Symptom Cluster Research in Women with Breast Cancer: A Comparison of Three Subgrouping Techniques

Long-term behavioral symptom clusters among survivors of early-stage breast cancer: Development and validation of a predictive model

BACKGROUND

Fatigue, cognitive impairment, anxiety, depression, and sleep disturbance are cancer-related behavioral symptoms that may persist years after early-stage breast cancer, affecting quality of life. We aimed to generate a predictive model of long-term cancer-related behavioral symptoms clusters among breast cancer survivors 4 years after diagnosis.

METHODS

Patients with early-stage breast cancer were included from the CANcer TOxicity trial (ClinicalTrials.gov identifier NCT01993498). Our outcome was the proportion of patients reporting cancer-related behavioral symptoms clusters 4 years after diagnosis (≥3 severe symptoms). Predictors, including clinical, behavioral, and treatment-related characteristics; Behavioral Symptoms Score (BSS; 1 point per severe cancer-related behavioral symptom at diagnosis); and a proinflammatory cytokine (interleukin 1b; interleukin 6; tumor necrosis factor α) genetic risk score were tested using multivariable logistic regression, implementing bootstrapped augmented backwards elimination. A 2-sided P less than .05 defined statistical significance.

RESULTS

In the development cohort (n = 3555), 642 patients (19.1%) reported a cluster of cancer-related behavioral symptoms at diagnosis, and 755 (21.2%) did so 4 years after diagnosis. Younger age (adjusted odds ratio for 1-year decrement = 1.012, 95% confidence interval [CI] = 1.003 to 1.020), previous psychiatric disorders (adjusted odds ratio vs no = 1.27, 95% CI = 1.01 to 1.60), and BSS (adjusted odds ratio ranged from 2.17 [95% CI = 1.66 to 2.85] for BSS = 1 vs 0 to 12.3 [95% CI = 7.33 to 20.87] for BSS = 5 vs 0) were predictors of reporting a cluster of cancer-related behavioral symptoms (area under the curve = 0.73, 95% CI = 0.71 to 0.75). Genetic risk score was not predictive of these symptoms. Results were confirmed in the validation cohort (n = 1533).

CONCLUSION

Younger patients with previous psychiatric disorders and higher baseline symptom burden have greater risk of long-term clusters of cancer-related behavioral symptoms. Our model might be implemented in clinical pathways to improve management and test the effectiveness of risk-mitigation interventions among breast cancer survivors.

Differences in Circulating Extracellular Vesicle and Soluble Cytokines in Older Versus Younger Breast Cancer Patients With Distinct Symptom Profiles

Because extracellular vesicle (EV)-associated cytokines, both encapsulated and surface bound, have been associated with symptom severity, and may vary over the lifespan, they may be potential biomarkers to uncover underlying mechanisms of various conditions. This study evaluated the associations of soluble and EV-associated cytokine concentrations with distinct symptom profiles reported by 290 women with breast cancer prior to surgery. Patients were classified into older (≥60 years, n = 93) and younger (< 60 years, n = 197) cohorts within two previously identified distinct symptom severity profiles, that included pain, depressive symptoms, sleep disturbance, and fatigue (i.e., High Fatigue Low Pain and All Low). EVs were extracted using ExoQuick. Cytokine concentrations were determined using Luminex multiplex assay. Mann Whitney U test evaluated the differences in EV and soluble cytokine levels between symptom classes and between and within the older and younger cohorts adjusting for Karnofsky Performance Status (KPS) score, body mass index (BMI), and stage of disease. Partial correlation analyses were run between symptom severity scores and cytokine concentrations. Results of this study suggest that levels of cytokine concentrations differ between EV and soluble fractions. Several EV and soluble pro-inflammatory cytokines had positive associations with depressive symptoms and fatigue within both age cohorts and symptom profiles. In addition, in the older cohort with High Fatigue Low Pain symptom profile, EV GM-CSF concentrations were higher compared to the All Low symptom profile (p < 0.05). Albeit limited by a small sample size, these exploratory analyses provide new information on the association between cytokines and symptom profiles of older and younger cohorts. Of note, unique EV-associated cytokines were found in older patients and in specific symptom classes. These results suggest that EVs may be potential biomarker discovery tools. Understanding the mechanisms that underlie distinct symptom class profiles categorized by age may inform intervention trials and offer precision medicine approaches.

