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Bergsneider BH, Armstrong TS, Conley YP, Cooper B, Hammer M, Levine JD, Paul S, Miaskowski C, Celiku O. Symptom Network Analysis and Unsupervised Clustering of Oncology Patients Identifies Drivers of Symptom Burden and Patient Subgroups With Distinct Symptom Patterns. Cancer Med 2024; 13:e70278. [PMID: 39377555 DOI: 10.1002/cam4.70278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 08/20/2024] [Accepted: 09/20/2024] [Indexed: 10/09/2024] Open
Abstract
BACKGROUND Interindividual variability in oncology patients' symptom experiences poses significant challenges in prioritizing symptoms for targeted intervention(s). In this study, computational approaches were used to unbiasedly characterize the heterogeneity of the symptom experience of oncology patients to elucidate symptom patterns and drivers of symptom burden. METHODS Severity ratings for 32 symptoms on the Memorial Symptom Assessment Scale from 3088 oncology patients were analyzed. Gaussian Graphical Model symptom networks were constructed for the entire cohort and patient subgroups identified through unsupervised clustering of symptom co-severity patterns. Network characteristics were analyzed and compared using permutation-based statistical tests. Differences in demographic and clinical characteristics between subgroups were assessed using multinomial logistic regression. RESULTS Network analysis of the entire cohort revealed three symptom clusters: constitutional, gastrointestinal-epithelial, and psychological. Lack of energy was identified as central to the network which suggests that it plays a pivotal role in patients' overall symptom experience. Unsupervised clustering of patients based on shared symptom co-severity patterns identified six patient subgroups with distinct symptom patterns and demographic and clinical characteristics. The centrality of individual symptoms across the subgroup networks differed which suggests that different symptoms need to be prioritized for treatment within each subgroup. Age, treatment status, and performance status were the strongest determinants of subgroup membership. CONCLUSIONS Computational approaches that combine unbiased stratification of patients and in-depth modeling of symptom relationships can capture the heterogeneity in patients' symptom experiences. When validated, the core symptoms for each of the subgroups and the associated clinical determinants may inform precision-based symptom management.
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Affiliation(s)
- Brandon H Bergsneider
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
- School of Medicine, Stanford University, Stanford, California, USA
| | - Terri S Armstrong
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Yvette P Conley
- School of Nursing, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Bruce Cooper
- School of Nursing, University of California San Francisco, San Francisco, California, USA
| | - Marilyn Hammer
- Phyllis F Cantor Center for Research in Nursing and Patient Care Services, Dana Farber Cancer Institute, Boston, Massachusetts, USA
| | - Jon D Levine
- School of Medicine, University of California San Francisco, San Francisco, California, USA
| | - Steven Paul
- School of Nursing, University of California San Francisco, San Francisco, California, USA
| | - Christine Miaskowski
- School of Nursing, University of California San Francisco, San Francisco, California, USA
- School of Medicine, University of California San Francisco, San Francisco, California, USA
| | - Orieta Celiku
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
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Doppenberg-Smit GE, Lamers F, van Linde ME, Braamse AMJ, Sprangers MAG, Beekman ATF, Verheul HMW, Dekker J. Network analysis used to investigate the interplay among somatic and psychological symptoms in patients with cancer and cancer survivors: a scoping review. J Cancer Surviv 2024:10.1007/s11764-024-01543-0. [PMID: 38530627 DOI: 10.1007/s11764-024-01543-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 01/22/2024] [Indexed: 03/28/2024]
Abstract
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.
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Affiliation(s)
- G Elise Doppenberg-Smit
- Department of Psychiatry, Amsterdam UMC, Location Vrije Universiteit Amsterdam, de Boelelaan 1117, Amsterdam, the Netherlands.
- Amsterdam Public Health, Mental Health Program, Amsterdam, the Netherlands.
