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

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

SUBMITTER: Bergsneider BH 

PROVIDER: S-EPMC9938652 | biostudies-literature | 2023 Jan-Dec

REPOSITORIES: biostudies-literature