ArticleInternational journal of chronic obstructive pulmonary disease2024
A Diagnostic Nomogram for Predicting Hypercapnic Respiratory Failure in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease.
Article in International journal of chronic obstructive pulmonary disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Construction and validation of a prognostic model for in-hospital multiple organ dysfunction syndrome in ICU patients with respiratory failure based on ultrasound and laboratory parameters.Journal of thoracic disease · 2026Article
- Predictive Value of SAPS II for 28-Day All-Cause Mortality in AECOPD Patients Admitted to the ICU.International journal of chronic obstructive pulmonary disease · 2026Article
- Development and validation of a predictive model for acute exacerbation in chronic obstructive pulmonary disease patients with comorbid insomnia.Frontiers in medicine · 2025Article
- Nutritional and Inflammatory Predictors of All-Cause Mortality in COPD Patients with Hypercapnic Respiratory Failure: A Two-Center Prospective Cohort Study.International journal of chronic obstructive pulmonary disease · 2025Observational
- Inflammatory markers as predictors of in-hospital mortality in acute exacerbation of chronic obstructive pulmonary disease (AECOPD) patients with acute respiratory failure: insights from the MIMIC-IV database.Journal of thoracic disease · 2024Article
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Abstract
Purpose: To develop and validate a nomogram for assessing the risk of developing hypercapnic respiratory failure (HRF) in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Patients and Methods: From January 2019 to August 2023, a total of 334 AECOPD patients were enrolled in this research. We employed the Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression to determine independent predictors and develop a nomogram. This nomogram was appraised by the area under the receiver operating characteristic curve (AUC), calibration curve, Hosmer-Lemeshow goodness-of-fit test (HL test), decision curve analysis (DCA), and clinical impact curve (CIC). The enhanced bootstrap method was used for internal validation. Results: Sex, prognostic nutritional index (PNI), hematocrit (HCT), and activities of daily living (ADL) were independent predictors of HRF in AECOPD patients. The developed nomogram based on the above predictors showed good performance. The AUCs for the training, internal, and external validation cohorts were 0.841, 0.884, and 0.852, respectively. The calibration curves and HL test showed excellent concordance. The DCA and CIC showed excellent clinical usefulness. Finally, a dynamic nomogram was developed (https://a18895635453.shinyapps.io/dynnomapp/). Conclusion: This nomogram based on sex, PNI, HCT, and ADL demonstrated high accuracy and clinical value in predicting HRF. It is a less expensive and more accessible approach to assess the risk of developing HRF in AECOPD patients, which is more suitable for primary hospitals, especially in developing countries with high COPD-related morbidity and mortality.
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