Network Analysis of the Multidimensional Symptom Experience of Oncology

Symptom networks in glioma patients: understanding the multidimensionality of symptoms and quality of life

PURPOSE

To comprehend the complex relationship between symptoms and health-related quality of life (HRQoL) in patients with diffuse glioma, we applied symptom network analysis to identify patterns of associations between depression, cognition, brain tumor-related symptoms, and HRQoL. Additionally, we aimed to compare global strength between symptom networks to understand if symptoms are more tightly connected in different subgroups of patients.

METHODS

We included 256 patients and stratified the sample based on disease status (preoperative vs. postoperative), tumor grade (grade II vs. III/IV), and fatigue status (non-fatigued vs. fatigued). For each subgroup of patients, we constructed a symptom network. In these six networks, each node represented a validated subscale of a questionnaire and an edge represented a partial correlation between two nodes. We statistically compared global strength between networks.

RESULTS

Across the six networks, nodes were highly correlated: fatigue severity, depression, and social functioning in particular. We found no differences in GS between the networks based on disease characteristics. However, global strength was lower in the non-fatigued network compared to the fatigued network (5.51 vs. 7.49, p < 0.001).

CONCLUSIONS

Symptoms and HRQoL are highly interrelated in patients with glioma. Interestingly, nodes in the network of fatigued patients were more tightly connected compared to non-fatigued patients.

IMPLICATIONS FOR CANCER SURVIVORS

We introduce symptom networks as a method to understand the multidimensionality of symptoms in glioma. We find a clear association between multiple symptoms and HRQoL, which underlines the need for integrative symptom management targeting fatigue in particular.

Symptom cluster among cancer survivors from a nationally representative survey: a network analysis

PURPOSE

To identify the symptom cluster among cancer survivors and examine their subgroup differences via network analysis based on nationally representative data.

METHODS

This cross-sectional study included 2966 survivors participating in the 2020 National Health Interview Survey (NHIS). Participants self-reported the presence of 14 symptoms capturing four clusters (physical, somatic, sleep, and psychologic problems). Network analysis models were used to reveal the relationships between symptoms and those interactions. Network comparison tests were applied to compare subgroups.

RESULTS

The core symptoms of the symptom cluster were fatigue (Bet = 33, Clo = 0.0067, Str = 0.9397), pain (Bet = 11, Clo = 0.0060, Str = 0.9226), wake up well rested (Bet = 25, Clo = 0.0057, Str = 0.8491), and anxiety (Bet = 5, Clo = 0.0043, Str = 0.9697) among cancer survivors. The core symptoms, network structure, and global strength were invariant between time since diagnoses (< 2 years vs. ≥ 2 years) or between numbers of cancers (1 vs. ≥ 2), yet varied between the comorbidity group and non-comorbidity group (≥ 1 vs. 0).

CONCLUSIONS

Fatigue would be a potential target for alleviating other symptoms through a negative feedback loop of other related symptoms of cancer survivors. In particular, cancer survivors with other chronic diseases should be the focus of attention and strengthen targeted intervention.

Kinetics of IFNγ-Induced Cytokines and Development of Immune-Related Adverse Events in Patients Receiving PD-(L)1 Inhibitors

Immune checkpoint inhibitors (ICI) have the potential to trigger unpredictable immune-related adverse events (irAEs), which can be severe. The underlying mechanisms of these events are not fully understood. As PD-L1 is upregulated by IFN, the heightened immune activation resulting from PD-1/PD-L1 inhibition may enhance the IFN response, triggering the expression of IFN-inducible genes and contributing to irAE development and its severity. In this study, we investigated the interplay between irAEs and the expression of IFN-inducible chemokines and cytokines in 134 consecutive patients with solid tumours treated with PD-(L)1 inhibitors as monotherapy or in combination with chemotherapy or other immunotherapy agents. We compared the plasma levels of IFN-associated cytokines (CXCL9/10/11, IL-18, IL-10, IL-6 and TGFβ) at various time points (at baseline, at the onset of irAE and previous to irAE onset) in three patient groups categorized by irAE development and severity: patients with serious irAEs, mild irAEs and without irAEs after PD-(L)1 inhibitors. No differences were observed between groups at baseline. However, patients with serious irAEs exhibited significant increases in CXCL9/10/11, IL-18 and IL-10 levels at the onset of the irAE compared to baseline. A network analysis and correlation patterns highlighted a robust relationship among these chemokines and cytokines at serious-irAE onset. Combining all of the analysed proteins in a cluster analysis, we identified a subgroup of patients with a higher incidence of serious irAEs affecting different organs or systems. Finally, an ROC analysis and a decision tree model proposed IL-18 levels ≥ 807 pg/mL and TGFβ levels ≤ 114 pg/mL as predictors for serious irAEs in 90% of cases. In conclusion, our study elucidates the dynamic changes in cytokine profiles associated with serious irAE development during treatment with PD-(L)1 inhibitors. The study's findings offer valuable insights into the intricate IFN-induced immune responses associated with irAEs and propose potential predictive markers for their severity.

A multiphase study protocol of identifying, and predicting cancer-related symptom clusters: applying a mixed-method design and machine learning algorithms

Objectives

In recent years, there has been increasing attention on the cluster approach to symptom management. Two significant challenges in the symptom cluster (SC) approach are identifying and predicting these clusters. This multiphase protocol aims to identify SCs in patients with advanced cancer as the primary objective, with the secondary objective of developing machine learning algorithms to predict SCs identified in the first phase.

Methods

The 2-MIXIP study consists of two main phases. The first phase involves identifying SCs, and the second phase focuses on developing predictive algorithms for the identified SCs. The identification of SCs involves a parallel mixed-method design (quantitative and qualitative). Quantitative and qualitative methods are conducted simultaneously and given equal importance. The data are collected and analyzed independently before being integrated. The quantitative part is conducted using a descriptive-analytical method. The qualitative analysis is conducted using a content analysis approach. Then, the identified SCs from both parts are integrated to determine the final clusters and use them in the second phase. In the second phase, we employ a tree-based machine learning method to create predictive algorithms for SCs using key demographic and clinical patient characteristics.

Conclusion

The findings of the 2-MIXIP study can help manage cancer patients' symptoms more effectively and enhance clinical decision-making by using SCs prediction. Furthermore, the results of this study can provide guidance for clinical trials aimed at managing symptoms.

Is poor sleep quality associated with poor neurocognitive outcome in cancer survivors? A systematic review

PURPOSE

Cancer-related neurocognitive impairment and poor sleep are prevalent in cancer survivors and have a negative impact on their quality of life. This systematic review studies the association between sleep disturbance and neurocognitive functioning, as well as the potential positive effects of sleep interventions on neurocognitive functioning in cancer survivors. In addition, we aimed at determining the potential positive effects of sleep interventions on neurocognitive functioning in this population.

METHODS

Following PRISMA guidelines for reporting systematic reviews and meta-analyses, a comprehensive PubMed, Embase, PsycINFO, and CINAHL search was performed. Inclusion criteria were adult cancer survivors, self-reported or objective measures of neurocognitive functioning and sleep quality, or reports on the association between sleep and neurocognitive functioning.

RESULTS

Of the 4,547 records retrieved, 17 studies were retained for this review. Twelve studies were correlational, and five reported on interventions aimed at improving sleep quality. All studies that included self-reported neurocognitive functioning found that poorer sleep was associated with worse neurocognitive functioning. In four out of eight studies, poorer sleep was associated with objective neurocognitive impairment. Three out of five interventional studies showed neurocognitive functioning improved with improved sleep.

