Risk factors and a nomogram for predicting cognitive frailty in Chinese patients with lung cancer receiving drug therapy: A single‐center cross‐sectional study
Abstract 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...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-04-01
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| Series: | Thoracic Cancer |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/1759-7714.15256 |
| _version_ | 1797208907006869504 |
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| author | Jinping Li Yan Wang Minfeng Zhai Mengyuan Qin Dandi Zhao Qian Xiang Zaoyuan Shao Panrong Wang Yan Lin Yiting Dong Yan Liu |
| author_facet | Jinping Li Yan Wang Minfeng Zhai Mengyuan Qin Dandi Zhao Qian Xiang Zaoyuan Shao Panrong Wang Yan Lin Yiting Dong Yan Liu |
| author_sort | Jinping Li |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-24T09:46:15Z |
| format | Article |
| id | doaj.art-2b971ed4fe7c427a8ae118d98a1b3669 |
| institution | Directory Open Access Journal |
| issn | 1759-7706 1759-7714 |
| language | English |
| last_indexed | 2024-04-24T09:46:15Z |
| publishDate | 2024-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Thoracic Cancer |
| spelling | doaj.art-2b971ed4fe7c427a8ae118d98a1b36692024-04-15T01:29:47ZengWileyThoracic Cancer1759-77061759-77142024-04-01151188489410.1111/1759-7714.15256Risk factors and a nomogram for predicting cognitive frailty in Chinese patients with lung cancer receiving drug therapy: A single‐center cross‐sectional studyJinping Li0Yan Wang1Minfeng Zhai2Mengyuan Qin3Dandi Zhao4Qian Xiang5Zaoyuan Shao6Panrong Wang7Yan Lin8Yiting Dong9Yan Liu10Department 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 ChinaDepartment 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 ChinaDepartment 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 ChinaDepartment 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 ChinaDepartment 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 ChinaDepartment 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 ChinaDepartment 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 ChinaDepartment 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 ChinaDepartment 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 ChinaCAMS 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 ChinaNursing department, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences & Peking Union Medical College Beijing ChinaAbstract 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.https://doi.org/10.1111/1759-7714.15256cognitive frailtylung cancernomogrampredicting modelrisk factors |
| spellingShingle | Jinping Li Yan Wang Minfeng Zhai Mengyuan Qin Dandi Zhao Qian Xiang Zaoyuan Shao Panrong Wang Yan Lin Yiting Dong Yan Liu 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 cognitive frailty lung cancer nomogram predicting model risk factors |
| title | Risk factors and a nomogram for predicting cognitive frailty in Chinese patients with lung cancer receiving drug therapy: A single‐center cross‐sectional study |
| title_full | Risk factors and a nomogram for predicting cognitive frailty in Chinese patients with lung cancer receiving drug therapy: A single‐center cross‐sectional study |
| title_fullStr | Risk factors and a nomogram for predicting cognitive frailty in Chinese patients with lung cancer receiving drug therapy: A single‐center cross‐sectional study |
| title_full_unstemmed | Risk factors and a nomogram for predicting cognitive frailty in Chinese patients with lung cancer receiving drug therapy: A single‐center cross‐sectional study |
| title_short | Risk factors and a nomogram for predicting cognitive frailty in Chinese patients with lung cancer receiving drug therapy: A single‐center cross‐sectional study |
| title_sort | risk factors and a nomogram for predicting cognitive frailty in chinese patients with lung cancer receiving drug therapy a single center cross sectional study |
| topic | cognitive frailty lung cancer nomogram predicting model risk factors |
| url | https://doi.org/10.1111/1759-7714.15256 |
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