Li Jinping, Wang Yan, Zhai Minfeng, Qin Mengyuan, Zhao Dandi, Xiang Qian, Shao Zaoyuan, Wang Panrong, Lin Yan, Dong Yiting, Liu Yan
Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
CAMS Key Laboratory of Translational Research on Lung Cancer, State Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Thorac Cancer. 2024 Apr;15(11):884-894. doi: 10.1111/1759-7714.15256. Epub 2024 Mar 7.
To identify independent factors of cognitive frailty (CF) and construct a nomogram to predict cognitive frailty risk in patients with lung cancer receiving drug therapy.
In this cross-sectional study, patients with lung cancer undergoing drug therapy from October 2022 to July 2023 were enrolled. The data collected includes general demographic characteristics, clinical data characteristics and assessment of tools for cognitive frailty and other factors. Logistic regression was harnessed to determine the influencing factors, R software was used to establish a nomogram model to predict the risk of cognitive frailty. The enhanced bootstrap method was employed for internal verification of the model. The performance of the nomogram was evaluated by using calibration curves, the area under the receiver operating characteristic curve, and decision curve analysis.
A total of 372 patients were recruited, with a cognitive frailty prevalence of 56.2%. Age, education background, diabetes mellitus, insomnia, sarcopenia, and nutrition status were identified as independent factors. Then, a nomogram model was constructed and patients were classified into high- and low-risk groups with a cutoff value of 0.552. The internal validation results revealed good concordance, calibration and discrimination. The decision curve analysis presented prominent clinical utility.
The prevalence of cognitive frailty was higher in lung cancer patients receiving drug therapy. The nomogram could identify the risk of cognitive frailty intuitively and simply in patients with lung cancer, so as to provide references for early screening and intervention for cognitive frailty at the early phases of drug treatment.
识别认知衰弱（CF）的独立因素，并构建列线图以预测接受药物治疗的肺癌患者的认知衰弱风险。
在这项横断面研究中，纳入了2022年10月至2023年7月期间接受药物治疗的肺癌患者。收集的数据包括一般人口统计学特征、临床数据特征以及认知衰弱评估工具和其他因素。采用逻辑回归确定影响因素，使用R软件建立列线图模型以预测认知衰弱风险。采用增强型自助法对模型进行内部验证。通过校准曲线、受试者操作特征曲线下面积和决策曲线分析评估列线图的性能。
共招募了372例患者，认知衰弱患病率为56.2%。年龄、教育背景、糖尿病、失眠、肌肉减少症和营养状况被确定为独立因素。然后，构建了列线图模型，并将患者分为高风险组和低风险组，临界值为0.552。内部验证结果显示出良好的一致性、校准和区分度。决策曲线分析显示出显著的临床实用性。
接受药物治疗的肺癌患者认知衰弱患病率较高。该列线图能够直观、简便地识别肺癌患者的认知衰弱风险，为药物治疗早期阶段认知衰弱的早期筛查和干预提供参考。
