Symptom Clusters in Patients With Gastrointestinal Cancers Using Different Dimensions of the Symptom Experience

Symptom cluster study undergoing chemotherapy in breast cancer patients: Latent class analysis and contemporaneous network analysis

Objective

This study aims to explore the subgroups and networks of symptom clusters in breast cancer patients undergoing chemotherapy, and to provide effective interventions for the core symptoms.

Methods

A cross-sectional survey was conducted at four comprehensive hospitals in Foshan City, China, from August to November 2023. A total of 292 participants completed the social determinants of health questionnaire, the numerical rating scale (NRS), the Pittsburgh sleep quality index (PSQI), the Chinese version of the cancer fatigue scale (CFS), and the hospital anxiety and depression Scale (HADS). Latent class analysis (LCA) was utilized to distinguish subgroups, and network analysis was utilized to identify core symptoms among different subgroups.

Results

Breast cancer patients undergoing chemotherapy exhibit symptoms were divided into two subgroups: the high burden group of symptoms (72.3%, Class 1) and the low burden group of symptoms (27.7%, Class 2). Education attainment, work status, family monthly income per capita, and daily sleep duration (hours) were associated with subgroup membership. "Panic feelings" (# HADS-A11) were the core symptom in both the full sample and Class 2, while "tension or pain" (# HADS-A1) was the core symptom in Class 1.

Conclusions

The core symptoms of fear, enjoyment, nervousness, and pain varied across subgroups of patients and could inform the current strategies for symptom management in breast cancer chemotherapy patients.

The change of symptom clusters in gastric cancer patients during the perioperative period: a longitudinal study

AIM AND OBJECTIVES

The purpose of this study was to describe the number, type and trajectory of symptom clusters during the perioperative period in patients with gastric cancer at four different time points. The study also aimed to identify the changes and consistency of these symptom clusters over time.

DESIGN

This was a longitudinal study.

METHODS

This study was conducted in a tertiary cancer hospital with 205 patients with gastric cancer. The M.D. Anderson Symptom Inventory Gastrointestinal Cancer Module was used to assess the incidence and severity of symptom clusters. Exploratory factor analysis was used to extract symptom clusters.

RESULTS

The study identified four symptom clusters in patients with gastric cancer during the perioperative period: gastrointestinal symptom cluster, physical symptom cluster, psychological symptom cluster, and sleep disturbance symptom cluster. These clusters were observed across two to four time points.

CONCLUSION

The findings of this study provide scientific evidence for medical staff and researchers to better understand the symptoms of patients with gastrointestinal cancer during the perioperative period. These findings can help develop individualized interventions for managing symptoms.

RELEVANCE TO CLINICAL PRACTICE

Gastric cancer patients suffered from various symptom clusters, which lasted from one day before surgery to one month after surgery. They should be given careful consideration by clinical staff.

Symptom networks in older adults with cancer: A network analysis

INTRODUCTION

Due to aging, older adults with cancer (OAC) may be confronted with a complex interplay of multiple age-related issues; coupled with receiving cancer treatment, OAC may experience multiple concurrent symptoms that require the identification of the core symptom for effective management. Constructing symptom networks will help in the identification of core symptoms and help achieve personalized and precise interventions. Currently, few studies have used symptom networks to identify core symptoms in OAC. Our objectives were to construct symptom networks of OAC, explore the core symptoms, and compare the differences in symptom networks among various subgroups.

MATERIALS AND METHODS

Secondary analysis was performed using data from 485 OAC collected in 2021 from a cross-sectional survey named the Shanghai CANcer Survivor (SCANS) Report. The MD Anderson Symptom Inventory (MDASI) was used to assess the incidence and severity of cancer-related symptoms. We used the R package to construct symptom networks and identify the centrality indices. The network comparison test was used to compare network differences among the subgroups.

RESULTS

The most common and severe symptoms reported were fatigue, disturbed sleep, and difficulty remembering. The network density was 0.718. Vomiting (rs = 1.81, rb = 2.13), fatigue (rs = 1.54, rb = 1.93), and sadness (rs = 0.81, rb = 0.69) showed the highest strength values, which suggested that these symptoms were more likely to co-occur with other symptoms. The network comparison tests showed significant differences in symptom network density between the subgroups categorized as survival "< 5 years" and survival "≥ 5 years" (p = 0.002), as well as between the those with comorbidities and those without comorbidities (p = 0.037).

