The symptom phenotype of oncology outpatients remains relatively stable from prior to through 1 week following chemotherapy

Worse Morning Energy Profiles Are Associated with Significant Levels of Stress and Decrements in Resilience in Patients Receiving Chemotherapy

OBJECTIVES

Evidence suggests that lower levels of morning energy are associated with higher levels of stress and lower levels of resilience in patients receiving chemotherapy. Study purposes were to identify subgroups of patients with distinct morning energy profiles; evaluate for differences among the profiles in demographic and clinical characteristics, as well as measures of stress, resilience, and coping.

METHODS

A total of 1,343 outpatients receiving chemotherapy completed a demographic questionnaire and measures of global, cancer-related, and cumulative life stress, and resilience at study enrollment. Morning energy was assessed using the Lee Fatigue Scale at six time points over two cycles of chemotherapy. Latent profile analysis was used to identify subgroups of patients with distinct morning energy profiles. Differences among the subgroups were evaluated using parametric and nonparametric tests.

RESULTS

Three morning energy profiles were identified (i.e., High (17.3%), Low (60.3%), Very Low (22.4%)). Compared to High class, the other two morning energy classes were less likely to be employed; had a lower functional status and a higher comorbidity burden; and were more likely to self-report depression and back pain. For all three types of stress, significant differences were found among the three classes with scores that demonstrated a dose response effect (i.e., High < Low < Very Low; as decrements in morning energy increased, stress scores increased). Compared to High class, Very Low class reported higher rates of physical and sexual abuse. The resilience scores exhibited a dose response effect as well (i.e., High > Low > Very Low). Patients with the two worst energy profiles reported a higher use of disengagement coping strategies.

CONCLUSIONS

Findings highlight the complex relationships among decrements in morning energy, various types of stress, resilience, and coping in patients undergoing chemotherapy.

IMPLICATIONS FOR NURSING PRACTICE

Clinicians need to assess for stress and adverse childhood experiences to develop individualized management plans to increase patients' energy levels.

Symptom Network Analysis and Unsupervised Clustering of Oncology Patients Identifies Drivers of Symptom Burden and Patient Subgroups With Distinct Symptom Patterns

BACKGROUND

Interindividual variability in oncology patients' symptom experiences poses significant challenges in prioritizing symptoms for targeted intervention(s). In this study, computational approaches were used to unbiasedly characterize the heterogeneity of the symptom experience of oncology patients to elucidate symptom patterns and drivers of symptom burden.

METHODS

Severity ratings for 32 symptoms on the Memorial Symptom Assessment Scale from 3088 oncology patients were analyzed. Gaussian Graphical Model symptom networks were constructed for the entire cohort and patient subgroups identified through unsupervised clustering of symptom co-severity patterns. Network characteristics were analyzed and compared using permutation-based statistical tests. Differences in demographic and clinical characteristics between subgroups were assessed using multinomial logistic regression.

RESULTS

Network analysis of the entire cohort revealed three symptom clusters: constitutional, gastrointestinal-epithelial, and psychological. Lack of energy was identified as central to the network which suggests that it plays a pivotal role in patients' overall symptom experience. Unsupervised clustering of patients based on shared symptom co-severity patterns identified six patient subgroups with distinct symptom patterns and demographic and clinical characteristics. The centrality of individual symptoms across the subgroup networks differed which suggests that different symptoms need to be prioritized for treatment within each subgroup. Age, treatment status, and performance status were the strongest determinants of subgroup membership.

CONCLUSIONS

Computational approaches that combine unbiased stratification of patients and in-depth modeling of symptom relationships can capture the heterogeneity in patients' symptom experiences. When validated, the core symptoms for each of the subgroups and the associated clinical determinants may inform precision-based symptom management.

Increases in stress and adverse childhood experiences are associated with the co-occurrence of anxiety and depression in oncology patients

PURPOSE

Identify subgroups of patients with distinct joint anxiety AND depression profiles and evaluate for differences in demographic and clinical characteristics, as well as stress, resilience, and coping.

DESIGN

Longitudinal study.

PARTICIPANTS

Patients (n = 1328) receiving chemotherapy.

METHODS

Measures of state anxiety and depression were done six times over two cycles of chemotherapy. All of the other measures were completed prior to second or third cycle of chemotherapy. Latent profile analysis was used to identify the distinct joint anxiety and depression profiles.

FINDINGS

Three classes were identified (i.e. Low Anxiety and Low Depression (57.5%); Moderate Anxiety and Moderate Depression (33.7%), High Anxiety and High Depression (8.8%)). For all of the stress measures, a dose response effect was seen among the profiles. Two worst profiles reported higher occurrence rates for a number of adverse childhood experiences.

IMPLICATIONS FOR PROVIDERS

Patients need referrals for stress reduction techniques and mental health and social services.

Prediction of morning fatigue severity in outpatients receiving chemotherapy: less may still be more

INTRODUCTION

Fatigue is the most common and debilitating symptom experienced by cancer patients undergoing chemotherapy (CTX). Prediction of symptom severity can assist clinicians to identify high-risk patients and provide education to decrease symptom severity. The purpose of this study was to predict the severity of morning fatigue in the week following the administration of CTX.

