학술논문
Automated detection of immune effector cell‐associated neurotoxicity syndrome via quantitative EEG
Document Type
article
Author
Christine A. Eckhardt; Haoqi Sun; Preeti Malik; Syed Quadri; Marcos Santana Firme; Daniel K. Jones; Meike vanSleuwen; Aayushee Jain; Ziwei Fan; Jin Jing; Wendong Ge; Husain H. Danish; Caron A. Jacobson; Daniel B. Rubin; Eyal Y. Kimchi; Sydney S. Cash; Matthew J. Frigault; Jong Woo Lee; Jorg Dietrich; M. Brandon Westover
Source
Annals of Clinical and Translational Neurology, Vol 10, Iss 10, Pp 1776-1789 (2023)
Subject
Language
English
ISSN
2328-9503
Abstract
Abstract Objective To develop an automated, physiologic metric of immune effector cell‐associated neurotoxicity syndrome among patients undergoing chimeric antigen receptor‐T cell therapy. Methods We conducted a retrospective observational cohort study from 2016 to 2020 at two tertiary care centers among patients receiving chimeric antigen receptor‐T cell therapy with a CD19 or B‐cell maturation antigen ligand. We determined the daily neurotoxicity grade for each patient during EEG monitoring via chart review and extracted clinical variables and outcomes from the electronic health records. Using quantitative EEG features, we developed a machine learning model to detect the presence and severity of neurotoxicity, known as the EEG immune effector cell‐associated neurotoxicity syndrome score. Results The EEG immune effector cell‐associated neurotoxicity syndrome score significantly correlated with the grade of neurotoxicity with a median Spearman's R2 of 0.69 (95% CI of 0.59–0.77). The mean area under receiving operator curve was greater than 0.85 for each binary discrimination level. The score also showed significant correlations with maximum ferritin (R2 0.24, p = 0.008), minimum platelets (R2 –0.29, p = 0.001), and dexamethasone usage (R2 0.42, p