DISCUSSION

Our study identified symptom networks in 485 OAC. Vomiting, fatigue, and sadness were important symptoms in the symptom networks of OAC. The symptom networks differed among populations with different survival durations and comorbidities. Our network analysis provides a reference for future targeted symptom management and interventions in OAC. In the future, conducting dynamic research on symptom networks will be crucial to explore interaction mechanisms and change trends between symptoms.

Chemotherapy-related symptom networks in distinct subgroups of Chinese patients with gastric cancer

Objective

This study aims to identify distinct subgroups among gastric cancer patients undergoing chemotherapy (CTX), delineate associated symptom networks, and ascertain the clinical and sociodemographic variables contributing to diverse symptom patterns.

Methods

Conducted in eastern China, our investigation involved gastric cancer patients receiving CTX. We gathered data using the M.D. Anderson Symptom Inventory Gastrointestinal Cancer Module along with clinical and sociodemographic variables. Subgroups were discerned based on symptom severity through latent profile analysis, and subsequent comparisons were made regarding the symptom networks in different subgroups.

Results

The analysis encompassed 677 eligible gastric cancer patients, revealing three profiles: "Profile 1: low class" (n = 354, 52.3%), "Profile 2: moderate class" (n = 222, 32.8%), and "Profile 3: all high class" (n = 101, 14.9%). Nausea-vomiting exhibited robust associations in the symptom networks of all subgroups, whereas sadness-distress, and taste change-lack of appetite were notably linked with Profile 1 and Profile 2. Distress emerged as a core symptom in Profile 1, lack of appetite dominated the symptom network in Profile 2, and fatigue attained the highest strength in Profile 3. Distinct symptom profiles were influenced by variables such as education level, CTX combined with surgical or herbal treatment, psychological resilience, and social support.

Conclusions

Patients within different subgroups manifest individualized patterns of symptom profiles. Analyzing demographics, disease characteristics, and psychosocial information among diverse subgroups facilitates healthcare providers in devising more personalized and targeted symptom management strategies, thereby alleviating the symptom burden on patients.

Stability and consistency of symptom clusters in younger versus older patients receiving chemotherapy

BACKGROUND

By 2035, the number of newly diagnosed cancer cases will double and over 50% will be in older adults. Given this rapidly growing demographic, a need exists to understand how age influences oncology patients' symptom burden. The study purposes were to evaluate for differences in the occurrence, severity, and distress of 38 symptoms in younger (< 60 years) versus older (≥ 60 years) oncology patients undergoing chemotherapy and to evaluate for differences in the stability and consistency of symptom clusters across the two age groups.

METHODS

A total of 1329 patients were dichotomized into the younger and older groups. Patients completed demographic and clinical questionnaires prior to the initiation of their second or third cycle of chemotherapy. A modified version of Memorial Symptom Assessment Scale was used to evaluate the occurrence, severity, and distress of 38 common symptoms associated with cancer and its treatment. Differences between the two age groups in demographic and clinical characteristics and ratings of occurrence, severity, and distress for the 38 symptoms were evaluated using parametric and nonparametric tests. Exploratory factor analyses were done within each age group to identify symptom clusters using symptom occurrence rates.

RESULTS

Compared to the younger group (14.8 (± 7.0)), older adults reported a lower mean number of symptoms (12.9 (± 7.2)). Older patients experienced lower occurrence rates for almost 50% of the symptoms. Regarding symptom clusters, an eight-factor solution was selected for both age groups. Across the two age groups, the eight symptom clusters (i.e., physical and cognitive fatigue, respiratory, psychological, hormonal, chemotherapy-related toxicity, weight gain, gastrointestinal, epithelial) were stable. However, symptoms within the physical and cognitive, chemotherapy-related toxicity, and gastrointestinal clusters were not consistent across the age groups.

CONCLUSIONS

To be able to provide tailored and effective symptom management interventions to older oncology patients, routine assessments of the core symptoms unique to the symptom clusters identified for this group warrants consideration. The underlying mechanism(s) for these inconsistencies in symptom burden is an important focus for future studies.

Identification of subgroups of patients with gastrointestinal cancers based on symptom severity and frequency: A latent profile and latent class analysis

PURPOSE

Identify subgroups of patients with gastrointestinal cancer with different frequency and severity of symptoms and assess differences in demographics, clinical characteristics, and degree of interference with daily life.

METHODS

This was a cross-sectional study. A total of 202 patients with gastrointestinal cancers completed the Chinese version of the MD Anderson Symptom Inventory for Gastrointestinal Cancer Module by convenience sampling. Subgroups of patients were identified using latent profile analysis and latent class analysis. Chi-squared, Mann-Whitney-U, and Kruskal-Wallis tests assessed differences among subgroups.

