Wang Na, Wang Guangdong, Li Mengcong, Liu Tingting, Ji Wenwen, Hu Tinghua, Shi Zhihong
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.
J Inflamm Res. 2024 Nov 7;17:8395-8406. doi: 10.2147/JIR.S461961. eCollection 2024.
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.
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.
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).
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.
慢性阻塞性肺疾病急性加重(AECOPD)与显著不良预后相关。淋巴细胞与单核细胞比值(LMR)、中性粒细胞与淋巴细胞比值(NLR)、嗜酸性粒细胞与淋巴细胞比值(ELR)、嗜碱性粒细胞与淋巴细胞比值(BLR)、血小板与淋巴细胞比值(PLR)以及血红蛋白、白蛋白、淋巴细胞和血小板(HALP)是炎症、免疫状态和营养状况的重要指标。本研究评估了这些指标在AECOPD中的预测价值,并基于这些指标建立预测模型以评估AECOPD的预后。
我们回顾性收集了2609例AECOPD患者的数据。评估的结局包括呼吸衰竭(RF)的发生、重症监护病房(ICU)住院时间、机械通气(MV)以及30天再入院情况。我们评估了LMR、NLR、PLR、BLR、ELR和HALP对AECOPD患者预后的预测能力。此外,基于这些指标,我们利用LASSO回归和多变量分析建立了AECOPD患者预后的预测模型。使用曲线下面积(AUC)评估这些指标的预测价值和模型的性能。
LMR对RF的AUC为0.612,对ICU住院时间的AUC为0.715,对MV的AUC为0.714,对30天再入院的AUC为0.624。其他指标,包括NLR、PLR、BLR、EMR和HALP,在预测AECOPD这些结局时的AUC范围为0.621至0.699。使用LASSO回归和多变量分析建立的模型对RF的AUC为0.717,对ICU住院时间的AUC为0.773,对MV的AUC为0.780,对30天再入院的AUC为0.682。将LMR、NLR、PLR、BLR、ELR和HALP分别纳入模型进一步提高了预测性能,尤其是LMR(对RF的AUC为0.753,对ICU住院时间的AUC为0.797,对MV的AUC为0.802,对30天再入院的AUC为0.697)、NLR(对RF的AUC为0.753,对ICU住院时间的AUC为0.796,对MV的AUC为0.802,对30天再入院的AUC为0.698)和HALP(对RF的AUC为0.752,对ICU住院时间的AUC为0.790,对MV的AUC为0.797,对30天再入院的AUC为0.697)。
LMR、NLR、PLR、BLR、ELR和HALP指标在预测AECOPD患者结局方面表现良好。将这些指标整合到预后模型中可显著提高其预测效能。