METHODS

Outpatients (n = 1217) completed questionnaires 1 week prior to and 1 week following administration of CTX. Morning fatigue was measured using the Lee Fatigue Scale (LFS). Separate prediction models for morning fatigue severity were created using 157 demographic, clinical, symptom, and psychosocial adjustment characteristics and either morning fatigue scores or individual fatigue item scores. Prediction models were created using two regression and five machine learning approaches.

RESULTS

Elastic net models provided the best fit across all models. For the EN model using individual LFS item scores, two of the 13 individual LFS items (i.e., "worn out," "exhausted") were the strongest predictors.

CONCLUSIONS

This study is the first to use machine learning techniques to accurately predict the severity of morning fatigue from prior to through the week following the administration of CTX using total and individual item scores from the Lee Fatigue Scale (LFS). Our findings suggest that the language used to assess clinical fatigue in oncology patients is important and that two simple questions may be used to predict morning fatigue severity.

Higher Stress in Oncology Patients is Associated With Cognitive and Evening Physical Fatigue Severity

CONTEXT

Cognitive and physical fatigue are common symptoms experienced by oncology patients. Exposure to stressful life events (SLE), cancer-related stressors, coping styles, and levels of resilience may influence the severity of both dimensions of fatigue.

OBJECTIVES

Evaluate for differences in global, cancer-specific, and cumulative life stress, as well as resilience and coping in oncology patients (n=1332) with distinct cognitive fatigue AND evening physical fatigue profiles.

METHODS

Latent profile analysis, which combined the two symptom scores, identified three subgroups of patients with distinct cognitive fatigue AND evening physical fatigue profiles (i.e., Low, Moderate, High). Patients completed measures of global, cancer-specific, and cumulative life stress as well measures of resilience and coping. Differences among the latent classes in the various measures were evaluated using parametric and nonparametric tests.

RESULTS

Compared to Low class, the other two classes reported higher global and cancer-specific stress. In addition, they reported higher occurrence rates for sexual harassment and being forced to touch prior to 16 years of age. Compared to the other two classes, High class reported lower resilience scores and higher use of denial, substance use, and behavioral disengagement.

CONCLUSION

To decrease both cognitive and evening physical fatigue, clinicians need to assess for relevant stressors and initiate interventions to increase resilience and the use of engagement coping strategies. Additional research is warranted on the relative contribution of various social determinants of health to both cognitive and physical fatigue in oncology patients receiving chemotherapy.

Associations Between Demographic, Clinical, and Symptom Characteristics and Stress in Oncology Patients Receiving Chemotherapy

BACKGROUND

Patients undergoing cancer treatment experience global stress and cancer-specific stress. Both types of stress are associated with a higher symptom burden.

OBJECTIVE

In this cross-sectional study, we used a comprehensive set of demographic, clinical, and symptom characteristics to evaluate their relative contribution to the severity of global and cancer-specific stress.

METHODS

Patients (N = 941) completed study questionnaires before their second or third cycle of chemotherapy.

RESULTS

Consistent with our a priori hypothesis, we found both common and distinct characteristics associated with higher levels of global stress and cancer-specific stress. A significant proportion of our patients had scores on the Impact of Event Scale-Revised suggestive of subsyndromal (29.4%) or probable (13.9%) posttraumatic stress disorder. Four of the 5 stepwise linear regression analyses for the various stress scales explained between 41.6% and 54.5% of the total variance. Compared with various demographic and clinical characteristics, many of the common symptoms associated with cancer and its treatments uniquely explained a higher percentage of the variance in the various stress scales. Symptoms of depression made the largest unique contribution to the percentage of total explained variance across all 5 scales.

CONCLUSION

Clinicians need to assess for global stress, cancer-specific stress, and depression in patients receiving chemotherapy.

IMPLICATIONS FOR PRACTICE

Patients may benefit from integrative interventions (eg, mindfulness-based stress reduction, cognitive behavioral therapy, acupuncture) that simultaneously address stress and symptoms commonly associated with cancer and its treatments.

Network analysis to identify symptoms clusters and temporal interconnections in oncology patients

Oncology patients experience numerous co-occurring symptoms during their treatment. The identification of sentinel/core symptoms is a vital prerequisite for therapeutic interventions. In this study, using Network Analysis, we investigated the inter-relationships among 38 common symptoms over time (i.e., a total of six time points over two cycles of chemotherapy) in 987 oncology patients with four different types of cancer (i.e., breast, gastrointestinal, gynaecological, and lung). In addition, we evaluated the associations between and among symptoms and symptoms clusters and examined the strength of these interactions over time. Eight unique symptom clusters were identified within the networks. Findings from this research suggest that changes occur in the relationships and interconnections between and among co-occurring symptoms and symptoms clusters that depend on the time point in the chemotherapy cycle and the type of cancer. The evaluation of the centrality measures provides new insights into the relative importance of individual symptoms within various networks that can be considered as potential targets for symptom management interventions.

