Symptom Profiles of Latina Breast Cancer Survivors: A Latent Class Analysis

Risk factors for self-reported high symptom cluster burdens in patients with breast cancer undergoing chemotherapy in China: A cross-sectional study

Background and Aims

Further exploration is needed to recognize symptom clusters and categorize subgroups with distinct cluster patterns and associated risks, focusing on symptoms that are highly self-reported by patients with breast cancer undergoing chemotherapy. This study aimed to identify subgroups and risk factors for self-reported high symptom cluster burden among patients with breast cancer undergoing chemotherapy.

Methods

A total of 647 participants who met the inclusion criteria were included in the study, with data collected on demographics, disease information, self-reported symptoms, and psychosocial factors. Latent class analysis was utilized to identify the subgroup, while logistic regression was used to pinpoint predictive risk factors.

Results

Latent class analysis revealed three subgroups: the "high burden of all symptoms group" (n = 107, 16.54%), the "high burden of psychological symptoms group" (n = 103, 15.92%), and the "low burden of all symptoms group" (n = 437, 67.54%). Patients in the high burden of all symptom group and high burden of psychological symptom group exhibited significantly worse function outcomes (p < 0.001). Predictive risk factors for the "high burden of all symptom group" included older age, lower self-efficacy, worse body image, and a higher financial burden. Similarly, patients with high burden of psychological symptom were more likely to have low self-efficacy, poor body image, and a high financial burden.

Conclusion

The study demonstrated the importance of giving more attention to patients with breast cancer who are at risk of developing into membership of high symptom cluster burden group.

Relationship of cancer-related fatigue with psychoneurophysiological (PNP) symptoms in breast cancer survivors

PURPOSE

Cancer-related fatigue (CRF) is a highly prevalent and debilitating symptom reported by breast cancer survivors (BCS). CRF has been associated with the co-occurrence of anxiety, depression, poor sleep quality, cognitive impairment, which are collectively termed as psychoneurophysiological (PNP) symptoms. CRF and these PNP symptoms are often reported during and after treatment with long-lasting distress. It is unclear how CRF and these PNP symptoms influence each other. This study aimed to explore predictive factors (i.e., PNP symptoms and social-demographic factors) of CRF, and test exploratory path models of the relationships of CRF with PNP symptoms (depression, anxiety, sleep disturbance, pain, and cognitive function) in BCS.

METHODS

This paper is part of a larger descriptive, correlational, and cross-sectional study. Validated and reliable instruments assessed CRF, depression, anxiety, sleep disturbance, pain, and cognitive function. Descriptive statistics, Pearson correlation, multiple linear regression models, and path analysis were employed.

RESULTS

Patients (N = 373) who reported less bodily pain had worst CRF (r = -0.45, p < .01). Significant predictors of CRF included depression, sleep disorder, bodily pain, perceived cognitive ability, and dispositional (state) optimism. Depression alone accounted for 31% of the variance in CRF. An integrative path model with bodily pain, neuropathic pain, CRF, and depression showed a good fit across different indices (CFI = 0.993, RMSEA = 0.047, 90% CI 0-0.12, SRMR = 0.027).

CONCLUSIONS

This study identified significant predictors of CRF and revealed a good fit mediation model with significant pathways for CRF, suggesting that a common etiology may underpin the co-occurrence of CRF with PNP symptoms (pain and depression). However, further investigation with longitudinal design is necessary to explore the causal relationships of these symptoms. Evidence-based strategies/interventions are needed to reduce or eliminate the burden of these symptoms on the lives of BCS.

Cancer-related symptoms among young and middle-aged women undergoing chemotherapy for breast cancer: Application of latent class analysis and network analysis

PURPOSE

To identify subgroups and symptom networks of cancer-related symptoms for women under 60 years who are undergoing chemotherapy for breast cancer.