RESULTS

In terms of symptom severity, low (70.3%), Moderate (13.4%), and high (16.3%) classes were identified. Compared with the other two classes, the Moderate group had a higher proportion of patients with a history of tobacco and alcohol, esophageal cancer, and gastric cancer (P < 0.05). In terms of symptom frequency, all -high (57.9%), high physical symptoms (9.9%), and all-low (32.2%) classes were identified. All-high groups had a younger age and a higher proportion of patients with cancer stage IV (P < 0.05). The high group had the most interference with daily life in both perspectives (P < 0.001), and psycho-emotional symptoms were frequent and severe.

CONCLUSIONS

The two perspectives of symptom severity and frequency can play a complementary role in identifying high-risk groups. Clinical practitioners should strengthen psychological interventions in young and advanced cancer patients and provide pharmaceutical and non-pharmaceutical interventions for dysphagia symptoms in esophageal and gastric cancer patients with a history of tobacco and alcohol.

Symptom clusters in oncology outpatients: stability and consistency across a cycle of chemotherapy

OBJECTIVES

Improved understanding of the stability and consistency of symptom clusters across time, symptom dimensions and cancer diagnoses will lead to refinements in symptom assessments and management, and provide direction for mechanistic studies. Study purposes were to describe the occurrence, severity and distress of 38 symptoms; evaluate the stability and consistency of symptom clusters across a cycle of chemotherapy, three symptom dimensions and four distinct cancer types; and identify common and distinct symptom clusters.

METHODS

Oncology outpatients (n=1329) completed the Memorial Symptom Assessment Scale prior to their next cycle of chemotherapy (T1), 1 week after chemotherapy (T2) and 2 weeks after chemotherapy (T3). Symptom clusters were identified using exploratory factor analysis using unweighted least squares. GEOMIN rotated factor loadings with absolute values ≥0.40 were considered meaningful. Clusters were stable if they were identified across each time point and/or dimension. Clusters were consistent if the same two or three symptoms with the highest factor loadings were identified across each time point and/or dimension.

RESULTS

Patients reported 13.9 (±7.2) symptoms at T1, 14.0 (±7.0) at T2 and 12.2 (±6.8) at T3. Psychological, weight gain, gastrointestinal and respiratory clusters were stable across time and dimensions. Only the psychological, weight gain and respiratory clusters were consistent across time and dimensions.

CONCLUSION

Given the stability of the psychological, weight gain and gastrointestinal clusters across cancer diagnoses, symptoms within these clusters need to be routinely assessed. However, respiratory and hormonal clusters are unique to specific cancer types and the symptoms within these clusters are variable.

Factors Associated with the Nutritional Status of Women with Non-Metastatic Breast Cancer in a Brazilian High Complexity Oncology Center

BACKGROUND

Breast cancer poses a significant public health concern owing to its high prevalence and the risk of mortality associated with delayed diagnosis and treatment. The aim of this study was to assess the nutritional status of women with non-metastatic breast cancer and to identify factors associated with it.

METHODS

A cross-sectional observational study was conducted at a High Complexity Oncology Assistance Center in the southeast region of Brazil, with the aim of assessing the nutritional status in women undergoing treatment for stage I, II, or III breast cancer. Patients in palliative care or undergoing reconstructive surgery were excluded. Data collection took place between June 2022 and March 2023 and included questionnaires, physical examinations, laboratory tests, and anthropometric assessments. Nutritional status was assessed using measures such as BMI and skinfold thickness, while nutritional risk was assessed using the Nutritional Risk Screening (NRS-2002) tool.

RESULTS

Significant associations were found between nutritional risk and educational level (p = 0.03) and BMI (p = 0.01). Binary logistic regression analysis revealed a significant association between educational level and nutritional risk, indicating that lower educational level was associated with higher odds of nutritional risk (OR = 4.59; 95% CI = 1.01-21.04; p = 0.049). In addition, regarding BMI, it was observed that a BMI above 20.5 kg/m2 was associated with a higher likelihood of nutritional risk (OR = 0.09; 95% CI = 0.01-0.89; p = 0.039).

CONCLUSIONS

It is crucial to consider the nutritional status of breast cancer patients, alongside clinical factors, to offer comprehensive and personalized care. Gaining insight into the sociodemographic variables linked to nutritional risk can significantly contribute to our understanding of breast cancer. This knowledge, in turn, can aid in identifying effective strategies for public policy, health promotion, and prevention efforts aimed at tackling this condition.