The prevalence and risk of symptom and function clusters in colorectal cancer survivors

PURPOSE

Our purpose was to describe the prevalence and predictors of symptom and function clusters in a diverse cohort of colorectal cancer survivors.

METHODS

We used data from a cohort of 909 adult colorectal cancer survivors. Participants were surveyed at a median of 9 months after diagnosis to ascertain the co-occurrence of eight distinct symptom and functional domains. We used factor analysis to identify co-occurring domains and latent profile analysis (LPA) to identify subgroups of survivors with different symptom and function clusters. Multinomial logistic regression models were used to identify risk/protective factors.

RESULTS

Factor analysis demonstrated a single underlying factor structure that included all eight health domains with depression and anxiety highly correlated (r = 0.87). The LPA identified three symptom and function clusters, with 30% of survivors in the low health-related quality of life (HRQOL) profile having the highest symptom burden and lowest functioning. In multivariable models, survivors more likely to be in the low HRQOL profile included being non-White, female, those with a history of cardiac or mental health conditions, and chemotherapy recipients. Survivors less likely to be in the low HRQOL profile included those with older age, greater financial well-being, and more spirituality.

CONCLUSION

Nearly one-third of colorectal cancer survivors experienced a cluster of physical and psychosocial symptoms that co-occur with clinically relevant deficits in function.

IMPLICATIONS FOR CANCER SURVIVORS

Improving the identification of risk factors for having the highest symptom and lowest function profile can inform the development of clinical interventions to mitigate their adverse impact on cancer survivors' HRQOL.

Analyzing the symptoms in colorectal and breast cancer patients with or without type 2 diabetes using EHR data

This research extracted patient-reported symptoms from free-text EHR notes of colorectal and breast cancer patients and studied the correlation of the symptoms with comorbid type 2 diabetes, race, and smoking status. An NLP framework was developed first to use UMLS MetaMap to extract all symptom terms from the 366,398 EHR clinical notes of 1694 colorectal cancer (CRC) patients and 3458 breast cancer (BC) patients. Semantic analysis and clustering algorithms were then developed to categorize all the relevant symptoms into eight symptom clusters defined by seed terms. After all the relevant symptoms were extracted from the EHR clinical notes, the frequency of the symptoms reported from colorectal cancer (CRC) and breast cancer (BC) patients over three time-periods post-chemotherapy was calculated. Logistic regression (LR) was performed with each symptom cluster as the response variable while controlling for diabetes, race, and smoking status. The results show that the CRC and BC patients with Type 2 Diabetes (T2D) were more likely to report symptoms than CRC and BC without T2D over three time-periods in the cancer trajectory. We also found that current smokers were more likely to report anxiety (CRC, BC), neuropathic symptoms (CRC, BC), anxiety (BC), and depression (BC) than non-smokers.

Distinct profiles of multiple co-occurring symptoms in patients with gastrointestinal cancers receiving chemotherapy

PURPOSE

Identify subgroups of gastrointestinal (GI) cancer patients with distinct multiple co-occurring symptom profiles and evaluate for differences among these subgroups in demographic and clinical characteristics and quality of life (QOL) outcomes.

METHODS

Patients with GI cancers (n = 399) completed the Memorial Symptom Assessment Scale (MSAS) that was used to assess for multiple co-occurring symptoms. Latent class analysis (LCA) was used to identify subgroups of patients with distinct symptom profiles using symptom occurrence ratings. Differences in demographic and clinical characteristics and QOL outcomes among the subgroups were evaluated using parametric and nonparametric tests.

RESULTS

All Low (36.6%), Moderate (49.4%), and All High (14.0%) classes were identified. Compared to the All Low class, patients in the other two classes were significantly younger and were more likely to report depression and back pain. Compared to the other two classes, patients in the All High class had fewer years of education and a higher number of comorbidities. Significant differences were found among the three classes for comorbidity burden and total number of MSAS symptoms (i.e., All Low < Moderate < All High), as well as for performance status (i.e., All Low > Moderate > All High). A higher symptom burden was associated with poorer QOL outcomes.

CONCLUSIONS

The first study to identify subgroups of patients with GI cancers based on distinct symptom profiles. LCA allowed for the identification of risk factors associated with a higher symptom burden. Clinicians can use this information to identify high-risk patients and develop personalized symptom management interventions.

Subgroups of patients undergoing chemotherapy with distinct cognitive fatigue and evening physical fatigue profiles

PURPOSE

The purpose was to model cognitive fatigue and evening physical fatigue together to determine subgroups of patients with distinct cognitive fatigue AND evening physical fatigue profiles. Once these profiles were identified, differences among the subgroups in demographic and clinical characteristics, co-occurring symptoms, and quality of life outcomes were evaluated.

METHODS

Oncology patients (n = 1332) completed self-report measures of cognitive fatigue and evening physical fatigue, six times over two cycles of chemotherapy. Latent profile analysis, which combined the two symptom scores, was done to identify subgroups of patients with distinct cognitive fatigue AND evening physical fatigue profiles.

RESULTS

Three distinct profiles (i.e., Low [20.5%], Moderate [39.6%], and High [39.6%]) were identified. Compared to the Low class, patients in the High class were younger, female, and more likely to live alone and had a higher comorbidity burden and a lower functional status. In addition, these patients had a higher symptom burden and a poorer quality of life.