METHODS

A cross-sectional survey in Mainland China was conducted between August 2020 and November 2021. Participants completed questionnaires that collected demographic and clinical characteristics and featured the PROMIS-57 and the PROMIS-Cognitive Function Short Form.

RESULTS

A total of 1033 participants were included in the analysis, and three-class model was identified: "severe symptom group" (17.6%; Class 1), "moderately severe anxiety, depression, and pain-interference group" (38.0%; Class 2), and "mild symptom group" (44.4%; Class 3). Patients who were in menopause (OR = 3.05, P < .001), undergoing a combination of medical treatments (OR = 2.39, P = .003), and who had experienced complications (OR = 1.86, P = .009) were more likely to belong to Class 1. However, having two or more children increased the likelihood of belonging to Class 2. Additionally, network analysis showed that severe fatigue level was the core symptom among the full sample. As for Class 1, feeling helpless and severe fatigue level were the core symptoms. Regarding Class 2, the impact of pain interfere on the ability to participate in social activities and feeling hopeless were found to be the targeted symptoms for intervention.

CONCLUSION

Menopause, receiving a combination of medical treatments, and experiencing complications characterize the group with the most symptom disturbance. Moreover, different interventions should be performed for core symptoms in patients with varied symptom disturbances.

Tryptophan and Kynurenine Pathway Metabolites and Psychoneurological Symptoms Among Breast Cancer Survivors

BACKGROUND

Among breast cancer survivors, pain, fatigue, depression, anxiety, and sleep disturbance are common psychoneurological symptoms that cluster together. Inflammation-induced activation of the tryptophan-kynurenine metabolomic pathway may play an important role in these symptoms.

AIMS

This study investigated the relationship between the metabolites involved in the tryptophan-kynurenine pathway and psychoneurological symptoms among breast cancer survivors.

DESIGN

Cross-sectional study.

SETTING

Participants were recruited at the oncology clinic at the University of Illinois Hospital & Health Sciences System.

PARTICIPANTS/SUBJECTS

79 breast cancer survivors after major cancer treatment.

METHODS

We assessed psychoneurological symptoms with the PROMIS-29 and collected metabolites from fasting blood among breast cancer survivors after major cancer treatment, then analyzed four major metabolites involved in the tryptophankynurenine pathway (tryptophan, kynurenine, kynurenic acid, and quinolinic acid). Latent profile analysis identified subgroups based on the five psychoneurological symptoms. Mann-Whitney U tests and multivariable logistic regression compared targeted metabolites between subgroups.

RESULTS

We identified two distinct symptom subgroups (low, 81%; high, 19%). Compared with participants in the low symptom subgroup, patients in the high symptom subgroup had higher BMI (p = .024) and were currently using antidepressants (p = .008). Using multivariable analysis, lower tryptophan levels (p = .019) and higher kynurenine/tryptophan ratio (p = .028) were associated with increased risk of being in the high symptom subgroup after adjusting for BMI and antidepressant status.

CONCLUSION

The tryptophan-kynurenine pathway and impaired tryptophan availability may contribute to the development of psychoneurological symptoms.

George SM, Ream M, Antoni MH. A latent profile analysis to assess physical, cognitive and emotional symptom clusters in women with breast cancer

OBJECTIVE

Current research on the physical and psychological functioning of breast cancer survivors often takes an approach where symptoms are studied independently even though they often occur in clusters This paper aims to identify physical and psychological symptom clusters among breast cancer survivors while assessing clinical, psychosocial and demographic characteristics that predict subgroup membership.

DESIGN

Using post-surgical data collected from 240 women with stage 0-III breast cancer, symptom clusters were identified using latent profile analysis of patient-reported symptoms.

MAIN OUTCOME MEASURES

Baseline measures included the Pittsburg Sleep Quality Index, the Fatigue Symptom Inventory, the Hamilton Rating Scales for depression and anxiety and the Impact of Event Scale.

