ArticleJournal of inflammation research2024
Construction and Comparison of Multiple Serum-Based Prognostic Models for Predicting the Prognosis of Acute Exacerbations of Chronic Obstructive Pulmonary Disease.
Article in Journal of inflammation research, 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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Multimodal machine learning predicts type 2 respiratory failure in COPD exacerbations: a multicenter XGBoost model with clinical nomogram.Frontiers in medicine · 2026Article
- Construction and evaluation of a 180-day readmission prediction model for chronic heart failure patients based on sCD40L.Medicine · 2025Article
- Development and validation of the machine learning model for acute exacerbation of chronic obstructive pulmonary disease prediction based on inflammatory biomarkers.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
- Association between HALP (hemoglobin, albumin, lymphocyte, and platelet) score and poor outcomes in acute ischemic stroke patients with type 2 diabetes mellitus: a study from the Third China National Stroke Registry.Frontiers in neurology · 2024Article
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7 authors.
Funding
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Abstract
Purpose: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with significant poor prognosis. Lymphocyte-to-Monocyte Ratio (LMR), Neutrophil-to-Lymphocyte Ratio (NLR), Eosinophil-to-Lymphocyte Ratio (ELR), Basophil-to-Lymphocyte Ratio (BLR), Platelet-to-Lymphocyte Ratio (PLR), and Hemoglobin, Albumin, Lymphocyte, and Platelet (HALP) are vital indicators for inflammation, immune status, and nutritional condition. This study evaluated the predictive value of these indicators in AECOPD and developed predictive models to assess the prognosis of AECOPD based on these indicators. Patients and Methods: We retrospectively collected data from 2609 AECOPD patients. The outcomes assessed included occurrence of respiratory failure (RF), intensive care unit (ICU) stay, mechanical ventilation (MV), and 30-day readmission. We evaluated the predictive ability of LMR, NLR, PLR, BLR, ELR, and HALP for predicting the prognosis of AECOPD patients. Furthermore, based on these indicators, we utilized LASSO regression and multivariable analysis to develop models for predicting the prognosis of AECOPD patients. The predictive value of these indicators and the performance of the models were assessed using AUCs. Results: LMR exhibited AUCs of 0.612 for RF, 0.715 for ICU stay, 0.714 for MV, and 0.624 for 30-day readmission. Other indicators, including NLR, PLR, BLR, EMR, and HALP, showed AUCs ranging from 0.621 to 0.699 for predicting these outcomes in AECOPD. The models developed using LASSO regression and multivariable analysis yielded AUCs of 0.717 for RF, 0.773 for ICU stay, 0.780 for MV, and 0.682 for 30-day readmission. Incorporating LMR, NLR, PLR, BLR, ELR, and HALP into the models individually further enhanced predictive performance, particularly with LMR (AUCs of 0.753 for RF, 0.797 for ICU stay, 0.802 for MV, and 0.697 for 30-day readmission), NLR (AUCs of 0.753 for RF, 0.796 for ICU stay, 0.802 for MV, and 0.698 for 30-day readmission), and HALP (AUCs of 0.752 for RF, 0.790 for ICU stay, 0.797 for MV, and 0.697 for 30-day readmission). Conclusion: Indicators of LMR, NLR, PLR, BLR, ELR, and HALP showed good performance in predicting outcomes for AECOPD patients. The integration of these indicators into prognostic models significantly enhances their predictive efficacy.
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