ArticleThoracic cancer2024
Risk factors and a nomogram for predicting cognitive frailty in Chinese patients with lung cancer receiving drug therapy: A single-center cross-sectional study.
Article in Thoracic cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Risk factors for frailty in older adults with cancer: a systematic review.BMC geriatrics · 2026Pooled it
- Influencing factors of cognitive frailty in older patients with esophageal cancer undergoing chemotherapy: a mixed-methods study.Frontiers in oncology · 2026Article
- The concept of multidimensional frailty in geriatric oncology: a systematic scoping review.BMC geriatrics · 2025Article
- Development and Validation of a Machine Learning-Based Predictive Model for Postoperative Frailty in Patients with Non-Small Cell Lung Cancer and Its Relation to Early Recovery.Annals of surgical oncology · 2025Article
- Development and validation of a nomogram for predicting cognitive frailty in patients on cancer.BMC geriatrics · 2025Article
- Construction and validation of a cognitive frailty risk prediction model for elderly patients with colorectal cancer.Frontiers in aging neuroscience · 2025Article
- Risk factors and a nomogram for predicting cognitive frailty in Chinese patients with lung cancer receiving drug therapy: A single-center cross-sectional study.Thoracic cancer · 2024Article
- Risk factors analysis of cognitive frailty among geriatric adults in nursing homes based on logistic regression and decision tree modeling.Frontiers in aging neuroscience · 2024Article
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Authors and funding
11 authors.
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Abstract
backgroundTo identify independent factors of cognitive frailty (CF) and construct a nomogram to predict cognitive frailty risk in patients with lung cancer receiving drug therapy.
methodsIn 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.
resultsA 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.
conclusionsThe 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.
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