1212P - Predictive value of a near-term prediction model for severe irAEs in cancer treatment with ICIs

Background

Severe irAEs caused by ICIs affect treatment efficacy and benefit. Current research on irAEs is mainly focused on early prediction, with a lack of near-term prediction. Studies have reported that baseline neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and absolute eosinophil count (AEC) can predict irAEs. Our aim is to explore the near-term predictive value of NLR, PLR, and AEC for grade 3 or higher irAEs caused by PD-1 inhibitors.

Methods

Data were collected from cancer patients treated with PD-1 inhibitors in our department from January 2020 to May 2022. NLR, PLR, and AEC data were collected one cycle before and during the occurrence of irAEs (median cycle number was 2nd and 3rd cycles, respectively). Logistic analysis was used to analyze the correlation between NLR, PLR, AEC, and irAEs, and to construct a prediction model. The model's performance was assessed by obtaining sensitivity and specificity through the ROC.

Results

Out of the 138 cancer patients, 47 experienced grade 1-2 irAEs, and 18 had grade 3 or higher irAEs (including 2 fatal irAEs). Multivariate analysis showed that the current cycle's NLR (OR, 1.839, p=0.000) and PLR (OR, 0.994, p=0.029) were independent risk factors for irAEs. Model A predicted the occurrence of irAEs in the next cycle with an area under the curve (AUC) of 0.788, a sensitivity of 69.2%, and a specificity of 68.5%. When the Model A was ≥38.8% (cutoff value), Model B (AUC =0.900) was entered, predicting the occurrence of grade 3 or higher irAEs in the next cycle with a sensitivity of 67.7% and a specificity of 72.6%. Similarly, Model C predicted the occurrence of irAEs in the current cycle (AUC=0.865) yielded a sensitivity of 81.5% and a specificity of 86.3%. When the Model C was ≥52.8%, Model D was entered, predicting the occurrence of grade 3 or higher irAEs in the current cycle (AUC=0.888) with a sensitivity of 83.3% and a specificity of 83.0%.

Conclusions

The model consisting of NLR, PLR, and AEC in this study can predict the occurrence of grade 3 or higher irAEs within one cycle. Compared with early prediction, our near-term prediction model maximizes the number of cycles of immune therapy obtained by patients, thereby improving the effectiveness of immunotherapy, which has significant clinical value.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Has not received any funding.

Disclosure

All authors have declared no conflicts of interest.