Dataset Information

Latent Transition Analysis of the Symptom Experience of Cancer Patients Undergoing Chemotherapy.

ABSTRACT: BACKGROUND:Symptom clusters reflect the person's experience of multiple cooccurring symptoms. Although a variety of statistical methods are available to address the clustering of symptoms, latent transition analysis (LTA) characterizes patient membership in classes defined by the symptom experience and captures changes in class membership over time. OBJECTIVES:The purposes of this article are to demonstrate the application of LTA to cancer symptom data and to discuss the advantages and disadvantages of LTA relative to other methods of managing and interpreting data on multiple symptoms. METHODS:Data from a total of 495 adult cancer patients who participated in randomized clinical trials of two symptom management interventions were analyzed. Eight cancer- and treatment-related symptoms reflected the symptom experience. Latent transition analysis was employed to identify symptom classes and evaluate changes in symptom class membership from baseline to the end of the interventions. RESULTS:Three classes, "A (mild symptoms)," "B (physical symptoms)," and "C (physical and emotional symptoms)," were identified. Class A patients had less comorbidity, better physical and emotional role effect, and better physical function than the other classes did. The number of symptoms, general health perceptions, and social functioning were significantly different across the three classes and were poorest in Class C. Emotional role functioning was poorest in Class C. Older adults were more likely to be in Class B than younger adults were. Younger adults were more likely to be in Class C (p < .01). Among patients in Class C at baseline, 41.8% and 29.0%, respectively, transitioned to Classes A and B at the end of the interventions. DISCUSSION:These results demonstrate that symptom class membership characterizes differences in the patient symptom experience, function, and quality of life. Changes in class membership represent longitudinal changes in the course of symptom management. Latent class analysis overcomes the problem of multiple statistical testing that separately addresses each symptom.

SUBMITTER: Jeon S

PROVIDER: S-EPMC6399048 | biostudies-literature | 2019 Mar/Apr

REPOSITORIES: biostudies-literature

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Latent Transition Analysis of the Symptom Experience of Cancer Patients Undergoing Chemotherapy.

Jeon Sangchoon S Sikorskii Alla A Given Barbara A BA Given Charles W CW Redeker Nancy S NS

Nursing research 20190301 2

<h4>Background</h4>Symptom clusters reflect the person's experience of multiple cooccurring symptoms. Although a variety of statistical methods are available to address the clustering of symptoms, latent transition analysis (LTA) characterizes patient membership in classes defined by the symptom experience and captures changes in class membership over time.<h4>Objectives</h4>The purposes of this article are to demonstrate the application of LTA to cancer symptom data and to discuss the advantage ...[more]

PMID: 30540701

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Project description:BackgroundIn oncology, the prevalence of symptoms is preferentially analyzed in isolation instead of being considered in clusters. However, clinical practice shows that symptoms rarely occur separately but rather form clusters that share common underlying mechanisms in terms of intensity and severity, creating a synergistic effect among them, which can even help predict the development of future symptoms.ObjectiveTo identify and gather evidence on the prevalence, composition, severity, and predictors of cancer symptom clusters in adult cancer patients undergoing chemotherapy.MethodsThis systematic review and meta-analysis protocol was developed in compliance with PRISMA-P. Observational and experimental study designs will be included. MEDLINE/PubMed, Cochrane Library, Embase, CINAHL, PsycINFO, Web of Science, Scopus, LILACS, clinical trials.gov-NIH, the British Library, Google Scholar, and preprints [medRXiv] will be searched with no restrictions on idioms, dates, or settings. Two investigators will independently select the studies, perform data extraction, and critically appraise the risk of bias of the included studies. Heterogeneity among the studies will be assessed using the I2 statistic. If meta-analysis was feasible, a random-effect model analysis will be carried out. For data analysis, the pooled effect will be estimated considering 95% confidence interval and α = 5%. In addition, the certainty of evidence will be rated based on Cochrane methods in accordance with the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE).DiscussionTo the best of our knowledge, this systematic review and meta-analysis will be the first to identify and critically assess evidence regarding the prevalence, composition, severity, and predictors of cancer symptom clusters in adult cancer patients undergoing chemotherapy. We intend to provide health professionals with subsidies to reflect on a better understanding of symptom clusters in adult cancer patients, with the aim of contributing to the development of evidence-based therapeutic interventions and success in clinical practice.Prospero registration numberCRD42021248406.

Project description:BackgroundThe expression of depressive symptoms in older people with cancer is heterogeneous because of specific features of age or cancer comorbidity. We aimed to identify depressive symptom profiles in this population and describe the associated features including survival.Materials and methodsPatients ≥70 years who were referred to geriatric oncology clinics were prospectively included in the ELCAPA study. In this subanalysis, depressive symptoms were used as indicators in a latent class analysis. Multinomial multivariable logistic regression and Cox models examined the association of each class with baseline characteristics and mortality.ResultsFor the 847 complete-case patients included (median age, 79 years; interquartile range, 76-84; women, 47.9%), we identified five depressive symptom classes: "no depression/somatic only" (38.8%), "no depression/pauci-symptomatic" (26.4%), "severe depression" (20%), "mild depression" (11.8%), and "demoralization" (3%). Compared with the no depression/pauci-symptomatic class, the no depression/somatic only and severe depression classes were characterized by more frequent comorbidities with poorer functional status and higher levels of inflammation. "Severe" and "mild" depression classes also featured poorer nutritional status, more medications, and more frequent falls. Severe depression was associated with poor social support, inpatient status, and increased risk of mortality at 1 year (adjusted hazard ratio, 1.62, 95% confidence interval, 1.06-2.48) and 3 years (adjusted hazard ratio, 1.49; 95% confidence interval, 1.06-2.10).ConclusionA data-driven approach based on depressive symptoms identified five different depressive symptom profiles, including demoralization, in older patients with cancer. Severe depression was independently and substantially associated with poor survival.Implications for practiceOlder patients with cancer present with distinct profiles of depressive symptomatology, including different severity levels of depression and the demoralization syndrome. Clinicians should use a systematic assessment of depressive symptoms to adequately highlight these distinct profiles. Geriatric and oncological features are differently associated with these profiles. For instance, severe depression was associated with more frequent comorbidities with poorer functional, poor nutritional status, polypharmacy, frequent falls, inpatient status and poor social support. Also, severe depression was independently and substantially associated with poor survival so that the identification and management of depression should be considered a high priority in this population.

Dataset Information

Latent Transition Analysis of the Symptom Experience of Cancer Patients Undergoing Chemotherapy.

Publications

Latent Transition Analysis of the Symptom Experience of Cancer Patients Undergoing Chemotherapy.

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