Yang Liangliang, Xu Xiaolong, Liu Qingquan
School of Clinical Medicine, Beijing University of Chinese Medicine, Beijing 100029, China.
Department of Critical Care Medicine, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing 100010, China.
Appl Bionics Biomech. 2022 May 30;2022:6149884. doi: 10.1155/2022/6149884. eCollection 2022.
Based on clinical data, the risk prediction model of pulmonary infection in patients with advanced cancer was established to predict the risk of pulmonary infection in patients with advanced cancer, and intervention measures were given in advance.
The clinical data of 2755 patients were divided into infection group and control group according to whether they were complicated with lung infection. 1609 patients' data from January 2016 to December 2018 served as the training set, and 1166 patients' data from January 2019 to December 2020 served as the testing set. Demographics, whether the primary cancer was lung cancer, lung metastasis, the pathological classification of lung cancer patients, the number of metastases, history of surgery, history of chemotherapy, history of radiotherapy, history of central venous catheterization, history of hypertension, diabetes, and whether with myelosuppression were recorded. The presence of concurrent pulmonary infection was recorded and defined as the primary outcome variable. Stepwise forward algorithms were applied to informative predictors based on Akaike's information criterion. Multivariable logistic regression analysis was used to develop the nomogram. An independent testing dataset was used to validate the nomogram. Receiver-operating characteristic curves and the Hosmer-Lemeshow test were used to assess model performance.
The sample included 2755 patients with advanced cancer. An independently validated dataset included 1166 patients with advanced cancer. In the training dataset, gender, age, lung cancer as primary cancer, the pathological classification of lung cancer patients, history of chemotherapy, history of radiation therapy, history of surgery, the number of metastases, presence of central venous catheterization, and myelosuppression were identified as predictors and assembled into the nomogram. The area under curve demonstrated adequate discrimination in the validation dataset (0.77; 95% confidence interval, 0.74 to 0.79). The nomogram was well calibrated, with a Hosmer-Lemeshow statistic of 12.4 ( = 0.26) in the testing dataset.
The present study has proposed an effective nomogram with potential application in facilitating the individualized prediction of risk of pulmonary infection in patients with advanced cancer.
基于临床数据建立晚期癌症患者肺部感染风险预测模型，以预测晚期癌症患者肺部感染风险，并提前给出干预措施。
将2755例患者的临床资料根据是否合并肺部感染分为感染组和对照组。2016年1月至2018年12月的1609例患者数据作为训练集，2019年1月至2020年12月的1166例患者数据作为测试集。记录人口统计学资料、原发癌是否为肺癌、肺转移情况、肺癌患者的病理分类、转移灶数量、手术史、化疗史、放疗史、中心静脉置管史、高血压病史、糖尿病史以及是否存在骨髓抑制。记录并发肺部感染情况，并将其定义为主要结局变量。基于赤池信息准则，对信息性预测因子应用逐步向前算法。采用多变量逻辑回归分析构建列线图。使用独立测试数据集验证列线图。采用受试者操作特征曲线和Hosmer-Lemeshow检验评估模型性能。
样本包括2755例晚期癌症患者。一个独立验证数据集包括1166例晚期癌症患者。在训练数据集中，性别、年龄、原发癌为肺癌、肺癌患者的病理分类、化疗史、放疗史、手术史、转移灶数量、中心静脉置管情况以及骨髓抑制被确定为预测因子，并纳入列线图。曲线下面积显示在验证数据集中具有足够的区分度（0.77；95%置信区间，0.74至0.79）。列线图校准良好，测试数据集中Hosmer-Lemeshow统计量为12.4（P = 0.26）。
本研究提出了一种有效的列线图，在促进晚期癌症患者肺部感染风险的个体化预测方面具有潜在应用价值。
