Development and validation of a predictive model for acute exacerbation in chronic obstructive pulmonary disease patients with comorbid insomnia Article Swipe
Qianqian Gao
,
Hongbin Zhu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fmed.2025.1511874
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fmed.2025.1511874
Aim To develop and validate a risk prediction model for estimating the likelihood of insomnia in patients with acute exacerbations of chronic obstructive pulmonary disease (AECOPD). Methods This prospective study enrolled 253 patients with AECOPD treated at the Department of Respiratory and Critical Care Medicine, Chaohu Hospital Affiliated with Anhui Medical University, between September 2022 and April 2024. Patients were randomly assigned to a training set and a testing set in a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was conducted in the training set to identify factors associated with insomnia in patients with AECOPD. A nomogram was constructed based on four identified variables to visualize the prediction model. Model validation involved the Hosmer-Lemeshow test, and its performance was assessed through receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Model interpretability was further enhanced using SHapley Additive exPlanations (SHAP). Results PSQI grade, marital status (widowed), white blood cell (WBC) count, and eosinophil percentage (EOS%) were identified as significant predictors of insomnia in patients with AECOPD. The nomogram based on these predictors exhibited excellent predictive performance, with areas under the ROC curve (AUCs) of 0.987 and 0.933 for the training and testing sets, respectively. The calibration curves and Hosmer-Lemeshow test demonstrated strong agreement between predicted and observed outcomes, while DCA confirmed the model’s superior clinical utility. Conclusion This study established a risk prediction model based on four variables to estimate the probability of insomnia in patients with AECOPD. The model exhibited excellent predictive accuracy and clinical applicability, offering valuable guidance for early identification and management of insomnia in this population.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fmed.2025.1511874
- https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1511874/pdf
- OA Status
- gold
- Cited By
- 1
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408682902
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4408682902Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fmed.2025.1511874Digital Object Identifier
- Title
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Development and validation of a predictive model for acute exacerbation in chronic obstructive pulmonary disease patients with comorbid insomniaWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-03-21Full publication date if available
- Authors
-
Qianqian Gao, Hongbin ZhuList of authors in order
- Landing page
-
https://doi.org/10.3389/fmed.2025.1511874Publisher landing page
- PDF URL
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https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1511874/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1511874/pdfDirect OA link when available
- Concepts
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Medicine, Nomogram, Receiver operating characteristic, Acute exacerbation of chronic obstructive pulmonary disease, Exacerbation, Lasso (programming language), Internal medicine, COPD, Area under the curve, Stepwise regression, Physical therapy, World Wide Web, Computer scienceTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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38Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works_count | 38 |
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| abstract_inverted_index.a | 5, 63, 67, 71, 227 |
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| abstract_inverted_index.for | 9, 194, 257 |
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| abstract_inverted_index.(WBC) | 156 |
| abstract_inverted_index.0.933 | 193 |
| abstract_inverted_index.0.987 | 191 |
| abstract_inverted_index.2024. | 57 |
| abstract_inverted_index.Anhui | 49 |
| abstract_inverted_index.April | 56 |
| abstract_inverted_index.Least | 74 |
| abstract_inverted_index.Model | 113, 137 |
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| abstract_inverted_index.curve | 134, 188 |
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| abstract_inverted_index.model | 8, 230, 246 |
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| abstract_inverted_index.(AUCs) | 189 |
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| abstract_inverted_index.Chaohu | 45 |
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| abstract_inverted_index.strong | 208 |
| abstract_inverted_index.(LASSO) | 80 |
| abstract_inverted_index.(SHAP). | 146 |
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| abstract_inverted_index.(AECOPD). | 25 |
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| abstract_inverted_index.Shrinkage | 76 |
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