluation of symptom clusters using techniques such as exploratory factor analysis or cluster analysis 4–6 .…”

Section: Introductionmentioning

confidence: 99%

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Network Analysis of the Multidimensional Symptom Experience of Oncology

Papachristou

Barnaghi

Cooper

et al. 2019

Sci Rep

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116

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Oncology patients undergoing cancer treatment experience an average of fifteen unrelieved symptoms that are highly variable in both their severity and distress. Recent advances in Network Analysis (NA) provide a novel approach to gain insights into the complex nature of co-occurring symptoms and symptom clusters and identify core symptoms. We present findings from the first study that used NA to examine the relationships among 38 common symptoms in a large sample of oncology patients undergoing chemotherapy. Using two different models of Pairwise Markov Random Fields (PMRF), we examined the nature and structure of interactions for three different dimensions of patients’ symptom experience (i.e., occurrence, severity, distress). Findings from this study provide the first direct evidence that the connections between and among symptoms differ depending on the symptom dimension used to create the network. Based on an evaluation of the centrality indices, nausea appears to be a structurally important node in all three networks. Our findings can be used to guide the development of symptom management interventions based on the identification of core symptoms and symptom clusters within a network.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Network Analysis of the Multidimensional Symptom Experience of Oncology

Papachristou

Barnaghi

Cooper

et al. 2019

Sci Rep

Self Cite

116

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“…LCA is a statistical method that may be used to define subgroups of patients (latent classes) using observed variables (10); this technique has been used previously to identify clinical phenotypes of palliative care in patients with cancer (11, 12). In this study, the observed variables were the initial reason(s) for palliative care consultation.…”

Section: Methodsmentioning

confidence: 99%

Latent Class Analysis of Specialized Palliative Care Needs in Adult Intensive Care Units From a Single Academic Medical Center

Wang

Ing

Blinderman

et al. 2019

Journal of Pain and Symptom Management

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Context: In the intensive care unit (ICU), 14% of patients meet criteria for specialized palliative care, but whether subgroups of patients differ in their palliative care needs is unknown. Objectives: To use latent class analysis (LCA) to separate ICU patients into different classes of palliative care needs, and determine if such classes differ in their palliative care resource requirements. Methods: Retrospective cohort study of ICU patients who received specialized palliative care, August 2013 – August 2015. Reason(s) for consultation were extracted from the initial note and entered into a LCA model to generate mutually exclusive patient classes. Differences in “high use” of palliative care (defined as having ≥ 5 palliative care visits) between classes was assessed using logistic regression, adjusting for age, race, Charlson comorbidity index and length of stay. Results: In a sample of 689 patients, a four-class model provided the most meaningful groupings: 1) Pain and Symptom Management (n=218, 31.6%), 2) Goals of Care and Advance Directives (GCAD) (n=131, 19.0%), 3) All Needs (n=112, 16.3%) and 4) Supportive Care (n=228, 33.1%). In comparison to GCAD patients, all other classes were more likely to require “high use” of palliative care, (adjusted odds ratio (aOR) 2.61, [1.41-4.83] for “All Needs”, aOR 2.01 [1.16-3.50] for “Pain and Symptom Management”, aOR 1.94 [1.12-3.34] for “Supportive Care”). Conclusion: Based on the initial reason for consultation, we identified four classes of palliative care needs amongst critically ill patients, and GCAD patients were least likely to be high-utilizers. These findings may help inform allocation of palliative care resources for this population.

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“…This study is part of a longitudinal study, funded by the National Cancer Institute, that evaluated the symptom experience of oncology outpatients receiving CTX. [21][22][23] Patients were eligible if they: were >18 years of age; had a diagnosis of breast, gastrointestinal (GI), gynecological (GYN), or lung cancer; had received CTX within the preceding four weeks; were scheduled to receive at least two additional cycles of CTX; were able to read, write, and understand English; and provided written informed consent. Patients were recruited from two Comprehensive Cancer Centers, a Veterans Affairs hospital, and four community-based oncology programs.…”

Section: Patients and Settingsmentioning

confidence: 99%

Identification of subgroups of chemotherapy patients with distinct sleep disturbance profiles and associated co-occurring symptoms

Tejada

Viele

Kober

et al. 2019

Sleep

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Study Objectives Purposes of this study were to identify subgroups of patients with distinct sleep disturbance profiles and to evaluate for differences in demographic, clinical, and various sleep characteristics, as well for differences in the severity of co-occurring symptoms among these subgroups. Methods Outpatients with breast, gynecological, gastrointestinal, or lung cancer (n = 1331) completed questionnaires six times over two chemotherapy cycles. Self-reported sleep disturbance was evaluated using the General Sleep Disturbance Scale (GSDS). Latent profile analysis was used to identify distinct subgroups. Results Three latent classes with distinct sleep disturbance profiles were identified (Low [25.5%], High [50.8%], Very High [24.0%]) across the six assessments. Approximately 75% of the patients had a mean total GSDS score that was above the clinically meaningful cutoff score of at least 43 across all six assessments. Compared to the Low class, patients in High and Very High classes were significantly younger, had a lower functional status, had higher levels of comorbidity, and were more likely to be female, more likely to have childcare responsibilities, less likely to be employed, and less likely to have gastrointestinal cancer. For all of the GSDS subscale and total scores, significant differences among the latent classes followed the expected pattern (Low < High < Very High). For trait and state anxiety, depressive symptoms, morning and evening fatigue, decrements in attentional function, and decrements in morning and evening energy, significant differences among the latent classes followed the expected pattern (Low < High < Very High). Conclusions Clinicians need to perform in-depth assessments of sleep disturbance and co-occurring symptoms to identify high-risk patients and recommend appropriate interventions.

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

Cited by 32 publications

References 61 publications

Network Analysis of the Multidimensional Symptom Experience of Oncology

Network Analysis of the Multidimensional Symptom Experience of Oncology

Latent Class Analysis of Specialized Palliative Care Needs in Adult Intensive Care Units From a Single Academic Medical Center

Identification of subgroups of chemotherapy patients with distinct sleep disturbance profiles and associated co-occurring symptoms

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