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

BACKGROUND: 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.

METHODS: 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.

RESULTS: 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.

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.