Inflammatory Markers, Metabolic Profile, and Psychoneurological Symptoms in Women with Breast Cancer: A Literature Review

Breast cancer is one of the most prevalent cancers in women. The improvement in breast cancer treatment has significantly increased the proportion of survival rate for women with breast cancer. Despite the advancement in breast cancer treatment, a great proportion of survivors suffer from co-occurring psychoneurological symptoms which impact their quality of life. The most frequently reported psychoneurological symptoms among women with breast cancer are depressive symptoms, anxiety, fatigue, sleep disturbances, and pain. These symptoms usually appear as a cluster. Inflammatory activation and serum metabolic alterations have been associated with the etiology of cancer and with various chronic neurocognitive disorders. However, to date, no studies considered the combined effects of inflammatory markers and metabolites in the development of psychoneurological symptoms in women with breast cancer especially those who were treated with chemotherapy. Further clarification of the relationships between the inflammatory markers, serum metabolic alterations, and psychoneurological symptoms in women with breast cancer should be pursued.

A Preliminary Investigation of Psychoneurological Symptoms in Low-Income Mothers

BACKGROUND

Exposure to chronic stressors may contribute to the development of psychoneurological symptoms (i.e., fatigue, cognitive dysfunction, sleep disturbance, depressed mood, and pain) that can compromise maternal function.

OBJECTIVES

In two studies of low-income mothers, we investigated the presence of psychoneurological symptoms and explored associations between mothers' stressors and psychoneurological symptoms as well as between symptoms and function. We also considered the possible mediating role of the symptoms between stressors and function.

METHODS

We conducted secondary analyses of psychoneurological symptoms in two studies of low-income mothers of infants and toddlers in the United States. Study 1 sampled Latina women with limited English proficiency, whereas Study 2 was conducted with English-speaking women from diverse backgrounds. In both studies, symptoms were measured using items from the Center for Epidemiological Studies Depression Scale and the Medical Outcomes Study Short-Form Health Survey. Maternal function was measured through self-report and researcher observation. In Study 2, stressors were measured using the Everyday Stressors Index. Multiple linear regressions were used to investigate associations while controlling for relevant covariates.

RESULTS

In both studies, mothers endorsed a wide range of psychoneurological symptoms. In Study 1, psychoneurological symptoms had significant negative associations with role function, social function, and developmental stimulation. In Study 2, psychoneurological symptoms had significant negative associations with role function, social function, and physical function. Using Aroian test for mediation, we found that psychoneurological symptoms mediated all significant relationships between stressors and maternal functions in Study 2.

DISCUSSION

In two samples of low-income mothers, psychoneurological symptoms were prevalent and associated with chronic stressors and with maternal function and may mediate the association between those two factors. These findings extend prior research on depressive symptoms in mothers by investigating pain as an additional key symptom. The studies advance symptom science by highlighting psychoneurological symptoms in a heterogeneous sample without known health conditions.

Two Methods for Calculating Symptom Cluster Scores

BACKGROUND

Symptom clusters are conventionally distilled into a single score using composite scoring, which is based on the mathematical assumption that all symptoms are equivalently related to outcomes of interest; this may lead to a loss of important variation in the data.

OBJECTIVES

This article compares two ways of calculating a single score for a symptom cluster: a conventional, hypothesis-driven composite score versus a data-driven, reduced rank regression score that weights the symptoms based on their individual relationships with key outcomes.

METHODS

We conducted a secondary analysis of psychoneurological symptoms from a sample of 356 low-income mothers. Four of the psychoneurological symptoms (fatigue, cognitive dysfunction, sleep disturbance, and depressed mood) were measured with the Center for Epidemiological Studies Depression Scale; the fifth (pain) was measured using an item from the Medical Outcomes Study 12-item Short Form Health Survey (SF-12). Mothers' function was measured using the 12-item Short Form Health Survey. The composite score was calculated by summing standardized scores for each individual psychoneurological symptom. In contrast, reduced rank regression weighted the individual symptoms using their respective associations with mothers' function; the weighted individual symptom scores were summed into the reduced rank regression symptom score.

