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Network Analysis of the Multidimensional Symptom Experience of Oncology.

ABSTRACT: Oncology patients undergoing cancer treatment experience an average of fifteen unrelieved symptoms that are highly variable in both their severity and distress. Recent advances in Network Analysis (NA) provide a novel approach to gain insights into the complex nature of co-occurring symptoms and symptom clusters and identify core symptoms. We present findings from the first study that used NA to examine the relationships among 38 common symptoms in a large sample of oncology patients undergoing chemotherapy. Using two different models of Pairwise Markov Random Fields (PMRF), we examined the nature and structure of interactions for three different dimensions of patients' symptom experience (i.e., occurrence, severity, distress). Findings from this study provide the first direct evidence that the connections between and among symptoms differ depending on the symptom dimension used to create the network. Based on an evaluation of the centrality indices, nausea appears to be a structurally important node in all three networks. Our findings can be used to guide the development of symptom management interventions based on the identification of core symptoms and symptom clusters within a network.

SUBMITTER: Papachristou N

PROVIDER: S-EPMC6381090 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Network Analysis of the Multidimensional Symptom Experience of Oncology.

Papachristou Nikolaos N Barnaghi Payam P Cooper Bruce B Kober Kord M KM Maguire Roma R Paul Steven M SM Hammer Marilyn M Wright Fay F Armes Jo J Furlong Eileen P EP McCann Lisa L Conley Yvette P YP Patiraki Elisabeth E Katsaragakis Stylianos S Levine Jon D JD Miaskowski Christine C

Scientific reports 20190219 1

Oncology patients undergoing cancer treatment experience an average of fifteen unrelieved symptoms that are highly variable in both their severity and distress. Recent advances in Network Analysis (NA) provide a novel approach to gain insights into the complex nature of co-occurring symptoms and symptom clusters and identify core symptoms. We present findings from the first study that used NA to examine the relationships among 38 common symptoms in a large sample of oncology patients undergoing ...[more]

PMID: 30783135

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Project description:BackgroundInterindividual 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.MethodsSeverity 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.ResultsNetwork 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.ConclusionsComputational 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.

Project description:BackgroundRemote patient monitoring (RPM) of symptoms using electronic patient reported outcomes (ePROs) has been shown to reduce symptom burden and hospitalizations, increase dose intensity and improve quality of life of patients during systemic therapy being recommended by international guidelines in routine oncology practice. However, implementation in routine care has been slow and faces several challenges. In this study we report on the real-world multi-center implementation of a RPM pathway encompassing weekly patient symptom ePRO reporting with electronic alert notifications triggered to providers for severe or worsening symptoms.MethodsAn RPM pathway was implemented in 33 European cancer centers in France and Belgium between November 2021 and August 2023. The implementation process followed a standardized phasic process of Exploration, Preparation, Implementation and Sustainment. Patient-level and system-level implementation metrics were collected and evaluated according to the Reach, Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) framework.FindingsAcross the 33 cancer centers, the RPM pathway was implemented for 3015 patients cared for by 168 providers. The RPM pathway enabled effective and timely symptom management with 94.6% of all alerts (10,132/10,711) evolving to an improvement two weeks later, among which 88.4% (9468/10,711) showed ≥2 grades of improvement on the 5-point scale of the Patient-Reported Outcomes Common Terminology (PRO-CTCAE). The median time to alert management by the care team was 13 h 41 min (25th percentile: 1 h 42 min, 75th percentile: 1 day + 19 h 54 min), with 80% (36,269/45,334) of alerts managed by a nurse navigator telephone call. Patient adherence with weekly ePRO reporting was 82% (2472/3015). In an experience survey, 87% (32/38) of providers were satisfied with integrating the solution into their organization and 90% (276/307) of the patients felt that ePRO reporting positively impacted their care. As of March 2024, the pathway has been maintained in all participating centers, with activation of an additional 18 centers following data lock, and reimbursement for this RPM pathway approved in France in October 2023.InterpretationThese findings demonstrate the feasibility of implementing and maintaining an RPM pathway during routine care across a diverse group of cancer centers in the European setting, with high levels of patient and provider engagement, and positive clinical impact.FundingPart of this work was funded Breast Cancer Research Foundation (Career Development Award to Maria Alice Franzoi) and Resilience (nurse navigation and technology support).

Project description:Background: Many genomic alterations have been identified that are critical to the malignant phenotype. Some of these, termed "driver mutations," are critical for tumor proliferation and progression. The landscape of targeted therapy has expanded as well. Next-generation sequencing (NGS) of tumors reveals cancer-related genomic alterations and provides therapeutic recommendations for specific targeted therapy. We analyzed our experience with FoundationOne, a validated NGS genomic profiling test, in a community oncology network. Methods: NGS results from May 2014 to September 2016 from a community oncology network in Western Pennsylvania were analyzed. Medical records were reviewed for primary site, stage, biopsy site, time of testing, prior treatment, FDA-approved therapy in patient's and other tumor types and potential clinical trials based upon mutations detected. Two co-primary endpoints for this study were to determine the percentage of patients having mutations with a FDA-approved targeted agent and the percentage of patients in whom a treatment decision was made based on these NGS results. Results: One Fifty-Seven NGS results were available for analysis. 82% patients had a mutation with a FDA-approved targeted agent available while 18% patients had no FDA-approved targeted agent for the mutation detected. Clinical trials were available for 93% cases. The NGS results were utilized in treatment decisions in 18% patients (n = 28) with, 7% (n = 11) initiating a targeted agent, 6% (n = 9) were on an appropriate targeted agent prior to testing and 5% (n = 8) being unable to start a targeted agent because of insurance denial, clinical deterioration or patient preference. 38% cases were tested early in the disease course (at diagnosis, during or shortly after first-line treatment) and 62% at progression. Conclusions: NGS is a valuable tool to identify molecular targets for personalizing cancer care. From our experience, the actual number of patients starting a targeted agent based on NGS results is low but it provides substantial information in terms of providing additional treatment options, identifying resistance conferring mutations and facilitating clinical trial enrollment. Optimal time of testing, early or late in disease course, financial implications of testing and using targeted therapy and survival benefit of targeted therapy need further studies.

Dataset Information

Network Analysis of the Multidimensional Symptom Experience of Oncology.

Publications

Network Analysis of the Multidimensional Symptom Experience of Oncology.

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