Predictive value of near-term prediction models for severe immune-related adverse events in malignant tumor PD-1 inhibitor therapy
Immune-related adverse events (irAEs) impact outcomes, with most research focusing on early prediction (baseline data), rather than near-term prediction (one cycle before the occurrence of irAEs and the current cycle). We aimed to explore the near-term predictive value of neutrophil/lymphocyte ratio...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Human Vaccines & Immunotherapeutics |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21645515.2024.2398309 |
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| author | Yunyi Du Ying Zhang Wenqi Zhao Yuexiang Zhang Fei Su Xiaoling Zhang Weiling Li Wenqing Hu Yongai Li Jun Zhao |
| author_facet | Yunyi Du Ying Zhang Wenqi Zhao Yuexiang Zhang Fei Su Xiaoling Zhang Weiling Li Wenqing Hu Yongai Li Jun Zhao |
| author_sort | Yunyi Du |
| collection | DOAJ |
| description | Immune-related adverse events (irAEs) impact outcomes, with most research focusing on early prediction (baseline data), rather than near-term prediction (one cycle before the occurrence of irAEs and the current cycle). We aimed to explore the near-term predictive value of neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), absolute eosinophil count (AEC) for severe irAEs induced by PD-1 inhibitors. Data were collected from tumor patients treated with PD-1 inhibitors. NLR, PLR, and AEC data were obtained from both the previous and the current cycles of irAEs occurrence. A predictive model was developed using elastic net logistic regression Cutoff values were determined using Youden’s Index. The predicted results were compared with actual data using Bayesian survival analysis. A total of 138 patients were included, of whom 47 experienced grade 1–2 irAEs and 18 experienced grade 3–5 irAEs. The predictive model identified optimal α and λ through 10-fold cross-validation. The Shapiro-Wilk test, Kruskal-Wallis test and logistic regression showed that only current cycle data were meaningful. The NLR was statistically significant in predicting irAEs in the previous cycle. Both NLR and AEC were significant predictors of irAEs in the current cycle. The model achieved an area under the ROC curve (AUC) of 0.783, with a sensitivity of 77.8% and a specificity of 80.8%. A probability ≥ 0.1345 predicted severe irAEs. The model comprising NLR, AEC, and sex may predict the irAEs classification in the current cycle, offering a near-term predictive advantage over baseline models and potentially extending the duration of immunotherapy for patients. |
| format | Article |
| id | doaj-art-6ddbeb11c972409dae8f6bd9bc4970d2 |
| institution | OA Journals |
| issn | 2164-5515 2164-554X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Human Vaccines & Immunotherapeutics |
| spelling | doaj-art-6ddbeb11c972409dae8f6bd9bc4970d22025-08-20T02:16:46ZengTaylor & Francis GroupHuman Vaccines & Immunotherapeutics2164-55152164-554X2024-12-0120110.1080/21645515.2024.2398309Predictive value of near-term prediction models for severe immune-related adverse events in malignant tumor PD-1 inhibitor therapyYunyi Du0Ying Zhang1Wenqi Zhao2Yuexiang Zhang3Fei Su4Xiaoling Zhang5Weiling Li6Wenqing Hu7Yongai Li8Jun Zhao9Department of Oncology, Changzhi People’s Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, ChinaDepartment of Respiratory, Pengzhou People’s Hospital, Chengdu, Sichuan, ChinaDepartment of Statistics, University of Auckland, AucklandDepartment of Oncology, Changzhi People’s Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, ChinaDepartment of Oncology, Graduate of School of Changzhi Medical College, Changzhi, Shanxi, ChinaDepartment of Oncology, Changzhi People’s Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, ChinaDepartment of Oncology, The People’s Hospital of Jianyang City, Chengdu, Sichuan, ChinaDepartment of Gastrointestinal Surgery, Changzhi People’s Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, ChinaDepartment of Radiology, Changzhi People’s Hospital, Changzhi, Shanxi, ChinaDepartment of Oncology, Changzhi People’s Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, ChinaImmune-related adverse events (irAEs) impact outcomes, with most research focusing on early prediction (baseline data), rather than near-term prediction (one cycle before the occurrence of irAEs and the current cycle). We aimed to explore the near-term predictive value of neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), absolute eosinophil count (AEC) for severe irAEs induced by PD-1 inhibitors. Data were collected from tumor patients treated with PD-1 inhibitors. NLR, PLR, and AEC data were obtained from both the previous and the current cycles of irAEs occurrence. A predictive model was developed using elastic net logistic regression Cutoff values were determined using Youden’s Index. The predicted results were compared with actual data using Bayesian survival analysis. A total of 138 patients were included, of whom 47 experienced grade 1–2 irAEs and 18 experienced grade 3–5 irAEs. The predictive model identified optimal α and λ through 10-fold cross-validation. The Shapiro-Wilk test, Kruskal-Wallis test and logistic regression showed that only current cycle data were meaningful. The NLR was statistically significant in predicting irAEs in the previous cycle. Both NLR and AEC were significant predictors of irAEs in the current cycle. The model achieved an area under the ROC curve (AUC) of 0.783, with a sensitivity of 77.8% and a specificity of 80.8%. A probability ≥ 0.1345 predicted severe irAEs. The model comprising NLR, AEC, and sex may predict the irAEs classification in the current cycle, offering a near-term predictive advantage over baseline models and potentially extending the duration of immunotherapy for patients.https://www.tandfonline.com/doi/10.1080/21645515.2024.2398309Immune-related adverse events (irAEs)PD-1 inhibitorsneutrophil/lymphocyte ratio (NLR)platelet/lymphocyte ratio (PLR)absolute eosinophil count (AEC)prediction model |
| spellingShingle | Yunyi Du Ying Zhang Wenqi Zhao Yuexiang Zhang Fei Su Xiaoling Zhang Weiling Li Wenqing Hu Yongai Li Jun Zhao Predictive value of near-term prediction models for severe immune-related adverse events in malignant tumor PD-1 inhibitor therapy Human Vaccines & Immunotherapeutics Immune-related adverse events (irAEs) PD-1 inhibitors neutrophil/lymphocyte ratio (NLR) platelet/lymphocyte ratio (PLR) absolute eosinophil count (AEC) prediction model |
| title | Predictive value of near-term prediction models for severe immune-related adverse events in malignant tumor PD-1 inhibitor therapy |
| title_full | Predictive value of near-term prediction models for severe immune-related adverse events in malignant tumor PD-1 inhibitor therapy |
| title_fullStr | Predictive value of near-term prediction models for severe immune-related adverse events in malignant tumor PD-1 inhibitor therapy |
| title_full_unstemmed | Predictive value of near-term prediction models for severe immune-related adverse events in malignant tumor PD-1 inhibitor therapy |
| title_short | Predictive value of near-term prediction models for severe immune-related adverse events in malignant tumor PD-1 inhibitor therapy |
| title_sort | predictive value of near term prediction models for severe immune related adverse events in malignant tumor pd 1 inhibitor therapy |
| topic | Immune-related adverse events (irAEs) PD-1 inhibitors neutrophil/lymphocyte ratio (NLR) platelet/lymphocyte ratio (PLR) absolute eosinophil count (AEC) prediction model |
| url | https://www.tandfonline.com/doi/10.1080/21645515.2024.2398309 |
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