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

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.

Investigation of core symptoms and symptom clusters in maintenance hemodialysis patients: A network analysis

PURPOSE

To construct a symptom network of maintenance hemodialysis patients and identify the core symptoms and core symptom clusters. Finally, this study provides a reference for accurate symptom management.

DESIGN AND METHOD

A correlational cross-sectional design. A total of 368 patients who underwent maintenance hemodialysis were enrolled from two hemodialysis centers in Chengdu, Sichuan Province, China. A symptom network was constructed with the R coding language to analyze the centrality index. Symptom clusters were extracted by exploratory factor analysis, and core symptom clusters were preliminarily determined according to the centrality index of the symptom network.

FINDINGS

The most common symptoms in maintenance hemodialysis patients were fatigue, dry skin, and pruritus. In the symptom network, headache had the highest mediation centrality (rB = 2.789) and closeness centrality (rC = 2.218) and the greatest intensity of numbness or tingling in the feet (rS = 1.952). A total of six symptom clusters were extracted, including pain and discomfort symptom clusters, emotional symptom clusters, gastrointestinal symptom clusters, sleep disorder symptom clusters, dry symptom clusters, and sexual dysfunction symptom clusters. The cumulative variance contribution rate was 69.269%.

CONCLUSIONS

Fatigue, dry skin, and pruritus are the sentinel symptoms of maintenance hemodialysis patients, headache is the core symptom and bridge symptom, and pain symptom clusters are the core symptom clusters of MHD patients. Nurses can develop interventions based on core symptoms and symptom clusters to improve the effectiveness of symptom management in maintenance hemodialysis patients.

CLINICAL RELEVANCE

Understanding the core symptoms and symptom groups that plague maintenance hemodialysis patients is critical to providing accurate symptom management. To ensure that maintenance hemodialysis patients receive effective support during treatment, reduce the adverse effects of symptoms, and improve the quality of life of patients.

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.

Chemotherapy-related symptoms in children with leukemia: application of latent profile analysis and network analysis

PURPOSE

Children with leukemia may experience a range of chemotherapy-related symptoms. Identifying subgroups and their distinct characteristics of symptoms may improve symptom management. We aimed to identify subgroups and their distinct characteristics of chemotherapy-related symptoms in children with leukemia.

METHODS

A cross-sectional survey was conducted among 500 children with leukemia, who completed questionnaires that assessed their demographic and clinical characteristics, as well as the Memorial Symptom Assessment Scale. Latent profile analysis was conducted to identify subgroups of symptoms. Additionally, multiple regression analysis and network analysis were utilized to reveal the characteristics of each subgroup.

RESULTS

Four subgroups were identified: "Profile 1: low symptom burden subgroup" (26.2%), "Profile 2: moderate symptom burden subgroup in transitional period" (14.8%), "Profile 3: moderate psychological symptom burden subgroup" (35.6%), and "Profile 4: high symptom burden subgroup" (23.4%). Multiple logistic regression analysis indicated that lower primary caregiver's education level, lower family monthly income, self-paid medical expenses, induction remission period, and consolidation enhancement period were associated with more severe symptoms of subgroups. Network analysis further revealed that nausea was the core symptom in Profiles 1 and 2, while the core symptom in Profile 3 was "I don't look like myself." Additionally, worrying was the core symptom in Profile 4.

CONCLUSION

There exists heterogeneity in chemotherapy-related symptoms. Four subgroups and their corresponding characteristics of children with varying symptom severity were identified. Identifying these subgroups will facilitate personalized care, maximize intervention effectiveness, and alleviate symptom burden.

Symptoms experienced after transcatheter arterial chemoembolization in patients with primary liver cancer: A network analysis

Objective

This study aimed to establish a symptom network for patients with primary liver cancer posttranscatheter arterial chemoembolization (TACE), identifying core and bridge symptoms. The goal is to provide a foundation for precise and comprehensive nursing interventions.