CONCLUSION

Based on clinically meaningful cutoff scores, 80% of the patients in this study had moderate to high levels of both cognitive fatigue and evening physical fatigue. In addition, these patients experienced high levels of other common symptoms (e.g., anxiety, depression, sleep disturbance, and pain). These co-occurring symptoms and other modifiable characteristics associated with membership in the Moderate and High classes may be potential targets for individualized symptom management interventions.

Prescribed Walking for Glycemic Control and Symptom Management in Patients Without Diabetes Undergoing Chemotherapy

BACKGROUND

Hyperglycemia may potentiate symptom experiences. Exercise is a nonpharmacological intervention that can potentially improve glycemic control and mitigate symptom experiences in patients undergoing chemotherapy for cancer.

OBJECTIVES

The primary objective was to assess the feasibility of patients engaging in a walking exercise study for 6 months. We also evaluated the effects of a prescribed walking program on glycemic control and for changes over time in the severity of pain, fatigue, depression, and sleep disturbance in patients undergoing chemotherapy for breast, lung, gynecologic, or gastrointestinal cancer.

METHODS

A randomized pilot intervention study was conducted to evaluate differences within and between a prescribed walking program intervention group and a control group. All patients were followed for 6 months, had glycosylated hemoglobin A1c measured at enrollment and 6 months, and completed symptom questionnaires at enrollment, 3 months, and 6 months. Data were analyzed using descriptive statistics and analysis of covariance.

RESULTS

Most of the patients who enrolled completed the 6-month study. The few who withdrew expressed feeling overwhelmed. The sample was predominately non-Hispanic White female patients with breast cancer with a normal-to-slightly-overweight body mass index. The intervention group had a slight decrease in glycosylated hemoglobin A1c at 6 months. In addition, at 6 months, compared to the control group, the intervention group had significantly less sleep disturbance and depression. No other within- or between-group differences were found.

DISCUSSION

It is feasible for patients undergoing chemotherapy to participate in a prescribed walking program. Exercise, such as walking, may decrease hyperglycemia and symptom severity. Additional research with larger samples is warranted.

Prediction of evening fatigue severity in outpatients receiving chemotherapy: less may be more

Background

Fatigue is the most common and debilitating symptom experienced by oncology patients undergoing chemotherapy. Little is known about patient characteristics that predict changes in fatigue severity over time.

Purpose

To predict the severity of evening fatigue in the week following the administration of chemotherapy using machine learning approaches.

Methods

Outpatients with breast, gastrointestinal, gynecological, or lung cancer (N=1217) completed questionnaires one week prior to and one week following administration of chemotherapy. Evening fatigue was measured with the Lee Fatigue Scale (LFS). Separate prediction models for evening fatigue severity were created using clinical, symptom, and psychosocial adjustment characteristics and either evening fatigue scores or individual fatigue item scores. Prediction models were created using two regression and three machine learning approaches.

Results

Random forest (RF) models provided the best fit across all models. For the RF model using individual LFS item scores, two of the 13 individual LFS items (i.e., "worn out", "exhausted") were the strongest predictors.

Conclusion

This study is the first to use machine learning techniques to predict evening fatigue severity in the week following chemotherapy from fatigue scores obtained in the week prior to chemotherapy. Our findings suggest that the language used to assess clinical fatigue in oncology patients is important and that two simple questions may be used to predict evening fatigue severity.

Distinct Subgroups of Patients With Lung Cancer Receiving Chemotherapy: A Latent Transition Analysis

Objectives

To identify subgroups of patients with lung cancer receiving chemotherapy based on the severity dimension of symptom experience, and to examine changes in membership between these subgroups over time.

Methods

Patients who were scheduled to receive chemotherapy completed the Chinese version of the MD Anderson Symptom Inventory and the revised lung cancer module with a total of 19 symptom items. Data were collected at three time points: two weeks before chemotherapy (T1), after chemotherapy cycle 1 (T2), and after chemotherapy cycle 3 or above (T3). The latent profile analysis and latent transition analysis were used to identify underlying subgroups and describe changes in subgroup membership over time.

Results

From the total sample (N = 195), 160 patients completed the symptom assessment at T1, T2, and T3. Two distinct latent symptom profiles of patients could be identified at T1, T2, and T3, which were classified as "Mild" and "Moderate-Severe" profiles. From T1 to T2 and T3, members in the Mild profile were more likely to move to the Moderate-Severe profile. Chemotherapy protocols, prior surgery treatment, and level of education can predict the transitions.

Conclusion

Results provide a better understanding of the patient's different symptom experiences and characteristics. These could help clinicians to anticipate symptom patterns and develop interventions in lung cancer patients who were scheduled to receive chemotherapy for the first time.

A COVID-19 screening tool for oncology telephone triage

PURPOSE

Symptoms associated with COVID-19 infection have made the assessment and triage of cancer patients extremely complicated. The purpose of this paper is to describe the development and implementation of a COVID-19 screening tool for oncology telephone triage.