CONCLUSIONS

While poor sleep in cancer survivors is associated with self-reported neurocognitive impairment, the association between poor sleep and objective neurocognitive impairment is less evident.

IMPLICATIONS FOR CANCER SURVIVORS

It is important that care providers are aware of the association between sleep and neurocognitive functioning and that improving sleep quality can be a way to decrease neurocognitive impairment in cancer survivors.

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.

Symptom networks analysis among people with Meniere's disease: Application for nursing care

Objectives

This study aimed to explore and visualize the relationships among multiple symptoms in patients with Meniere's disease (MD) and aid clinical nurses in the design of accurate, individualized interventions.

Methods

This study included 790 patients with MD at the Eye and ENT Hospital of Fudan University from October 2014 to December 2021. A self-designed symptom checklist was used to assess 15 MD-related symptoms and construct contemporaneous networks with all 15 symptoms in R software. Qgraph package and Fruchterman-Reingold layout were used for network visualization. Bootstrapping methods were performed to assess network accuracy and stability, and three centrality indices were adopted to describe relationships among symptoms.

Results

Symptom networks showed good accuracy and stability. "Anxiety and nervousness"(98.2%), "aural fullness"(84.4%) and "tinnitus"(82.7%) were the common symptom in MD patients, while "tinnitus", "aural fullness" and "decline in word recognition", were more serious. MD patients with longer disease duration had higher prevalence and severity for all symptoms (P < 0.05). Symptom networks showed good accuracy and stability. "Decline in word recognition," "fatigue," and "anxiety and nervousness" were at the center of the symptom networks, which had the largest strength values and closeness. "Decline in word recognition," "headache," and "spatial discrimination and poor orientation" were the symptoms with the highest betweenness with the strongest bridging effect. The ≥1-year disease group exhibited higher centralities for "drop attack" and "anxiety and nervousness," and a lower centrality for "headache" compared with the <1-year disease group.

Conclusions

The symptom networks of MD patients with varying disease durations were revealed. Clinicians and nurses must provide precision interventions tailored to modifying symptom severity and centrality. Nursing interventions should focus on word recognition issues and associated discomfort in MD patients with multiple symptoms.

Identifying central symptom clusters and correlates in patients with lung cancer post-chemotherapy: A network analysis

Objective

This study aims to investigate the network structures of symptoms and symptom clusters in patients with lung cancer post-chemotherapy, with a focus on identifying the central symptom cluster. Understanding the central cluster is crucial for targeted and effective symptom management.

Methods

Symptom occurrence and severity were assessed using the Memorial Symptom Assessment Scale (MSAS). Principal component analysis (PCA) was employed to explore symptom clusters, while network analysis unveiled the network structure and pinpointed the central symptom cluster.

Results

The study included 512 patients with lung cancer. Four distinct symptom clusters emerged: sickness behavior, psychological, lung cancer-specific, and epithelial. The sickness behavior symptom cluster was identified as the central symptom cluster.

Conclusions

This research designates the sickness behavior symptom cluster as central in post-chemotherapy patients with lung cancer, offering valuable insights for clinical nurses in devising more effective symptom management strategies.

Trial registration

ChiCTR2300070944 (Chinese Clinical Trial Register).

Network analysis used to investigate the interplay among somatic and psychological symptoms in patients with cancer and cancer survivors: a scoping review

PURPOSE

Patients with cancer often experience multiple somatic and psychological symptoms. Somatic and psychological symptoms are thought to be connected and may reinforce each other. Network analysis allows examination of the interconnectedness of individual symptoms. The aim of this scoping review was to examine the current state of knowledge about the associations between somatic and psychological symptoms in patients with cancer and cancer survivors, based on network analysis.

METHODS

This scoping review followed the five-stage framework of Arksey and O'Malley. The literature search was conducted in May, 2023 in PubMed, APA PsycINFO, Embase Cochrane central, and CINAHL databases.

RESULTS

Thirty-two studies were included, with eleven using longitudinal data. Seventeen studies reported on the strength of the associations: somatic and psychological symptoms were associated, although associations among somatic as well as among psychological symptoms were stronger. Other findings were the association between somatic and psychological symptoms was stronger in patients experiencing more severe symptoms; associations between symptoms over time remained rather stable; and different symptoms were central in the networks, with fatigue being among the most central in half of the studies.

IMPLICATIONS FOR CANCER SURVIVORS

Although the associations among somatic symptoms and among psychological symptoms were stronger, somatic and psychological symptoms were associated, especially in patients experiencing more severe symptoms. Fatigue was among the most central symptoms, bridging the somatic and psychological domain. These findings as well as future research based on network analysis may help to untangle the complex interplay of somatic and psychological symptoms in patients with cancer.

Network evolution of core symptoms after lung cancer thoracoscopic surgery:A dynamic network analysis

OBJECTIVES

To investigate relationships between various symptoms occurring 1-2 and 5-6 days following days after thoracoscopic surgery, to identify core symptoms, and to monitor changes in core symptoms over time following lung cancer thoracoscopic surgery.

METHODS

We evaluated symptoms using the Anderson Symptom Scale (Chinese version) and the Lung Cancer-Specific Symptoms Template in 214 lung cancer patients hospitalized in the Department of Thoracic Surgery of a provincial hospital in Jiangsu Province from March 2023 to September 2023. Data was collected at 1-2 days and 5-6 days postoperatively. Symptom networks were constructed for each time point, and centrality indicators were analyzed to identify core symptoms while controlling for influencing factors.

RESULTS

According to the network analysis, fatigue (rs = 26.00、rc = 0.05、rb = 1.02) had the highest strength, closeness, and betweenness in the symptom network 1-2 days after lung cancer surgery. At 5-6 days after surgery, shortness of breath (rs = 27.00) emerged as the symptom with the highest strength, fatigue (rc = 0.04) had the highest closeness, and cough (rb = 1.08) ranked highest in betweenness within the symptom network.

CONCLUSION

Fatigue stands out as the most core symptom in the network 1-2 days after lung cancer surgery. Shortness of breath, fatigue and cough are the most core symptoms in the symptom network 5-6 days after surgery. Therefore, clinical staff can improve the postoperative symptom experience of lung cancer patients by developing symptom management programmes tailored to these core symptoms.

Exploring Symptom Clusters in Chinese Patients with Diabetic Kidney Disease: A Network Analysis

Purpose

The research on symptom management in patients with diabetic kidney disease (DKD) has shifted from separate symptoms to symptom clusters and networks recently. This study aimed to evaluate the unpleasant symptoms of DKD patients, and to investigate how these symptom clusters could affect patients.

Methods

408 DKD patients were recruited in this cross-sectional study. The symptoms of DKD patients were measured using the modified Dialysis Symptom Index. Network analysis was employed to evaluate the symptom network and the characteristics of individual nodes, while factor analysis was utilized to identify symptom clusters.

Results

Blurred vision was the most prevalent symptom among DKD patients. The symptoms identified as the most distressing, severe, and frequent were light headache or dizziness, arteriovenous fistula/catheterization pain, and diarrhea, respectively. Five symptom clusters were obtained from factor analysis, and the most central symptom cluster in the entire symptom network was sexual dysfunction.

