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

Brandon H Bergsneider; Elizabeth Vera; Ophir Gal; Alexa Christ; Amanda L King; Alvina Acquaye; Anna Choi; Heather E Leeper; Tito Mendoza; Lisa Boris; Eric Burton; Nicole Lollo; Marissa Panzer; Marta Penas-Prado; Tina Pillai; Lily Polskin; Jing Wu; Mark R Gilbert; Terri S Armstrong; Orieta Celiku

doi:10.1093/noajnl/vdac188

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

Neurooncol Adv. 2022 Dec 26;5(1):vdac188. doi: 10.1093/noajnl/vdac188. eCollection 2023 Jan-Dec.

Authors

Brandon H Bergsneider¹, Elizabeth Vera¹, Ophir Gal¹, Alexa Christ¹, Amanda L King¹, Alvina Acquaye¹, Anna Choi¹, Heather E Leeper¹, Tito Mendoza¹, Lisa Boris¹, Eric Burton¹, Nicole Lollo¹, Marissa Panzer¹, Marta Penas-Prado¹, Tina Pillai¹, Lily Polskin¹, Jing Wu¹, Mark R Gilbert¹, Terri S Armstrong¹, Orieta Celiku¹

Affiliation

¹ Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA.

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.

Keywords: Gaussian Graphical Models; Precision health approaches to symptom care; network analysis; patient-reported outcomes; primary brain tumor patient symptoms.

Published by Oxford University Press on behalf of the Society for Neuro-Oncology and the European Association of Neuro-Oncology 2022.