Risk Factors of Health-Related Quality of Life among Gastrointestinal Cancer Survivors in the U.S.: With a Focus on Social and Behavioral Determinants of Health (SBDH)

BACKGROUND

Increasing numbers of long-term gastrointestinal (GI) cancer survivors highlight the importance of understanding the factors contributing to their health-related quality of life (HRQoL). We investigated the risk factors of HRQoL, including demographics, clinical characteristics, and social and behavioral determinants of health (SBDH).

METHODS

Data on adult GI cancer survivors (n = 3201) from the Behavioral Risk Factors Surveillance System (BRFSS) surveys from 2014-2021 (except for 2015) were analyzed. Unadjusted/adjusted logistic regression was used.

RESULTS

The majority were women (54%) and white (78%), with a median age of 67. Survivors who were 65 years or older, diagnosed with colorectal cancer, or who had fewer comorbidities were more likely to report significantly better HRQoL. Significant social factors of poor HRQoL included unmarried, racial and ethnic minorities, poor socioeconomic status, and poor healthcare access. Significant behavioral factors of poor HRQoL were lack of physical activity, heavy alcohol consumption, and current smoking, with lack of physical activity being the most significant factor.

CONCLUSIONS

The SBDH has a critical role in HRQoL. Future studies are warranted to develop a tailored survivorship intervention, such as physical rehabilitation, and to explore machine learning/artificial intelligence-based predictive models to identify cancer survivors at a high risk of developing poor HRQoL.

GI Symptoms in Pancreatic Cancer

This review will apply a multidisciplinary approach to GI symptoms with attention to symptom assessment (instruments and qualitative aspects), differential diagnosis, and recent findings relevant to management of symptoms and underlying diseases. We conclude that further development of supportive interventions for GI symptoms for both patient and caregivers has the potential to reduce distress from GI symptoms, and anticipate better symptom control with advances in scientific knowledge and improvement of the evidence base.

Flavonoids' Dual Benefits in Gastrointestinal Cancer and Diabetes: A Potential Treatment on the Horizon?

Diabetes and gastrointestinal cancers (GI) are global health conditions with a massive burden on patients' lives worldwide. The development of both conditions is influenced by several factors, such as diet, genetics, environment, and infection, which shows a potential link between them. Flavonoids are naturally occurring phenolic compounds present in fruits and vegetables. Once ingested, unabsorbed flavonoids reaching the colon undergo enzymatic modification by the gut microbiome to facilitate absorption and produce ring fission products. The metabolized flavonoids exert antidiabetic and anti-GI cancer properties, targeting major impaired pathways such as apoptosis and cellular proliferation in both conditions, suggesting the potentially dual effects of flavonoids on diabetes and GI cancers. This review summarizes the current knowledge on the impact of flavonoids on diabetes and GI cancers in four significant pathways. It also addresses the synergistic effects of selected flavonoids on both conditions. While this is an intriguing approach, more studies are required to better understand the mechanism of how flavonoids can influence the same impaired pathways with different outcomes depending on the disease.

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.

Symptom clusters in outpatients with cancer using different dimensions of the symptom experience

PURPOSE

Relatively few studies have evaluated for symptom clusters across multiple dimensions. It is unknown whether the symptom dimension used to create symptom clusters influences the number and types of clusters that are identified. Study purposes were to describe ratings of occurrence, severity, and distress for 38 symptoms in a heterogeneous sample of oncology patients (n = 1329) undergoing chemotherapy; identify and compare the number and types of symptom clusters based on three dimensions (i.e., occurrence, severity, and distress); and identify common and distinct clusters.

METHODS

A modified version of the Memorial Symptom Assessment Scale was used to assess the occurrence, severity, and distress ratings of 38 symptoms in the week prior to patients' next cycle of chemotherapy. Symptom clusters for each dimension were identified using exploratory factor analysis.

RESULTS

Patients reported an average of 13.9 (±7.2) concurrent symptoms. Lack of energy was both the most common and severe symptom while "I don't look like myself" was the most distressing. Psychological, gastrointestinal, weight gain, respiratory, and hormonal clusters were identified across all three dimensions. Findings suggest that psychological, gastrointestinal, and weight gain clusters are common while respiratory and hormonal clusters are distinct.