Conclusion

This study identified five symptom clusters in Chinese DKD patients, with sexual dysfunction emerging as the most central cluster. These findings carry significant clinical implications, underscoring the necessity of assessing symptom clusters and their associations to enhance symptom management in DKD patients. Further research is essential to elucidate the underlying mechanisms of symptoms and to clarify the associations among symptoms in DKD patients across different disease trajectories or treatment modalities.

Physical symptoms and anxiety and depression in older patients with advanced cancer in China: a network analysis

BACKGROUND

Little is understood about the association between psychosomatic symptoms and advanced cancer among older Chinese patients.

METHODS

This secondary analysis was part of a multicenter cross-sectional study based on an electronic patient-reported outcome platform. Patients with advanced cancer were included between August 2019 and December 2020 in China. Participants (over 60 years) completed the MD Anderson Symptom Inventory (MDASI) and Hospital Anxiety and Depression Scale (HADS) to measure symptom burden. Network analysis was also conducted to investigate the network structure, centrality indices (strength, closeness, and betweenness) and network stability.

RESULTS

A total of 1022 patients with a mean age of 66 (60-88) years were included; 727 (71.1%) were males, and 295 (28.9%) were females. A total of 64.9% of older patients with advanced cancer had one or more symptoms, and up to 80% had anxiety and depression. The generated network indicated that the physical symptoms, anxiety and depression symptom communities were well connected with each other. Based on an evaluation of the centrality indices, 'distress/feeling upset' (MDASI 5) appears to be a structurally important node in all three networks, and 'I lost interest in my own appearance' (HADS-D4) had the lowest centrality indices. The network stability was relatively high (> 0.7).

CONCLUSION

The symptom burden remains high in older patients with advanced cancer in China. Psychosomatic symptoms are highly interactive and often present as comorbidities. This network can be used to provide targeted interventions to optimize symptom management in older patients with advanced cancer in China.

TRIAL REGISTRATION

Chinese Clinical Trial Registry (ChiCTR1900024957), registered on 06/12/2020.

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.

Examining racial differences in the network structure and properties of specific cognitive domains among older adults

Older adults oftentimes experience cognitive aging which leads to varying degrees of cognitive impairment. Previous studies have found that racial and ethnic disparities exist in the prevalence and severity of cognitive impairment among older adults. Yet, little is known on the relationship among specific cognitive domains and how this relationship differs between African American and White older adults. This is a secondary data analysis of Wave II (2010-2011) data from the National Social Life, Health, and Aging Project (NSHAP). A total of 2,471 older adults aged between 65 and 85 years old (African American n = 452, White n = 2019) were included. Network analysis was used to visualize and characterize the network structure and to examine network stability. Then, network comparison test was conducted to compare the network properties of the cognitive network structure between African American and White older adults. African American older adults had a lower cognitive function in all cognitive domains than White older adults. While there was no significant difference in global strength, there was a significant difference in the network structure and strength centrality measure between the two groups (p < 0.05). The invariance edge strength test found the language-visuospatial edge to be significantly stronger in African American older adults. Clinicians need to understand the different cognitive function across multiple cognitive domains between African American and White older adults and routinely offer targeted and timely cognitive assessment and management in this population.

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.

A network approach to investigating the inter-relationship between health-related quality of life dimensions and depression in 1735 Chinese patients with heterogeneous cancers

Background

We aimed to explore the inter-connection between depression and HRQOL dimensions in cancer patients using a network approach, which might provide new insights for precise interventions to improve cancer patients' overall HRQOL.

Methods

Between June 1, 2016, and August 31, 2017, a total of 1735 eligible patients with heterogeneous types of cancer were recruited. The Zung Self-Rating Depression Scale (SDS) and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30) were used to measure patients' depression status and HRQOL, respectively. A regularized partial correlation network was established. Central and bridge symptoms/functions were identified using expected influence and bridge expected influence. A directed acyclic graph (DAG) was used to explore the possible causal relationship between depression and HRQOL dimensions.

Results

In this study, depression and 15 dimensions of the EORTC-QLQ-C30 scale were highly inter-correlated and could be represented as a network. We found that nearly two-thirds of cancer patients experienced various degrees of depression, and depression was consistently the central symptom in the network, in addition to nausea/vomiting, pain, and physical function. DAG and bridge symptoms indicated that depression might influence overall HRQOL in cancer patients mainly through emotional function, pain, physical function, and sleeplessness, particularly in cancer patients with moderate-to-severe depression. The disparity in network structures between mild and moderate-to-severe depression suggested that the relationship between depression and HRQOL dimensions might be bidirectional.

Conclusion

The prevalence of depression remained high in Chinese patients with cancer, and depression may influence various symptoms and functions within the HRQOL network. Screening and early treatment of depression were warranted to improve the overall HRQOL of cancer patients, in addition to adequate treatment of pain and nausea/vomiting and improvement in physical function.

Supporting data driven translational patient-centered care using network analysis to visualize symptom distress in children with serious illness

PURPOSE

There are an increasing number of techniques and tools to improve the capacity for children to relay their perceptions of their symptom experience while undergoing blood and marrow transplant (BMT). Network analysis (NA) is a tool that can illustrate associations between symptoms and the distress they cause. We aimed to develop a biopsychosocial assessment clinical analytic tool to examine symptom relationships for children undergoing BMT to find actionable relationships for intervention to improve clinical outcomes including mood.

DESIGN AND METHODS

This pilot study used an analytical mobile application tool to support a wide scope of 15 biopsychosocial symptom distress levels and five mood assessments. Children recorded their symptom distress and mood using the app. NA was used to explore relationships between symptom distress and mood.

RESULTS

Four children, 11-14 years old, undergoing BMT used the app daily during hospitalization. We found a strong presence of emotional distress and its associations symptom distress and mood. Multiple symptom associations were identified including associations between the set of symptoms difficulty breathing and fever (0.557), sad and worried (0.429). Of note, pain distress had a strong capacity to bridge other symptoms and was connected directly to many symptoms.

PRACTICE IMPLICATIONS

We found the significance of patient struggles with emotional and symptom distress and the importance of this relationship to other clinical outcomes. This provides valuable insights and an improved understanding of the child's symptoms. Our findings support early assessment, intervention, and improved symptom communication to enhance sense of well-being and the child's care experience.

The Mediating Effect of Resilience on the Relationship Between Symptom Burden and Anxiety/Depression Among Chinese Patients with Primary Liver Cancer After Liver Resection

Purpose

Primary liver cancer (PLC) is a common cancer of the digestive system. Patients with PLC often experience a heavy symptom burden and along with a significant levels of anxiety and depression after liver resection. High levels of symptom burden can lead to increased anxiety and depression, whereas high levels of resilience can alleviate these conditions. Therefore, we aimed to explore the relationships among symptom burden, resilience, and anxiety/depression in Chinese patients with PLC after liver resection and to determine whether resilience mediates the relationship between symptom burden and anxiety/depression.

Patients and Methods

A total of 223 postoperative PLC patients were recruited from two public hospitals in Anhui Province, China. All participants completed the MD Anderson Symptom Inventory (MDASI), Hospital Anxiety and Depression Scale (HADS), and Connor-Davidson Resilience Scale (CD-RISC). The mediating effect of resilience was estimated using the bootstrap method via IBM SPSS AMOS 26.0.