METHODS

An Ambulatory Oncology Clinical Nurse Educator and three faculty members worked on the development of an oncology specific triage tool based on the challenges that oncology nurses were having with the generic COVID triage tool. A thorough search of the published literature, as well as pertinent websites, verified that no screening tool for oncology patients was available.

RESULTS

The screening tool met a number of essential criteria: (1) simple and easy to use, (2) included the most common signs and symptoms as knowledge of COVID-19 infection changed, (3) was congruent with the overall screening procedures of the medical center, (4) included questions about risk factors for and environmental exposures related to COVID-19, and (5) assessed patient's current cancer history and treatment status. Over a period of 3 weeks, the content and specific questions on the tool were modified based on information obtained from a variety of sources and feedback from the triage nurses.

CONCLUSION

Within 1 month, the tool was developed and implemented in clinical practice. Oncology clinicians can modify this tool to triage patients as well as to screen patients in a variety of outpatient settings (e.g., chemotherapy infusion units, radiation therapy departments). The tool will require updates and modifications based on available resources and individual health care organizations' policies and procedures.

Co-occurrence of decrements in physical and cognitive function is common in older oncology patients receiving chemotherapy

PURPOSE

Older adults receiving cancer chemotherapy are at increased risk for decrements in physical (PF) and cognitive (CF) function.

OBJECTIVES

Study identified subgroups of patients with distinct PF and CF profiles; risk factors associated with subgroup membership; and impact of subgroup membership on quality of life (QOL).

METHODS

In 366 older oncology patients, PF and CF were assessed using the Physical Component Summary (PCS) of the SF-12 and Attentional Function Index, respectively. Latent profile analysis was used to identify subgroups of older patients with distinct PF/CF profiles.

RESULTS

Three distinct PF/CF profiles were identified (i.e., Very Low PF + Moderate CF (15.6%); Low PF + Low CF (39.3%), Normal PF + Normal CF (45.1%)). Compared to the both Normal class, patients in the other two classes had a lower functional status, a worse comorbidity profile, and were less likely to exercise on a regular basis. Compared to the Both Normal class, patients in the Both Low class were less likely to be married/partnered, more likely to live alone, less likely to be employed, and more likely to report depression and back pain. Compared to the other two classes, patients in the Both Low class had a lower annual household income and were receiving chemotherapy with a worse toxicity profile.

CONCLUSION

First study to use a person-centered analytic approach to identify subgroups of older adults with distinct PF/CF profiles. Fifty-five percent of the older adults had statistically significant and clinically meaningful decrements in both PF AND CF that had negative effects on all aspects of QOL.

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

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.

Higher levels of stress and different coping strategies are associated with greater morning and evening fatigue severity in oncology patients receiving chemotherapy

PURPOSE

A cancer diagnosis and associated treatments are stressful experiences for most patients. Patients' perceptions of stress and their use of coping strategies may influence fatigue severity. This study extends our previous work describing distinct profiles of morning (i.e., Very Low, Low, High, and Very High) and evening (i.e., Low, Moderate, High, and Very High) fatigue in oncology patients by evaluating for differences in stress and coping strategies among these fatigue classes.

METHODS

This longitudinal study evaluated for changes in morning and evening fatigue in oncology patients (n = 1332) over two cycles of chemotherapy (CTX). Patients completed measures of cumulative exposure to stressful life events (SLEs) (i.e., the Life Stressor Checklist-Revised), general stress (i.e., Perceived Stress Scale [PSS]), cancer-specific stress (i.e., Impact of Event Scale-Revised [IES-R]), and coping strategies (i.e., Brief Cope). Differences among the latent classes were evaluated using analyses of variance, Kruskal-Wallis, or chi-square tests.

RESULTS

Patients in both the Very High morning and evening fatigue classes reported higher numbers of and a higher impact from previous SLEs and higher PSS scores than the other fatigue classes. The IES-R scores for the Very High morning fatigue class met the criterion for subsyndromal PTSD. Patients in the Very High evening fatigue class used a higher number of engagement coping strategies compared with the Very High morning fatigue class.

CONCLUSIONS

Our findings suggest that interventions to reduce stress and enhance coping warrant investigation to decrease fatigue in patients undergoing CTX.

Co-occuring symptoms in older oncology patients with distinct attentional function profiles

PURPOSE

Evaluate how subgroups of older adults with distinct attentional function profiles differ on the severity of nine common symptoms and determine demographic and clinical characteristics and symptom severity scores associated with membership in the low and moderate attentional function classes.

METHODS

Three subgroups of older oncology outpatients were identified using latent profile analysis based on Attentional Function Index (AFI) scores. Symptoms were assessed prior to the second or third cycle of CTX. Logistic regressions evaluated for associations with attentional function class membership.

RESULTS

For trait anxiety, state anxiety, depression, sleep disturbance, morning fatigue, and evening fatigue scores, differences among the latent classes followed the same pattern (low > moderate > high). For morning and evening energy, compared to high class, patients in low and moderate classes reported lower scores. For pain, compared to moderate class, patients in low class reported higher scores. In the logistic regression analysis, compared to high class, patients with lower income, higher comorbidity, higher CTX toxicity score, and higher levels of state anxiety, depression, and sleep disturbance were more likely to be in low AFI class. Compared to high class, patients with higher comorbidity and trait anxiety and lower morning energy were more likely to be in moderate AFI class.

