Risk factors and a nomogram for predicting cognitive frailty in Chinese patients with lung cancer receiving drug therapy: a single-center cross-sectional study

Author:

Li Jinping1,Wang Yan1,Zhai Minfeng1,Qin Mengyuan1,Zhao Dandi1,Xiang Qian1,Shao Zaoyuan1,Wang Panrong1,Lin Yan1,Dong Yiting1,Liu Yan1

Affiliation:

1. National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College

Abstract

Abstract Purpose To identify independent factors of cognitive frailty and construct a nomogram to predict cognitive frailty risk in patients with lung cancer receiving drug therapy. Methods This is a cross-sectional study that enrolled patients with lung cancer undergoing drug therapy from October 2022 to July 2023. The collected data 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. Results A total of 372 patients were recruited, with a CF prevalence of 56.2%. Age, education background, diabetes Mellitus, insomnia, sarcopenia, and nutrition status were identified as independent factors by multivariate logistic regression analysis. Then, a nomogram model was constructed and patients were classified into high- and low-risk groups with a cutoff value of 0.552. For the entire cohort, the result of Hosmer-Lemeshow good of fit test was χ2 = 3.685 (P = 0.884), the area under the curve was 0.764 (95%CI:0.716–0.812), and the calibration curves presented good concordance with actual observations, which were consistent in both the training set and the validation set. Additionally, decision curve analysis demonstrated that the application of this nomogram could help improve clinical outcomes. Conclusions 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.

Publisher

Research Square Platform LLC

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