Results

The mean HADS score was 12.37 ± 6.03 points in postoperative PLC patients. Among these patients, 78 (34.98%) had anxiety, and 64 (28.70%) had depression, as indicated by a subscale score ≥ 8. Pearson correlation analysis revealed that anxiety/depression was positively associated with symptom burden (p < 0.05) and negatively associated with resilience (p < 0.05). Furthermore, resilience partially mediated the relationship between symptom burden and anxiety/depression (β = 0.04; 95% confidence interval: 0.01-0.08).

Conclusion

The levels of anxiety and depression in postoperative PLC patients should be decreased. Resilience partially mediated the relationship between symptom burden and anxiety/depression, but the indirect effect was much weaker than the direct effect of symptom burden on anxiety/depression. Consequently, rather than focusing primarily on resilience interventions, joint symptom-psychological interventions focusing on symptoms should be considered for patients with PLC after hepatectomy to reduce the levels of anxiety/depression.

Identification of Core Symptom Cluster in Patients With Digestive Cancer: A Network Analysis

BACKGROUND

A lack of identified core symptom clusters in digestive cancer patients hinders achieving precision symptom intervention. There are few studies on identifying digestive cancer symptom clusters based on network analysis.

OBJECTIVES

The aims of this study were to construct the symptom network of digestive cancer patients and identify the core symptom cluster.

METHODS

A cross-sectional study was conducted among 202 digestive cancer patients. The Chinese version of the MD Anderson Symptom Inventory for gastrointestinal cancer scale was used to assess the symptoms by convenience sampling. R software was used to construct a symptom network and identify core symptom clusters. Edge weight and centrality difference tests were used to test the accuracy of core symptom cluster identification.

RESULTS

The most common symptoms were distress, poor appetite, and sadness. The most serious symptoms were poor appetite, disturbed sleep, and fatigue. The core symptom cluster of the psychoemotional symptom group was distress, sadness, and numbness. The centrality index showed that the top 3 in strength were distress (Rs = 1.11), fatigue (Rs = 1.09), and sadness (Rs = 1.04). The edge weight difference test showed that the psychoemotional symptom group had high stability.

CONCLUSIONS

The psychoemotional symptoms of digestive cancer patients should be given priority for intervention. Network analysis must be extended to the symptom research of cancer patients as soon as possible to provide a scientific basis for symptom management.

IMPLICATIONS FOR PRACTICE

Nurses must perform comprehensive psychological and emotional assessments, initiate referrals for psychoemotional symptom management and psychological services, and administer pharmacologic and nonpharmacologic interventions to improve appetite loss in digestive cancer patients.

The course of self-perceived cognitive functioning among patients with lymphoma and the co-occurrence with fatigue and psychological distress

PURPOSE

To investigate the proportion of patients with lymphoma with persistent clinically relevant cognitive impairment, and its relation to  treatment, fatigue, and psychological distress.

METHODS

Patients with diffuse-large-B-cell-lymphoma (DLBCL), follicular-lymphoma (FL), and chronic-lymphocytic-leukemia (CLL)/small-lymphocytic-lymphoma (SLL), diagnosed between 2004-2010 or 2015-2019, were followed up to 8 years post-diagnosis. Sociodemographic and clinical data were obtained from the Netherlands Cancer Registry and the Population-based HAematological Registry for Observational Studies. The EORTC QLQ-C30 was used to assess cognitive functioning and fatigue, and the HADS to assess psychological distress. Individual growth curve models were performed. Results were compared with an age- and sex-matched normative population.

RESULTS

A total of 924 patients were included (70% response rate). Persistent cognitive impairment was twice as high in patients (30%) compared to the normative population (15%). Additionally, 74% of patients reported co-occurring symptoms of persistent fatigue and/or psychological distress. Patients with FL (- 23 points, p < 0.001) and CLL/SLL (- 10 points, p < 0.05) reported clinically relevant deterioration of cognitive functioning, as did the normative population (FLnorm - 5 points, DLBCLnorm - 4 points, both p < 0.05). Younger age, higher fatigue, and/or psychological distress at inclusion were associated with worse cognitive functioning (all p's < 0.01). Treatment appeared less relevant.

CONCLUSION

Almost one-third of patients with lymphoma report persistent cognitive impairment, remaining present up to 8 years post-diagnosis. Early onset and co-occurrence of symptoms highlight the need for clinicians to discuss symptoms with patients early.

IMPLICATIONS FOR CANCER SURVIVORS

Early recognition of cognitive impairment could increase timely referral to suitable supportive care (i.e., lifestyle interventions) and reduce (long-term) symptom burden.

Exploring core symptoms and interrelationships among symptoms in children with acute leukemia during chemotherapy: A network analysis

PURPOSE

Children with acute leukemia have suffered from a considerable symptom burden during chemotherapy. However, few studies have focused on exploring the mechanisms among symptoms in children with acute leukemia. Our study aims to explore core symptoms and describe the interrelationships among symptoms in children with acute leukemia during chemotherapy.

METHODS

From January 2021 to March 2023, 469 children with acute leukemia were recruited from 20 Chinese cities. The Memorial Symptom Assessment Scale 10-18 (MSAS 10-18) was used to evaluate the prevalence and severity of symptoms during chemotherapy. A network analysis was performed by the R software based on 31 symptoms. Centrality indices and density were used to explore core symptoms and describe interrelationships among symptoms in the network during chemotherapy.

RESULTS

Worrying and feeling irritable were the central symptoms across the three centrality indices, including strength, closeness, and betweenness. Lack of energy was the most prevalent symptom; however, it was less central than other symptoms. The density of the "induction and remission" network significantly differed from other cycles' counterparts (p < 0.001). Global strength was greater in the " ≥ 8 years group " network than the " < 8 years group " network (p = 0.023).

CONCLUSION

Network analysis provides a novel approach to identifying the core symptoms and understanding the interrelationships among symptoms. Our study indicates the need to assess emotional symptoms in children with acute leukemia during chemotherapy, especially during the induction and remission phases, as well as in older children. Future research is imperative to construct trajectories of dynamic symptom networks and centrality indices in longitudinal data to investigate the causal relationships among symptoms.

Age Differences in Core Symptoms and Symptom Relationships in Patients With Irritable Bowel Syndrome: A Network Analysis

INTRODUCTION

Irritable bowel syndrome (IBS) is a common disorder of gut-brain interaction, characterized by symptoms of abdominal pain and changes in bowel habits. It often co-occurs with extraintestinal somatic and psychological symptoms. However, the nature of the interrelationships among these symptoms is unclear. Although previous studies have noted age differences in IBS prevalence and specific symptom severity, it remains unknown whether specific symptoms and symptom relationships may differ by age.

METHODS

Symptom data were collected in 355 adults with IBS (mean age 41.4 years, 86.2% female). Network analysis was used to examine the interrelationships among 28 symptoms and to identify the core symptoms driving the symptom structure between young (≤45 years) vs older (>45 years) adults with IBS. We evaluated 3 network properties between the 2 age groups: network structure, edge (connection) strength, and global strength.

RESULTS

In both age groups, fatigue was the top core symptom. Anxiety was a second core symptom in the younger age group, but not the older age group. Intestinal gas and/or bloating symptoms also exerted considerable influences in both age groups. The overall symptom structure and connectivity were found to be similar regardless of age.