- Cancer Centre Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, the Netherlands.
| | - Femke Lamers
- Department of Psychiatry, Amsterdam UMC, Location Vrije Universiteit Amsterdam, de Boelelaan 1117, Amsterdam, the Netherlands
- Amsterdam Public Health, Mental Health Program, Amsterdam, the Netherlands
| | - Myra E van Linde
- Department of Medical Oncology, Amsterdam UMC, Location Vrije Universiteit Amsterdam, de Boelelaan 1117, Amsterdam, the Netherlands
| | - Annemarie M J Braamse
- Amsterdam Public Health, Mental Health Program, Amsterdam, the Netherlands
- Cancer Centre Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, the Netherlands
- Department of Medical Psychology, Amsterdam UMC, Location University of Amsterdam, Amsterdam, the Netherlands
| | - Mirjam A G Sprangers
- Amsterdam Public Health, Mental Health Program, Amsterdam, the Netherlands
- Cancer Centre Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, the Netherlands
- Department of Medical Psychology, Amsterdam UMC, Location University of Amsterdam, Amsterdam, the Netherlands
| | - Aartjan T F Beekman
- Department of Psychiatry, Amsterdam UMC, Location Vrije Universiteit Amsterdam, de Boelelaan 1117, Amsterdam, the Netherlands
- Amsterdam Public Health, Mental Health Program, Amsterdam, the Netherlands
| | - Henk M W Verheul
- Department of Medical Oncology, Erasmus MC, Dr. Molewaterplein 40, Rotterdam, the Netherlands
| | - Joost Dekker
- Department of Psychiatry, Amsterdam UMC, Location Vrije Universiteit Amsterdam, de Boelelaan 1117, Amsterdam, the Netherlands
- Amsterdam Public Health, Mental Health Program, Amsterdam, the Netherlands
- Cancer Centre Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, the Netherlands
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Bergsneider BH, Vera E, Gal O, Christ A, King AL, Acquaye A, Choi A, Leeper HE, Mendoza T, Boris L, Burton E, Lollo N, Panzer M, Penas-Prado M, Pillai T, Polskin L, Wu J, Gilbert MR, Armstrong TS, Celiku O. Discovery of clinical and demographic determinants of symptom burden in primary brain tumor patients using network analysis and unsupervised clustering. Neurooncol Adv 2022; 5:vdac188. [PMID: 36820236 PMCID: PMC9938652 DOI: 10.1093/noajnl/vdac188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
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.
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Affiliation(s)
- Brandon H Bergsneider
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Elizabeth Vera
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Ophir Gal
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Alexa Christ
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Amanda L King
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Alvina Acquaye
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Anna Choi
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Heather E Leeper
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Tito Mendoza
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Lisa Boris
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Eric Burton
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Nicole Lollo
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Marissa Panzer
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Marta Penas-Prado
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Tina Pillai
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Lily Polskin
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Jing Wu
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Mark R Gilbert
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Terri S Armstrong
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Orieta Celiku
- Corresponding Author: Orieta Celiku, PhD, Neuro-Oncology Branch, National Cancer Institute, 37 Convent Drive, Bethesda, MD 20892, USA ()
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Neijenhuijs KI, Peeters CFW, van Weert H, Cuijpers P, Leeuw IVD. Symptom clusters among cancer survivors: what can machine learning techniques tell us? BMC Med Res Methodol 2021; 21:166. [PMID: 34399698 PMCID: PMC8369803 DOI: 10.1186/s12874-021-01352-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 07/21/2021] [Indexed: 12/25/2022] Open
Abstract
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.