Methods

A total of 1207 post-TACE patients were included using a consecutive sampling method. Data collection involved a general information questionnaire, the Anderson Symptom Assessment Scale, and a primary liver cancer-specific symptom module. The symptom network was constructed using the R language.

Results

In the overall network, distress exhibited the highest strength (rs = 1.31) and betweenness (rb = 62). Fatigue had the greatest closeness (rc = 0.0043), while nausea and vomiting (r = 0.76 ± 0.02) had the highest marginal weights. Nausea had the highest bridge strength (rbs = 5.263). In the first-time TACE-treated symptom network, sadness (rbs = 5.673) showed the highest bridge strength, whereas in the non-first-time symptom network, fever (rbs = 3.061) had the highest bridge strength.

Conclusions

Distress serves as a core symptom, and nausea acts as a bridge symptom after TACE treatment in liver cancer patients. Interventions targeting bridge symptoms should be tailored based on the number of treatments, enhancing the quality of symptom management.

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.

Evaluation of a psychoneurological symptom cluster in patients with breast or digestive cancer: a longitudinal observational study

BACKGROUND

A psychoneurological symptom cluster composed of cancer-related fatigue, emotional distress, sleep difficulties, and pain is very common among patients with cancer. Cognitive difficulties are also frequently associated with this cluster. Network analyses allow for an in-depth understanding of the relationships between symptoms in a cluster. This paper details the study protocol of a longitudinal assessment of the psychoneurological symptom cluster in two distinct cohorts: breast cancer and digestive cancer survivors, using network analyses.

METHODS

Over two years, the symptoms involved in the psychoneurological symptom cluster, along with other common symptoms (e.g., digestive symptoms, financial difficulties) and variables (i.e., self-compassion, coping strategies) will be assessed in two cohorts: breast cancer survivors (N = 240) and digestive cancer survivors (N = 240). Online questionnaires will be completed at baseline, then 6, 12 and 24 months later. Network analyses will be used to assess the configuration of the symptom cluster at each measurement time and in each cohort. Comparison of networks between two measurement times or between the two cohorts will also be done with network comparison tests.

DISCUSSION

This study will enable a better understanding of the relationships between common symptoms endured by patients with cancer. The results will be employed to develop more cost-effective interventions which, ultimately, will significantly improve the quality of life of patients with breast or digestive cancer.

TRIAL REGISTRATION

ClinicalTrials.gov (NCT05867966). Registered on the 27th of April 2023. url: https://classic.

CLINICALTRIALS

gov/ct2/show/NCT05867966 .

Exploring the dynamics of perioperative symptom networks in colorectal cancer patients: a cross-lagged panel network analysis

BACKGROUND

Colorectal cancer incidence is on the rise, necessitating precise symptom management. However, causal relationships among symptoms have been challenging to establish due to reliance on cross-sectional data. Cross-lagged panel network (CLPN) analysis offers a solution, leveraging longitudinal data for insight.

OBJECTIVE

We employed CLPN analysis to construct symptom networks in colorectal cancer patients at three perioperative time points, aiming to identify predictive relationships and intervention opportunities.

METHODS

We evaluated the prevalence and severity of symptoms throughout the perioperative period, encompassing T1 the first day of admission, T2 2-3 days postoperatively, and T3 discharge, utilizing the M. D. Anderson Symptom Inventory Gastrointestinal Cancer Module (MDASI-GI). To identify crucial nodes in the network and explore predictive and interactive effects among symptoms, CLPNs were constructed from longitudinal data in R.

RESULTS

The analysis revealed a stable network, with disturbed sleep exhibiting the highest out-EI (outgoing expected influence) during T1. Distress had a sustained impact throughout the perioperative. Disturbed sleep at T1 predicted T2 bloating, fatigue, distress, and pain. T1 distress predicted T2 sadness severity. T2 distress primarily predicted T3 fatigue, disturbed sleep, changes in taste, and bloating. T2 shortness of breath predicted T3 changes in taste and loss of appetite. Furthermore, biochemical markers like RBC and ALB had notable influence on symptom clusters during T1→T2 and T2→T3, respectively.

