Institutional-Level Monitoring of Immune Checkpoint Inhibitor IrAEs
  Using a Novel Natural Language Processing Algorithmic Pipeline
Michael Shapiro Herut Dor Anna Gurevich-Shapiro Tal Etan Ido Wolf
Background: Immune checkpoint inhibitors (ICIs) have revolutionized cancer
treatment but can result in severe immune-related adverse events (IrAEs).
Monitoring IrAEs on a large scale is essential for personalized risk profiling
and assisting in treatment decisions.
  Methods: In this study, we conducted an analysis of clinical notes from
patients who received ICIs at the Tel Aviv Sourasky Medical Center. By
employing a Natural Language Processing algorithmic pipeline, we systematically
identified seven common or severe IrAEs. We examined the utilization of
corticosteroids, treatment discontinuation rates following IrAEs, and
constructed survival curves to visualize the occurrence of adverse events
during treatment.
  Results: Our analysis encompassed 108,280 clinical notes associated with
1,635 patients who had undergone ICI therapy. The detected incidence of IrAEs
was consistent with previous reports, exhibiting substantial variation across
different ICIs. Treatment with corticosteroids varied depending on the specific
IrAE, ranging from 17.3% for thyroiditis to 57.4% for myocarditis. Our
algorithm demonstrated high accuracy in identifying IrAEs, as indicated by an
area under the curve (AUC) of 0.89 for each suspected note and F1 scores of
0.87 or higher for five out of the seven IrAEs examined at the patient level.
  Conclusions: This study presents a novel, large-scale monitoring approach
utilizing deep neural networks for IrAEs. Our method provides accurate results,
enhancing understanding of detrimental consequences experienced by ICI-treated
patients. Moreover, it holds potential for monitoring other medications,
enabling comprehensive post-marketing surveillance to identify susceptible
populations and establish personalized drug safety profiles.