恶性肿瘤患者基于临床和血液学标记物的免疫检查点抑制剂相关不良事件预测模型的建立

许辉茹; 冯慧晶; 任秀宝; 张俊萍

doi:10.12354/j.issn.1000-8179.2022.20211708

恶性肿瘤患者基于临床和血液学标记物的免疫检查点抑制剂相关不良事件预测模型的建立

Establishment of a prediction model for immune checkpoint inhibitors-related adverse effects based on clinical and hematological markers in patients with malignant tumors

摘要

摘要:
目的本研究的目的是建立基于临床和血液学参数的免疫检查点抑制剂（immune checkpoint inhibitors，ICIs）治疗的恶性肿瘤患者的免疫相关不良事件（immune-related adverse effects，irAEs）预测模型，如果经过验证，这些标记物的优点是易于整合到临床使用中，成本低廉。
方法本研究是对2016年1月到2020年12月在天津医科大学肿瘤医院和山西白求恩医院接受至少一剂ICIs治疗的恶性肿瘤患者的回顾性研究。收集了基线特征、治疗细节和不良事件的数据。采用t检验、χ²检验和Logistic回归等方法确定影响因素，建立预测模型。
结果任何级别和3级及以上的irAEs发生率分别为16.03%（76/474）和2.32%（11/474），其中最常见的分别为内分泌毒性37.1%（39/105）和肺炎7.6%（8/105）。多因素分析显示，2线治疗irAEs发生的风险更大比值比（Odds Ratio，OR）=3.302；球蛋白（OR=1.086）与irAEs的发生呈正相关，而直接胆红素（direct bilirubin，DBIL）（OR=0.723）与其呈负相关（Ｐ<0.05）。最终建立了基于“ICIs类型、治疗线数、球蛋白、DBIL和淋巴细胞/单核细胞比值（lymphocyte to monocyte ratio，LMR）”的预测模型，其受试者工作特征（receiver operating characteristic，ROC）曲线下面积（area under the curve，AUC）为0.775，95%CI：0.711～0.840，Ｐ<0.05，临界值为0.118，敏感度为92.5%，特异度为56.6%。
结论基于ICIs类型、治疗线数、球蛋白、DBIL和LMR的预测模型对单纯接受ICIs的恶性肿瘤患者的irAEs预测效果较好，其中治疗线数、球蛋白和 DBIL是irAEs发生的独立预测因素。ICIs作为2线治疗以及治疗前高球蛋白和低DBIL 的人群发生irAEs的风险较高。

Abstract:
Objective To establish a predictive model for immune-related adverse events (irAEs) based on clinical and hematological markers in patients with malignancies treated with immune checkpoint inhibitors (ICIs). If validated, these markers have the advantage of being easily integrated into clinical practice with minimal costs.
Methods This was a retrospective study of patients with malignant tumors treated with at least one dose of ICIs at Tianjin Medical University Cancer Institute & Hospital and Shanxi Bethune Hospital from January 2016 to December 2020. Data on baseline characteristics, treatment details, and adverse events were collected. Student’s t-test, chi-square test, and Logistic regression were used to identify risk factors for irAEs to establish a prediction model.
Results The incidence rates of any grade and grade 3 or higher irAEs were 16.03% (76/474) and 2.32% (11/474), respectively. Endocrinopathy disorders (39/105, 37.1%) and pneumonitis (8/105, 7.6%) were the most commonly observed irAEs in the respective categories. Multivariate Logistic regression analysis showed that the risk of irAEs was higher in patients undergoing second-line treatment odds ratio (OR) = 3.302; globulin level (OR = 1.086) was positively correlated with the occurrence of irAEs, whereas direct bilirubin level (DBIL) (OR = 0.723) showed a negative correlation (P < 0.05). A prediction model based on “ICI type, line of treatment, globulin level, DBIL level, and lymphocyte to monocyte ratio (LMR)” was established. The area under the curve (AUC) of the receiver operating characteristic (ROC) was 0.775 (95% CI: 0.711～0.840) with a cut-off value of 0.118, and the sensitivity and specificity were 92.5% and 56.6%, respectively.
Conclusions The prediction model based on “ICI type, line of treatment, globulin level, DBIL level, and LMR” demonstrated good predictive performance for irAEs in patients receiving ICIs alone, wherein the line of treatment, globulin level, and DBIL level were independent predictors for the onset of irAEs. Patients undergoing second-line ICIs therapy and exhibiting high globulin levels and low DBIL levels at baseline have a higher risk of irAEs.

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