CONCLUSIONS

Psychological, gastrointestinal, weight gain, hormonal, and respiratory clusters are stable across occurrence, severity, and distress in oncology patients receiving chemotherapy. Given the stability of these clusters and the consistency of the symptoms across dimensions, the use of a single dimension to identify these clusters may be sufficient. However, comprehensive and disease-specific inventories need to be used to identify distinct clusters.

HIV Symptom Clusters are Similar Using the Dimensions of Symptom Occurrence and Distress

CONTEXT

People living with HIV infection (PLWH) in the United States continue to experience a high symptom burden despite improvements in antiretroviral therapy.

OBJECTIVES

The purpose of this study was to determine if the number and types of symptom clusters differed based on whether symptom occurrence rates or distress ratings were used to create the clusters.

METHODS

Data from 2,000 patients with complete symptom occurrence rates and distress scores on the 20-item HIV Symptom Index from their first ambulatory clinic visit at one of six national HIV centers of excellence in the Center for AIDS Research Network of Integrated Clinical Systems were used in these analyses. Exploratory factor analysis was used to create the symptom clusters.

RESULTS

The same four symptom clusters (i.e., gastrointestinal, psychological, pain, body image) were identified using occurrence rates and distress ratings. For both dimensions of the symptom experience, the psychological, pain, and body image clusters each had the same symptoms. For the gastrointestinal cluster, four symptoms loaded on the occurrence dimension and six symptoms loaded on the distress dimension.

CONCLUSION

The number and types of symptom clusters were relatively similar across the occurrence and distress dimensions of the symptom experience. Symptom clusters in PLWH may provide insights into the development of targeted interventions for multiple co-occurring symptoms.

Symptom clusters in patients receiving chemotherapy: A systematic review

BACKGROUND AND PURPOSE

Since 2001, symptom cluster research has grown considerably. However, because multiple methodological considerations remain, ongoing synthesis of the literature is needed to identify gaps in this area of symptom science. This systematic review evaluated the progress in symptom clusters research in adults receiving primary or adjuvant chemotherapy since 2016.

METHODS

Eligible studies were published in English between 1 January 2017 and 17 May 2021; evaluated for and identified symptom clusters 'de novo;' and included only adults being treated with primary or adjuvant chemotherapy. Studies were excluded if patients had advanced cancer or were receiving palliative chemotherapy; symptoms were measured after treatment; symptom clusters were pre-specified or a patient-centred analytic approach was used. For each study, symptom instrument(s); statistical methods and symptom dimension(s) used to create the clusters; whether symptoms were allowed to load on more than one factor; method used to assess for stability of symptom clusters and associations with secondary outcomes and biomarkers were extracted.

RESULTS

Twenty-three studies were included. Memorial Symptom Assessment Scale was the most common instrument and exploratory factor analysis was the most common statistical method used to identify symptom clusters. Psychological, gastrointestinal, and nutritional clusters were the most commonly identified clusters. Only the psychological cluster remained relatively stable over time. Only five studies evaluated for secondary outcomes.

DISCUSSION

While symptom cluster research has evolved, clear criteria to evaluate the stability of symptom clusters and standardised nomenclature for naming clusters are needed. Additional research is needed to evaluate the biological mechanism(s) for symptom clusters.

PROSPERO REGISTRATION NUMBER

CRD42021240216.

Serum biomarkers predict adjuvant chemotherapy-associated symptom clusters in radical resected colorectal cancer patients

BACKGROUND

Due to individual heterogeneity, patients at the same stage of colorectal cancer (CRC) who receive the same chemotherapy may experience different symptom clusters. Identifying the precise factors that predict symptom clusters is of great clinical significance for precision care and for improving the quality of life of patients. The present study investigated the relationship between serum biomarkers and adjuvant chemotherapy-related symptom clusters in radically resected CRC patients.

METHODS

Serum biomarkers and clinical/pathological characteristics of the radical resected CRC patients were collected before the first cycle of adjuvant chemotherapy. A demographic questionnaire and M.D. Anderson Gastrointestinal Cancer Symptom Scale (MDASI-GI) were performed on the third day after chemotherapy and exploratory factor analysis was performed to determine the symptoms clusters. Multiple linear regression and correlation analysis were also performed to evaluate the correlation between serum biomarkers and chemotherapy-related symptom clusters.

