Metareview-informed Explainable Cytokine Storm Detection during CAR-T cell Therapy

TL;DR

This paper introduces a meta-review informed machine learning approach for early and interpretable detection of cytokine storms in CAR-T cell therapy patients, leveraging clinical data and prior studies.

Contribution

It pioneers a method that combines meta-review insights with machine learning to improve CRS detection and interpretability in clinical settings.

Findings

Effective identification of cytokine storm onset in real-world data

Supports clinicians with interpretable, evidence-based diagnostics

Demonstrates potential for swift CRS diagnosis

Abstract

Cytokine release syndrome (CRS), also known as cytokine storm, is one of the most consequential adverse effects of chimeric antigen receptor therapies that have shown promising results in cancer treatment. When emerging, CRS could be identified by the analysis of specific cytokine and chemokine profiles that tend to exhibit similarities across patients. In this paper, we exploit these similarities using machine learning algorithms and set out to pioneer a meta--review informed method for the identification of CRS based on specific cytokine peak concentrations and evidence from previous clinical studies. We argue that such methods could support clinicians in analyzing suspect cytokine profiles by matching them against CRS knowledge from past clinical studies, with the ultimate aim of swift CRS diagnosis. During evaluation with real--world CRS clinical data, we emphasize the potential of our proposed method of producing interpretable results, in addition to being effective in identifying the onset of cytokine storm.