CONCLUSION

Prioritizing disturbed sleep during T1 and addressing distress throughout the perioperative phase is recommended. Effective symptom management not only breaks the chain of symptom progression, enhancing healthcare impact, but also eases patient symptom burdens.

Symptom profiles and related factors among patients with advanced cancer: A latent profile analysis

Objective

This study aimed to investigate symptom subgroups and associated influencing factors in patients with advanced cancer.

Methods

A cross-sectional study was conducted, involving 416 patients with advanced cancer. The study examined five symptoms: fatigue, pain, sleep impairment, anxiety, and depression. Latent Profile Analysis (LPA) was utilized to classify symptom subgroups. A multiple logistic regression analysis was conducted to explore factors associated with the identified symptom subgroups.

Results

The analysis revealed three distinct subgroups among the participants: "all low" (58.2%), characterized by normal symptoms except for moderate sleep quality; "all moderate" (35.1%), exhibiting normal symptoms except for poor sleep quality and fatigue; and "all high" (6.7%), experiencing normal pain, moderate depression, moderate anxiety, poor sleep quality, and fatigue. Malnutrition risk, cancer diagnosis, and cancer survivorship duration were found to be associated with a more severe symptom burden.

Conclusions

Patients in the "all high" subgroup faced an increased risk of malnutrition and a longer cancer survivorship duration. Additionally, patients in the "all moderate" subgroup were distinguished by having a breast cancer diagnosis. These findings have significant implications for allocating medical resources and implementing person-centered symptom management strategies.

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.

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.

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.

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.

Contemporaneous symptom networks and correlates during endocrine therapy among breast cancer patients: A network analysis

Background

Endocrine therapy-related symptoms are associated with early discontinuation and quality of life among breast cancer survivors. Although previous studies have examined these symptoms and clinical covariates, little is known about the interactions among different symptoms and correlates. This study aimed to explore the complex relationship of endocrine therapy-related symptoms and to identify the core symptoms among breast cancer patients.

Methods

This is a secondary data analysis conducted based on a multicenter cross-sectional study of 613 breast cancer patients in China. All participants completed the 19-item Chinese version of the Functional Assessment of Cancer Therapy-Endocrine Subscale (FACT-ES). Multivariate linear regression analysis was performed to identify significant factors. A contemporaneous network with 15 frequently occurring symptoms was constructed after controlling for age, payment, use of aromatase inhibitors, and history of surgery. Network comparison tests were used to assess differences in network structure across demographic and treatment characteristics.

Results

All 613 participants were female, with an average age of 49 years (SD = 9.4). The average duration of endocrine therapy was 3.6 years (SD = 2.3) and the average symptom score was 18.99 (SD = 11.43). Irritability (n = 512, 83.52%) and mood swings (n = 498, 81.24%) were the most prevalent symptoms. Lost interest in sex (mean = 1.95, SD = 1.39) and joint pain (mean = 1.57, SD = 1.18) were the most severe symptoms. The edges in the clusters of emotional symptoms ("irritability-mood swings"), vasomotor symptoms ("hot flashes-cold sweats-night sweats"), vaginal symptoms ("vaginal discharge-vaginal itching"), sexual symptoms ("pain or discomfort with intercourse-lost interest in sex-vaginal dryness"), and neurological symptoms ("headaches-dizziness") were the thickest in the network. There were no significant differences in network structure (P = 0.088), and global strength (P = 0.330) across treatment types (selective estrogen receptor modulators vs. aromatase inhibitors). Based on an evaluation of the centrality indices, irritability and mood swings appeared to be structurally important nodes after adjusting for the clinical covariates and after performing subgroup comparisons.

Conclusion

Endocrine therapy-related symptoms are frequently reported issues among breast cancer patients. Our findings demonstrated that developing targeted interventions focused on emotional symptoms may relieve the overall symptom burden for breast cancer patients during endocrine therapy.

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.