Cao Shuhui, Zhang Yao, Zhou Yan, Rong Wenwen, Wang Yue, Ling Xuxinyi, Zhang Lincheng, Li Jingwen, Tomita Yusuke, Watanabe Satoshi, Nakada Takeo, Seki Nobuhiko, Hida Toyoaki, Dermime Said, Zhong Runbo, Zhong Hua
Department of Pulmonary, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
Statistical Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
Transl Lung Cancer Res. 2022 Apr;11(4):607-616. doi: 10.21037/tlcr-22-171.
Immune checkpoint inhibitor (ICI) therapy is an emerging type of treatment for lung cancer (LC). However, hyperprogressive disease (HPD) has been observed in patients treated with ICIs that lacks a prognostic prediction model. There is an urgent need for a simple and easily implementable predictive model to predict the occurrence of HPD. This study aimed to establish a novel scoring system based on a nomogram for the occurrence of HPD.
We retrospectively identified 1473 patients with stage III-IV LC or inoperable stage I-II LC (1147 in training set, and 326 in testing set), who had undergone ICI therapy at the Shanghai Chest Hospital between January 2017 and March 2022. Available computed tomography (CT) data from the previous treatment, before ICI administration, and at least 2 months after the first the course of ICI administration is collected to confirm HPD. Data from these patients' common blood laboratory test results before ICI administration were analyzed by the univariable and multivariable logistic regression analysis, then used to develop nomogram predictive model, and made validation in testing set.
A total of 1,055 patients were included in this study (844 in the training set, and 211 in the testing set). In the training set, 93 were HPD and 751were non-HPD. Multivariate logistic regression analyses demonstrated that lactate dehydrogenase [LDH, P<0.001; odds ratio (OR) =0.987; 95% confidence interval (CI): 0.980-0.995], mean corpuscular hemoglobin concentration (MCHC, P=0.038; OR =1.021; 95% CI: 1.003-1.033), and erythrocyte sedimentation rate (ESR, P=0.012; OR =0.989; 95% CI: 0.977-0.997) were significantly different. The prediction model was established and validated based on these 3 variables. The concordance index were 0.899 (95% CI: 0.859-0.918) and 0.924 (95% CI: 0.866-0.983) in training set and testing set, and the calibration curve was acceptable.
This model, which was developed from a laboratory examination of LC patients undergoing ICI treatment, is the first nomogram model to be developed to predict HPD occurrence and exhibited good sensitivity and specificity.
免疫检查点抑制剂（ICI）疗法是肺癌（LC）治疗的一种新兴方式。然而，在接受ICI治疗的患者中观察到了超进展性疾病（HPD），目前缺乏预后预测模型。迫切需要一种简单且易于实施的预测模型来预测HPD的发生。本研究旨在建立一种基于列线图的新型评分系统，用于预测HPD的发生。
我们回顾性纳入了1473例III-IV期LC或无法手术的I-II期LC患者（训练集1147例，测试集326例），这些患者于2017年1月至2022年3月在上海胸科医院接受了ICI治疗。收集患者上次治疗前、ICI给药前以及首次ICI给药疗程后至少2个月的可用计算机断层扫描（CT）数据，以确认HPD。对这些患者ICI给药前的常见血液实验室检查结果进行单变量和多变量逻辑回归分析，然后用于建立列线图预测模型，并在测试集中进行验证。
本研究共纳入1055例患者（训练集844例，测试集211例）。在训练集中，93例为HPD，751例为非HPD。多变量逻辑回归分析表明，乳酸脱氢酶[LDH，P<0.001；比值比（OR）=0.987；95%置信区间（CI）：0.980-0.995]、平均红细胞血红蛋白浓度（MCHC，P=0.038；OR =1.021；95%CI：1.003-1.033）和红细胞沉降率（ESR，P=0.012；OR =0.989；95%CI：0.977-0.997）有显著差异。基于这3个变量建立并验证了预测模型。训练集和测试集的一致性指数分别为0.899（95%CI：0.859-0.918）和0.924（95%CI：0.866-0.983），校准曲线可接受。
该模型是通过对接受ICI治疗的LC患者进行实验室检查而开发的，是首个用于预测HPD发生的列线图模型，具有良好的敏感性和特异性。
