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

Autor:	Bergsneider BH; Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA., Vera E; Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA., Gal O; Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA., Christ A; Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA., King AL; Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA., Acquaye A; Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA., Choi A; Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA., Leeper HE; Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA., Mendoza T; Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA., Boris L; Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA., Burton E; Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA., Lollo N; Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA., Panzer M; Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA., Penas-Prado M; Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA., Pillai T; Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA., Polskin L; Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA., Wu J; Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA., Gilbert MR; Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA., Armstrong TS; Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA., Celiku O; Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA.
Jazyk:	angličtina
Zdroj:	Neuro-oncology advances [Neurooncol Adv] 2022 Dec 26; Vol. 5 (1), pp. vdac188. Date of Electronic Publication: 2022 Dec 26 (Print Publication: 2023).
DOI:	10.1093/noajnl/vdac188
Abstrakt:	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. (Published by Oxford University Press on behalf of the Society for Neuro-Oncology and the European Association of Neuro-Oncology 2022.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu