Authors
Huiwen Xu, Mostafa Mohamed, Marie Flannery, Luke Peppone, Erika Ramsdale, Kah Poh Loh, Megan Wells, Leah Jamieson, Victor G Vogel, Bianca Alexandra Hall, Karen Mustian, Supriya Mohile, Eva Culakova
Publication date
2023/3/1
Journal
JAMA network open
Volume
6
Issue
3
Pages
e234198-e234198
Publisher
American Medical Association
Description
Importance
Older adults with advanced cancer who have high pretreatment symptom severity often experience adverse events during cancer treatments. Unsupervised machine learning may help stratify patients into different risk groups.
Objective
To evaluate whether clusters identified from baseline patient-reported symptom severity were associated with adverse outcomes.
Design, Setting, and Participants
This secondary analysis of the Geriatric Assessment Intervention for Reducing Toxicity in Older Patients With Advanced Cancer (GAP70+) Trial (2014-2019) included patients who completed the National Cancer Institute Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) before starting a new cancer treatment regimen and received care at community oncology sites across the United States. An unsupervised machine learning algorithm (k-means with …
Total citations
Scholar articles
H Xu, M Mohamed, M Flannery, L Peppone… - JAMA network open, 2023