Department of Oncology III, Liaoning People's Hospital, 33 Wenyi Road, Shenhe District, Shenyang, 110022, Liaoning Province, China.
Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.
BMC Pulm Med. 2022 Sep 1;22(1):331. doi: 10.1186/s12890-022-02127-3.
Cancer is one of the main causes of death worldwide. Although immunotherapy brings hope for cancer treatment, it is also accompanied by immune checkpoint inhibitor-related adverse events (irAEs). Immune checkpoint inhibitor pneumonia (CIP) is a potentially fatal adverse event, but there is still a lack of effective markers and prediction models to identify patients at increased risk of CIP.
A total of 369 cancer patients treated between 2017 and 2022 with immune checkpoint inhibitors at Shengjing Hospital of China Medical University and Liaoning People's Hospital were recruited for this study. Independent variables were selected by differences and binary logistic regression analysis, and a risk assessment nomogram was constructed for CIP risk. The accuracy and discriminative abilities of the nomogram were evaluated by calibration plots, receiver operating characteristic curves (ROCs) and decision curve analyses (DCAs).
Binary logistic regression analysis showed that smoking history, acute phase proteins [interleukin (IL-6) and C-reactive protein (CRP)], CD8 + T lymphocyte count and serum alveolar protein [surface protein-A (SP-A) and Krebs Von den Lungen-6 (KL-6)] were significantly associated with CIP risk. A nomogram consisting of these variables was established and validated by different analyses.
We developed an effective risk nomogram for CIP prediction in immune-checkpoint inhibitor administrated cancer patients, which will further assist early detection of immunotherapy-related adverse events.
癌症是全球主要死亡原因之一。虽然免疫疗法为癌症治疗带来了希望，但它也伴随着免疫检查点抑制剂相关的不良反应（irAEs）。免疫检查点抑制剂肺炎（CIP）是一种潜在致命的不良反应，但仍缺乏有效的标志物和预测模型来识别 CIP 风险增加的患者。
本研究共纳入了 2017 年至 2022 年在中国医科大学盛京医院和辽宁省人民医院接受免疫检查点抑制剂治疗的 369 例癌症患者。通过差异和二项逻辑回归分析选择自变量，并构建 CIP 风险的风险评估列线图。通过校准图、接收者操作特征曲线（ROCs）和决策曲线分析（DCAs）评估列线图的准确性和判别能力。
二项逻辑回归分析显示，吸烟史、急性期蛋白（IL-6 和 CRP）、CD8+T 淋巴细胞计数和血清肺泡蛋白[表面蛋白-A（SP-A）和 Krebs Von den Lungen-6（KL-6）]与 CIP 风险显著相关。建立了一个包含这些变量的列线图，并通过不同的分析进行了验证。
我们开发了一种用于预测接受免疫检查点抑制剂治疗的癌症患者 CIP 的有效风险列线图，这将有助于进一步早期检测免疫治疗相关的不良反应。