RESULTS

The composite score and reduced rank regression score were highly correlated at .93. The cluster of psychoneurological symptoms accounted for 53.7% of the variation in the mothers' function. Depressed mood and pain accounted for almost all the explained variation in mothers' function at 37.2% and 15.0%, respectively.

DISCUSSION

The composite score approach was simpler to calculate, and the high correlation with the reduced rank regression score indicates that the composite score reflected most of the variation explained by the reduced rank regression approach in this data set. However, the reduced rank regression analysis provided additional information by identifying pain and depressed mood as having the strongest association with a mother's function, which has implications for understanding which symptoms to target in future interventions. Future studies should also explore composite versus reduced rank regression approaches given that reduced rank regression may yield different insights in other data sets.

Symptom Clusters in Patients With Pancreatic Cancer Undergoing Surgical Resection: Part I

OBJECTIVES

To describe patient-reported symptoms and symptom clusters in patients with pancreatic cancer (PC) undergoing surgical resection.

SAMPLE & SETTING

143 patients with stage II PC undergoing surgical resection alone or with subsequent adjuvant chemoradiation or chemotherapy were recruited to participate in a nested, longitudinal, exploratory study through convenience sampling techniques from Thomas Jefferson University Hospital, a National Cancer Institute-designated cancer center.

METHODS & VARIABLES

The Functional Assessment in Cancer Therapy-Hepatobiliary questionnaire was used to assess 17 PC symptoms preoperatively and at three, six, and nine months postoperatively. Exploratory and confirmatory factor analyses were used to identify symptom clusters.

RESULTS

Fatigue, trouble sleeping, poor appetite, trouble digesting food, and weight loss were consistently reported as the most prevalent and severe symptoms. Sixteen distinct symptom clusters were identified within nine months of surgery. Four core symptom clusters persisted over time.

IMPLICATIONS FOR NURSING

Findings may be used to provide anticipatory patient and family guidance and to inform clinical assessments of symptoms and symptom clusters in this population.

Differences in symptom clusters before and twelve months after breast cancer surgery

PURPOSE

Given the inter-relatedness among symptoms, research efforts are focused on an evaluation of symptom clusters. The purposes of this study were to evaluate for differences in the number and types of menopausal-related symptom clusters assessed prior to and at 12-months after surgery using ratings of occurrence and severity and to evaluate for changes in these symptom clusters over time.

METHODS

Prior to and at 12 months after surgery, 392 women with breast cancer completed the Menopausal Symptoms Scale. Exploratory factor analyses were used to identify the symptom clusters.

RESULTS

Of the 392 women evaluated, the mean number of symptoms (out of 46) was 13.2 (±8.5) at enrollment and 10.9 (±8.2) at 12 months after surgery. Using occurrence and severity, three symptom clusters were identified prior to surgery. Five symptom clusters were identified at 12 months following surgery. Two symptom clusters (i.e., pain/discomfort and hormonal) were relatively stable across both dimensions and time points. Two symptom clusters were relatively stable across both dimensions either prior to surgery (i.e., sleep/psychological/cognitive) or at 12 months after surgery (i.e., sleep). The other four clusters (i.e., irritability, psychological/cognitive, cognitive, psychological) were identified at one time point using a single dimension.

CONCLUSIONS

While some menopausal-related symptom clusters were consistent across time and dimensions, the majority of symptoms clustered together differently depending on whether they were evaluated prior to or at 12 months after breast cancer surgery. An increased understanding of how symptom clusters change over time may assist clinicians to focus their symptom assessments and management strategies.

Cytokine Gene Polymorphisms Associated With Symptom Clusters in Oncology Patients Undergoing Radiation Therapy

CONTEXT

Most of the reviews on the biological basis for symptom clusters suggest that inflammatory processes are involved in the development and maintenance of the symptom clusters. However, no studies have evaluated for associations between genetic polymorphisms and common symptom clusters (e.g., mood disturbance, sickness behavior).

OBJECTIVES

Examine the associations between cytokine gene polymorphisms and the severity of three distinct symptom clusters (i.e., mood-cognitive, sickness-behavior, treatment-related) in a sample of patients with breast and prostate cancer (n = 157) at the completion of radiation therapy.

