NCOG-39. DISCOVERY OF CLINICAL AND DEMOGRAPHIC DETERMINANTS OF SYMPTOM BURDEN IN PRIMARY BRAIN TUMOR PATIENTS USING NETWORK ANALYSIS AND UNSUPERVISED CLUSTERING

• Published in: Neuro-oncology (Charlottesville, Va.) Vol. 24; no. Supplement_7; pp. vii205 - vii206
• Main Authors: Bergsneider, Brandon, Vera, Elizabeth, Gal, Ophir, Christ, Alexa, King, Amanda, Acquaye, Alvina, Choi, Anna, Leeper, Heather, Mendoza, Tito, Boris, Lisa, Burton, Eric, Lollo, Nicole, Panzer, Marissa, Penas-Prado, Marta, Pillai, Valentina, Polskin, Lily, Wu, Jing, Gilbert, Mark, Armstrong, Terri, Celiku, Orieta
• Format: Journal Article
• Language: English
• Published: 14.11.2022
• ISSN: 1522-8517; 1523-5866
• DOI: 10.1093/neuonc/noac209.790

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. Network Analysis (NA) identifies complex symptom co-severity patterns across large patient cohorts. Unsupervised clustering unbiasedly stratifies patients into clinically relevant subgroups based on symptom patterns. This is the first study to use NA and unsupervised clustering to explore PBT patients’ clinical and demographic determinants of symptom burden. METHODS Symptom severity data reported using the MDASI-BT from a two-institutional cohort of 1,128 PBT patients was analyzed. Gaussian Graphical Model networks were constructed for the entire cohort and for subgroups identified by unsupervised clustering. Network characteristics were analyzed and compared using permutation-based statistical tests. RESULTS NA on the entire cohort revealed that the majority of PBT patients experience symptoms on four core dimensions that drive overall symptom burden: cognitive, physical, focal neurologic, and affective. These dimensions substantially overlap with factor groupings defined during initial construct validation of the MDASI-BT. Fatigue/drowsiness scored the highest in all network centrality measures, indicating a pivotal role in the symptom experience. Unsupervised clustering identified four 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, but not for PC3 and PC4 due to their small size. The PC1 network closely resembles the all-patient network, and these patients have the highest interference scores among the subgroups with fatigue/drowsiness as the primary driver. PC2 represents an older subgroup in which cognitive symptoms drive symptom burden. CONCLUSIONS This novel study identified clinically relevant subgroups of patients with unique symptom burdens. With further validation, our approach may inform more personalized and effective symptom management by identifying symptoms to prioritize for targeting in individual patients.