CONCLUSIONS

Consistent with the hypothesis that an increased risk for persistent cognitive decline is likely related to a variety of physical and psychological factors, for six of the nine symptoms, a "dose response" effect was observed with higher symptom severity scores associated with a progressive decline in attentional function.

Stability of Symptom Clusters in Patients With Lung Cancer Receiving Chemotherapy

CONTEXT

Patients with lung cancer who undergo chemotherapy (CTX) experience multiple symptoms. Evaluation of how these symptoms cluster together and how these symptom clusters change over time are salient questions in symptom clusters research.

OBJECTIVES

The purposes of this analysis, in a sample of patients with lung cancer (n = 145) who were receiving CTX, were to 1) evaluate for differences in the number and types of symptom clusters at three time points (i.e., before their next cycle of CTX, the week after CTX, and two weeks after CTX) using ratings of symptom occurrence and severity and 2) evaluate for changes in these symptom clusters over time.

METHODS

At each assessment, a modified version of the Memorial Symptom Assessment Scale was used to assess the occurrence and severity of 38 symptoms. Exploratory factor analyses were used to extract the symptom clusters.

RESULTS

Across the two symptom dimensions (i.e., occurrence and severity) and the three assessments, six distinct symptom clusters were identified; however, only three of these clusters (i.e., lung cancer specific, psychological, nutritional) were relatively stable across both dimensions and across time. Two additional clusters varied by time but not by symptom dimension (i.e., epithelial/gastrointestinal and epithelial). A sickness behavior cluster was identified at each assessment with the exception of the week before CTX using only the severity dimension.

CONCLUSION

Findings provide insights into the most common symptom clusters in patients with lung cancer undergoing CTX. Most common symptoms within each cluster appear to be relatively stable across the two dimensions, as well as across time.

Network Analysis of the Multidimensional Symptom Experience of Oncology

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.

Conley Y, Hammer M, Katsaragakis S, M. Kober K, D. Levine J, McCann L, Patiraki E, P. Furlong E, A. Fox P, M. Paul S, Ream E, Wright F, Miaskowski C. Learning from data to predict future symptoms of oncology patients

Effective symptom management is a critical component of cancer treatment. Computational tools that predict the course and severity of these symptoms have the potential to assist oncology clinicians to personalize the patient's treatment regimen more efficiently and provide more aggressive and timely interventions. Three common and inter-related symptoms in cancer patients are depression, anxiety, and sleep disturbance. In this paper, we elaborate on the efficiency of Support Vector Regression (SVR) and Non-linear Canonical Correlation Analysis by Neural Networks (n-CCA) to predict the severity of the aforementioned symptoms between two different time points during a cycle of chemotherapy (CTX). Our results demonstrate that these two methods produced equivalent results for all three symptoms. These types of predictive models can be used to identify high risk patients, educate patients about their symptom experience, and improve the timing of pre-emptive and personalized symptom management interventions.

Characteristics Associated With Physical Function Trajectories in Older Adults With Cancer During Chemotherapy

CONTEXT

Studies on physical function trajectories in older adults during chemotherapy remain limited.

OBJECTIVES

The objective of this study was to determine demographic, clinical, and symptom characteristics associated with initial levels as well as trajectories of physical function over two cycles of chemotherapy in adults aged ≥65 years with breast, gastrointestinal, gynecological, or lung cancer.

METHODS

Older adults with cancer (n = 363) who had received chemotherapy within the preceding four weeks were assessed six times over two cycles of chemotherapy using the Short Form-12 Physical Component Summary (PCS) score. Hierarchical linear modeling was used to evaluate for interindividual variability in initial levels and trajectories of PCS scores.

RESULTS

Mean age was 71.4 years (SD 5.5). Mean PCS score at enrollment was 40.5 (SD .45). On average, PCS scores decreased slightly (i.e., 0.21 points) at each subsequent assessment. Lower PCS scores at enrollment were associated with older age, greater comorbidity, being unemployed, lack of regular exercise, higher morning fatigue, lower evening energy, occurrence of pain, lower trait anxiety, and lower attentional function. Only higher morning fatigue and lower enrollment PCS scores were associated with decrements in physical function over time.

CONCLUSION

While several symptoms were associated with decrements in PCS scores at enrollment in older adults with cancer receiving chemotherapy, morning fatigue was the only symptom associated with decreases in physical function over time. Regular assessments of symptoms and implementation of evidence-based interventions should be considered to maintain physical function in older adults during chemotherapy.

Risk Factors Associated With Chemotherapy-Induced Nausea in the Week Before the Next Cycle and Impact of Nausea on Quality of Life Outcomes

CONTEXT

Despite current advances in antiemetic treatments, between 19% and 58% of oncology patients experience chemotherapy-induced nausea (CIN).