DISCUSSION

Network analysis suggests fatigue is a critical target for symptom management in adults with IBS, regardless of age. Comorbid anxiety is likely an important treatment focus for young adults with IBS. Rome V criteria update could consider the importance of intestinal gas and bloating symptoms. Additional replication with larger diverse IBS cohorts is warranted to verify our results.

Identifying central symptom clusters and correlates in children with acute leukemia undergoing chemotherapy: a network analysis

Background

Previous studies have examined symptom clusters in children with acute leukemia, yet a knowledge gap persists regarding central symptom clusters and their influencing factors. By identifying these central clusters and associated factors, healthcare providers can enhance their understanding and effective management of symptoms. Our study seeks to address this gap by identifying symptom clusters, exploring central clusters, and investigating the demographic and health-related factors associated with these clusters in children with acute leukemia undergoing chemotherapy.

Methods

A total of 586 children with acute leukemia from January 2021 to April 2023 were recruited from China. They were investigated using Memorial Symptom Assessment Scale 10-18 during chemotherapy. The principal component analysis was used to identify the symptom clusters. An association network was conducted to describe the relationships among symptoms and clusters. A multiple linear model was used to investigate the associated factors for the severity of overall symptoms and each symptom cluster.

Results

Five clusters were identified, including oral and skin cluster, somatic cluster, self-image disorder cluster, gastrointestinal cluster and psychological cluster. Gastrointestinal cluster was the most central symptom cluster. Age, sex, clinical classification, number of having chemotherapy and education degree and marital status of the primary caregiver are associated with the severity of these five symptom clusters.

Conclusion

Our study highlights the importance of evaluating symptom clusters in children with acute leukemia during chemotherapy. Specifically, addressing gastrointestinal symptoms is crucial for effective symptom management and overall care.

Data Science and Precision Oncology Nursing: Creating an Analytic Ecosystem to Support Personalized Supportive Care across the Trajectory of Illness

OBJECTIVES

The authors' objective is to present an overarching framework of an analytic ecosystem using diverse data domains and data science approaches that can be used and implemented across the cancer continuum. Analytic ecosystems can improve quality practices and offer enhanced anticipatory guidance in the era of precision oncology nursing.

DATA SOURCES

Published scientific articles supporting the development of a novel framework with a case exemplar to provide applied examples of current barriers in data integration and use.

CONCLUSION

The combination of diverse data sets and data science analytic approaches has the potential to extend precision oncology nursing research and practice. Integration of this framework can be implemented within a learning health system where models can update as new data become available across the continuum of the cancer care trajectory. To date, data science approaches have been underused in extending personalized toxicity assessments, precision supportive care, and enhancing end-of-life care practices.

IMPLICATIONS FOR NURSING PRACTICE

Nurses and nurse scientists have a unique role in the convergence of data science applications to support precision oncology across the trajectory of illness. Nurses also have specific expertise in supportive care needs that have been dramatically underrepresented in existing data science approaches thus far. They also have a role in centering the patient and family perspectives and needs as these frameworks and analytic capabilities evolve.

Exploration of symptom clusters during hemodialysis and symptom network analysis of older maintenance hemodialysis patients: a cross-sectional study

BACKGROUND

Symptom networks can provide empirical evidence for the development of personalized and precise symptom management strategies. However, few studies have established networks of symptoms experienced by older patients on maintenance hemodialysis. Our goal was to examine the type of symptom clusters of older maintenance hemodialysis patients during dialysis and construct a symptom network to understand the symptom characteristics of this population.

METHODS

The modified Dialysis Symptom Index was used for a cross-sectional survey. Network analysis was used to analyze the symptom network and node characteristics, and factor analysis was used to examine symptom clusters.

RESULTS

A total of 167 participants were included in this study. The participants included 111 men and 56 women with a mean age of 70.05 ± 7.40. The symptom burdens with the highest scores were dry skin, dry mouth, itching, and trouble staying asleep. Five symptom clusters were obtained from exploratory factor analysis, of which the clusters with the most severe symptom burdens were the gastrointestinal discomfort symptom cluster, sleep disorder symptom cluster, skin discomfort symptom cluster, and mood symptom cluster. Based on centrality markers, it could be seen that feeling nervous and trouble staying asleep had the highest strength, and feeling nervous and feeling irritable had the highest closeness and betweenness.

CONCLUSIONS

Hemodialysis patients have a severe symptom burden and multiple symptom clusters. Dry skin, itching, and dry mouth are sentinel symptoms in the network model; feeling nervous and trouble staying asleep are core symptoms of patients; feeling nervous and feeling irritable are bridge symptoms in this symptom network model. Clinical staff can formulate precise and efficient symptom management protocols for patients by using the synergistic effects of symptoms in the symptom clusters based on sentinel symptoms, core symptoms, and bridge symptoms.

Frequency and burden of potentially treatable symptoms in glioma patients with stable disease

Background & aims

Glioma patients experience a multitude of symptoms that negatively affect their health-related quality of life. Symptoms vary greatly across disease phases, and the patients' stable phase might be particularly suitable for assessing and treating symptoms. Identifying symptoms and patients' needs is a first step toward improving patient care. In glioma patients with stable disease, we assessed the frequency and burden of patient-reported symptoms, examined how these symptoms co-occur, and also determined whether patients would consider treatment to ameliorate specific symptoms.

Methods

In this retrospective study, patients rated the frequency and burden of seventeen symptoms on a seven-point Likert scale and stated whether they would consider treatment for these symptoms. Correlations between frequency, burden, and considering treatment were evaluated with Kendall's Tau correlation coefficients. Based on partial correlations between symptom frequencies we visualized the symptoms as a network.

Results

Fifty-two glioma patients with stable disease were included (31 WHO grade II/III, 21 WHO grade IV). The top five symptoms were fatigue, memory problems, reduced physical fitness, concentration problems, and drowsiness. Fatigue had the highest median frequency (4.5, interquartile range 2.5). Over half of the patients experienced three or more symptoms simultaneously and associations between all symptoms were depicted as a network. Overall, 35% of patients would consider treatment for at least one symptom. The wish to undergo symptom treatment correlated only moderately with symptom frequency and burden (range of correlations 0.24-0.57 and 0.28-0.61, respectively).

Conclusion

Glioma patients with stable disease experience multiple symptoms with a consequently high symptom burden. Despite the high prevalence of symptoms, the inclination for symptom management interventions was relatively low. The most frequent and burdensome symptoms and the way they are interrelated could serve as a roadmap for future research on symptom management in these patients.

Gastrointestinal Symptom Cluster is Associated With Epigenetic Regulation of Lymphotoxin Beta in Oncology Patients Receiving Chemotherapy

OBJECTIVES

While the gastrointestinal symptom cluster (GISC) is common in patients receiving chemotherapy, limited information is available on its underlying mechanism(s). Emerging evidence suggests a role for inflammatory processes through the actions of the nuclear factor kappa B (NF-κB) signaling pathway. This study evaluated for associations between a GISC and levels of DNA methylation for genes within this pathway.

METHODS

Prior to their second or third cycle of chemotherapy, 1071 outpatients reported symptom occurrence using the Memorial Symptom Assessment Scale. A GISC was identified using exploratory factor analysis. Differential methylation analyses were performed in two independent samples using EPIC (n = 925) and 450K (n = 146) microarrays. Trans expression-associated CpG (eCpG) loci for 56 NF-κB signaling pathway genes were evaluated. Loci significance were assessed using an exploratory false discovery rate (FDR) of 25% for the EPIC sample. For the validation assessment using the 450K sample, significance was assessed at an unadjusted p-value of 0.05.