METHODS

Symptom severity was assessed using the Memorial Symptom Assessment Scale. Symptom clusters were created using exploratory factor analysis. The associations between cytokine gene polymorphisms and the symptom cluster severity scores were evaluated using regression analyses.

RESULTS

Polymorphisms in C-X-C motif chemokine ligand 8 (CXCL8), interleukin (IL13), and nuclear factor kappa beta 2 (NFKB2) were associated with severity scores for the mood-cognitive symptom cluster. In addition to interferon gamma (IFNG1), the same polymorphism in NFKB2 (i.e., rs1056890) that was associated with the mood-cognitive symptom cluster score was associated with the sickness-behavior symptom cluster. Polymorphisms in interleukin 1 receptor 1 (IL1R1), IL6, and NFKB1 were associated with severity factor scores for the treatment-related symptom cluster.

CONCLUSION

Our findings support the hypotheses that symptoms that cluster together have a common underlying mechanism and the most common symptom clusters in oncology patients are associated polymorphisms in genes involved in a variety of inflammatory processes.

Advancing Symptom Science Through Symptom Cluster Research: Expert Panel Proceedings and Recommendations

An overview of proceedings, findings, and recommendations from the workshop on "Advancing Symptom Science Through Symptom Cluster Research" sponsored by the National Institute of Nursing Research (NINR) and the Office of Rare Diseases Research, National Center for Advancing Translational Sciences, is presented. This workshop engaged an expert panel in an evidenced-based discussion regarding the state of the science of symptom clusters in chronic conditions including cancer and other rare diseases. An interdisciplinary working group from the extramural research community representing nursing, medicine, oncology, psychology, and bioinformatics was convened at the National Institutes of Health. Based on expertise, members were divided into teams to address key areas: defining characteristics of symptom clusters, priority symptom clusters and underlying mechanisms, measurement issues, targeted interventions, and new analytic strategies. For each area, the evidence was synthesized, limitations and gaps identified, and recommendations for future research delineated. The majority of findings in each area were from studies of oncology patients. However, increasing evidence suggests that symptom clusters occur in patients with other chronic conditions (eg, pulmonary, cardiac, and end-stage renal disease). Nonetheless, symptom cluster research is extremely limited and scientists are just beginning to understand how to investigate symptom clusters by developing frameworks and new methods and approaches. With a focus on personalized care, an understanding of individual susceptibility to symptoms and whether a "driving" symptom exists that triggers other symptoms in the cluster is needed. Also, research aimed at identifying the mechanisms that underlie symptom clusters is essential to developing targeted interventions.

Differences in Composition of Symptom Clusters Between Older and Younger Oncology Patients

CONTEXT

Older oncology patients have unique needs associated with the many physical, psychological, and social changes associated with the aging process. The mechanisms underpinning and the impact of these changes are not well understood. Identification of clusters of symptoms is one approach that has been used to elicit hypotheses about the biological and/or psychological basis for variations in symptom experiences.

OBJECTIVES

The purposes of this study were to identify and compare symptom clusters in younger (<60 years) and older (≥60 years) patients undergoing cancer treatment.

METHODS

Symptom data from one Australian study and two U.S. studies were combined to conduct this analysis. A total of 593 patients receiving active treatment were dichotomized into younger (<60 years) and older (≥60 years) groups. Separate exploratory factor analyses (EFAs) were undertaken within each group to identify symptom clusters from occurrence ratings of the 32 symptoms assessed by the Memorial Symptom Assessment Scale.

RESULTS

In both groups, a seven-factor solution was selected. Four partially concordant symptom clusters emerged in both groups (i.e., mood/cognitive, malaise, body image, and genitourinary). In the older patients, the three unique clusters reflected physiological changes associated with aging, whereas in the younger group the three unique clusters reflected treatment-related effects.

CONCLUSION

The symptom clusters identified in older patients typically included a larger and more diverse range of physical and psychological symptoms. Differences also may be reflective of variations in treatment approaches between age groups. Findings highlight the need for better understanding of variation in treatment and symptom burden between younger and older adults with cancer.