OBJECTIVES

Aims of this post hoc exploratory analysis were to determine occurrence, severity, and distress of CIN and evaluate for differences in demographic and clinical characteristics, symptom severity, stress; and quality of life (QOL) outcomes between oncology patients who did and did not report CIN in the week before chemotherapy (CTX). Demographic, clinical, symptom, and stress characteristics associated with CIN occurrence were determined.

METHODS

Patients (n = 1296) completed questionnaires that provided information on demographic and clinical characteristics, symptom severity, stress, and QOL. Univariate analyses evaluated for differences in demographic and clinical characteristics, symptom severity, stress, and QOL scores between the two patient groups. Multiple logistic regression analysis was used to evaluate for factors associated with nausea group membership.

RESULTS

Of the 1296 patients, 47.5% reported CIN. In the CIN group, 15% rated CIN as severe and 23% reported high distress. Factors associated with CIN included less education; having childcare responsibilities; poorer functional status; higher levels of depression, sleep disturbance, evening fatigue, and intrusive thoughts; as well as receipt of CTX on a 14-day CTX cycle and receipt of an antiemetic regimen that contained serotonin receptor antagonist and steroid. Patients in the CIN group experienced clinically meaningful decrements in QOL.

CONCLUSION

This study identified new factors (e.g., poorer functional status, stress) associated with CIN occurrence. CIN negatively impacted patients' QOL. Pre-emptive and ongoing interventions may alleviate CIN occurrence in high-risk patients.

Distinct attentional function profiles in older adults receiving cancer chemotherapy

PURPOSE

While attentional function is an extremely important patient outcome for older adults, research on changes in function in this group is extremely limited. The purposes of this study were to: identify subgroups of older patients (i.e., latent growth classes) based on changes in their level of self-reported attentional function; determine which demographic and clinical characteristics were associated with subgroup membership; and determine if these subgroups differed on quality of life (QOL) outcomes.

METHODS

Older oncology outpatients (n = 365) who were assessed for changes in attention and working memory using the Attentional Function Index a total of six times over two cycles of chemotherapy (CTX). QOL was assessed using the Medical Outcomes Study-Short Form 12 and the QOL-Patient Version Scale. Latent profile analysis (LPA) was used to identify subgroups of older adults with distinct attentional function profiles.

RESULTS

Three distinct attentional functional profiles were identified (i.e., low, moderate, and high attentional function). Compared to the high class, older adults in the low and moderate attentional function classes had lower functional status scores, a worse comorbidity profile and were more likely to be diagnosed with depression. In addition, QOL scores followed an expected pattern (low class < moderate class < high attentional function class).

CONCLUSIONS

Three distinct attentional function profiles were identified among a relatively large sample of older adults undergoing CTX. The phenotypic characteristics associated with membership in the low and moderate latent classes can be used by clinicians to identify high risk patients.

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

CONTEXT

Risk profiling of oncology patients based on their symptom experience assists clinicians to provide more personalized symptom management interventions. Recent findings suggest that oncology patients with distinct symptom profiles can be identified using a variety of analytic methods.

OBJECTIVES

The objective of this study was to evaluate the concordance between the number and types of subgroups of patients with distinct symptom profiles using latent class analysis and K-modes analysis.

METHODS

Using data on the occurrence of 25 symptoms from the Memorial Symptom Assessment Scale, that 1329 patients completed prior to their next dose of chemotherapy (CTX), Cohen's kappa coefficient was used to evaluate for concordance between the two analytic methods. For both latent class analysis and K-modes, differences among the subgroups in demographic, clinical, and symptom characteristics, as well as quality of life outcomes were determined using parametric and nonparametric statistics.

RESULTS

Using both analytic methods, four subgroups of patients with distinct symptom profiles were identified (i.e., all low, moderate physical and lower psychological, moderate physical and higher Psychological, and all high). The percent agreement between the two methods was 75.32%, which suggests a moderate level of agreement. In both analyses, patients in the all high group were significantly younger and had a higher comorbidity profile, worse Memorial Symptom Assessment Scale subscale scores, and poorer QOL outcomes.

CONCLUSION

Both analytic methods can be used to identify subgroups of oncology patients with distinct symptom profiles. Additional research is needed to determine which analytic methods and which dimension of the symptom experience provide the most sensitive and specific risk profiles.

Distinct Physical Function Profiles in Older Adults Receiving Cancer Chemotherapy

CONTEXT

Although physical function is an important patient outcome, little is known about changes in physical function in older adults receiving chemotherapy (CTX).

OBJECTIVES

Identify subgroups of older patients based on changes in their level of physical function; determine which demographic and clinical characteristics were associated with subgroup membership; and determine if these subgroups differed on quality-of-life (QOL) outcomes.

METHODS

Latent profile analysis was used to identify groups of older oncology patients (n = 363) with distinct physical function profiles. Patients were assessed six times over two cycles of CTX using the Physical Component Summary score from the Short Form 12. Differences, among the groups, in demographic and clinical characteristics and QOL outcomes were evaluated using parametric and nonparametric tests.

