Na Wang, Guangdong Wang, Mengcong Li, Tingting Liu, Wenwen Ji, Tinghua Hu, Zhihong Shi

Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, CN, 710061, People’s Republic of China

Correspondence: Zhihong Shi, Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Xi’an Jiaotong University, No. 277 Yanta Road, Yanta District, Xi’an, Shaanxi, CN, 710061, People’s Republic of China, Tel +862985323854, Email docszh@xjtufh.edu.cn

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.

Keywords: AECOPD, prediction, outcome, models