Development of a clinical nomogram for predicting sarcopenia in patients with chronic obstructive pulmonary disease based on NHANES data
BackgroundThe prevalence of sarcopenia in COPD patients is high, and the mutual influence between COPD and sarcopenia creates a vicious cycle. The goal of this research is to create a nomogram model that can forecast when sarcopenia will strike people with COPD.Methods2011 to 2018 data were retrieve...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1612403/full |
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| author | Xingfu Fan Jin Zhao Yang Luo Xiaofang Li Wenqin Tan Shiping Liu |
| author_facet | Xingfu Fan Jin Zhao Yang Luo Xiaofang Li Wenqin Tan Shiping Liu |
| author_sort | Xingfu Fan |
| collection | DOAJ |
| description | BackgroundThe prevalence of sarcopenia in COPD patients is high, and the mutual influence between COPD and sarcopenia creates a vicious cycle. The goal of this research is to create a nomogram model that can forecast when sarcopenia will strike people with COPD.Methods2011 to 2018 data were retrieved from four NHANES database cycles. The 7:3 proportion was applied to split the dataset randomly to separate validation and training datasets. Multivariate logistical regression and LASSO regression were applied to design nomogram design and to select predictors. In addition, multicollinearity existence among final predictor variables remaining in model were tested, among other variables. Calibration curve, decision curve analysis (DCA), and area under receiver operating characteristic curve (AUC) were applied in testing performance in prediction model.ResultsThe nomogram was constructed based on four predictive factors: gender, height, BMI, and WWI. The AUC for the training set was 0.94 (95% CI 0.91–0.97), and the AUC for the validation set was 0.91 (95% CI 0.83–0.98), indicating excellent predictive performance. Furthermore, the clinical applicability of the model has been thoroughly validated.ConclusionWe established a nomogram model to provide an easy and convenient way for early screening of sarcopenia in COPD patients, and to allow for effective guidance to perform early intervention and manage patient prognosis in an optimal way. |
| format | Article |
| id | doaj-art-a2ba8a08073248928800c99df71c30cd |
| institution | DOAJ |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-a2ba8a08073248928800c99df71c30cd2025-08-20T03:09:35ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-07-011210.3389/fmed.2025.16124031612403Development of a clinical nomogram for predicting sarcopenia in patients with chronic obstructive pulmonary disease based on NHANES dataXingfu FanJin ZhaoYang LuoXiaofang LiWenqin TanShiping LiuBackgroundThe prevalence of sarcopenia in COPD patients is high, and the mutual influence between COPD and sarcopenia creates a vicious cycle. The goal of this research is to create a nomogram model that can forecast when sarcopenia will strike people with COPD.Methods2011 to 2018 data were retrieved from four NHANES database cycles. The 7:3 proportion was applied to split the dataset randomly to separate validation and training datasets. Multivariate logistical regression and LASSO regression were applied to design nomogram design and to select predictors. In addition, multicollinearity existence among final predictor variables remaining in model were tested, among other variables. Calibration curve, decision curve analysis (DCA), and area under receiver operating characteristic curve (AUC) were applied in testing performance in prediction model.ResultsThe nomogram was constructed based on four predictive factors: gender, height, BMI, and WWI. The AUC for the training set was 0.94 (95% CI 0.91–0.97), and the AUC for the validation set was 0.91 (95% CI 0.83–0.98), indicating excellent predictive performance. Furthermore, the clinical applicability of the model has been thoroughly validated.ConclusionWe established a nomogram model to provide an easy and convenient way for early screening of sarcopenia in COPD patients, and to allow for effective guidance to perform early intervention and manage patient prognosis in an optimal way.https://www.frontiersin.org/articles/10.3389/fmed.2025.1612403/fullCOPDsarcopenianomogramNHANESrisk factors |
| spellingShingle | Xingfu Fan Jin Zhao Yang Luo Xiaofang Li Wenqin Tan Shiping Liu Development of a clinical nomogram for predicting sarcopenia in patients with chronic obstructive pulmonary disease based on NHANES data Frontiers in Medicine COPD sarcopenia nomogram NHANES risk factors |
| title | Development of a clinical nomogram for predicting sarcopenia in patients with chronic obstructive pulmonary disease based on NHANES data |
| title_full | Development of a clinical nomogram for predicting sarcopenia in patients with chronic obstructive pulmonary disease based on NHANES data |
| title_fullStr | Development of a clinical nomogram for predicting sarcopenia in patients with chronic obstructive pulmonary disease based on NHANES data |
| title_full_unstemmed | Development of a clinical nomogram for predicting sarcopenia in patients with chronic obstructive pulmonary disease based on NHANES data |
| title_short | Development of a clinical nomogram for predicting sarcopenia in patients with chronic obstructive pulmonary disease based on NHANES data |
| title_sort | development of a clinical nomogram for predicting sarcopenia in patients with chronic obstructive pulmonary disease based on nhanes data |
| topic | COPD sarcopenia nomogram NHANES risk factors |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1612403/full |
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