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

Identifying symptom clusters and temporal interconnections in patients with lung tumors after CT-guided microwave ablation: A network analysis

PURPOSE

To explore symptom clusters and interrelationships using a network analysis approach among symptoms in patients with lung tumors who underwent computed tomography (CT)-guided microwave ablation (MWA).

METHODS

A longitudinal study was conducted, and 196 lung tumor patients undergoing MWA were recruited and were measured at 24 h, 48 h, and 72 h after MWA. The Chinese version of the MD Anderson Symptom Inventory and the Revised Lung Cancer Module were used to evaluate symptoms. Network analyses were performed to explore the symptom clusters and interrelationships among symptoms.

RESULTS

Four stable symptom communities were identified within the networks. Distress, weight loss, and chest tightness were the central symptoms. Distress, and weight loss were also the most key bridge symptoms, followed by cough. Three symptom networks were temporally stable in terms of symptom centrality, global connectivity, and network structure.

CONCLUSION

Our findings identified the central symptoms, bridge symptoms, and the stability of symptom networks of patients with lung tumors after MWA. These network results will have important implications for future targeted symptom management intervention development. Future research should focus on developing precise interventions for targeting central symptoms and bridge symptoms to promote patients' health.

Patterns and predictors of symptom burden and posttraumatic growth among patients with cancer: a latent profile analysis

PURPOSE

The study identified different patterns of symptom burden and posttraumatic growth (PTG) among patients with cancer and to explored the effects of sociodemographic, disease-related, and family resilience factors, which could provide reference for the development of personalized nursing measures.

METHODS

A questionnaire survey was conducted with 329 patients with cancer who were undergoing treatment. Latent profile analysis (LPA) was used to explore the patterns of symptom burden and PTG among patients with cancer, and multiple logistic regression analysis was used to explore the influencing factors of different patterns.

RESULTS

Based on the fit indicators of LPA, a three-class pattern model of posttraumatic responses was shown to be optimal, including resisting, struggling, and growth groups. In the resisting group (34.34%), patients reported low symptom burden and low PTG; in the struggling group (19.15%), patients showed a high symptom burden and moderate PTG; in the growth group (46.51%), patients showed low symptom burden and high PTG. Moreover, patients with cancer with high levels of family resilience were more likely to fall into the struggling and growth groups. Specifically, those with lower scores in the optimistic attitude and higher scores in the family and social support dimension of family resilience were more likely to fall into the struggling group, whereas those with lower scores in the transcendence and spiritual belief dimensions of family resilience were more likely to fall into the resisting group. Additionally, patients with at least three children were more likely to fall into the struggling group.

CONCLUSIONS

This study showed heterogeneity in symptom burden and PTG patterns among patients with cancer. Patients' growth must include both psychological growth and the mitigated symptom burden. Family factors may be intervention targets to improve the growth patterns.

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.

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.

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.

Neurotransmitter Gene Polymorphisms Are Associated with Symptom Clusters in Patients Undergoing Radiation Therapy

OBJECTIVES

Purpose was to evaluate for associations between the severity of three distinct symptom clusters (ie, sickness-behavior, mood-cognitive, treatment-related) and polymorphisms for 16 genes involved in catecholaminergic, GABAergic, and serotonergic neurotransmission.

DATA SOURCES

Patients with breast and prostate cancer (n = 157) completed study questionnaires at the completion of radiation therapy. Memorial Symptom Assessment Scale was used to assess the severity of 32 common symptoms. Three distinct symptom clusters were identified using exploratory factor analysis. Associations between the symptom cluster severity scores and neurotransmitter gene polymorphisms were evaluated using regression analyses.

CONCLUSION

Severity scores for the sickness-behavior symptom cluster were associated with polymorphisms for solute carrier family 6 (SLC6A) member 2 (SLC6A2), SLC6A3, SLC6A1, and 5-hydroxytryptamine receptor (HTR) 2A (HTR2A) genes. For the mood-cognitive symptom cluster, severity scores were associated with polymorphisms for adrenoreceptor alpha 1D, SLC6A2, SLC6A3, SLC6A1, HTR2A, and HTR3A. Severity scores for the treatment-related symptom cluster were associated with polymorphisms for SLC6A2, SLC6A3, catechol-o-methyltransferase, SLC6A1, HTR2A, SLC6A4, and tryptophan hydroxylase 2.

IMPLICATIONS FOR NURSING PRACTICE

Findings suggest that polymorphisms for several neurotransmitter genes are involved in the severity of sickness-behavior, mood-cognitive, and treatment-related symptom clusters in oncology patients at the completion of radiation therapy. Four genes with various associated polymorphisms were common across the three distinct symptom clusters (ie, SLC6A2, SLC6A3, SLC6A1, HTR2A) which suggest that these clusters have common underlying mechanisms.

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.