Prediction Model and Its Value of IrAEs Based on Peripheral Blood Markers

Jun DENG; Jun WANG; Xi WANG; Change GAO; Xiao CHEN; Mingxia SHI

doi:10.12259/j.issn.2095-610X.S20250408

Volume 46 Issue 4

Apr. 2025

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Jun DENG, Jun WANG, Xi WANG, Change GAO, Xiao CHEN, Mingxia SHI. Prediction Model and Its Value of IrAEs Based on Peripheral Blood Markers[J]. Journal of Kunming Medical University, 2025, 46(4): 57-66. doi: 10.12259/j.issn.2095-610X.S20250408

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Jun DENG, Jun WANG, Xi WANG, Change GAO, Xiao CHEN, Mingxia SHI. Prediction Model and Its Value of IrAEs Based on Peripheral Blood Markers[J]. Journal of Kunming Medical University, 2025, 46(4): 57-66. doi: 10.12259/j.issn.2095-610X.S20250408

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Prediction Model and Its Value of IrAEs Based on Peripheral Blood Markers

doi: 10.12259/j.issn.2095-610X.S20250408

1.
Department of Oncology
2.
Department of Sports Medicine
3.
Clinical Pharmacy Center
4.
Department of Hematology, The First Affiliated Hospital of Kunming Medical University, Kunming Yunnan 650032, China

Received Date: 2024-10-19
Available Online: 2025-04-07
Publish Date: 2025-04-25

Abstract

Abstract

Objective To explore the predictive model and its value of irAEs based on peripheral blood markers. Methods The baseline clinical data, laboratory tests, and irAEs follow-up results of 825 malignant tumor patients treated with PD-1/PD-L1 antibodies in the First Affiliated Hospital of Kunming Medical University were retrospectively collected from December 2020 to December 2023. The patients were divided into irAEs group and non-irAEs group according to the presence or absence of irAEs. The differences between and within groups were analyzed by t-test, rank-sum test, chi-square test and Fisher exact probability method. LASSO, Ridge and Elastic-net logistic regressions were used to screen the predictors and establish the risk prediction models for irAEs. Results 136 patients experienced 178 irAEs, of which endocrine toxicity accounted for 42.64%, hepatitis 35.29%, pneumonia 20.58%, grade ≥ G3 accounted for 19.07%, involving more than two organs accounted for 24.26% of the total number of irAEs. Univariate analysis showed that baseline CD4⁺ T cell count, IL-6, IL-17, TSH, GLB and ALB were associated with irAEs. GLB, ALB, IL-17 and TSH were selected as the important risk factors by Ridge, LASSO and Elastic-Net logistic regression. The results showed that the AUC of the three algorithms were over 0.800. The AUC of internal validation set by LASSO-Logistic was 0.800 （95%CI 0.739～0.862）. The AUC of external validation set was 0.800 （95%CI 0.739～0.861） and the DCA curve results indicated the highest net return for this predictive model. Conclusion GLB, ALB, IL-17 and TSH are independent predictors of irAEs, and the predictive model of irAEs based on them is effective.
- ICIs,
- irAEs,
- Predictors,
- Predictive model

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References(21)

References

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