RESULTS

Three distinct classes were identified: (1) mild physical, cognitive and emotional symptoms, (2) moderate across all domains and (3) high levels of all symptoms. Lower socio-economic status, minority ethnicity, younger age, advanced disease stage along with lower self-efficacy and less internal locus of control were significantly associated with a higher likelihood of class 3 membership.

CONCLUSION

By identifying those most at risk for severe physical and psychological symptoms in the post-surgical period, our results can guide the development of tailored interventions to optimise quality of life during breast cancer treatment.

Latent Profile/Class Analysis Identifying Differentiated Intervention Effects

BACKGROUND

The randomized clinical trial is generally considered the most rigorous study design for evaluating overall intervention effects. Due to patient heterogeneity, subgroup analysis is often used to identify differential intervention effects. In research of behavioral interventions, such subgroups often depend on a latent construct measured by multiple correlated observed variables.

OBJECTIVES

The purpose of this article is to illustrate latent class analysis/latent profile analysis as a helpful tool to characterize latent subgroups, conduct exploratory subgroup analysis, and identify potential differential intervention effects using clinical trial data.

METHODS

After reviewing different approaches for subgroup analysis, latent class analysis/latent profile analysis was chosen to identify heterogeneous patient groups based on multiple correlated variables. This approach is superior in this specific scenario because of its ability to control type I error, assess intersection of multiple moderators, and improve interpretability. We used a case study example to illustrate the process of identifying latent classes as potential moderators based on both clinical and perceived risk scores and then tested the differential effects of health coaching in improving health behavior for patients with elevated risk of developing coronary heart disease.

RESULTS

We identified three classes based on one clinical risk score and four perceived risk measures for individuals with high risk of developing coronary heart disease. Compared to other classes we assessed, individuals in the class with low clinical risk and low perceived risk benefit most from health coaching to improve their physical activity levels.

DISCUSSION

Latent class analysis/latent profile analysis offers a person-centered approach to identifying distinct patient profiles that can be used as moderators for subgroup analysis. This offers tremendous opportunity to identify differential intervention effects in behavioral research.

Symptom trajectories in patients with breast cancer: An integrative review

Objective

This study aimed to synthesize the available knowledge of symptom trajectories in patients with breast cancer and identify predictors associated with these trajectories.

Methods

Whittemore and Knafl’s integrative review method was employed to guide a systematic search for literature in four databases (PubMed, Embase, Web of Science, and CINAHL). The retrieved articles’ publication time was limited to 2010 to 2020, and only peer-reviewed English articles were included.

Results

Twenty-seven articles were included. The findings of the relevant studies were analyzed using thematic analysis. They were grouped into two themes: symptom trajectories of patients with breast cancer (symptom trajectories in patients with breast cancer, in patients who underwent surgery, and in patients who received cancer-related treatment) and associated factors (socioeconomic factors, health characteristics, cancer-related treatment characteristics). Newly diagnosed patients tended to report high trajectories of depression and persistent sleep disturbance. Most patients who underwent surgery reported significant sleep disturbance and anxiety shortly after surgery. For patients who received cancer-related treatment, only a small proportion had a high level of physical activity after cancer-related therapy over time. A high body mass index, a low relationship quality, parental responsibilities, insufficient social support, a low educational background, and an unhealthy lifestyle may increase the risk of negative symptom trajectories in patients with breast cancer. Additionally, old patients were more likely to report cognitive impairment after chemotherapy, while young patients tended to report trajectories of persistent sexual dysfunction. Concurring symptoms and poor health status also contributed to adverse symptom trajectories.

Conclusion

The findings of this review add to the body of knowledge of the interindividual variability of symptom trajectories in patients with breast cancer. Despite the overall similarity in appraisal at baseline, the patients reported varied symptom trajectories over time. It is recommended that nurses consider sociodemographic, clinical and cancer-related treatment characteristics and perform targeted early preventive interventions for patients with breast cancer.