A two-step scoring model incorporating visceral-to-subcutaneous fat ratio and systemic immunoinflammatory index for predicting cytokine release syndrome severity in patients with gastric cancer receiving Claudin18.2-targ

Early human evidence such as case series or small samples is exploring possible benefits.

• Level A · Stronger Clinical Evidence
• Level B · Emerging clinical evidence with positive signals
• Level C · Early human research exploring benefits
• Level D · Scientific groundwork from lab and animal studies
• Emerging · Emerging topic under active research

Abstract (original English)

Cytokine release syndrome (CRS) greatly impacts survival in patients who undergo chimeric antigen receptor (CAR)-T cell therapy, and the identification of its determinants is still challenging. We analysed the impact of systemic immunoinflammatory index (SII) and body composition parameters derived from CT images on the severity of CRS in 45 patients with advanced gastric cancer treated with CLDN18.2-targeted CAR-T cells. The waist circumference, skeletal muscle index (SMI), skeletal muscle density (SMD), subcutaneous fat area (SFA), visceral fat area (VFA) and VFA-to-SFA ratio (VSR) on baseline CT were automatically segmented and calculated using a deep learning-based tool. The relationship between SII, body composition, and CRS severity was investigated by using ROC analysis, univariate and multivariate binary logistic regression. There were no significant differences in SMI, SMD, SFA and waist circumference between patients with CRS grade 1 and 2. CRS grade 2 patients exhibited significantly higher VSR and SII than patients with CRS grade 1 (P = 0.003 and 0.012, respectively). ROC analysis showed that the AUCs of VSR and SII for predicting CRS grade were 0.762 (0.620-0.905) and 0.721 (0.563-0.879), respectively. Logistic regression analysis demonstrated that SII > 553 × 10 9 /L and VSR ≥ 0.21 were significantly linked with high grade CRS (P = 0.035 and 0.014, respectively).

Evidence level

Early human evidence such as case series or small samples is exploring possible benefits.

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