RESULTS

A total of 4 chemotherapy-related symptom clusters were determined in the enrolled radical resected CRC patients, including a fatigue-psychological symptom cluster, gastrointestinal symptom cluster, neurotoxic symptom cluster, and constipation-abdominal distension symptom cluster. Further analysis showed that the gastrointestinal symptom cluster was significantly associated with age, gender, weight change status, neutrophil to lymphocyte ratio (NLR), and body mass index (BMI). Additionally, the fatigue-psychological symptom cluster was found to be significantly associated with high NLR. The neurotoxic symptom cluster was found to be significantly associated with low hemoglobin level.

CONCLUSIONS

Serum biomarkers and clinical characteristics of the radical resected CRC patients could be used to predict chemotherapy-related symptoms clusters.

Symptom Clusters and Quality of Life in Gastric Cancer Patients Receiving Chemotherapy

CONTEXT

Although gastric cancer is one of the most common tumors worldwide, there is little knowledge about symptom clusters and quality of life (QoL) in this population.

OBJECTIVES

The objectives were to identify the symptom clusters in gastric cancer patients receiving chemotherapy, and explore their effects on QoL.

METHODS

Gastric cancer patients receiving chemotherapy were recruited. Data were collected using the Memorial Symptom Assessment Scale Short Form, the Functional Assessment of Cancer Therapy-Gastric and the self-designed General Information Evaluation Form. The symptom clusters were extracted through the exploratory factor analysis. The influencing factors of symptom clusters and their effects on QoL were identified using multiple linear regression analysis.

RESULTS

A total of 322 participants were enrolled from three medical centers. Five factors were identified in this exploratory factor analysis based on symptom prevalence, namely fatigue related symptom cluster, epithelial symptom cluster, neurologic symptom cluster, malnutrition related symptom cluster and psychological symptom cluster (χ² = 31.470, P < 0.05). The affecting factors across symptom clusters and QoL subscales were relatively stable, but also different. Generally, fatigue related symptom cluster, malnutrition related symptom cluster and psychological symptom cluster demonstrated significantly negative effects on all aspects of QoL except social well being.

CONCLUSION

Five symptom clusters were identified in gastric cancer patients receiving chemotherapy in mainland China. The symptom clusters negatively contributed to the variance in all aspects of QoL except social well being. Further studies should examine interventions for symptom clusters, their influencing factors, and their effects on improving QoL.

Stability of Symptom Clusters in Patients With Gynecologic Cancer Receiving Chemotherapy

BACKGROUND

Patients with gynecologic cancer undergoing chemotherapy experience multiple co-occurring symptoms. Understanding how symptom clusters change over time is essential to the development of interventions that target multiple co-occurring symptoms.

OBJECTIVE

The aim of this study was to assess the relative stability of symptom clusters across a chemotherapy cycle in patients with gynecologic cancer.

METHODS

This is a longitudinal, descriptive study. Eligible patients (n = 232) were English-speaking adults (≥18 years old) with gynecologic cancer. Data were collected in the week before patients' second or third cycle of chemotherapy (T1) and at 1 (T2) and 2 (T3) weeks after chemotherapy. Three dimensions of the symptom experience (occurrence, severity, and distress) were assessed using a modified version of the Memorial Symptom Assessment Scale. Symptom clusters for each dimension and time point were identified through exploratory factor analysis.

RESULTS

A 5-factor solution was selected for each exploratory factor analysis. Hormonal, respiratory, and weight change clusters were identified across all dimensions and time points. A psychological symptom cluster was identified at T1 for occurrence and severity and at T2 and T3 for all 3 dimensions. A gastrointestinal symptom cluster was identified at T1 for occurrence and at T2 and T3 for all 3 dimensions. The hormonal, respiratory, psychological, and weight change symptom clusters exhibited common symptoms across dimensions and time points.

CONCLUSIONS

Hormonal, respiratory, weight change, and psychological symptom clusters are relatively stable across a cycle of chemotherapy in patients with gynecologic cancer.

IMPLICATIONS FOR PRACTICE

Clinicians need to assess patients for multiple co-occurring symptoms and initiate multimodal interventions.

Characterization of Symptoms and Symptom Clusters for Type 2 Diabetes Using a Large Nationwide Electronic Health Record Database

OBJECTIVE

A variety of symptoms may be associated with type 2 diabetes and its complications. Symptoms in chronic diseases may be described in terms of prevalence, severity, and trajectory and often co-occur in groups, known as symptom clusters, which may be representative of a common etiology. The purpose of this study was to characterize type 2 diabetes-related symptoms using a large nationwide electronic health record (EHR) database.