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Affiliation(s)
- Koen I Neijenhuijs
- Department of Clinical, Vrije Universiteit Amsterdam, Neuro- and Developmental Psychology, Amsterdam Public Health Research Institute, Van der Boechorststraat 1, 1081, BT, Amsterdam, The Netherlands.,Amsterdam UMC, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Carel F W Peeters
- Department of Epidemiology & Biostatistics, Amsterdam UMC, location VUmc, Boelelaan, 1117, Amsterdam, The Netherlands.,Mathematical & Statistical Methods Group (Biometris), Wageningen University & Research, Wageningen, The Netherlands
| | - Henk van Weert
- Department of General Practice, Amsterdam UMC, location AMC, Amsterdam Public Health, Meibergdreef 9, Amsterdam, The Netherlands
| | - Pim Cuijpers
- Department of Clinical, Vrije Universiteit Amsterdam, Neuro- and Developmental Psychology, Amsterdam Public Health Research Institute, Van der Boechorststraat 1, 1081, BT, Amsterdam, The Netherlands
| | - Irma Verdonck-de Leeuw
- Department of Clinical, Vrije Universiteit Amsterdam, Neuro- and Developmental Psychology, Amsterdam Public Health Research Institute, Van der Boechorststraat 1, 1081, BT, Amsterdam, The Netherlands. .,Amsterdam UMC, Cancer Center Amsterdam, Amsterdam, The Netherlands. .,Department of Otolaryngology-Head and Neck Surgery, Amsterdam UMC, location VUmc, Boelelaan, 1117, Amsterdam, The Netherlands.
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Shi Y, Hu X, Cui J, Cui L, Huang J, Ma X, Jiang T, Yao X, Lan F, Li J, Bi Z, Li J, Wang Y, Fu H, Wang J, Lin Y, Bai J, Guo X, Tu L, Xu J. Clinical data mining on network of symptom and index and correlation of tongue-pulse data in fatigue population. BMC Med Inform Decis Mak 2021; 21:72. [PMID: 33627103 PMCID: PMC7905588 DOI: 10.1186/s12911-021-01410-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 01/28/2021] [Indexed: 12/19/2022] Open
Abstract
Background Fatigue is a kind of non-specific symptom, which occurs widely in sub-health and various diseases. It is closely related to people's physical and mental health. Due to the lack of objective diagnostic criteria, it is often neglected in clinical diagnosis, especially in the early stage of disease. Many clinical practices and researches have shown that tongue and pulse conditions reflect the body's overall state. Establishing an objective evaluation method for diagnosing disease fatigue and non-disease fatigue by combining clinical symptom, index, and tongue and pulse data is of great significance for clinical treatment timely and effectively. Methods In this study, 2632 physical examination population were divided into healthy controls, sub-health fatigue group, and disease fatigue group. Complex network technology was used to screen out core symptoms and Western medicine indexes of sub-health fatigue and disease fatigue population. Pajek software was used to construct core symptom/index network and core symptom-index combined network. Simultaneously, canonical correlation analysis was used to analyze the objective tongue and pulse data between the two groups of fatigue population and analyze the distribution of tongue and pulse data. Results Some similarities were found in the core symptoms of sub-health fatigue and disease fatigue population, but with different node importance. The node-importance difference indicated that the diagnostic contribution rate of the same symptom to the two groups was different. The canonical correlation coefficient of tongue and pulse data in the disease fatigue group was 0.42 (P < 0.05), on the contrast, correlation analysis of tongue and pulse in the sub-health fatigue group showed no statistical significance. Conclusions The complex network technology was suitable for correlation analysis of symptoms and indexes in fatigue population, and tongue and pulse data had a certain diagnostic contribution to the classification of fatigue population.
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Affiliation(s)
- Yulin Shi
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Xiaojuan Hu
- Shanghai Innovation Center of TCM Health Service, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Ji Cui
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Longtao Cui
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Jingbin Huang
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Xuxiang Ma
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Tao Jiang
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Xinghua Yao
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Fang Lan
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Jun Li
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Zijuan Bi
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Jiacai Li
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Yu Wang
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Hongyuan Fu
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Jue Wang
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Yanting Lin
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Jingxuan Bai
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Xiaojing Guo
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Liping Tu
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China.
| | - Jiatuo Xu
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China.
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