RESULTS

Three groups of older oncology patients with distinct functional profiles were identified: Well Below (20.4%), Below (43.8%), and Above (35.8%) normative Physical Component Summary scores. Characteristics associated with membership in the Well Below class included the following: lower annual income, a higher level of comorbidity, being diagnosed with depression and back pain, and lack of regular exercise. Compared with the Above class, patients in the other two classes had significantly poorer QOL outcomes.

CONCLUSION

Almost 65% of older oncology patients reported significant decrements in physical function that persisted over two cycles of CTX. Clinicians can assess for those characteristics associated with poorer functional status to identify high-risk patients and initiate appropriate interventions.

Differences in Symptom Clusters Identified Using Ratings of Symptom Occurrence vs. Severity in Lung Cancer Patients Receiving Chemotherapy

CONTEXT

An important question in symptom clusters research is whether the number and types of symptom clusters vary based on the specific dimension of the symptom experience used to create the clusters.

OBJECTIVES

Given that lung cancer patients undergoing chemotherapy (CTX) report an average of 14 co-occurring symptoms and studies of symptom clusters in these patients are limited, the purpose of this study, in lung cancer patients undergoing CTX (n = 145), was to identify whether the number and types of symptom clusters differed based on whether symptom occurrence rates or symptom severity ratings were used to create the clusters.

METHODS

A modified version of the Memorial Symptom Assessment Scale was used to assess for the occurrence and severity of 38 symptoms, one week after the administration of CTX. Exploratory factor analysis was used to extract the symptom clusters.

RESULTS

Both the number and types of symptom clusters were relatively similar using symptom occurrence rates or symptom severity ratings. Five symptom clusters were identified using both symptom occurrence rates and severity ratings (i.e., sickness behavior, lung cancer specific, psychological, nutritional, and epithelial). Across these two dimensions, the specific symptoms within each of the symptom clusters were relatively similar.

CONCLUSIONS

Identification of symptom clusters in patients with lung cancer may assist with the development of more targeted symptom management interventions. Future studies are warranted to determine if symptom clusters change over a cycle of CTX in patients with lung cancer.

Differences in symptom clusters identified using symptom occurrence rates versus severity ratings in patients with breast cancer undergoing chemotherapy

PURPOSE

One of the unanswered questions in symptom clusters research is whether the number and types of symptom clusters vary based on the dimension of the symptom experience used to create the clusters. Given that patients with breast cancer receiving chemotherapy (CTX), report between 10 and 32 concurrent symptoms and studies of symptom clusters in these patients are limited, the purpose of this study, in breast cancer patients undergoing CTX (n = 515), was to identify whether the number and types of symptom clusters differed based on whether symptom occurrence rates or symptom severity ratings were used to create the clusters.

METHODS

A modified version of the Memorial Symptom Assessment Scale was used to assess for the occurrence and severity of 38 symptoms, one week after the administration of CTX. Exploratory factor analysis was used to extract the symptom clusters.

RESULTS

Both the number and types of symptom clusters were similar using symptom occurrence rates or symptom severity ratings. Five symptom clusters were identified using symptom occurrence rates (i.e., psychological, hormonal, nutritional, gastrointestinal, epithelial). Six symptom clusters (i.e., psychological, hormonal, nutritional, gastrointestinal, epithelial, chemotherapy neuropathy) were identified using symptom severity ratings. Across the two dimensions, the specific symptoms within each of the symptom clusters were similar.

CONCLUSIONS

Identification of symptom clusters in patients with breast cancer may be useful in guiding symptom management interventions. Future studies are warranted to determine if symptom clusters remain stable over a cycle of CTX in patients with breast cancer.

Patient factors and quality of life outcomes differ among four subgroups of oncology patients based on symptom occurrence

CONTEXT

Reviews of the literature on symptoms in oncology patients undergoing curative treatment, as well as patients receiving palliative care, suggest that they experience multiple, co-occurring symptoms and side effects.

OBJECTIVES

The purposes of this study were to determine if subgroups of oncology patients could be identified based on symptom occurrence rates and if these subgroups differed on a number of demographic and clinical characteristics, as well as on quality of life (QoL) outcomes.

METHODS

Latent class analysis (LCA) was used to identify subgroups (i.e. latent classes) of patients with distinct symptom experiences based on the occurrence rates for the 13 most common symptoms from the Memorial Symptom Assessment Scale.

RESULTS

In total, 534 patients with breast, head and neck, colorectal, or ovarian cancer participated. Four latent classes of patients were identified based on probability of symptom occurrence: all low class [i.e. low probability for all symptoms (n = 152)], all high class (n = 149), high psychological class (n = 121), and low psychological class (n = 112). Patients in the all high class were significantly younger compared with patients in the all low class. Furthermore, compared to the other three classes, patients in the all high class had lower functional status and higher comorbidity scores, and reported poorer QoL scores. Patients in the high and low psychological classes had a moderate probability of reporting physical symptoms. Patients in the low psychological class reported a higher number of symptoms, a lower functional status, and poorer physical and total QoL scores.

CONCLUSION

Distinct subgroups of oncology patients can be identified based on symptom occurrence rates. Patient characteristics that are associated with these subgroups can be used to identify patients who are at greater risk for multiple co-occurring symptoms and diminished QoL, so that these patients can be offered appropriate symptom management interventions.