METHODS

We acquired the Cerner Health Facts, a nationwide EHR database. The type 2 diabetes cohort (n = 1,136,301 patients) was identified using a rule-based phenotype method. A multistep procedure was then used to identify type 2 diabetes-related symptoms based on International Classification of Diseases, 9th and 10th revisions, diagnosis codes. Type 2 diabetes-related symptoms and co-occurring symptom clusters, including their temporal patterns, were characterized based the longitudinal EHR data.

RESULTS

Patients had a mean age of 61.4 years, 51.2% were female, and 70.0% were White. Among 1,136,301 patients, there were 8,008,276 occurrences of 59 symptoms. The most frequently reported symptoms included pain, heartburn, shortness of breath, fatigue, and swelling, which occurred in 21-60% of the patients. We also observed over-represented type 2 diabetes symptoms, including difficulty speaking, feeling confused, trouble remembering, weakness, and drowsiness/sleepiness. Some of these are rare and difficult to detect by traditional patient-reported outcomes studies.

CONCLUSION

To the best of our knowledge, this is the first study to use a nationwide EHR database to characterize type 2 diabetes-related symptoms and their temporal patterns. Fifty-nine symptoms, including both over-represented and rare diabetes-related symptoms, were identified.

A Computational Framework to Analyze the Associations Between Symptoms and Cancer Patient Attributes Post Chemotherapy Using EHR Data

Patients with cancer, such as breast and colorectal cancer, often experience different symptoms post-chemotherapy. The symptoms could be fatigue, gastrointestinal (nausea, vomiting, lack of appetite), psychoneurological symptoms (depressive symptoms, anxiety), or other types. Previous research focused on understanding the symptoms using survey data. In this research, we propose to utilize the data within the Electronic Health Record (EHR). A computational framework is developed to use a natural language processing (NLP) pipeline to extract the clinician-documented symptoms from clinical notes. Then, a patient clustering method is based on the symptom severity levels to group the patient in clusters. The association rule mining is used to analyze the associations between symptoms and patient attributes (smoking history, number of comorbidities, diabetes status, age at diagnosis) in the patient clusters. The results show that the various symptom types and severity levels have different associations between breast and colorectal cancers and different timeframes post-chemotherapy. The results also show that patients with breast or colorectal cancers, who smoke and have severe fatigue, likely have severe gastrointestinal symptoms six months after the chemotherapy. Our framework can be generalized to analyze symptoms or symptom clusters of other chronic diseases where symptom management is critical.

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.

Enzymatic Metabolism of Flavonoids by Gut Microbiota and Its Impact on Gastrointestinal Cancer

Gastrointestinal (GI) cancer is a prevalent global health disease with a massive burden on health care providers. Internal and external factors such as obesity, smoking, diet (red meat), low socioeconomic status and infection with Helicobacter pylori are the critical risk factors of GI cancers. Flavonoids are natural phenolic compounds found abundantly in fruits and vegetables. Upon ingestion, 90% of flavonoids consumed require further enzymatic metabolism by the gut microbiome to enhance their bioavailability and absorption. Several epidemiological studies reported that consumption of flavonoids and their enzymatic conversion by gut microbes is strongly associated with the reduced risk of GI cancer development. This review summarizes the current knowledge on the enzymatic conversion of flavonoids by the human gut microbiome. It also addresses the underlying anti-GI cancer effects on metabolic pathways such as apoptosis and cellular proliferation. Overall, metabolites produced from flavonoid's enzymatic conversion illustrate anti-GI cancer effects, but the mechanisms of action need further clarification.

Symptom Clusters in Patients With Gynecologic Cancer Receiving Chemotherapy

OBJECTIVES

To describe ratings of symptom occurrence, severity, and distress for 38 symptoms and to identify and compare the number and types of symptom clusters identified using these ratings. Although patients with gynecologic cancer experience multiple co-occurring symptoms, little is known about how these symptoms cluster together.

SAMPLE & SETTING

Eligible patients (N = 232) had gynecologic cancer and were receiving chemotherapy.

METHODS & VARIABLES

Symptoms were assessed using the Memorial Symptom Assessment Scale. Symptom clusters were identified through exploratory factor analysis. Geomin-rotated factor loadings with absolute values of 0.3 or greater were considered meaningful. Factor solutions (i.e., symptom clusters) were assessed for simple structure and clinical relevance.

RESULTS

Lack of energy, hair loss, and "I don't look like myself" were the most common, severe, and distressing symptoms. Hormonal, respiratory, and weight change clusters were identified across all three dimensions.