RESULTS

For the EPIC sample, the GISC was associated with increased expression of lymphotoxin beta (LTB) at one differentially methylated trans eCpG locus (cg03171795; FDR = 0.168). This association was not validated in the 450K sample.

CONCLUSIONS

This study is the first to identify an association between a GISC and epigenetic regulation of a gene that is involved in the initiation of gastrointestinal immune responses. Findings suggest that increased LTB expression by hypermethylation of a trans eCpG locus is involved in the occurrence of this cluster in patients receiving chemotherapy. LTB may be a potential therapeutic target for this common cluster.

Discovery of clinical and demographic determinants of symptom burden in primary brain tumor patients using network analysis and unsupervised clustering

Background

Precision health approaches to managing symptom burden in primary brain tumor (PBT) patients are imperative to improving patient outcomes and quality of life, but require tackling the complexity and heterogeneity of the symptom experience. Network Analysis (NA) can identify complex symptom co-severity patterns, and unsupervised clustering can unbiasedly stratify patients into clinically relevant subgroups based on symptom patterns. We combined these approaches in a novel study seeking to understand PBT patients' clinical and demographic determinants of symptom burden.

Methods

MDASI-BT symptom severity data from a two-institutional cohort of 1128 PBT patients were analyzed. Gaussian Graphical Model networks were constructed for the all-patient cohort and subgroups identified by unsupervised clustering based on co-severity patterns. Network characteristics were analyzed and compared using permutation-based statistical tests.

Results

NA of the all-patient cohort revealed 4 core dimensions that drive the overall symptom burden of PBT patients: Cognitive, physical, focal neurologic, and affective. Fatigue/drowsiness was identified as pivotal to the symptom experience based on the network characteristics. Unsupervised clustering discovered 4 patient subgroups: PC1 (n = 683), PC2 (n = 244), PC3 (n = 92), and PC4 (n = 109). Moderately accurate networks could be constructed for PC1 and PC2. The PC1 patients had the highest interference scores among the subgroups and their network resembled the all-patient network. The PC2 patients were older and their symptom burden was driven by cognitive symptoms.

Conclusions

In the future, the proposed framework might be able to prioritize symptoms for targeting individual patients, informing more personalized symptom management.

Exploring the prevalence and burden of sleep disturbance in primary brain tumor patients

BACKGROUND

Sleep disturbance (SD) is common in patients with cancer and has been associated with worse clinical outcomes. This cross-sectional study explored the prevalence of SD in a primary brain tumor (PBT) population, identified associated demographic and clinical characteristics, and investigated co-occurrence of SD with other symptoms and mood disturbance.

METHODS

Demographic, clinical characteristics, MD Anderson Symptom Inventory-Brain Tumor, and Patient Reported Outcome Measurement Information System Depression and Anxiety Short-Forms were collected from PBT patients at study entry. Descriptive statistics, Chi-square tests, and independent t-tests were used to report results.

RESULTS

The sample included 424 patients (58% male, 81% Caucasian) with a mean age of 49 years (range 18-81) and 58% with high-grade gliomas. Moderate-severe SD was reported in 19% of patients and was associated with younger age, poor Karnofsky Performance Status, tumor progression on MRI, and active corticosteroid use. Those with moderate-severe SD had higher overall symptom burden and reported more moderate-severe symptoms. These individuals also reported higher severity in affective and mood disturbance domains, with 3 to 4 times higher prevalence of depressive and anxiety symptoms, respectively. The most frequently co-occurring symptoms with SD were, drowsiness, and distress, though other symptoms typically associated with tumor progression also frequently co-occurred.

CONCLUSIONS

PBT patients with moderate-severe SD are more symptomatic, have worse mood disturbance, and have several co-occurring symptoms. Targeting interventions for sleep could potentially alleviate other co-occurring symptoms, which may improve life quality for PBT patients. Future longitudinal work examining objective and detailed subjective sleep reports, as well as underlying genetic risk factors, will be important.

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.

Advances in Conceptual and Methodological Issues in Symptom Cluster Research: A 20-Year Perspective

Two conceptual approaches are used to evaluate symptom clusters: "clustering" symptoms (ie, variable-centered analytic approach) and "clustering" patients (ie, person-centered analytic approach). However, these methods are not used consistently and conceptual clarity is needed. Given the emergence of novel methods to evaluate symptom clusters, a review of the conceptual basis for older and newer analytic methods is warranted. Therefore, this article will review the conceptual basis for symptom cluster research; compare and contrast the conceptual basis for using variable-centered versus patient-centered analytic approaches in symptom cluster research; review their strengths and weaknesses; and compare their applications in symptom cluster research.

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.

Contemporaneous symptom networks of multidimensional symptom experiences in cancer survivors: A network analysis

BACKGROUND

Symptom networks can provide empirical evidence for the development of personalized and precise symptom management strategies. However, few studies have explored the symptom networks of multidimensional symptom experiences in cancer survivors. The objectives of this study were to generate symptom networks of multidimensional symptom experiences in cancer survivors and explore the centrality indices and density in these symptom networks METHODS: Data from 1065 cancer survivors were obtained from the Shanghai CANcer Survivor (SCANS) Report. The MD Anderson Symptom Inventory was used to assess the prevalence and severity of 13 cancer-related symptoms. We constructed contemporaneous networks with all 13 symptoms after controlling for covariates.

RESULTS

Distress (r_s  = 9.18, r_c  = 0.06), sadness (r_s  = 9.05, r_c  = 0.06), and lack of appetite (r_s  = 9.04, r_c  = 0.06) had the largest values for strength and closeness. The density of the "less than 5 years" network was significantly different from that of the "5-10 years" and "over 10 years" networks (p < 0.001). We found that while fatigue was the most severe symptom in cancer survivorship, the centrality of fatigue was lower than that of the majority of other symptoms.

CONCLUSION

Our study demonstrates the need for the assessment of centrality indices and network density as an essential component of cancer care, especially for survivors with <5 years of survivorship. Future studies are warranted to develop dynamic symptom networks and trajectories of centrality indices in longitudinal data to explore causality among symptoms and markers of interventions.

Pandemic impact on patients with advanced non-COVID-19 illness and their family carers receiving specialised palliative home care: a qualitative study

OBJECTIVES

To investigate the experiential impact of the COVID-19 pandemic on patients with non-COVID, life-threatening disease and their family carers.

DESIGN

An interpretative qualitative design informed by phenomenological hermeneutics and based on data from in-depth interviews, performed between June and September 2020.

SETTING

Patients receiving specialised palliative home care and their family carers living in Sweden.

PARTICIPANTS

22 patients (male/female 11/11) and 17 carers (male/female 5/12) aged 50 years and older. All the patients received specialised palliative home care and most were diagnosed with cancer.

INCLUSION CRITERIA

aged 18 years or older, diagnosed with an incurable life-threatening, non-COVID disease, sufficient strength to participate and capacity to provide informed consent. Participants were selected through a combination of convenient and consecutive sampling.

RESULTS

The significance of the pandemic for both patients and carers showed a continuum from being minimally affected in comparison to the severe underlying disease to living in isolation with constant fear of becoming infected and falling ill with COVID-19, which some likened to torture.The imposed restrictions on social contact due to the pandemic were particularly palpable for this group of people with a non-COVID-19, life-limiting condition, as it was said to steal valuable moments of time that had already been measured.Most patients and carers found access to specialised palliative home care was maintained despite the pandemic. This care was of paramount importance for their sense of security and was often their sole visiting social contact.

CONCLUSIONS

In the pandemic situation, highly accessible support from healthcare and social care at home is particularly important to create security for both patients and carers. Thus, to provide appropriate support, it is important for healthcare and social care personnel to be aware of the great diversity of reactions patients in palliative care and their carers may have to a pandemic threat.

Adverse events after BNT162b2 mRNA COVID-19 vaccination in health care workers and medical students in Japan

To control the coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the promotion of vaccination is important. However, adverse reactions following vaccination remain a concern. To investigate adverse events in the vaccinated Japanese population, we conducted a survey-based study among health care workers, including medical doctors and nurses; other medical staff; and medical university faculty, staff, and students in a single medical school and affiliated hospital in Japan. In addition, we analyzed the association of different adverse events with individual factors (e.g., age, sex) by performing network analysis. While young age and female sex are often considered risk factors for more severe adverse events, the regression models showed neither age nor sex was associated with local injection-site reactions after the second dose in this study. In contrast to local reactions, systemic adverse events were associated with young age and female sex. However, myalgia was unique in that it was not associated with younger age even though the network analysis showed that myalgia was consistently related to arthralgia and belonged to the group of systemic events after both the first and second vaccine doses. Further study is needed to ensure safe and effective vaccination to aid in controlling the COVID-19 pandemic.

Enhancing Informed Decisions for Coastal Groundwater Sustainability: A Network Analysis of Water-Related Indicator Results from 122 Cities

In many places around the globe, groundwater has been threatened by various pressures, which calls for better management strategies for groundwater sustainability. In this study, we suggest a novel framework for identifying factors critical to coastal groundwater based on results from City Blueprint (CB) assessments. By compiling the 5856 indicator results of the City Blueprint Approach (CBA) from 122 cities and analyzing the correlation between these indicators, we constructed City Blueprint networks (CBN) by using a complex network modeling approach for three groups of cities: all 122 cities, 40 coastal, and 82 non-coastal cities. These networks were then analyzed for their node centralities to identify major factors that influence coastal groundwater management. Interestingly, our analysis revealed that groundwater has various indirect but important links with the factors that are typically unexplored in the literature. We also assessed the CB of the two largest coastal cities in South Korea. By combining the results of network analysis and CB assessment of the two cities, we could identify the indicators that are potentially at risk regarding coastal groundwater. We propose the CBN as a novel approach to unveil underestimated or hidden factors related to the target system (e.g., groundwater), which allows extensive options for sustainable groundwater management.

Symptom Clusters and Key Symptoms Among Midlife Perimenopausal and Postmenopausal Women With and Without Metabolic Syndrome

BACKGROUND

Midlife perimenopausal and postmenopausal women with metabolic syndrome experience multiple symptoms concurrently.

OBJECTIVE

The study objectives were to examine the relationship among symptoms through network visualization and identify and compare symptom clusters and key symptoms across symptom occurrence and symptom severity dimensions in midlife perimenopausal and postmenopausal women with and without metabolic syndrome.

METHODS

Cross-sectional data from the Study of Women's Health Across the Nation (Visit 5) were used for analysis. A machine-learning-based network analysis and the Walktrap algorithm were used to fulfill the study objectives.

RESULTS

The number and types of symptom clusters differed between the groups. Midlife perimenopausal and postmenopausal women with metabolic syndrome experienced the psychological/somatic/genital cluster (key symptom: frequent mood change), the sleep/urinary cluster (sleep disturbance), and the vasomotor cluster (cold sweat) in the symptom occurrence dimension and the psychological/somatic/sexual cluster (anxiety), the sleep/urinary cluster (sleep disturbance), and the vasomotor/genital cluster (night sweat) in the symptom severity dimension. In contrast, midlife perimenopausal and postmenopausal women without metabolic syndrome experienced the psychological cluster (anxiety), the sleep/somatic/genitourinary cluster (sleep disturbance), and the vasomotor cluster (night sweat) in the symptom occurrence dimension and the psychological/somatic cluster (anxiety), the sleep/urinary cluster (sleep disturbance), the vasomotor cluster (night sweat), and the sexual/genital cluster (vaginal dryness) in the symptom severity dimension.

DISCUSSION

The study findings may serve as a knowledge basis for effective assessment and management of symptom clusters and key symptoms in clinical settings and provide directions for future development of targeted symptom management interventions.

Holistic Needs Assessment of Cancer Survivors-Supporting the Process Through Digital Monitoring of Circadian Physiology

The year 2022 could represent a significant juncture in the incorporation of mHealth solutions in routine cancer care. With the recent global COVID-19 pandemic leading a surge in both observation- and intervention-based studies predominantly aimed at remote monitoring there has been huge intellectual investment in developing platforms able to provide real time analytics that are readily usable. Another fallout from the pandemic has seen record waiting times and delayed access to cancer therapies leading to exhausting pressures on global healthcare providers. It seems an opportune time to utilize this boom in platforms to offer more efficient “at home” clinical assessments and less “in department” time for patients. Here, we will focus specifically on the role of digital tools around cancer survivorship, a relevant aspect of the cancer journey, particularly benefiting from integrative approaches. Within that context a further concept will be introduced and that is of the likely upsurge in circadian-based interpretation of continuous monitoring and the engendered therapeutic modifications. Chronobiology across the 24-hour span has long been understood to control key bodily aspects and circadian dysregulation plays a significant role in the risk of cancer and also the response to therapy and therefore progressive outcome. The rapid improvement in minimally invasive monitoring devices is, in the opinion of the authors, likely to advance introducing chronobiological amendments to routine clinical practices with positive impact on cancer survivors.

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.

Symptom clusters among cancer survivors: what can machine learning techniques tell us?

PURPOSE

Knowledge regarding symptom clusters may inform targeted interventions. The current study investigated symptom clusters among cancer survivors, using machine learning techniques on a large data set.

METHODS

Data consisted of self-reports of cancer survivors who used a fully automated online application 'Oncokompas' that supports them in their self-management. This is done by 1) monitoring their symptoms through patient reported outcome measures (PROMs); and 2) providing a personalized overview of supportive care options tailored to their scores, aiming to reduce symptom burden and improve health-related quality of life. In the present study, data on 26 generic symptoms (physical and psychosocial) were used. Results of the PROM of each symptom are presented to the user as a no well-being risk, moderate well-being risk, or high well-being risk score. Data of 1032 cancer survivors were analysed using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) on high risk scores and moderate-to-high risk scores separately.

RESULTS

When analyzing the high risk scores, seven clusters were extracted: one main cluster which contained most frequently occurring physical and psychosocial symptoms, and six subclusters with different combinations of these symptoms. When analyzing moderate-to-high risk scores, three clusters were extracted: two main clusters were identified, which separated physical symptoms (and their consequences) and psycho-social symptoms, and one subcluster with only body weight issues.

CONCLUSION

There appears to be an inherent difference on the co-occurrence of symptoms dependent on symptom severity. Among survivors with high risk scores, the data showed a clustering of more connections between physical and psycho-social symptoms in separate subclusters. Among survivors with moderate-to-high risk scores, we observed less connections in the clustering between physical and psycho-social symptoms.