IMPLICATIONS FOR NURSING

Research that explores how symptom clusters change over time and their underlying mechanisms is warranted.

Characteristics of Cancer Symptom Clusters Reported through a Patient-Centered Symptom Cluster Assessment

In the scope of symptom cluster research, few investigators have obtained patients' perceptions of their symptom clusters, even though this information is central to designing effective interventions. In this cross-sectional study, 38 adults with cancer completed measures of demographics, health outcomes (functional status, well-being, quality of life) and a symptom cluster assessment that captured symptom occurrence, severity, distress, clustering, a priority cluster, causal attributions, duration, directional relationships, and cluster interference with daily life. Participants described 72 distinct symptom clusters. Symptoms were most frequently attributed to the cancer diagnosis. Participants' priority symptom cluster typically included two symptoms of continuous duration and one intermittent symptom. Temporal order and direction of symptom relationships varied, with 75 different relationships described among symptom pairs. Greater symptom cluster burden and interference were related to poorer health outcomes. This patient-centered view of symptom clusters revealed substantial variability in symptom cluster characteristics with important implications for symptom management.

Stable Symptom Clusters and Evolving Symptom Networks in Relation to Chemotherapy Cycles

CONTEXT

The existence of stable symptom clusters with variations or changes in cluster membership and the merging of symptom clusters over time urge us to investigate how symptom relationships change over time.

OBJECTIVES

To identify stable symptom clusters and understand networks among symptoms using longitudinal data.

METHODS

Secondary data analysis was conducted using data from a nonblinded randomized clinical trial, which evaluated the effect and feasibility of the developed cancer symptom management system. For the present study, data from all participants of the original trial were analyzed (N = 249). The severity of 20 symptoms was measured before the start of chemotherapy (CTx) and during the initial four cycles of CTx. Symptom clusters were identified using principal component and hierarchical cluster analyses, and network analysis was used to explore the relationships among symptoms.

RESULTS

Three common symptom clusters were identified. The first cluster consisted of anxiety, depression, sleep disturbance, pain, and dyspnea. Fatigue, difficulty concentrating, and drowsiness formed a second stable cluster throughout the CTx cycles. The third cluster comprised loss of appetite, taste change, nausea, and vomiting. In terms of the symptom networks, close relationships were recognized, irrespective of symptom severity level, between anxiety and depression, fatigue and drowsiness, and loss of appetite and taste change. Fatigue was the most central symptom with the highest strength. Edge thickening after starting CTx demonstrated evolving symptom networks in relation to CTx cycles.

CONCLUSION

Stable symptom clusters and evolving networks were identified. The most central symptom was fatigue; however, the paucity of studies that investigated symptom networks and central symptoms calls for further investigations on these phenomena. Identification of central symptoms and underlying mechanisms will guide efficient symptom management. Future studies will need to focus on developing comprehensive interventions for managing symptom clusters or targeting central symptoms.

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.

Stability of Symptom Clusters in Patients With Gastrointestinal Cancers Receiving Chemotherapy

CONTEXT

Limited evidence suggests that patients with gastrointestinal (GI) cancers receiving chemotherapy (CTX) experience an average of 13 co-occurring symptoms. An alternative to counting symptoms is to evaluate for symptom clusters.

OBJECTIVES

In a sample of patients with GI cancers receiving CTX (n = 399), we evaluated the occurrence, severity, and distress of 38 symptoms in the week before patients' second or third cycle of CTX (Time 1 [T1]), approximately one week after CTX (Time 2 [T2]), and approximately two weeks after CTX (Time 3 [T3]); evaluated for differences in the number and types of symptom clusters at each of these three assessments using ratings of occurrence, severity, and distress; and evaluated for changes in symptom clusters over time.

METHODS

Modified version of the Memorial Symptom Assessment Scale collected data on 38 common symptoms. Exploratory factor analyses were used to create the symptom clusters.

RESULTS

Five distinct symptom clusters were identified across the three symptom dimensions and the three assessments (i.e., psychological, CTX-related, weight change, GI, and epithelial). Psychological, CTX-related, and weight change clusters were relatively stable across all three symptom dimensions and time. Across all three symptom dimensions, GI cluster was identified only at T1 and epithelial cluster was identified at T2 and T3.

CONCLUSION

The number and types of symptom clusters appear to be relatively stable over time and across the symptom dimensions. Ongoing assessment and management of these clusters is warranted across the entire course of CTX. The underlying mechanism for these clusters warrants investigation.