Haptotherapy for patients with cancer; experience of haptotherapists and reasons for consultation: A survey among haptotherapists

BACKGROUND AND PURPOSE

Haptotherapy is a type of mind-body therapy that makes use of affective touch. This study aims to provide insights in reasons for haptotherapy for patients with cancer, and in experiences of haptotherapists with these patients.

MATERIALS AND METHODS

In a cross-sectional study, a survey was conducted among 536 Dutch haptotherapists. Multiple-choice and open-ended questions were analyzed both quantitatively and qualitatively.

RESULTS

Of 272 (50.7%) responding haptotherapists, 167 (61.5%) had experience with treating people with cancer. Most frequently, combinations of emotional problems and a disturbed body experience were reported as reasons for haptotherapy. Haptotherapists emphasized the need for affective touch to restore patients' body connection.

CONCLUSION

Two-thirds of the respondents treat patients with cancer, addressing the interaction of body and mind. Reasons for consultation cover a wide range of problems in multiple dimensions, in which a disturbed body experience in combination with emotional problems is mentioned most often.

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.

Network Analysis of Outpatients to Identify Predictive Symptoms and Combinations of Symptoms Associated With Positive/Negative SARS-CoV-2 Nasopharyngeal Swabs

Background: Limited data exist on early predictive clinical symptoms or combinations of symptoms that could be included in the case definition of coronavirus disease 2019 (COVID-19), particularly for mild-to-moderate disease in an outpatient setting. Methods: A cohort study of individuals presenting with clinical symptoms to one of the largest dedicated networks of COVID-19 test centers in Geneva, Switzerland, between March 2 and April 23, 2020. Individuals completed a symptom questionnaire, received a nurse-led check-up, and nasopharyngeal swabs were obtained. An analysis of clinical features predicting the positivity and negativity of the SARS-CoV-2 RT-PCR test was performed to determine the relationship between symptoms and their combinations. Results: Of 3,248 patients included (mean age, 42.2 years; 1,504 [46.3%] male), 713 (22%) had a positive RT-PCR; 1,351 (41.6%) consulted within 3 days of symptom onset. The strongest predictor of a positive SARS-CoV-2 RT-PCR was anosmia, particularly in early disease, followed by fever, myalgia, and cough. Symptoms predictive of a negative test were breathing difficulties, abdominal symptoms, thoracic pain and runny nose. Three distinct networks of symptoms were identified, but did not occur together: respiratory symptoms; systemic symptoms related to fever; and other systemic symptoms related to anosmia. Conclusions: Symptoms and networks of symptoms associated with a positive/negative SARS-CoV-2 RT-PCR are emerging and may help to guide targeted testing. Identification of early COVID-19-related symptoms alone or in combination can contribute to establish a clinical case definition and provide a basis for clinicians and public health authorities to distinguish it from other respiratory viruses early in the course of the disease, particularly in the outpatient setting.

Lifestyle and Symptom Management Needs: A Network Analysis of Family Caregiver Needs of Cancer Patients

Previous research on the needs of family cancer caregivers (FCCs) have not elucidated associations between specific caregiving needs. Network analysis, a statistical approach that allows the estimation of complex relationship patterns, helps facilitate the understanding of associations between needs and provides the opportunity to identify and direct interventions at relevant and specific targets. No studies to date, have applied network analysis to FCC populations. The aim of the study is to explore the network structure of FCC needs in a cohort of caregivers in Singapore. FCCs (N = 363) were recruited and completed a self-report questionnaire on socio-demographic data, medical data on their loved ones, and the Needs Assessment of Family Caregivers-Cancer scale. The network was estimated using state-of-the-art regularized partial correlation model. The most central needs were having to deal with lifestyle changes and managing care-recipients cancer-related symptoms. The strongest associations were between (1) having enough insurance coverage and understanding/navigating insurance coverage, (2) managing cancer-related pain and managing cancer-related symptoms, (3) being satisfied with relationships and having intimate relationships, and (4) taking care of bills and paying off medical expenses. Lifestyle changes, living with cancer, and symptom management are central to FCCs in Singapore. These areas deserve special attention in the development of caregiver support systems. Our findings highlight the need to improve access to social and medical support to help FCCs in their transition into the caregiving role and handle cancer-related problems.

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.

Oncology patients' perceptions of and experiences with COVID-19

Purpose

No information is available on cancer patients’ knowledge of and experiences with COVID-19. We undertook an evaluation of differences in COVID-19 symptom occurrence rates, COVID-19 testing rates, clinical care activities, knowledge of COVID-19, and use of mitigation procedures between patients who were and were not receiving active cancer treatment.

Methods

Patients enrolled were > 18 years of age; had a diagnosis of cancer; and were able to complete the emailed study survey online.

Results

Of the 174 patients who participated, 27.6% (n = 48) were receiving active treatment, 13.6% were unemployed because of COVID-19, 12.2% had been tested for COVID-19, and 0.6% had been hospitalized for COVID-19. Patients who were not on active treatment reported a higher mean number of COVID-19 symptoms (3.1 (± 4.2) versus 1.9 (± 2.6)), and patients who reported a higher number of COVID-19 symptoms were more likely to be tested. Over 55% of the patients were confident that their primary care provider could diagnose COVID-19, and the majority of the patients had high levels of adherence with the use of precautionary measures (e.g., social distancing, use of face coverings).

Conclusion

The high level of COVID-19 symptoms and the significant overlap of COVID-19 and cancer-related symptoms pose challenges for clinicians who are assessing and triaging oncology patients for COVID-19 testing. For patients on active treatment, clinicians face challenges with how to assess and manage symptoms that, prior to COVID-19, would be ascribed to acute toxicities associated with cancer treatments or persistent symptoms in cancer survivors.

Stability and Impact of Symptom Clusters in Patients With End-Stage Renal Disease Undergoing Dialysis

CONTEXT

Patients with end-stage renal disease undergoing dialysis experience multiple concurrent symptoms. These symptoms cluster together and have negative impacts on patient outcomes. However, information on changes in symptom clusters over time is limited.

OBJECTIVES

This longitudinal study examined the stability of symptom clusters and their impacts on health-related quality of life and functional status over a period of one year.

METHODS

Eligibility criteria were patients diagnosed with end-stage renal disease; had received dialysis consecutively for at least three months; and had given written informed consent. Dialysis Symptom Index, Kidney Disease Quality of Life 36, and Karnofsky Performance Status Scale were used to evaluate the impacts of symptom clusters and outcomes. Exploratory factor analyses and multiple regression analyses were used to determine symptom clusters and their associations with patient outcomes.

RESULTS

Among the 354 recruited patients, 271 completed the 12-month assessment. Four symptom clusters were identified across the three assessments, namely, uremic, gastrointestinal, skin, and emotional. Within each cluster, the specific symptoms were varied. The uremic symptom cluster accounted for the largest amount of variability. Across the three assessments, a higher uremic cluster factor score was associated with poorer physical well-being, whereas a higher emotional cluster factor score was consistently associated with poorer mental well-being.

CONCLUSION

Symptoms in patients on dialysis clustered in relatively stable patterns. The four symptom clusters identified had consistent negative effects on various aspects of patients' well-being. Our findings suggest the need for ongoing symptom assessment and early recognition of symptoms that may contribute to adverse patient outcomes.